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QUATERNARY SULFUR CYCLEDYNAMICS
by
Stefan Markovic
A thesis submitted in conformity with the requirementsfor the degree of Doctor of Philosophy
Department of Earth SciencesUniversity of Toronto
© Copyright by Stefan Markovic 2014
Quaternary Sulfur Cycle Dynamics
Stefan Markovic
Doctor of Philosophy
Department of Earth SciencesUniversity of Toronto
2014
Abstract
The past 3 million years of Earth history are characterized by
dramatic changes, which greatly affected the biogeochemical cycling
of elements like carbon, sulfur and phosphorous. Here I investigate
the effect of these changes on sulfur fluxes and microbially mediated
sulfur cycling in marine sediments.
Climate driven sea level fluctuations and dynamic topography have
greatly affected the areal extent of the continental shelf during the
Quaternary. In turn this affects organic matter burial rates and the
relative importance of different organic matter remineralization
pathways. Since microbial sulfur cycling is the dominant organic
matter re-mineralization pathway in marine sediments, this must have
affected marine sulfur cycling. Furthermore, previous studies
suggested that Quaternary sea level fluctuations caused a
considerable increase in the erosion of shelf sediments, which is
ii
closely tied to the re-oxidation of pyrite. Here, I use the marine barite
record of sulfur and oxygen isotope ratios (δ34S and δ18O) of
seawater sulfate for the past 4Ma to evaluate the implications of
Quaternary sea level changes on the sulfur cycling.
Quantitative interpretation of δ34S and δ18O data suggests that
erosion during sea level lowstands was only partly compensated by
increased sedimentation during times of rising sea levels and sea level
highstands. Furthermore, my findings indicate that shelf systems
reached a new equilibrium state about 700 kyr ago, which
considerably slowed or terminated erosion of shelf sediments.
Modeling results also show that microbial sulfur cycling changes
proportionally with shelf area, resulting in a 15% reduction of
microbial sulfur cycling over the last 2 Ma. This results in a 1-1.5‰
drop in the marine sulfate δ18O isotope value. While further work is
needed to understand how shelf area changes affect the cycling of
carbon, phosphorous and other elements, results presented here
highlight the dynamic role of continental shelves in the global
biogeochemical cycles.
iii
AcknowledgmentsWriting acknowledgments is an ungrateful task – unavoidably some
of those people who should be thanked are missed in the end. In my
case, it is an impossible task to thank all those former colleagues,
teachers and professors, whose help and support brought me here
after a long journey through geology which started 14 years ago in
my first year of high school. For that reason, I wish to first thank
those who may be missed in the following list.
For his continuing support and scientific guidance, I wish to thank my
advisor, Dr. Ulrich G. Wortmann. For the past four years, Uli's
unfading optimism and excitement about each new puzzling finding
(which I would often interpret as this-makes-no-sense) were just what
was needed to counter my grad student existential crisis. For that, I
am very grateful.
Fruitful discussions with my supervisory committee members Dr.
Joerg Bollmann and Dr. Maria Dietrich, as well as collaborators Dr.
Adina Paytan, Prof. Benjamin Brunner, Dr. Alexandra Turchyn and
Dr. Yongbo Peng were instrumental in shaping this thesis and finding
solutions where my work appeared to be at an impasse. Furthermore,
I gratefully acknowledge Dr. Adina Paytan and Dr. Zhongwu Ma for
sharing their barite samples and Dr. Bridget Bergquist for providing
seawater samples.
Since I was and still am a novice in the field of mass spectrometry,
my work often required a lot of practical support in order to extract
meaningful data. For that, I gratefully acknowledge Hong Li who
patiently supervised me in the lab and kept our Mass Spec running
iv
smoothly. In addition, Hong Li along with Dr. Georges Lacrampe-
Couloume, was always ready to answer any question I came up with.
Many others provided invaluable help during my studies. I want to
specially acknowledge Dr. Mike Gorton who, although not my
supervisor, was a constant source of support, practical and theoretical
guidance in pretty much all fields I had the (mis)fortune to touch
during the course of my studies. Also, help of George Kretschmann.
Dr. Kim Tait and Brendt C. Hyde in various instances is gratefully
acknowledged. Furthermore, I gratefully acknowledge Dr Boswell
Wing, my external appraiser, for helpful comments which improved
the quality of this thesis.
I would also like to especially acknowledge my friends and
colleagues at the Department of Earth Sciences for providing personal
support during times of my grad student existential crisis as well as
great company during happy occasions. Thank you guys, it was a
great pleasure meeting you over the past four years.
I wish to also acknowledge the continuing support of my family,
during many years of my education. Without your help, I wouldn't be
here.
Finally, my greatest thanks go to my life partner Amina Abdalla who
provided me with emotional support at all times, particularly when I
was struggling.
v
Table of ContentsChapter 1: Introduction.................................................................1
1.1 Overview.....................................................................................1
1.2 The Biogeochemical sulfur cycle................................................2
1.2.1 Processes in the oxic sulfur cycle............................................5
1.2.2 Stable isotopes of sulfur and oxygen as tracers of biogeochemical processes in the sulfur cycle..............................8
1.2.3 Organic matter decomposition in shelf vs abyssal environments..............................................................................11
1.2.4 Marine barite – a proxy for sulfur and oxygen isotopic composition of seawater sulfate.................................................12
1.2.5 A Few notes on modeling.......................................................13
1.3 Open questions..........................................................................16
1.4 Research objectives...................................................................17
1.5 Thesis outline............................................................................18
1.6 Statement of authorship............................................................20
Chapter 2: Purification of marine barite for oxygen isotope measurement using sodium carbonate digestion...................22
2.1 Abstract......................................................................................22
2.2 Introduction...............................................................................22
2.2.1 Experiments conducted..........................................................24
2.3 Methods.....................................................................................26
2.3.1 Isotope Analysis.....................................................................27
vi
2.3.2 Mineral composition and chemistry of precipitates and residuals......................................................................................29
2.4 Results and Discussion..............................................................29
2.5 Conclusion.................................................................................34
Chapter 3: Pleistocene sediment offloading and the global sulfur cycle............................................................................................35
3.1 Abstract......................................................................................35
3.2 Introduction...............................................................................35
3.3 Geological Setting.....................................................................37
3.4 Methods.....................................................................................38
3.4.1 Age model...............................................................................40
3.4.2 Isotope analysis......................................................................40
3.4.3 Statistical Analysis.................................................................40
3.4.4 Sulfur cycle model..................................................................41
3.4.5 Model forcing.........................................................................43
3.5 Results and Discussion..............................................................47
3.6 Conclusions...............................................................................51
Chapter 4: Shelf area fluctuations and related impacts on microbial sulfur cycling............................................................53
4.1 Abstract......................................................................................53
4.2 Introduction...............................................................................54
4.3 Background...............................................................................55
4.4 Geological settings....................................................................59
4.5 Methods.....................................................................................60
vii
4.5.1 Age model...............................................................................60
4.5.2 Isotope Analysis.....................................................................61
4.5.3 Statistical Analysis.................................................................61
4.5.4 Sulfur cycle model..................................................................62
4.5.5 Steady state model run...........................................................63
4.5.6 Model forcing.........................................................................65
4.6 Results and discussion...............................................................69
4.7 Quantitative Interpretation........................................................74
4.8 Conclusion.................................................................................77
Chapter 5: Sulfur and oxygen isotopic composition of contemporary seawater sulfate and authigenic core top barite....................................................................................................79
5.1 Abstract......................................................................................79
5.2 Introduction...............................................................................79
5.3 Sampling locations....................................................................81
5.4 Methods.....................................................................................83
5.4.1 Core top barite separation.......................................................83
5.4.2 Isotope analysis......................................................................84
5.4.3 Statistical evaluation...............................................................85
5.5 Results and Discussion..............................................................87
5.5.1 Comparison with previously published records.....................89
5.5.2 Statistical analysis..................................................................92
5.6 Conclusions...............................................................................96
5.7 Data Tables................................................................................97
viii
Chapter 6: Final Remarks............................................................99
6.1 Conclusions...............................................................................99
6.2 Outlook....................................................................................101
References....................................................................................103
Appendix......................................................................................119
ix
List of TablesTable 1.1 Size of different sulfur reservoirs expressed in mol S .....3
Table 1.2 Stable isotopes of sulfur and their abundances.................8
Table 2.1 Impure BaSO4 used in experiment 3..............................26
Table 2.2 The outline of experiments and results...........................30
Table 2.3 Oxygen isotope data after sodium carbonate digestion experiments................................................................................30
Table 3.1 Model fluxes and sulfur isotope ratios in the steady state............................................................................................43
Table 4.1 Model fluxes and sulfur and sulfate oxygen isotope ratios in the steady state.......................................................................65
Table 5.1 Published range of sulfur isotope ratios of seawater sulfate. .......................................................................................91
Table 5.2 Published range of seawater sulfate oxygen isotope ratios...........................................................................................92
Table 5.3 δ18OSO4 ratios of core top samples...................................97
Table 5.4 δ18OSO4 ratios of dissolved seawater sulfate....................98
x
List of FiguresFig. 1.1 Conceptual scheme of sulfur cycle......................................4
Fig. 1.2 Schematic representation of the oxic sulfur cycle...............6
Fig. 1.3 An illustration how changing balance between pyrite weathering and burial affects sulfur isotopic composition of seawater sulfate.............................................................................9
Fig. 1.4 Processes controlling seawater sulfate oxygen ratio..........11
Fig. 1.5 Schematic drawing of sulfur cycle and impacts of Quaternary sea level variations...................................................15
Fig.2.1 Measured oxygen yield vs δ18O..........................................31
Fig. 2.2 The typical XRD pattern of barium sulfate shows no lines of other mineral phases...............................................................33
Fig. 2.3 SEM image of reprecipited barium sulfate. EDS analysis of the same sample gives composition of pure barium sulfate.......34
Fig. 3.1 Sulfate δ34S results.............................................................48
Fig. 3.2 Model output – seawater sulfate δ34S composition............50
Fig. 4.1 Major fluxes controlling oxygen isotope ratio of seawater sulfate..........................................................................................56
Fig. 4.2 Simplified schematic representation of the microbially mediated sulfur cycling (MMSC)...............................................58
Fig. 4.3 Sulfate δ18O results.............................................................70
Fig. 4.4 Sulfate oxygen isotope record by Turchyn and Schrag (2004)..........................................................................................72
Fig. 4.5 The effect of sea level variation on MMSC fluxes............75
xi
Fig. 4.6 Model output – seawater sulfate δ18O value when both changes of microbially mediated sulfur cycling and pyrite weathering are included (red solid line) and with only pyrite weathering (blue solid line)........................................................77
Fig. 5.1 Schematic diagram of barite precipitation (after Jacquet 2007)...........................................................................................80
Fig. 5.2 δ34S composition of seawater sulfate at KM703 station 11 (black), IAPSO seawater standard (blue) and mean with 1σ spread of results for core top barite (red)...................................87
Fig. 5.3 δ18O composition of seawater sulfate at KM703 station 11 (black), IAPSO seawater standard (blue) and mean with 1σ spread of results for core top barite (red)...................................88
Fig. 5.4 Calculated kernel density distribution (Gaussian) of the seawater sulfate δ34S (blue) compared to that of of core top barite δ34S ratios (red).................................................................93
Fig. 5.5 Calculated kernel density distribution (Gaussian) of the seawater sulfate δ18O (blue) compared to that of of core top barite δ18O ratios (red)................................................................94
Fig. 5.6 Schematic diagram of barite precipitation showing different sources of sulfate (modified from Jacquet 2007).......................95
xii
Chapter 1 1
Chapter 1
Introduction
1.1 Overview
Sulfur is a multivalent element naturally occurring in a broad range
of oxidation states from fully reduced (-II) to fully oxidized (VI).
Due to the variety of redox states and reactivity towards both
metals and non-metals sulfur produces many compounds which are
ubiquitous in natural environments. In its reduced form sulfur is
present in metallic sulfides, sometimes forming deposits of
important economic minerals. Dissolved species of sulfur are
widely present in natural waters. On the other hand, concentrations
of inorganic sulfate and biologically produced dimethyl-sulfate
aerosols in the atmosphere are small and their presence is short
lived. However, they initiate condensation in clouds and as such
have an important effect on the albedo of the Earth (e.g.,
Brimblecombe 2003).
As a biologically active element sulfur is a component of many
important compounds, e.g., amino acids (cysteine and methionine),
enzymes and cofactors (e.g., Coenzyme A, Ferredoxins) and
various sulfate esters and sulfonates (Canfield 2001,
Brimblecombe 2003, Canfield et al. 2005). Anaerobic
microorganisms “respire” sulfate to oxidize organic matter through
microbial sulfate reduction, which is one of the most important
processes for organic matter remineralization in the ocean.
On the other hand, sulfur is widely cycled between its many
inorganic and organic forms. Since the fundamentals of sulfur
Chapter 1 2
cycling were described by Lindgren (1923) scores of researchers
have extensively studied its various aspects. These studies
revealed that the sulfur cycle is intimately linked with
biogeochemical cycling of other biologically active elements
including carbon, oxygen, nitrogen, phosphorus and sulfur (e.g.,
Canfield and Marais 1993, Canfield and Farquhar 2012, Wortmann
and Paytan 2012).
In this thesis, I focus on the sulfur cycle in the context of changing
global climate and paleoenvironmental conditions in the most
recent geological period – the Quaternary. The hallmark of this
period are glaciations. While the intricate relationship between
glaciations and biogeochemical cycling of other elements like
carbon is widely studied, much less is known how the Quaternary
glaciations affected the global cycling of sulfur. Here I investigate
how Quaternary sea level variations affected global sulfur fluxes*
and microbially mediated sulfur cycling in marine sediments.
1.2 The Biogeochemical sulfur cycle
Sulfur is the fourteenth most abundant element in the earth's crust
(average S abundance ~0.07% wt%, Wedepohl 1995) forming a
wide range of sulfide and sulfate minerals. Because of the
oxygenated state of the Earth's surface, presently the most
abundant form of sulfur in the hydrosphere is fully oxidized sulfate
ion, second only to chloride in seawater, and to bicarbonate in
freshwater. Estimates of the total amount of sulfur in different
reservoirs are presented in Table 1.1.
___________
* The term flux is used here in geochemical and geological jargon and refers tomass transfer from one reservoir to another, i.e., inputs and outputs (expressed inunits of mass/time). This is different from common usage in physics orengineering where it refers to flow across a unit of area.
Chapter 1 3
Reservoir Size [mol S]
Upper mantle 6,230 *1018
Oceanic crust 161*1018
Continental crust 185*1018
Endogenic reservoirs 6,576*1018
Sediments Oceanic, reduced (sulfide) 8*1018
Continental, reduced (sulfide) 145*1018
Continental, oxidized (sulfate) 192*1018
Ocean water (sulfate) 40*1018
Fresh waters (sulfate) 40*1015
Atmosphere (SO2) 56*109
Exogenic reservoirs 385*1018
Land phytomassa 4–10*1013
Oceanic biotab 0.9–4*1012
Table 1.1 Size of different sulfur reservoirs expressed in mol S(from Lerman and Clauer 2007 and references therein: Holser et al.(1988); aBased on the C/S molar ratios in land plants from 600:1 to1470:1 (sources cited in Lerman, 1988, p. 23, p. 33) and mass of6x1016 mol C. bBased on the C/S molar ratios in oceanic planktonof 106:1.7 (Redfield et al., 1963) and 106:0.4 (Hedges et al., 2002)and the mass of 2.5x1014 mol C
The global biogeochemical sulfur cycle represents the exchange of
sulfur compounds among different Earth surface reservoirs (Fig.
1.1). Continental weathering of sulfur containing minerals (e.g.
pyrite and gypsum) is the dominant source of sulfate to the ocean
while pyrite and evaporite precipitation are the main sinks. In the
process, a portion of sedimentary sulfur is subducted, transferred
back to the mantle or returned to the surface through volcanic or
hydrothermal activity.
Chapter 1 4
Since the sulfur inputs and outputs are small compared to the
current total amount of sulfate in the ocean ~40*1018 mol S (Fig.
1.1), the residence time of sulfate in the ocean is usually
considered to be very long ~20Ma (Jørgensen and Kasten 2006).
However, this long residence time does not account for fast
recycling in sediments where sulfate is first reduced to hydrogen
sulfide and then reoxidized back to sulfate by the group of
processes collectively referred hereafter as an oxic sulfur cycle
(Jørgensen 1982, Jørgensen and Kasten 2006, Fig. 1.1). When the
oxic sulfur cycle is taken into account the residence time of sulfate
is much shorter ~0.5Ma (Jørgensen and Kasten 2006).
Sulfur is both a macro-element for organisms and an electron
donor for organic matter respiration. As a macro-nutrient, sulfur is
a building block of proteins and various enzymes, co-enzymes and
vitamins (Canfield 2001). The uptake of inorganic sulfate and its
subsequent incorporation in organic molecules is called
assimilatory sulfur metabolism (Canfield 2001). However, this
type of sulfur metabolism has a minor role in sulfur cycling (see
Fig. 1.1 Conceptual scheme of sulfur cycle (See also Chapter3 for more in-depth discussion about fluxes).
Chapter 1 5
Table 1.1 and the total amount of S in oceanic biota) compared to
dissimilatory sulfur metabolism which uses sulfur (or its
compounds) as electron donor for oxidation of organic matter. The
principal reaction of this metabolism is microbial sulfate reduction
(MSR) which oxidizes organic matter or methane and reduces
sulfate to sulfide. Because of the abundance of dissolved sulfate in
sea water, microbial sulfate reduction (MSR) accounts for ~50% of
organic matter remineralization in the ocean (e.g. Jørgensen 1982,
Sørensen et al. 1979, Canfield et al. 1993) and, over geologic time
scales is the fundamental control of OM burial, and thus
atmospheric oxygen concentration (Berner 1982, Wortmann and
Chernyavsky 2007, Wortmann and Paytan, 2012).
In sediments, MSR reduces sulfate to hydrogen sulfide, a fraction
of which reacts with iron to form pyrite entering the rock cycle as a
long term sink for sulfate (up to 5% of total sulfide produced
during MSR, e.g., Jørgensen 1982; Jørgensen and Kasten 2006; see
pyrite burial on Fig. 1.1). The amount of pyrite being formed
depends on the availability of reactive phases of iron – ferric
oxyhydroxides and oxides (Canfield et al. 1992; Jørgensen 1982,
Raiswell and Canfield 1998). Since the supply rate of reactive iron
phases is usually much lower than the production of sulfide by
MSR (Raiswell and Canfield 1998), only a small fraction of sulfide
gets buried as pyrite, while the rest is oxidized back to sulfate in
the oxic sulfur cycle (e.g., Jørgensen 1982; Fig. 1.1&Fig. 1.2). The
oxidation usually takes place within pore waters and rarely in the
overlaying water column (e.g., Jørgensen and Kasten 2006).
1.2.1 Processes in the oxic sulfur cycle
The oxic sulfur cycle is a complex network of parallel and
superimposing reactions (e.g., Jørgensen and Kasten 2006, Fig.
1.2). The initial products of inorganic reoxidation are intermediate
Chapter 1 6
oxidation state sulfur compounds (Yao and Millero 1993&1996;
Zhang and Millero 1993, eq. 1-2):
(1)
(2)
After the first reoxidation step, multiple pathways are possible.
The intermediate compounds can be oxidized to sulfate through
inorganic reactions with O2, Mn or Fe or disproportionated by
bacteria (Fig. 1.2).
During microbial disproportionation, sulfur compounds of
intermediate oxidation states – elemental sulfur, sulfite and
thiosulfate, are simultaneously reduced to hydrogen sulfide and
oxidized to sulfate (Jørgensen 1990; Thamdrup et al. 1993;
Canfield and Thamdrup 1994, eq. 3-5):
(3)
(4)
(5)
The disproportionation reactions are energetically favorable only if
Fig. 1.2 Schematic representation of the oxic sulfur cycle. Notethat equations 1-10 are given in text.
S2O32-+H 2O→H2 S+SO4
2-
4 S0+4 H2 O→3 H2 S+SO 4
2-+2 H+
4 SO32-+2 H+
→H 2 S+3SO42-
H2 S+MnO2→So+Mn2+
+2OH -
3H2 S+2 FeOOH →So+2FeS+4 H2 O
Chapter 1 7
sulfide is effectively stripped away (Thamdrup et al. 1993). Sulfide
can react with reduced iron to form relatively stable iron sulfides
or get oxidized with Fe(III) and Mn(IV) (Thamdrup et al. 1993;
Canfield and Thamdrup 1994; Canfield and Thamdrup 1996;
Jørgensen and Nelson 2004; Jørgensen and Kasten 2006):
(6)
(7)
While in most environments, iron and manganese oxidize sulfide
in anaerobic zones (Aller and Rude 1988; Aller 1990, Zhang and
Millero 1993), in some rare cases where MSR rates in sediments
are very high (or iron concentrations very low), dissolved sulfide
may make contact with molecular oxygen dissolved in seawater
(Jørgensen and Nelson 2004). In those environments sulfide is
oxidized by molecular oxygen (Jørgensen and Nelson 2004;
Jørgensen and Kasten 2006):
(8)
Chemolithoautotrophic sulfur oxidizing bacteria (e.g., different
Thiobacilli and Beggiatoa) utilize this reaction to fix carbon
(Jørgensen and Nelson 2004; Schulz and Jørgensen 2001):
(9)
where CH2O represents organic matter production during
chemolithoautotrophic bacterial sulfide oxidation.
In addition to fully aerobic bacterial sulfide oxidation, some
species of filamentous bacteria (e.g., Thiobacillus, Thiomicrospira,
Thioploca) also produce energy by coupling sulfide oxidation with
denitrification (Jørgensen and Nelson 2004; Schulz and Jørgensen
2001):
(10)
4 MnO2+HS -+7 H+
→4 Mn2++SO4
2-+4 H 2O
8 FeOOH+HS -+15 H+
→8 Fe2++SO4
2-+12H 2O
H 2 S+2 O2→SO42-+2 H+
4 H2 S+7 O2+CO2+H2 O→4 SO42-+CH2 O+8 H+
5 H2 S+8 NO3-→5 SO4
2-+4 N 2+4 H2O+2 H+
Chapter 1 8
1.2.2 Stable isotopes of sulfur and oxygen as tracers of biogeochemical processes in the sulfur cycle
Biogeochemical processes in the sulfur cycle produce specific
sulfur and oxygen isotope signatures in the resulting sulfur
compounds. This allows us to use stable isotope ratios of sulfur
and oxygen as tools for investigating sulfur cycling. Here I
concentrate on the seawater sulfate stable isotope ratios of sulfur
and oxygen as a proxy for past and present changes affecting the
sulfur cycle.
Isotope of sulfur Average crustal abundance [%]32S 95.0
33S 0.76
34S 4.22
36S 0.014
Table 1.2 Stable isotopes of sulfur and their abundances
Sulfur has four stable isotopes with relative crustal abundances
shown in Table 1.2. In the following, I use the standard delta
notation to express the S-isotope composition (δ34S) as a difference
in “parts per thousand” between the 34S/32S ratio of sample versus
the internationally standard – Vienna Canyon Diablo Troilite
(VCDT) (11):
(11)
where 34S/32S represents the molar ratio of the heavy over the lightisotope of sulfur.
δ S34=(
Ssample34
Ssample32
SVCDT34
SVCDT32
−1)∗1000[‰]
Chapter 1 9
The ratio between 34S/32S changes as a result of biogeochemical
redox transformations. For example, the central process in the
sulfur cycle is MSR which preferentially breaks the 32S-O bond,
resulting in S-isotope partitioning between the reduced (sulfide)
and oxidized (sulfate) reservoir (of up to 70 ‰, Wortmann et al.
2001, Rudnicki et al. 2001, Brunner and Bernasconi 2005, Sim et
al. 2011). This sulfide is buried as pyrite during sedimentation and
as a result the overlying water column becomes depleted in 32S. On
the other hand, the weathering of pyrite produces sulfate rich in
Fig. 1.3 An illustration how changing the balance between pyriteweathering and burial affects the sulfur isotopic composition ofseawater sulfate. Pyrite is enriched in the lighter S isotope (32S)and therefore pyrite weathering delivers sulfate with negativeδ34S. On the other hand, pyrite burial preferentially removes 32S
which increases δ34S of residual seawater sulfate.
Chapter 1 10
32S. Therefore, the balance between pyrite burial and weathering
controls sulfur isotopic composition of seawater sulfate (Fig. 1.3,
see also Chapter 3 for more details).
The oxygen isotope composition of sulfate (δ18O) is reported using
standard δ-notation relative to international oxygen standard –
Vienna Standard Mean Oceanic Water (VSMOW):
δ O18=(
O sample18
O sample16
OVSMOW18
OVSMOW16
−1)∗1000 [‰] (12)
where 18O/16O is the ratio of the heavy over the light isotope of
oxygen (in sulfate) in the sample and the standard (VSMOW),
respectively.
Since oxygen isotope exchange between dissolved sulfate ion and
ambient water is very slow at low temperature and marine pH
conditions (Lloyd 1967; Lloyd 1968; Chiba and Sakai 1985; Van
Stempvoort and Krouse 1994), it is the balance between abiotic
and microbial processes in the oxic sulfur cycle and input of
sulfate from pyrite weathering that controls oxygen isotopic
composition of seawater sulfate (Fig. 1.4; see Chapter 4 for
detailed discussion). Whereas, abiotic oxidation in the oxic sulfur
cycle produces sulfate with δ18O signatures close to the oxygen
isotope ratio of seawater (~ 0‰, Taylor et al. 1984; Van
Stempvoort and Krouse 1994), microbial processes
(disproportionation, microbial sulfur oxidation, MSR) offset
sulfate δ18O by up to 29‰ (e.g. Fritz et al. 1989, Van Stempvoort
and Krouse 1994, Böttcher and Thamdrup 2001, Böttcher et al.
2001, Wortmann et al. 2007, Turchyn et al. 2010, Balci et al.
2012). On the other hand, sulfate input from pyrite weathering is
on average ~ 0‰, (Taylor et al. 1984; Van Stempvoort and Krouse
Chapter 1 11
1994, Balci et al. 2007).
1.2.3 Organic matter decomposition in shelf vs abyssal environments
Although the shelf underlies only 7-8% of the total area of the
ocean, most of organic matter (OM) is buried in the shelf
Fig. 1.4 Processes controlling the seawater sulfate oxygen isotoperatio. Increased rates of microbial processes (disproportionation,microbial sulfur oxidation, MSR) result in higher seawater sulfate
δ18O because these processes produce sulfate enriched in 18O. On
the other hand, abiotic oxidation of hydrogen sulfide produced
during MSR and pyrite weathering produces sulfate with δ18O
close to 0‰ which lowers seawater sulfate δ18O.
Chapter 1 12
sediments (up to 80-90%, e.g., Berner 1982, Hedges and Keil
1995, Wollast 1991). Decomposition of this OM drives a sequence
of microbial respiration processes, the order of which is controlled
by the energy yield of the respective redox reaction (e.g. Froelich
et al. 1979). Aerobic respiration and denitrification, are
energetically the most favorable, followed by manganese, iron and
sulfate reduction (MSR) and ultimately by fermentation and
methanogensis (Froelich et al. 1979). In the shelf areas OM supply
is high and thus OM respiration is limited by the availability of a
respective oxidant. While the energy yield of MSR is low, marine
sulfate concentrations are high, rendering OM respiration through
sulfate reduction a major control on carbon burial in shelf
sediments and upper slope (e.g., Jørgensen 1982, Sørensen et al.
1979, Canfield et al. 1993, Jørgensen and Kasten 2006, Thullner et
al. 2009).
In the lower slope and especially abyssal environments, microbial
respiration is limited by drastically lower supply of OM (e.g.,
Canfield 1993). In those environments dissolved oxygen penetrates
several decimeters or meters in the sediments (Hensen et al. 2006)
and aerobic respiration dominates OM decomposition (e.g.,
Jørgensen 1982, Sørensen et al. 1979, Canfield 1993, Canfield et
al. 1993, Thullner et al. 2009). Correspondingly, microbial sulfate
reduction rates are 5 orders of magnitude lower than in shelf
sediments (Jørgensen and Kasten 2006, Thullner et al. 2009).
1.2.4 Marine barite – a proxy for sulfur and oxygen isotopic composition of seawater sulfate
It is generally believed that barite crystals forming in the water
column, precipitate in isotopic equilibrium with the ambient
seawater, and thus record δ34S and δ18O composition of seawater
sulfate (Paytan et al. 1998, Turchyn and Schrag 2004). Barite is a
Chapter 1 13
particularly heavy mineral and these crystals quickly sink to the
ocean floor and in the process take a snapshot of the isotopic
composition of seawater sulfate at the time of their formation.
Therefore, we can use the sulfur and oxygen isotopic signature of
marine barite as a tool to investigate secular changes of the sulfur
cycle.
1.2.5 A few notes on modeling
While the details about my sulfur cycle model can be found in the
subsequent chapters, here I would like to give the reader some
pointers about how is this model constructed and used. The
difference between my sulfur cycle model and dozens of others
which were published in the past 50 years is in the separation of
sulfur cycling on the shelf (which is sensitive to sea level
variations) from sulfur cycling in deep water environments (which
is not affected by sea level). A schematic drawing of sulfate inputs
and outputs is shown on Fig. 1.5.
The model is calibrated in such a way that it achieves steady state
using inputs and outputs which approximate modern conditions
and were selected from the range of published values (Fig. 1.1, see
also Chapter 3&4 for details). Because there is considerable
uncertainty with respect to these flux estimates, I conducted a
series of sensitivity experiments. It can be shown that taking
specific estimate instead of some other will not affect the overall
conclusions.
Unlike many other S-cycle models (e.g. Kurtz et al. 2003,
Wortmann and Chernyavsky 2007), my model specifically
explores the impacts of sea level variations on fluxes sensitive to
shelf area change - pyrite weathering and burial and microbial
sulfur cycling (see Chapters 3 and 4 for details).
From a modeling point of view, I achieve this by first calculating
Chapter 1 14
shelf area at each time step using Miller at al. (2011) sea level
estimates. Next, I take the fluxes that are sensitive to shelf area and
divide them in two boxes, the one of which is constant and
represents background flux and the other which varies in direct
proportion to calculated shelf area. The details of how the model
is constructed and what specific equations are used to force each
flux can be found in the following chapters (Chapter 3&4).
Chapter 1 15
Fig. 1.5 Schematic drawing of sulfur cycle and impacts ofQuaternary sea level variations. High sea levels before theQuaternary resulted in wide shelf areas characterized by high MSRand pyrite burial rates. During glaciations in the late Quaternarysea level was much lower which resulted in significant shelfsediment erosion and pyrite weathering. This increased pyriteweathering is only partially compensated by increased burialduring interglacials of the same period.
Chapter 1 16
1.3 Open questions
Our knowledge of the past changes affecting the sulfur cycle is
fragmentary. The impeding issues vary from analytical problems to
a lack of detailed knowledge of the processes in sedimentary
environments, e.g.,:
1) Inputs and outputs and reservoir sizes in the sulfur cycle are not
well constrained. There are many reasons for this. Some of the
processes like evaporite precipitation are difficult to quantify
because they occur in isolated basins across the globe.
Precipitation of carbonate associated sulfate – CAS (also
structurally substituted sulfate – SSS) is very difficult to quantify
as it depends on the location of carbonate precipitation (i.e., deep
water vs. shallow water carbonates) and the behavior of sulfate
during carbonate diagenesis is poorly constrained (Bottrell and
Newton 2006).
2) The sulfur and oxygen isotope records of marine sulfate
suggests that major perturbations of sulfur cycle must have
occurred over the Cenozoic (Paytan et al. 1998&2004; Turchyn
and Schrag 2004&2006, Kurtz et al. 2003, Wortmann and
Chernyavsky 2007, Wortmann and Paytan 2012). However, the
magnitude, timing and causes of these changes are currently poorly
constrained, and the understanding of the response of the oxic
sulfur cycle to changing paleoenvironmental conditions is in its
infancy (Turchyn and Schrag 2006).
3) Currently the only Quaternary marine barite O-isotope record
shows a noted negative O-isotope offset of ~-2‰ between marine
barite and present day oxygen isotopic composition of seawater
sulfate (Turchyn and Schrag 2004). Turchyn and Schrag (2004)
suggested that this offset represents a kinetic isotope fractionation
during barite precipitation. However, kinetic isotope fractionation
Chapter 1 17
in other sulfate minerals (evaporitic gypsum or anhydrite) is
known to cause a positive oxygen isotope offset (e.g. Lloyd 1968;
Holser et al. 1979), which should also be expected in the case of
barite.
4) Use of marine barite as a proxy for the oxygen isotopic
composition of past seawater sulfate depends on obtaining pure
barite samples that do not contain other oxygen bearing mineral
phases (e.g. iron oxides, silicates, rutile). Previous barite separation
methods (e.g., Turchyn and Schrag 2004) often did not pay
enough attention to this possibility. Turchyn and Schrag e.g, use
lithium polytungstate (LST) with a density of up to 2.85 g/ml for
gravity separation of barite from other phases. However, this
method fails for minerals with densities exceeding those of LST
(silicates, rutile, iron oxides). These minerals also carry oxygen
and therefore may introduce an error during O isotope
measurements. Therefore an alternative method for obtaining pure
barium sulfate is needed.
1.4 Research objectives
The goal of this thesis is to address some of the aforementioned
questions. Towards that end, I concentrate my research on marine
barites of Holocene and Quaternary age. I do this because, in
addition to being able to provide a good time control, it is possible
to correlate the barite record with other isotope data (e.g. δ18O and
δ13C of carbonates) and paleoclimate and paleoenvironmental
studies. This provides an opportunity to increase our understanding
of global S-cycling during times of rapid environmental and
climatic change in the Quaternary.
Chapter 1 18
My research has the following objectives:
1) To develop and test a method of purification of marine barite
contaminated with other mineral phases (oxides, silicates, rutile)
using sodium carbonate digestion and check for potential O-
isotope fractionation during this process. This purification step is
necessary because those minerals contain oxygen which is released
during high temperature pyrolysis and thus affects oxygen isotope
measurement.
2) To produce a high resolution (50-100kyr) record of sulfur and
oxygen isotopic composition of marine sulfate in Quaternary using
marine barite extracted from Integrated Ocean Drilling Program
(IODP) core samples and develop a model describing the observed
changes. My model is testing hypotheses that sea level variations
in the Quaternary caused:
a) A net increase of shelf sediment erosion and associated pyrite
weathering (Objective 2a).
b) A decrease in MSR rates along with decreased microbial sulfur
cycling through disproportionation and microbial oxidation
pathways (Objective 2b).
3) To quantify the O-isotope offset between marine barite
separated from core top samples and seawater sulfate. This tests
the fidelity of marine barite proxy as recorder of seawater sulfate
δ18O composition.
1.5 Thesis outline
The thesis starts with a methodological chapter on developing and
testing the method for barite purification (Objective 1), followed
by chapters on the sulfur (Objective 2a) and oxygen (Objective 2b)
isotopic composition of seawater sulfate and sulfur and oxygen
isotopic composition of modern seawater sulfate and core top
barites (Objective 3). Following is the outline of specific chapters:
Chapter 1 19
Chapter 2 (Objective 1): Purification of marine barite foroxygen isotope measurement using sodium carbonate digestion(in preparation for submission to Rapid Communications in MassSpectrometry)
This chapter presents a method for recovery of pure barium sulfate
from mixtures of barite with silicates, iron or manganese oxides or
resistant mineral phases (e.g., rutile). The method involves barite
digestion using sodium carbonate and subsequent reprecipitation of
barium sulfate in acidic medium. The possibility and extent of
alteration of O-isotope signature of barite is specifically addressed
and evaluated. Additionally, the purity and recovery of barium
sulfate is examined. Finally, this chapter also addresses the error
associated with co-precipitation of hydration water with barium
sulfate.
Chapter 3 (Objective 2a): Pleistocene sediment offloading and
the global sulfur cycle (in review Earth and Planetary Science
Letters)
Here, I present a new high resolution (~50ky) S-isotope record of
seawater sulfate isotope compositions for the past 3Ma using
marine barite extracted from Integrated Ocean Drilling Program
(IODP) core samples. I test the impact of sea level variations on
the sulfur cycle with a box model which separates sulfur cycling in
different environments (shelf vs. deep water settings and
continental environment). Based on modeling data I propose that
Quaternary variations of seawater sulfate sulfur isotope ratios
primarily reflect increased erosion of shelf sediments from
continental shelves during glacial low-stands and to a lesser extent
the reduction of pyrite burial.
Chapter 4 (Objective 2b): Shelf area fluctuations and related
impacts on microbial sulfur cycling (in preparation for
submission to Geochimica and Cosmochimica Acta)
Chapter 1 20
This chapter expands on previous work by Turchyn and Schrag
(2004) and presents a new seawater sulfate O-isotope record for
the past 4Ma. Using box modeling, I evaluate the effect of
Quaternary shelf area change on the oxic sulfur cycle and propose
that observed variations of seawater sulfate δ18O ratios reflect
changes of two major fluxes: a) reduced microbial sulfur cycling in
continental shelves as a result of periodic glacials which exposed
shelves to weathering and erosion and b) increased importance of
abiotic oxidation in the deep water environments.
Chapter 5 (Objective 3): Sulfur and oxygen isotopic
compositions of contemporary seawater sulfate and authigenic
core top barite (in preparation for submission to Deep-Sea
Research or Paleoceanography)
This chapter presents sulfur and oxygen isotope compositions of
seawater sulfate from a depth transect in the South Pacific and a
number of core top (recent) barites from various locations across
the Pacific and Southern Oceans. Using kernel density functions I
evaluate the difference between the S- and O-isotope compositions
of dissolved sulfate and marine barite and discuss implications for
the use of barite proxy.
1.6 Statement of authorship
All experiments, sample preparation and measurements and data
interpretation are the work of Stefan Markovic, with supervision
and guidance from Hong Li and Dr. Ulrich G. Wortmann. Detailed
description of specific contributions follows:
Chapter 2: Purification of marine barite for oxygen isotope
measurement using sodium carbonate digestion
Stefan Markovic, A. Paytan, and Ulrich G. Wortmann
Chapter 1 21
Experiments are designed and conducted by SM. All sample
preparation was done by SM. SEM-EDS analysis was conducted
by Jorg Bollmann and SM. XRD analysis was done by SM.
Isotope analysis was done by SM with help from Hong Li. Data
interpretation and writing was done by SM with input from co-
authors.
Chapter 3: Pleistocene sediment offloading and the global
sulfur cycle
Stefan Markovic, A. Paytan, and Ulrich G. Wortmann
SM carried out all sample preparation and isotope measurements
with help from Hong Li. Data interpretation and writing was done
by SM with input from co-authors.
Chapter 4: Shelf area fluctuations and related impacts on
microbial sulfur cycling
Stefan Markovic, Adina Paytan, Alexandra V. Turchyn, Yongbo
Peng, Hong Li, Ulrich G. Wortmann
Samples were provided by Zhongwu Ma. SM carried out all
sample preparation, data interpretation and writing with input from
co-authors. Isotope analysis was done by SM with help from Hong
Li.
Chapter 5: Sulfur and oxygen isotopic composition of
contemporary seawater sulfate and authigenic core top barite
Stefan Markovic, A. Paytan, Bridget Bergquist and Ulrich G.
Wortmann
Core top barite samples were provided by Adina Paytan. Seawater
samples were provided by Bridget Bergquist. All sample
preparation was done by SM. Isotope analysis was done by SM
with assistance from Hong Li. All data interpretation and writing
was done by SM with input from co-authors.
Chapter 2 22
Chapter 2
Purification of marine barite for oxygenisotope measurement using sodium
carbonate digestion
2.1 Abstract
It is difficult to separate marine barite from other mineral phases,
e.g., silicates, iron or manganese oxides or resistant mineral phases
(e.g. rutile). These impurities represent a serious problem for
measurements of oxygen isotope ratio of sulfate, since they carry
oxygen isotope signatures which are unrelated to that of barite.
Here we present a method to recover pure barium sulfate which
involves barite digestion using sodium carbonate, reprecipitation in
an acidic medium, and subsequent heating treatment. Our results
suggests that this method is highly efficient in separating barium
sulfate from other mineral phases, thereby improving the precision
of sulfate δ18O measurements. Furthermore, we show that no
oxygen isotope exchange between water and sulfate occurs during
this treatment.
2.2 Introduction
Barite is a universal component of suspended matter and a minor
constituent in marine sediments (e.g., Dehairs et al. 1980). As a
very stable mineral, being insoluble under most conditions with the
notable exception of highly reducing environments where sulfate is
depleted (e.g., Paytan and Griffith 2007; Torres et al. 1996), barite
is considered a proxy of seawater sulfate sulfur (δ34S) and oxygen
(δ18O) isotopic ratios (Paytan et al. 1998&2004; Turchyn and
Chapter 2 23
Schrag 2004). However, the analysis of these isotope ratios
requires the separation of barite from other S and O bearing
mineral species.
Two methods were recently developed to separate barite from
sediments: a) sequential dissolution by Paytan et al. (1996) and b)
heavy liquid separation (Turchyn and Schrag 2004). Both methods
are unable to completely separate barite from the following
contaminants (Paytan et al. 1996&1998; Turchyn and Schrag
2004):
a) silicates
b) iron oxides
c) HF resistant mineral phases like rutile.
The presence of these impurities is particularly a problem for
oxygen isotope analysis since all of those above mentioned phases
contain oxygen which is released during high temperature
pyrolysis and thus affects oxygen isotope measurement. Therefore,
it is important to obtain pure barium sulfate.
Bao (2006) proposed treatment involving dissolution of barite with
excess Diethylenetriaminepentaacetic acid (DTPA),
[(HO2CCH2)2NCH2CH2]2NCH2CO2H and subsequent
reprecipitation of synthetic barium sulfate. However, this method
was primarily developed to purify barium sulfate precipitates
which contain crystalline lattice bound nitrate. The DTPA chelates
iron (DTPA-Fe formation constant K is 28, Hart 2012) and
manganese (DTPA-Mn K constant is 16, Hart 2012) ions more
strongly than barium ions (K constant of 8.87, Hart 2012), and
therefore this method cannot be used for barites contaminated with
iron and manganese oxides.
Barite can also be dissolved by slow digestion (>24h) in excess
Chapter 2 24
sodium carbonate at temperature of >90oC. This reaction releases
sulfate into solution, which can be separated from barium
carbonate residuals. Breit et al. (1985) used this method to extract
Sr from marine barite for 87Sr/86Sr ratio measurement. While
oxygen isotope exchange between sulfate and water is very slow at
low temperature and normal pH conditions (Rennie et al. 2014), it
is orders of magnitudes faster at elevated temperatures and extreme
pH conditions (Lloyd 1968; Chiba and Sakai 1985). Here we
explore if prolonged digestion used by Breit et al. (1985) will
affect barite δ18O signature.
The method proposed by Breit et al. (1985) aims to separate Sr
which concentrates in barium carbonate residuals. Since they
assume that barite digestion is quantitative the liquid with sulfate is
simply discarded. Therefore, the purity of reprecipitated barium
sulfate was not assessed. This is particularly important for oxygen
isotope measurements, because barium sulfate precipitated from
aqueous solutions readily incorporates ambient water (Walton and
Walden 1946a&b) or nitrate from solution (Michalski et al. 2008)
which could alter original barite δ18OSO4 composition (Michalski et
al. 2008, Hannon et al. 2008).
Here we adapt Breit et al. (1985) digestion method to separate
barite from a mixture of silicates and oxides. We examine the
efficiency of the method and purity of reprecipitated barium
sulfate. Additionally we examine the associated oxygen isotope
effect by comparing the δ18OSO4 composition of the original versus
the reprecipitated barite.
2.2.1 Experiments conducted
The following experiments were conducted in order to test: a)
whether the sodium carbonate dissolution and reprecipitation
affects the δ18O (Experiment 1), b) if hydration water is present in
Chapter 2 25
reprecipitated barium sulfate (Experiment 1), c) whether heat
treatment can be used to remove hydration water from barium
sulfate precipitate (Experiment 2) and d) whether we can separate
pure barium sulfate from mixture with quartz and iron oxides
(Experiment 3):
Experiment 1: Digestion and reprecipitation of synthetic
barium sulfate. In order to test the effect of digestion and
reprecipitation on the oxygen isotope composition of barite, we
digest, reprecipitate and measure the oxygen isotope composition
of reprecipitated synthetic barium sulfate with a known δ18OSO4
isotope value. The difference between δ18OSO4 isotope value before
and after treatment represents either oxygen isotope exchange
between sulfate and water or presence of hydration water. Since
the magnitude of oxygen isotope exchange is time dependent, we
conduct three sets of experiments with reaction times of 12h, 24h
and 48h. Each experiment is run in duplicate.
Experiment 2: Removing hydration water. Barium sulfate
precipitated from aqueous solutions contains variable amounts of
hydration water which cannot be removed by drying at low
temperatures (Walton and Walden 1946a&b, Hannon et al. 2008).
The impact of this hydration water on reprecipitated barium sulfate
δ18O isotope value is tested in Experiment 1. In order to remove
this water we heat reprecipitated barium sulfate samples from
Experiment 1 at 700oC for one hour. In the next step, we re-
measure δ18O to assess the impact of heating treatment on oxygen
isotope signatures of reprecipitated barium sulfate and test if the
treatment is sufficient to remove hydration water.
Experiment 3: Purification of impure barium sulfate. In order
to test the effectiveness of our method we digest and reprecipitate
barium sulfate from mixture containing silicate, iron oxide and
Chapter 2 26
synthetic barium sulfate (Table 2.1). Following reprecipitation, we
examine the recovered barium sulfate with X-ray diffraction
(XRD) and Scanning Electron Microscopy – Energy Dispersive
Spectroscopy (SEM-EDS) techniques to check for presence of
impurities. After this, the sample is heated at 700oC for one hour to
remove hydration water. Finally, we measure the oxygen isotope
ratio of the recovered barium sulfate to assess the cumulative
impact of our method on the original barium sulfate δ18O isotope
value.
Phase Amount [mg]
*µg
Ba content [w%]
Quartz* 10 n.d.
Goethite 5 n.d.
Barium sulfate (syn) 1000*
Table 2.1 Impure BaSO4 used in experiment 3. Note: the quartzused in this experiment is our internal standard with δ18O of 9.8+/-0.3 (VSMOW). The goethite is from departmental mineralcollection and its δ18O is 4.3+/-0.5 VSMOW.
2.3 Methods
Step 1. Barium sulfate dissolution. First, an impure barite is
weighed and placed in Axygen® 1.5mL MaxyClear snaplock
polupropylene microtubes. Next, we add 10 times the weight of
sodium carbonate (99.99%, Acros Organics) and 1 mL of miliQ
water (resistivity > 18MΩ). Microtubes are closed and placed in an
oven, previously set at 85 oC and left to react. Reaction between
barium sulfate and sodium carbonate is a simple exchange of
Chapter 2 27
carbonate for sulfate, which produces insoluble barium carbonate
while sulfate is released in solution:
BaSO4(s)+Na2CO3(aq)→BaCO3(s)+Na2SO4(aq)
Step 2. Reprecipitation. After dissolution step tubes are
centrifuged in order to separate barium carbonate and sodium
sulfate. The liquid containing sodium sulfate is decanted into larger
BD Falcon™ 10 mL tubes leaving behind barium carbonate
residue which is left in an oven to dry out and kept for subsequent
XRD analysis. The supernatant liquid is acidified to pH 1 using
trace metal grade hydrochloric acid and then 2-5 mL of 10%
solution of BaCl2 (99.99% Sigma-Aldrich) is added which
precipitates white BaSO4:
Na2SO4(aq)+BaCl2→BaSO4(s)+2NaCl(aq)
The barium sulfate precipitate is left at room temperature overnight
in the mother solution to “age”. Next day samples were centrifuged
and the liquid was decanted. Following this step, miliQ water
(resistivity > 18MΩ) is added to the tube, the remaining precipitate
is shaken, centrifuged and decanted again. This “washing” step is
repeated 5-7 times, to obtain precipitate free from BaCl2 and HCl
residues. To eliminate hydration water, reprecipitated barium
sulfate is heated at 700oC for one hour.
Samples for all experiments were prepared using known amount of
BaSO4. The weight of reprecipitated barium sulfate is
subsequently measured in order to estimate recovery.
2.3.1 Isotope Analysis
We analyze the oxygen isotope ratios with a continuous flow
isotope ratio monitoring mass spectrometry (CF-IRMS) system
using a Hekatech high temperature pyrolysis furnace coupled via a
Chapter 2 28
Finnigan Conflo III open split interface to a Finnigan MAT 253
mass spectrometer. Solid barite samples (~200µg+/-10µg) are
weighted into a silver capsule and introduced into the HT furnace
where BaSO4 is converted to CO gas at 1350o C under helium
atmosphere.
Measurements are calibrated using the following international
standards NBS 127 (+8.6‰, Vienna Standard Mean Oceanic Water
- VSMOW, IAEA SO5 +12.13‰ VSMOW and IAEA SO6 –
11.35‰VSMOW, USGS 32 +25.4‰VSMOW, Böhlke et al. 2003;
Brand et al. 2009) and an in-house synthetic BaSO4 standard
(Sigma-Aldrich, BaSO4 99.9%, 11.9+/-0.2‰,VSMOW). Repeated
measurements of the in-house standard (typically >10 per run) and
international standards (3-4 standards per run) result in a
reproducibility of 0.2‰ (1 standard deviation – σ).
In order to assess the purity of reprecipitated barium sulfate
samples we test the “yield” or efficiency of sample conversion to
CO during pyrolysis using the area of 12C16O (mass 28) peak. First,
we compared the mean average area of 12C16O (mass 28) peak for
our BaSO4 lab standard and microcrystalline cellulose powder
(99.99%, Sigma-Aldrich) and find no difference. Since cellulose
pyrolysis should result in 100% conversion to CO (Gehre and
Strauch 2003) this also means that lab standard conversion to CO
is ~100% efficient. Then we compare the mean of the area of mass
28 peak for BaSO4 lab standard during each run (typically >10 per
run) with the area of 28 peak for each sample.
Chapter 2 29
2.3.2 Mineral composition and chemistry of precipitates and residuals
X-ray diffraction (XRD) is used to determine the mineral
composition of bulk impure barium sulfate, reprecipitated synthetic
barium sulfate and residuals after dissolution step.
Samples for XRD analysis were prepared by mixing a small
amount of material (several mg) in ethyl-alcohol or acetone in
order to make thick suspension which is than evenly smeared on
zero reflection silica plate. XRD analysis was done on a Philips
XRD system with a PW 1830 HT generator, a PW 1050
goniometer, PW3710 control electronics, and X-Pert system
software. The scanning speed was 2s per 1”; 2 theta ranges were 2-
60o/70o.
Field emission scanning electron microscope system ZEISS
SUPRA VP55 with INCA 350 Energy Dispersive X-ray detector
(EDS) is used for micron scale imaging and microanalysis of
reprecipitated barium sulfate and residuals after dissolution step.
For XRD we estimate that under our set up detection limit for
phases other than barite is between ~5wt% for quartz and 10 wt%
for iron oxide and sodium carbonate phases. However, for SEM-
EDS detection limit of specific elements are minimum 0.1wt%.
2.4 Results and Discussion
The outline of experiments conducted is presented in Table 2.2.
The results of individual experiment are discussed bellow.
Chapter 2 30
Experiment Result
Experiment 1: Dissolvingsynthetic barium sulfate withknown δ18OSO4 ratio.
No oxygen isotope exchange.
Up to -1‰ oxygen isotope offset due topresence of hydration water.
Experiment 2: Heatingreprecipitated barium sulfate at700oC for one hour
Complete removal of hydration water.
Experiment 3: Purification ofimpure barium sulfate.
Barium sulfate free of other mineralphases is obtained after onedigestion/reprecipitation cycle.
Table 2.2 The outline of experiments and results
There is up to -1‰ isotope offset in reprecipitated barium
sulfate associated with incorporation of solution water.
Synthetic barium sulfate used in experiments is our lab standard
which has previously measured δ18O isotope value of 11.9‰ (+/-
0.2) out of >200 measurements. Following sodium carbonate
dissolution and reprecipitation (Experiment 1), the reprecipitated
barium sulfate shows δ18O values of 10.9+/-0.4‰ (Table 2.3).
Reaction time BaSO4 recovered*[wt%]
Yield[%]
δ18Ocalibrated ‰
[VSMOW]
No. ofmeasurements
12h 95 109+/-5 11+/-0.2 8
24h 96 108+/-4 11.2+/-0.15 4
48h 95 107+/-3 10.7+/-0.3 8
Experiment 2
1h at 700 oC N/A 99+/-1% 11.8+/-0.2 8
Experiment 3
Reaction time BaSO4 recovered[wt%]
Yield[%]
δ18Ocalibrated no. ofmeasurements
24h 90 97+/-4 11.9+/-0.2 8
Table 2.3 Oxygen isotope data after sodium carbonate digestionexperiments. Note:* The recovery of BaSO4 is calculated as a ratioof the weight of reprecipitated BaSO4 over the weight of initiallyused BaSO4.
Chapter 2 31
This negative 1‰ offset between original and reprecipitated
barium sulfate may represent oxygen isotope exchange between
water and sulfate ion, or the incorporation of water in barium
sulfate precipitate. However, oxygen isotope exchange between
water and sulfate is expected to result in significant positive
oxygen isotope enrichment of up to 40‰ (Zeebe 2010, Lloyd
1967), and therefore it is more likely that the observed offset is due
to water trapped in barite crystals during precipitation. We can test
this by measuring yield of CO during pyrolysis, because water
contains more oxygen per weight than barium sulfate and therefore
the presence of water in barium sulfate crystalline lattice would be
reflected in increased yields. This increase in yield is indeed
detected (Table 2.3). While our barite standard typically has yields
~100+/-5%, the reprecipitated barium sulfate shows increased
yields of 107+/-5% (Table 2.3). Since the amount of water
incorporated in crystalline lattice is reflected in the yield, there is
also a linear correlation between increased yield and decrease in
measured δ18O ratio (Fig. 2.1).
Fig.2.1 Measured oxygen yield vs δ18O
Chapter 2 32
While the presence of water in barium sulfate precipitates has been
well known since the 1940s (Walton and Walden 1946a&b), this is
largely ignored in oxygen isotope studies (Hannon et al. 2008).
Hannon et al. (2008) showed that water bound in barium sulfate
crystals has the same δ18O signature as water in the mother
solution. Using the known δ18O signature of our barium sulfate lab
standard and water used in the experiments we can calculate the
percentage of water which was incorporated:
δ18Omeasured=δ18OSO4*ASO4+ δ18OH2O*BH2O where ASO4 and BH2O are
fraction of O atoms coming from sulfate and crystalline water
respectively. From known barium sulfate δ18OSO4 and our water
δ18OH2O = -6.3‰ we can calculate ASO4 and BH2O using:
BH2O=[δ18Omeasured- δ18OSO4*ASO4]/ δ18OH2O, which in our case gives
~0.95 for ASO4 and 0.05 for BH2O. If we transform this to molar
percentage of sulfate and water this gives ~83% for sulfate and
~17% of water or sulfate to water molar ratio of 4.65:1. Similar
high molar ratios of water in barium sulfate precipitated from
aqueous solutions were previously reported by Walton and Walden
(1946a&b). This water is present within the crystalline structure of
barium sulfate and therefore it cannot be removed through drying
or vacuum treatment requiring instead a prolonged heating at
elevated temperatures of >450oC (Walton and Walden 1946a&b;
Hannon et al. 2008).
Based on our δ18O isotope measurements, heating samples at
700oC for 1hr is sufficient to remove hydration water
(Experiment 2) Barium sulfate samples from Experiment 1
which showed δ18O isotope values up to~1‰ lower than original
barium sulfate are heated at 700oC for 1hr. Following this
treatment, the measured oxygen isotope composition of
reprecipitated barium sulfate is indistinguishable from untreated
Chapter 2 33
barium sulfate – 11.8+/-0.2, while the yield of 99+/-1% indicates
complete removal of water (Experiment 2 - Table 2.3).
One cycle of sodium carbonate digestion and reprecipitation is
sufficient to purify heavily contaminated barium sulfate
(Experiment 3) The results of purity assessment suggest that
reprecipitated barium sulfate does not contain other phases
detectible within the analytical detection limit (5-10 wt% in case of
XRD and ~0.1wt% in case of SEM-EDS) (Fig. 2.2&2.3). Fe (III)
oxyhydroxides and quartz are extremely insoluble at the pH of our
sodium carbonate dissolution (>pH 10) and therefore separation
during barite digestion is likely complete. This is corroborated by
δ18O isotope values of recovered barium sulfate which are equal to
untreated barium sulfate – 11.9+/-0.2 (Table 2.3). The
effectiveness of the method is attested by recovery of ~90% of
initial barite (Table 2.3).
Fig. 2.2 The typical XRD pattern of barium sulfate shows no linesof other mineral phases
Chapter 2 34
2.5 Conclusion
We show that a sodium carbonate digestion and subsequent
reprecipitation is suitable for purification of barite for the purpose
of oxygen isotope measurements. This method is highly efficient
in separating BaSO4 from other minerals with recoveries of the
original barite greater than 90%. We show that during precipitation
up to 17% (molar %) of solution water is incorporated in barium
sulfate. The impact of this water on reprecipitated sulfate δ18O
isotope value, depends on the difference between δ18O of solution
water and original sulfate. In this study, the difference between
water and sulfate δ18O is -18‰ and presence of hydration water
results in up to -1‰ oxygen isotope offset. We show that heating
samples at 700oC for 1hr is sufficient to remove this offset and
hydration water.
Fig. 2.3 SEM image of reprecipited barium sulfate. EDS analysis ofthe same sample gives composition of pure barium sulfate.
Chapter 3 35
Chapter 3
Pleistocene sediment offloading and theglobal sulfur cycle
3.1 Abstract
Quaternary sea level fluctuations have greatly affected the
sediment budgets of the continental shelves. Previous studies
suggested that this caused a considerable increase in the net loss of
shelf sediments. Here we use a high resolution record of sulfur
isotope ratios (34S/32S) of marine sulfate to evaluate the
implications of the so called “shelf sediment offloading” on the
global sulfur cycle. We expect that during high sea-level stands
high sediment accumulation will enhance pyrite formation, while
as sea level drops an increase in shelf erosion will stimulate pyrite
oxidation. Modeling of the high resolution, marine barite based
δ34S record suggests that erosion during sea level low stands was
only partly compensated by increased sedimentation during times
of rising sea level and sea level high stands. Furthermore, our data
suggests that shelf systems reached a new equilibrium state about
700 kyr ago, which considerably slowed or terminated shelf
sediment offloading.
3.2 Introduction
Pliocene-Early Pleistocene was characterized by relatively small
(20-50m) but frequent sea level changes in the precession and
obliquity frequency bands (Miller et al. 2011). During the Mid-
Pleistocene, this pattern changed and large sea-level fluctuations in
Chapter 3 36
the 100~ky frequency range became dominant. At times, global
sea-level dropped as low as 130-150m bellow present day sea level
(Miller et al. 2011), exposing large areas of shelf to weathering and
erosion. These sea level changes must have fundamentally altered
the balance between sedimentation and erosion on continental
shelves. Hay and Southam (1977) proposed that the repeated
exposure and inundation of the continental shelves has led to a
massive transfer of sediments from continental shelves to the deep
ocean. They estimate that as much as 5*1021g of detrital sediment
may have been removed by this so called “sediment offloading”
(Hay and Southam 1977).
Although intuitively a convincing hypothesis, a quantitative
analysis which includes the rates of sediment delivery to the deep
ocean is missing. Hay and Southam (1977) hypothesized that the
pattern of sea level falls controls the sediment delivery into the
deep ocean. For example, during the first large sea level drop,
sediment transfer would be exceptionally large and the intensity of
sediment erosion will decrease with consequent events, as the
sediment reservoir available for erosion will become depleted (Hay
and Southam 1977, Hay 1998, Hay et al. 2002).
Adding/removing sediments from the shelf is closely coupled to
the burial/erosion of pyrite in those sediments. During
interglacials, high sea levels result in expanded shelf areas.
Coincidentally, the shelf areas are characterized by high pyrite
burial rates (Jørgensen 1982; Berner 1982). During sea level
lowstands, shelf areas are much smaller and formerly water
covered shelf areas are being replaced by low-lying coastal plains
transected by rivers. This affects sedimentary sulfur cycling in two
ways: 1) pyrite burial is reduced; 2) fine grained and unlithified
sediments in the exposed shelf (de Haas et al. 2002) are eroded
(Gibbs and Kump 1994; Foster and Vance 2006) and pyrite and
Chapter 3 37
organic sulfur (S) contained in the eroded sediments will be
oxidized.
Pyrite formation is mediated by microbial sulfate reduction (MSR)
and microbial sulfur disproportionation, which produce a large S-
isotope ratio difference between pyrite and concomitant seawater
sulfate (up to 70‰, Wortmann et al. 2001, Rudnicki et al. 2001,
Böttcher et al. 2001&2005, Brunner and Bernasconi 2005, Sim et
al. 2011). Accordingly, the burial of large amounts of pyrite will
result in a more positive sulfur isotope composition of seawater
sulfate (δ34S), whereas the oxidation of large amounts of pyrite will
cause a decrease of the seawater sulfate δ34S. In the following, we
take advantage of this relationship and use past changes of
seawater sulfate δ34S to track changes in pyrite burial/oxidation on
continental shelves and their relation to changes in global sea level.
Past changes of seawater sulfate δ34S are continuously recorded by
authigenic marine barite crystals (Paytan et al. 1998). Here we use
a new high resolution marine barite δ34S record of the last 3
Million years (Ma) to delineate the onset and duration of these
changes, which allows us to validate/test the shelf sediment
offloading hypothesis.
3.3 Geological Setting
We use sediment samples from Eastern Equatorial Pacific Sites
849D (0°.10993'N, 110°.31.197'W) and 851B (2°46.223′N,
110°34.308′W) obtained by advanced piston coring (APC) during
Leg 138 of the Ocean Drilling program (ODP). Site 849D is
located below a highly productive equatorial divergence zone at a
depth of 3839m (Mayer et al. 1992). Site 851B is located within
the northern limit of western-flowing South Equatorial Current at
Chapter 3 38
the depth of 3760m, within the equatorial high productivity zone
(Mayer et al. 1992).
Sediments at both locations consist of diatom nannofossil ooze. In
the past 5 Ma both sites have been above the carbonate
compensation depth (CCD), but sediments were subject to variable
rates of carbonate dissolution controlled by regional and temporal
lysocline changes (Mayer et al. 1992). Sedimentation rates were
moderate since the late Pliocene varying between 25-35m/Myr at
Site 849D and 15-20m/Myr at Site 851B.
Marine barite forms in the water column recording seawater S
isotope ratios (Griffith and Paytan 2012). After burial in the
sediment barite is stable during diagenesis except in environments
with high rates of sulfate reduction where sulfate in pore waters is
exhausted (e.g.,Torres et al. 1996; Griffith and Paytan 2012). In
sulfate reducing environments, barite is soluble releasing barium to
solution. This barium will diffuse and barite will reprecipitate
forming diagenetic barite with typically anomalously high δ34S
signatures (Paytan et al., 2002). Sites 849D an 851B are
characterized by low organic matter (OM) concentrations and high
sulfate concentrations in the interstitial waters (0.2 wt%, OM, 25-
28mM SO42-, Mayer et al. 1992). These conditions suggest that
sulfate reduction was not prevalent and that the barite samples in
sediments at these sites are not affected by barite dissolution and or
reprecipitation and thus originate from sinking particles in the
water column (e.g. marine barite).
3.4 Methods
The δ34S of seawater sulfate is uniform throughout the ocean
reflecting the long residence time of marine sulfate (~ 10-20Myr,
Jørgensen and Kasten 2006) compared to the ocean mixing time
Chapter 3 39
(~1600 yrs). The evolution of the δ34S of sulfate thus serves as a
proxy for past changes in the sulfur cycle (Paytan et al.
1998&2004; Wortmann and Chernyavsky 2007; Wortmann and
Paytan 2012). The use of the δ34S of authigenic marine barite
crystals allows for the reconstruction of a continuous seawater
sulfate δ34S record (Paytan et al., 1998&2004).
Here, we use the sequential dissolution method of Paytan et al.
(1996) to extract barite crystals from marine sediments. We have
modified the original method to better address concerns about
pyrite contamination (DeBond et al. 2012) and to improve the
workflow. Unlike the original method organic matter is removed
by heating the sample in the furnace at 700oC instead of oxidizing
it with hot bleach overnight. We also, change the order of the
extraction steps so that iron and manganese oxyhydroxides are
now dissolved with 0.2 N hydroxylamine hydrochloride in 25%
acetic acid at the end of the process. Between steps we centrifuge
samples, decant the supernatant and wash the residue three times
with ultrapure deionized water.
In order to prevent oxidation of reduced sulfur during the
carbonate leaching process, we add 50ml of 5% tin chloride
(SnCl2) solution to 1l of HCl to maintain reducing conditions
during the leaching step (instead of bubbling N2 gas as in the
original procedure). In addition, the HCl is flushed with Argon
before and during the carbonate dissolution. This is the step we
expect pyrite to be prone to oxidation if present in the sediments.
We examine the purity of the extracted barite with X-ray
diffraction. Furthermore we check for presence of diagenetic barite
using SEM imaging/EDS analysis (Paytan et al. 2002). If samples
contain residual mineral phases like rutile, we redissolve the
extracted barite with sodium carbonate and subsequently re-
Chapter 3 40
precipitated pure BaSO4 (Breit et al. 1985, see Chapter 2).
3.4.1 Age model
Sample ages are estimated using age model by Shackleton et al.
(1995). Their age model is made using magnetostratigraphy,
biostratigraphy, gamma ray attenuation porosity measurements
(GRAPE) coupled with orbital tuning of density estimations and
δ18O records of benthic foraminifera.
3.4.2 Isotope analysis
Sulfur isotopes are analyzed with a continuous flow isotope ratio
mass spectrometer system (CFIRMS) using an Eurovector
Elemental Analyzer (EA) coupled via a Finnigan Conflo III open
split interface to a Finnigan MAT 253 mass spectrometer. Solid
barite samples (200µg) are mixed in a tin cup with ~600µg of
vanadium pentoxide (V2O5) powder and introduced into the EA,
where the sulfate from barite (BaSO4) is converted to sulfur
dioxide gas (SO2) by flash combustion at 1700oC in an oxygen
atmosphere. Measurements are calibrated using international
sulfate standards NBS 127, IAEA SO5 and IAEA SO6 (relative to
Vienna Canyon Diablo Troilite-VCDT, +21.1 ‰, +0.49 ‰, –34.05
‰, respectively, Coplen et al. 2001) and an in-house synthetic
BaSO4 (Sigma-Aldrich) standard (8.6 ‰, VCDT). Repeated
measurements of the in-house standard (typically >10
measurements per run) and international standards (3-4
measurements per standard per run) yield an average
reproducibility of 0.15‰ (1 standard deviation-σ).
3.4.3 Statistical Analysis
The isotope data includes errors in sample assigned ages and
uncertainties of how well a single measurement represents the
seawater sulfate δ34S ratio. Note that the latter uncertainty not only
Chapter 3 41
includes analytical precision (which can be quantified), but also
sample origin, sample handling, and sample extraction. We
therefore have to assume that each measurement carries an
unknown error (or noise).
However, the δ34S of seawater sulfate at any given time (t) depends
to a certain degree on the δ34S of sulfate at a given time before (t-
∆t). This allows us to apply a “local regression smoothing”
technique (LOESS, Cleveland 1979) to estimate the likely value
for the δ34S of sulfate at any time of interest.
We use the default LOESS module provided by the statistical
software package R (R Core Team 2012). The 95% confidence
interval is calculated for each data point from the standard errors
returned by the LOESS function.
3.4.4 Sulfur cycle model
We describe the sulfur cycle using the following mass conservation
equation:
ddt
M SO4 (t )=Fwp (t )−Fbp (t ) +( Fwe+Fv−Fbe ) (1)
where MSO4 denotes mass of sulfate in the ocean calculated from
the sulfate concentration and the ocean volume; Fwp and Fwe denote
the pyrite and evaporite weathering input respectively; FV denotes
the volcanic flux, and Fbp and Fbe denote the of pyrite and evaporite
precipitation flux respectively.
We can formulate a similar mass conservation equation for
respective isotopes of sulfur (32S and 34S), e.g. (2):
ddt
M SO432 ( t )=Fwp
32 S ( t )−Fbp32 S ( t )+( F we
32 S+Fv32 S−Fbe
32 S ) (2)
Chapter 3 42
where M32SO4 denotes mass of 32S in the ocean calculated from
known mass of sulfate and its isotopic composition; Fwp32S and
Fbp32S denote 32S input from pyrite weathering and 32S removal by
pyrite burial respectively;FV32S denotes the 32S input from volcanic
flux; Fwe32S and Fbe
32S denote the 32S input from evaporite
weathering and removal by evaporite precipitation respectively.
In order to achieve an initial steady state we use modern values for
the sulfur isotope composition and volume of the fluxes as
boundary conditions (e.g., Berner 1982; Kump 1989; Hansen and
Wallmann 2003; Bottrell and Newton 2006; see Table 3.1. for
additional details). Note that the average isotopic composition of
buried pyrite (δ34Spyrite) is adjusted so that other fluxes are in steady
state.
From steady state condition (3):
(3)
we can calculate the average isotopic composition of pyrite
(δ34Spyrite) using 4&5:
Fbp (t )=Fbp32 S (t )+Fbp
34 S (t ) (4)
Fbp34 S (t )=Fwe
34 S (t )+Fwp34 S (t )+Fv
34 S (t )−Fbe34 S (t ) (5)
This yields δ34Spyrite of –17‰, which is in good agreement with
previous estimates (Strauss 1997; Seal 2006; Leavitt et al. 2013).
The average sulfur isotopic composition of pyrite tells us about the
offset between δ34Sseawater and δ34Spyrite, which represents the sulfur
isotope fractionation during sulfate reduction. In our case this
offset is –39‰. It is kept constant during subsequent non-steady
state runs.
ddt
M SO4 (t )=0
Chapter 3 43
Flux
Initial flux–steady state
[mol SO4/year]
Isotopiccomposition [‰]
References
δ34S (VCDT)
Pyriteweathering
1.9x1012 -14Kump 1989; Garrels and Lerman 1981; Petschand Berner 1998; Seal 2006;
Evaporiteweathering
1x1012 19
Kump 1989; Garrels and Lerman 1981; see also
Hansen and Wallmann 2003; for δ34Sevap see
Claypool et al. 1980
Volcanic flux 0.34x10123 Hansen and Wallmann 2003 and references
therein
Pyrite Burial 2x1012 -17*
Bottrell and Newton 2006; Turchyn and Schrag2004; see also Berner 1982; Petsch and Berner1998
Evaporiteprecipitation
1.24x1012 22& Kump 1989; see also Garrels and Lerman 1981;Petsch and Berner 1998
Note: The global sulfur fluxes are not well constrained. Our fluxes are well within the range of previouslypublished estimates (see reference list). The initial sulfate concentration is 27 mmol/l which is in therange of estimates based on fluid inclusions in halite by Horita et al. (2002) and Zimmermann (2000) forlate Miocene/Pliocene from.
*Steady state value calculated as a function of other known fluxes (see text).
&This is used for model initialization. Later on isotope composition of respective seawater sulfate.
Table 3.1 Model fluxes and sulfur isotope ratios in the steady state
3.4.5 Model forcing
The objective of our model is to evaluate the effect of sea level
changes on pyrite burial and weathering on the continental shelf
and use these changes to track shelf sediment offloading. This
requires that we consider two boxes for pyrite burial/erosion. The
first box allows for pyrite burial and erosion as a function of ocean
covered shelf area, whereas the second box describes pyrite burial
Chapter 3 44
in the deep sea. We assume that up to 90% of the total amount of
pyrite buried occurs in the continental shelf (e.g. Berner 1982;
Canfield et al. 1992; Jørgensen 1982&1983). In deep water
environments, the supply of OM is greatly reduced, and MSR and
pyrite burial rates are orders of magnitudes smaller than in the
shelf. In a first approximation, we can therefore treat pyrite burial
in the deep-water box as constant.
There are, however, caveats to this assumption. Pyrite burial could
increase if we increase the delivery of reactive OM to the deep
ocean by increasing export production or by introducing anoxic
conditions. Although, some researchers argued for increased
productivity (e.g. Murray et al. 1993; Filippelli et al., 2007) this is
disputed by others (e.g, Nameroff et al., 2004; Francois et al.,
1997; Dean et al., 1997). On the other hand, while redox proxies
support decreased oxygen levels in some parts of the deep glacial
ocean (François et al., 1997; Thomson et al., 1990; Mangini et al.,
2001; Dean et al., 1997), other areas, specifically continental
margins, show the opposite trend (i.e., higher oxygen levels,
Ganeshram et al. 2002). Overall Pleistocene trends of deep sea
oxygenation are difficult to assess because they are dependent on
several factors including circulation patterns, local productivity
and temperature which show a high degree of temporal and spatial
variability (e.g., Jaccard et al. 2009&2010; Keeling et al. 2010).
For the purpose of this model we thus assume that pyrite burial in
abyssal environments can be treated as constant.
Sediment offloading will also introduce pyrite and OM into the
abyssal box. However this redistributed pyrite cannot be counted
twice, and thus will not alter the overall pyrite burial. The case for
OM is however more involved, as the additional OM will promote
increased MSR. The extend of this OM support of MSR is
however less clear as the re-mobilized OM is dominantly
Chapter 3 45
refractory in nature.
We use the sea level estimates of Miller at al. (2011) to calculate
the size of the shelf area. The latter will then be used to force the
fluxes affected by sea level change: pyrite weathering and burial.
For the purpose of this study which focuses on the global average
sea level change, local sea level variations resulting from local
tectonic processes such as isostatic rebound, can be neglected.
We calculate the shelf area (A*s) as a function of sea level at any
given point in time using a model cubic polynomial fit (6 - after
Bjerrum et al. 2006) of the global mean hypsometric curve from
ETOPO5 (National Geophysical Data Center 1988):
A s*=A∗(1−0.307∗z3
+0.624∗z2+0.43∗z+0.99991) (6)
where A is the area of the ocean ~3.6*1014 m2 and z corresponds to
the sea level (m).
We then divide the fluxes which are affected by sealevel change
(pyrite weathering and pyrite burial) into two boxes. The first box
corresponds to constant weathering of pyrite on continents and
constant pyrite burial in continental slope and pelagic
environments. The second box represents pyrite weathering and
burial on the shelf and varies in proportion to calculated shelf area
(7-8). The pyrite weathering flux is calculated as follows (7):
Fwp* =Fwp
o ∗[1+Amax−A s
*
A s* ] (7)
where Amax is the maximum extent of shelf area; F *wp is the
calculated pyrite weathering flux corresponding to shelf change Aos
– A*s. Fo
wp is the minimum pyrite weathering flux corresponding to
maximum shelf extent (Amax). We assume Fowp to be 90% of the
Chapter 3 46
steady state value calculated for the modern conditions. This
assumption is based on the estimates of maximum shelf flooding
area in the past 3Ma. During times of maximum flooding the sea
level may have been up to 10m higher than the current sea level
(Miller et al. 2011), corresponding to a 10% larger shelf area. At
present some pyrite weathering takes place on this previously
inundated shelf area. Therefore, we assume that during times of
maximum extent of shelf inundation, pyrite weathering was lower
and only 90% of today, because pyrite rich shelf sediments were
inundated.
The pyrite burial flux is calculated as follows (8):
Fbp*=F bp-abyssal+Fbp-shelf∗
A s*−Amin
Amax−Amin
(8)
where Fbp-abyssal corresponds to the minimum pyrite burial which
takes place in slope and abyssal environments at minimum shelf
extent in this case 0.85*1012 molS/yr, Fbp-shelf is the portion of pyrite
that is buried on the shelf at the maximum shelf extent (Amax)
assumed to be 1.65*1012 molS/yr; Amin is minimum shelf extent.
These numbers are based on present day estimates of sulfate
reduction rates and pyrite burial in sediments at different water
depth (Jørgensen 1982; Jørgensen and Kasten 2006; Thullner et al.
2009).
When considering pyrite burial on the shelf we distinguish
between old pyrite and pyrite which can be re-mobilized. The
former represents the total shelf storage of pyrite (~1019 molS,
Charlson et al. 1992), while the later corresponds to the amount of
pyrite in shelf sediment that is offloaded in response to glacial sea
level changes. Hay and Southam (1977) estimate that 5*1021g of
shelf sediment was offloaded during Pleistocene. If we take an
average concentration of pyrite in shelf sediments as 0.2-0.3%
Chapter 3 47
(Berner 1982) this corresponds to a pyrite reservoir of 6*1017 mol
S. In agreement with modern observations of fast pyrite oxidation
in reworked shelf sediments (e.g., Amazon shelf, Aller et al. 1986)
we assume that that the resuspension of pyrite bearing sediments
promotes essentially complete oxidation of all pyrite in the
respective sediment volume.
3.5 Results and Discussion
The δ34S composition of seawater sulfate is uniform throughout the
ocean reflecting the long residence time of marine sulfate (~
10Myr, Jørgensen and Kasten 2006) compared to the ocean mixing
time (1600 yrs). The evolution of the δ34S of sulfate thus serves as
a proxy for past changes in the sulfur cycle (Paytan et al.
1998&2004; Wortmann and Chernyavsky 2007; Wortmann and
Paytan 2012).
Our results show that between 3Ma and ~1.5Ma the seawater
δ34SSO4 values fluctuate around ~22‰ (VCDT) with a standard
deviation (1 σ) of 0.2‰. In the interval between 1.5Ma and 0.7Ma
we observe a steady decline from ~22‰ (VCDT) to 20.7‰
(VCDT) (Fig. 3.1). This minimum is followed by an upwards trend
from 20.7‰ (VCDT) at 0.7Ma to 21.1‰ (VCDT) at 0.6Ma. In the
past 0.3Ma there is a decline from 21.1‰ (VCDT) to ~20.7‰
(VCDT) in the most recent sediments (Fig. 3.1).
Chapter 3 48
Considering the long residence time of sulfate in the ocean
(~107yr), a -1‰ shift between 1.5Ma and 0.7Ma, implies a massive
change in the balance of the sulfur input/output fluxes. Possible
explanations include: a) an order of magnitude increase of volcanic
and hydrothermal S release; b) a drastic increase in pyrite
weathering; c) a massive decrease in pyrite burial. An order of
magnitude increase of volcanic S-input is incompatible with the
geological record which shows no evidence for intensification of
volcanic activity in the Pleistocene compared to the earlier periods
of the Cenozoic (Kaiho and Saito 1994; Mason et al. 2004; Cogné
and Humler 2006; White et al. 2006).
Pyrite weathering could have been affected by changes in
continental erosion rates in the past 3Ma (e.g., Raymo et al. 1988).
Fig. 3.1 Sulfate δ34S results. The circles denote the measuredseawater sulfate δ34S ratio, the shaded area the 95% confidenceinterval of a LOESS approximation of the “true” δ34SSO4
Chapter 3 49
However recent evidence suggest that these changes were minor
(e.g., Foster and Vance 2006). Nonetheless, pyrite weathering is
not restricted to the continental interiors, but happens each time we
expose marine sediments to erosion.
Glacially induced sealevel drops will expose large swaths of
previously ocean covered shelf areas to subaerial weathering and
erosion. Coincidentally, the shallow shelf is also the location of the
highest pyrite burial rates (Jørgensen 1982). First order
approximations show that shelf area related changes in pyrite
burial/weathering rates are indeed large enough to explain the
observed variations in the marine sulfate δ34S.
In this context, it is interesting to note that the timing of the δ34S
shift roughly coincides with the Middle Pleistocene Transition
(MPT) period ~ 1.3 – 0.7 Ma (e.g. Clark et al. 2006). In this period
the climate system switched from 41kyr to 100kyr glacial-
interglacial periodicity (Lisiecki and Raymo 2005; Clark et al.
2006 and references therein). The 100kyr variations are primarily
controlled by the growth and collapse of ice sheets which gradually
increased in size across the MPT compared to earlier periods of
Pleistocene (Clark et al. 2006). The increase in ice volume resulted
in larger sea level fluctuations (up to 150m, e.g. Miller et al. 2011)
exposing large areas of continental shelf to weathering and erosion
which previously remained fully marine for tens of millions of
years (Clark et al. 2006).
In the following we use a box model to investigate the hypothesis
that the changes in the δ34S composition of marine sulfate are
driven by changes in pyrite burial and weathering.
We first calculate the ocean covered shelf area as a function of sea
level using the sealevel estimates by Miller et al. (2011). In a
subsequent step we calculate pyrite burial/weathering fluxes as a
Chapter 3 50
function of shelf area (see Methods section for a detailed
description).
If we lower the sea level by e.g., 100m (typical for the glaciations
in the past 1Ma, see Miller et al. 2011) the available shelf area is
reduced by 50%. The exposure and erosion of previously water
covered shelf areas, results in the reoxidation of sulfide minerals
(i.e., pyrite), which increases pyrite weathering flux from 1.9*1012
mol S/yr to 3.9* 1012 mol S/yr. At the same time, pyrite burial
decreases by ~50%, from 2*1012 mol S/yr at steady state to
1.1*1012 mol S/yr.
We start our model at 3 Ma (Late Pliocene) and forward model the
resulting sulfur isotopic composition of seawater sulfate as a
function of the sea level estimates published by Miller et al.
Fig. 3.2 Model output – seawater sulfate δ34S composition (solidline). The circles denote the measured seawater sulfate δ34Scomposition, the shaded area the 95% confidence interval of aLOESS approximation of the “true” δ34SSO4 composition
Chapter 3 51
(2011). Our model results capture the shape and magnitude of the
δ34S signal quite well (See Fig. 3.2). Specifically the decline of δ34S
values between 1.5 and 0.7 Ma is well represented. During this
time interval, larger sea level fluctuation of up to -150m (Lisiecki
and Raymo 2005; Clark et al. 2006; Miller et al. 2011) drastically
increase the transfer of shelf sediments into the deep ocean.
During the interglacial periods, sea level rise creates large
accommodation volumes, but Hay and Southam (1977) proposed
that the creation of accommodation space outstripped sediment
supply, resulting in a net loss of shelf sediment. This interpretation
is supported by our δ34S data, which suggest that the balance
between pyrite weathering and pyrite burial shifts in favor of pyrite
weathering with increasing sea level variations.
Interestingly, the steady decline of the seawater sulfate δ34S ratios
appears to slow down or to stop around ~700ka (Fig. 3.2). If we
accept the premise that pyrite burial/oxidation are linked to
sedimentation and subaerial shelf erosion, the stabilization of
seawater sulfate δ34S composition implies that sediment offloading
has come to an end, or in other words, shelf sedimentation and
erosion dynamics must have reached a new equilibrium, adapted to
the climate driven 100~ky sea level cycles.
3.6 Conclusions
This study shows that the intensification of Quaternary glaciations
in the past 1.5Ma and concomitant periodic changes in shelf area,
likely affected the balance of weathering fluxes of sulfate/sulfide
and the burial of pyrite. Quantitative modeling based on the sea
level estimates by Miller et al. (2011) and on our newly established
high resolution sulfur isotope record suggests that during glacial
periods, pyrite weathering drastically increases as a result of
subaerial shelf erosion. The increased erosion rates are not fully
Chapter 3 52
compensated by increased pyrite burial during sealevel high
stands.
The declining seawater δ34S ratios support the idea that the
transition to the climate driven 100kyr sea level variations resulted
in a net reduction of shelf sediment volume (i.e., the so called
“shelf sediment offloading”, Hay and Southam 1977).
Our data shows that the steady decline in the seawater δ34S ratios
stops around 700ka. We consider it likely that this stabilization
indicates the termination of the massive net “sediment offloading”
(Hay and Southam 1977), and heralds a new equilibrium between
shelf erosion during sea level lowstands and sediment resupply
during sea level high stands. The resuspension of previously
deposited sediments oxidized large amounts of pyrite back to
sulfate (Turchyn and Schrag 2004). Our model results suggest that
this would have increased the marine sulfate concentration by ~1.5
mM. This number is smaller than previous estimates (5mM,
Turchyn and Schrag 2004) but in good agreement with sulfate
concentration estimates based on fluid inclusions (Brennan et al.
2013) and estimates of ocean alkalinity budget based on boron
isotopes (Hoenisch et al. 2009).
It is likely that this same shelf sediment offloading may have
impacted additional elements that are predominantly buried in
shelf sediments such as phosphorus and carbon (e.g., Berner 1982,
Wollast 1991, Ruttenberg 2003) with possible implications to their
biogeochemical cycles as well as ocean productivity.
Chapter 4 53
Chapter 4
Shelf area fluctuations and relatedimpacts on microbial sulfur cycling
4.1 Abstract
Chemical reaction rates and the composition of the microbial
community in marine sediments are a function of electron acceptor
concentrations and the availability and reactivity of organic carbon.
High organic carbon burial in nearshore and shelf environments
stimulates higher microbial activity rates at these locations relative
to deep sea sediments. The areal extent of shelf environments is
greatly affected by sea level variations and dynamic topography.
Expanding on previous work (Turchyn and Schrag 2004&2006),
we provide a revised δ18O sulfate record for the last ~4Ma and
explore the effects of Quaternary sea level variations on the global
sulfur cycle. Our model suggests that microbial sulfur cycling
changes proportionally with shelf area, resulting in a 15%
reduction of microbial sulfur cycling over the last 2 Ma. This
results in a 1-1.5‰ drop in the marine sulfate δ18O. While further
work is needed to understand how shelf area changes affect the
cycling of carbon, phosphorous and other elements, our results
highlight the dynamic role of continental shelves in the global
biogeochemical cycles.
Chapter 4 54
4.2 Introduction
Continental shelves represent only 7-8% of the world’s ocean
floor, but are responsible for up to 80-90% of organic matter (OM)
burial (e.g., Berner 1982, Hedges and Keil 1995, Wollast 1991).
High burial rates on continental shelves are primarily a function of
high sedimentation rates, coupled with high organic matter supply
e.g., detrital organic matter from rivers and/or high primary
productivity promoted by terrestrial nutrient supply and coastal
upwelling (Hedges and Keil 1995). The abundant supply of
organic carbon and physical mixing by bioturbation results in high
rates of microbial activity in nearshore sediments (Goldhaber et al.
1977). It is generally assumed that ~ 80% of all microbially
mediated organic matter respiration (OM) at the ocean floor takes
place in shelf sediments (e.g., Jørgensen 1983). Since OM
respiration is the principal process determining whether carbon
(C), and phosphorous (P) will be buried, or returned to the ocean,
continental shelf areas play an important role in carbon and
phosphorous cycling. Furthermore, anaerobic carbon
mineralization by sulfate reducing microbes, links OM respiration
to the oxygen and sulfur cycles as well (Berner 1982, Wortmann
and Chernyavsky 2007, Wortmann & Paytan, 2012).
Changing the available shelf area not only affects organic matter
burial rates and marine redox capacity (Ozaki and Tajika 2013),
but will also affect the relative importance of different organic
matter remineralization pathways.
In the following, we investigate the influence of sea level
variations on microbial sulfur cycling in marine sediments. We
choose sulfur because it is responsible for 50% of all OM
remineralization in marine sediments (e.g., Jørgensen 1982), and
we can use stable isotopes to distinguish microbial and abiotic
Chapter 4 55
sulfur cycling.
4.3 Background
Decomposition of organic matter is mediated by a series of
microbial respiration processes, which are controlled by the free
energy yield of the respective redox reaction (e.g., Froelich et al.
1979). The energetically most favorable redox reactions are
aerobic respiration and denitrification, but both reactions are
limited by the low concentration of the respective electron
acceptors, oxygen and nitrate. Sulfate reduction, while
energetically less favorable is promoted in the absence of oxygen
and nitrate by high seawater sulfate concentrations (~28 mM in the
present day ocean) and thus sulfate reducing bacteria oxidize about
50% of organic carbon in recent marine sediments (e.g., Jørgensen
1982, Sørensen et al. 1979, Canfield et al. 1993).
Microbial sulfate reduction (MSR) reduces sulfate to hydrogen
sulfide, the majority of which is oxidized within the surface
sediments (Jørgensen 1982) by the group of processes collectively
referred hereafter as an oxic sulfur cycle. Both, MSR and the oxic
sulfur cycle, affect the oxygen isotope ratio of dissolved sulfate
(δ18OSO4) in the interstitial water (e.g. Fritz et al. 1989, Thamdrup
et al. 1993&1994, Blake et al. 2006, Bottrell and Newton 2006)
and we therefore refer to these processes as microbially mediated
sulfur cycling (MMSC).
Chapter 4 56
Fig. 4.1 Major fluxes controlling oxygen isotope ratio of seawatersulfate Note: the arrow width is proportional to the flux. Flux dataafter Berner 1982; Kump 1989; Hansen and Wallmann 2003;Jorgensen and Kasten 2006; see Methods section for full list); Allisotope values are relative to Vienna Standard Oceanic MeanWater [VSMOW].
The MMSC modified pore water sulfate will diffuse or get mixed
into the overlying water column, affecting the δ18OSO4 ratio of the
ocean sulfate reservoir. This sedimentary flux is considerably
larger than any of the other sulfate input and output fluxes (Fig.
4.1).
The MMSC is closely related to organic matter supply which
represents important distinction between shallow and deep
environments. High organic matter supply in shallow environments
promotes rapid sulfur turnover which favors microbially mediated
disproportionation (Jørgensen 1990; Thamdrup et al. 1993&1994;
Canfield and Thamdrup 1994, Canfield and Thamdrup 1996) and
microbially mediated oxidation which utilizes either nitrate,
Mn(IV) or Fe(III) (Kasten and Jørgensen 2006; Balci et al. 2012).
In the deeper settings, the supply of OM is greatly reduced, and
Chapter 4 57
abiotic oxidation processes become more important (Blake et al.
2006).
While it is difficult to quantify the relative importance of each
process, we can use sulfate δ18OSO4 signatures to broadly
differentiate between sulfate modified by abiotic processes and
sulfate that is affected by microbially mediated processes i.e.,
isotope exchange during microbial sulfate reduction, microbial
oxidation and disproportionation (Fig. 4.2). The δ18O ratios of
sulfate produced during abiotic oxidation is close to the oxygen
isotope ratio of ambient seawater (~ 0‰, Taylor et al. 1984; Van
Stempvoort and Krouse 1994). On the other hand, microbial
processes (disproportionation, microbial sulfur oxidation, MSR)
offset the δ18OSO4 ratio by up to 29‰ (e.g. Fritz et al. 1989, Van
Stempvoort and Krouse 1994, Böttcher and Thamdrup 2001,
Böttcher et al. 2001, Wortmann et al. 2007, Turchyn et al. 2010,
Balci et al. 2012).
The δ18OSO4 ratio of seawater sulfate is controlled by the flux of
MMSC isotopically modified sulfate, input from continental
weathering and volcanic emissions and output through evaporite
and carbonate precipitation (Bottrell and Newton 2006). Since the
residence time of marine sulfate bound oxygen (~ 500 ky,
Jørgensen and Kasten 2006) is short, compared to rate of oxygen
isotope exchange between dissolved seawater sulfate and ambient
water (106 to 107 yrs, Lloyd 1967; Lloyd 1968; Chiba and Sakai
1985; Van Stempvoort and Krouse 1994), dissolved sulfate
preserves its original δ18OSO4 signature. Furthermore, the marine
residence time of sulfate bound oxygen exceeds the ocean mixing
time (1600 yrs) by two orders of magnitude, so that the ocean can
be considered a well mixed reservoir. The seawater sulfate δ18O
ratio thus reflects the balance of its input and output fluxes at any
Chapter 4 58
given point in time.
In turn, the seawater sulfate δ18O ratio will be recorded by barite
(BaSO4) crystals forming in the water column. It is generally
assumed that these authigenic barites record the δ18OSO4 ratio of
ambient seawater sulfate (Turchyn and Schrag 2004&2006), which
enables us to trace the evolution of seawater sulfate oxygen isotope
ratio with time.
Fig. 4.2 Simplified schematic representation of the microbially
mediated sulfur cycling (MMSC). Note: The δ18O ratios of
sulfate produced during inorganic oxidation is close to that ofambient water (~ 0 permil, Taylor et al. 1984; Van Stempvoortand Krouse 1994) whereas microbial disproportionationimprints a distinct isotope offset (+8‰ to +21‰ relative toambient water, Böttcher and Thamdrup 2001, Böttcher et al.2001, Böttcher et al. 2005). The microbial oxidation takesplace in the presence of oxidant (O2, Fe(III) and Mn(IV)) andimparts a variable isotope offset (up to 8‰, Van Stempvoortand Krouse 1994, Balci et al. 2012). The reverse arrowbetween sulfate and sulfide represents oxygen isotopeexchange between sulfate and water during MSR (Fritz et al.1989)
Chapter 4 59
Although the marine barite record of seawater sulfate δ18O ratios
lacks resolution and precision to resolve the effect of shelf area
changes during individual glacial-interglacial cycles, we
hypothesize that it records the accumulative effect over
Pleistocene.
Here we use a highly resolved and revised marine barite sulfate
δ18O record to expand on the earlier work of Turchyn and Schrag
(2004&2006) to track the effect of Pleistocene shelf area changes,
on microbial sulfur cycling.
4.4 Geological settings
Once formed, barite is a very stable mineral. However, sediments
where microbial sulfate reduction has used all dissolved sulfate,
are prone to early diagenetic barite dissolution (e.g., Paytan and
Griffith 2007; Torres et al. 1996). Under these conditions barite can
dissolve and reprecipitate as diagenetic barite elsewhere in the
sediment column (Paytan et al., 2002). This process will change
the barite δ18O signature, and it is thus important to select samples
from locations where the concentration of sedimentary OM is low
enough to prevent exhaustion of the dissolved sulfate pool.
We use core samples from leg 138 of the Ocean Drilling program
(ODP), Site 849, which is located about 860 km west of the East
Pacific Rise (0.1831oN, 110.5197 oW) at a depth of 3850m. At Site
849 organic matter concentrations are low and interstitial water
sulfate concentrations remain high with depth (25-28mM, Mayer et
al. 1992). We are thus certain that our barite samples are not
affected by barite dissolution and or reprecipitation. This was
confirmed with the SEM imaging analysis of crystal size and
morphology which showed an absence of tabular, large (>10µm)
crystals suggestive of diagenetic barite (Paytan et al. 2002).
Chapter 4 60
The core top samples used in this study represent a wide range of
sites and locations (see Chapter 5 data tables).
4.5 Methods
We separate barites from sediments following the sequential
dissolution method by Paytan et al., (1996). Samples are treated
with: (I) HCl to remove carbonates; (II) sodium hypochlorite to
oxidize organic matter; (III) hydroxylamine hydrochloride to
remove iron and manganese oxyhydroxides; (IV) concentrated HF-
HNO3 mixtures with ratios 1:2, 1:1, 2:1 to remove silicates; (V)
aluminum chloride in 1M HNO3 to remove fluorides; (VI) heated
at 750oC in the furnace for 1h to oxidize highly refractory organic
matter and remove water sorbed on or trapped in barite crystalline
lattice. After steps I-V, we centrifuge the samples, decant the
supernatant and wash the residue three times with ultrapure
deionized water. We examine the purity of the extracted barite with
X-ray diffraction. Furthermore we check for presence of diagenetic
barite using SEM imaging/EDS analysis (Paytan et al., 2002).
To avoid contamination with residual mineral phases like rutile, we
redissolve the extracted barite with sodium carbonate (Breit et al.
1985) and subsequently re-precipitated pure BaSO4 (see Chapter 2
of this thesis). Lastly, we use samples with a known δ18O (our
BaSO4 lab standard) to ensure that the sample preparation did not
alter the original oxygen isotope ratios.
4.5.1 Age model
Sample ages are determined using an age model by Shackleton et
al. (1995). Their age model is constructed using
magnetostratigraphy, biostratigraphy, gamma ray attenuation
porosity measurements (GRAPE) coupled with orbital tuning of
density estimations and δ18O records of benthic foraminifera.
Chapter 4 61
4.5.2 Isotope Analysis
We analyze the oxygen isotope ratios with a continuous flow
isotope ratio monitoring mass spectrometry system using a
Hekatech high temperature pyrolysis furnace coupled via a
Finnigan Conflo III open split interface to a Finnigan MAT 253
mass spectrometer. Solid barite samples (~200µg) are weighed into
a silver capsule and introduced into the HT furnace where BaSO4
is converted to CO gas at 1350o C under a helium atmosphere.
Measurements are calibrated using international sulfate standards
NBS 127 (+8.6‰, Vienna Standard Mean Oceanic Water -
VSMOW, IAEA SO5 +12.13‰ VSMOW and IAEA SO6 –
11.35‰VSMOW, USGS 32 +25.4‰VSMOW, Böhlke et al. 2003;
Brand et al. 2009) and an in-house synthetic BaSO4 standard
(Sigma-Aldrich, 11.9+/-0.2‰,VSMOW). Repeated measurements
of the in-house standard (typically >10 per run) and international
standards (3-4 standards per run) yield a reproducibility of +/-
0.2‰ (1 standard deviation - σ).
4.5.3 Statistical Analysis
Uncertainties of the isotope data include errors in sample assigned
ages and uncertainties in how well a single measurement
represents the sulfate δ18O ratio of the ocean. Note that the latter
uncertainty comprises not only the analytical precision (which can
be quantified), but also sample origin, sample handling, and
sample extraction. We therefore have to assume that each
measurement carries an unknown error (or noise).
However, the sulfate δ18O at any given time (t) depends to a certain
degree on the sulfate δ18O at the time before. The degree of this
dependence is being constrained by the time interval between two
measurements relative to the residence time of sulfate bound
oxygen in the ocean (0.5Myr, Jørgensen and Kasten 2006). This
Chapter 4 62
coupling allows us to apply “local regression smoothing” (LOESS,
Cleveland 1979) to estimate the likely sulfate δ18O value.
We use the default LOESS module provided by the statistical
software package R (R Development Core Team 2008). The 95%
confidence interval is calculated for each data point from the
standard errors returned by the LOESS function and is roughly
equal to the 2 sigma value of our isotope measurements (~0.4
permil).
4.5.4 Sulfur cycle model
We describe the sulfur cycle using the following mass conservationequation:
(1)
where MSO4 denotes mass of sulfate in the ocean calculated from
concentration and ocean volume, FMSR(t) and FReox(t) denote time
dependent microbial sulfate reduction and sulfide reoxidation
respectively, Fwp(t) and Fbp(t) denote time dependent pyrite
weathering and burial fluxes respectively, FV denotes the volcanic
flux, Fwe and Fbe denote the of evaporite weathering and
precipitation flux respectively.
We can formulate similar mass conservation equations for
respective isotopes of oxygen (16O and 18O) and sulfur (32S and 34S),
e.g.:
(2)
where MS16
O4 denotes mass of 16O in the ocean calculated from
known mass of sulfate and its isotopic composition; FMSR[S16
O4] (t)
and FReox[S16
O4] (t) denote 16O removed by microbial sulfate reduction
ddt
M SO4 (t )=Fwp (t ) +(F we+F v−Fbe )−F MSR( t)+Freox (t)
ddt
MS16 O4
(t )=Fwp( S16 O4)
(t )+[Fwe (S16 O4)
+Fv(S 16O4 )
−Fbe(S 16O4)
]−FMSR( S16 O4)
(t )+Freox (S 16O4 )
(t )
Chapter 4 63
to sulfide and its return flux during sulfide reoxidation
respectively; Fwp[S16
O4] (t) and Fbp[S16
O4] (t)denote 16O input from
pyrite weathering and 16O loss as a result of pyrite burial
respectively; FV[S16
O2] denotes the 16O input from volcanic flux;
Fwe[S16
O4] and Fbe[S16
O4] denote the 16O input from evaporite
weathering and removal by evaporite precipitation respectively.
(3)
where M32SO4 denotes mass of 32S in the ocean calculated from
known mass of sulfate and its isotopic composition; Fwp32
SO4 and
Fbp32
SO4 denote 32S input from pyrite weathering and 32S removal by
pyrite burial respectively; FV32
SO4 denotes the 32S input from
volcanic flux; Fwe32
SO4 and Fbe32
SO4 denote the 32S input from
evaporite weathering and removal by evaporite precipitation
respectively; FMSR(S32
O4) and FReox(S32
O4) denote 32S removed by
microbial sulfate reduction to sulfide and reoxidized back to
sulfate during sulfide reoxidation respectively.
4.5.5 Steady state model run
In order to achieve an initial steady state we use modern boundary
conditions (e.g., Berner 1982; Kump 1989; Hansen and Wallmann
2003; Bottrell and Newton 2006; Jørgensen and Kasten (2006); see
Table 4.1. for additional details). Note that the isotopic
composition of sulfate originating from oxidized sulfide (δ18OFreox)
and average isotopic composition of buried pyrite (δ34Spyrite) are
calculated from known volume and isotopic composition of other
fluxes.
ddt
MS32 O4
(t )=Fwp( S32O4 )
(t )+[Fwe( S32 O4)
+Fv (S32 O4)
−Fbe( S32O4 )
]−FMSR(S32 O4)
( t)+ Freox (S 32O4)
(t)
Chapter 4 64
From steady state condition (4):
(4)
we can calculate the average isotopic composition of pyrite
(δ34Spyrite) using 5&6:
Fbp (t )=Fbp32 S (t )+Fbp
34 S (t ) (5)
Fbp34 S (t )=Fwe
34 S (t )+Fwp34 S (t )+Fv
34 S (t )−Fbe34 S (t ) (6)
This yields δ34Spyrite of –17‰, which is in good agreement with
previous estimates (Strauss 1997; Seal 2006; Leavitt et al. 2013).
The average sulfur isotopic composition of pyrite tells us about the
offset between δ34Sseawater and δ34Spyrite, which represents the sulfur
isotope fractionation during sulfate reduction. In our case this
offset is –39‰. It is kept constant during subsequent non-steady
state runs.
Similarly, we can calculate the oxygen isotopic composition of
sulfate produced during oxidative sulfur cycle (δ18OFreox) using and
7&8:
Freox (SO4 )=F reox(S16 O4 )
+Freox (S18 O4 )(7)
where FReox[S18
O4] and FReox[S16
O4] are masses of O18 and O16 isotopes
respectively in sulfate produced during internal sulfide reoxidation
Freox (S18 O4 )=FMSR (S18 O4)
−Fbp (S18 O4)(8)
where FMSR(S18
O4) equals to O18 which is “lost” during sulfate
reduction – note that of this flux only a portion equal to Fbp(S18
O4) is
effectively removed and represent actual sink.
ddt
M SO4 (t )=0
Chapter 4 65
Flux Initial flux–steady state
[mol SO4/year]
Isotopic composition[‰]
References
δ34S
(VCDT)
δ18O
(VSMOW)
Weatheringpyrite
1.9x1012 -14 0 Kump 1989; Garrels and Lerman 1981; Petsch and Berner 1998; Calmels et al. 2007, Van Stempvoort and Krouse 1994; Seal 2006;
Weatheringevaporite
1x1012 19 12 Kump 1989; Garrels and Lerman 1981;Hansenand Wallmann 2003; Claypool et al. 1980
Volcanic flux 0.34x1012 3 3 Hansen and Wallmann 2003; Van Stempvoort and Krouse 1994; Alt et al. 2010;
Pyrite Burial 2x1012 -17* 7.2§ Bottrell and Newton 2006; see also Berner 1982; Petsch and Berner 1998
Evaporiteprecipitation
1.24x1012 22§ 7.2§ Kump 1989; see also Garrels and Lerman 1981; Petsch and Berner 1998
MSR 7.5x1013 22§ 7.2§ Jørgensen and Kasten (2006); also see Jørgensen (1982)
Sulfideoxidation
7.3x1013# 22§ 7.7** Jørgensen and Kasten (2006)
Note: The initial sulfate concentration is 27 mmol/l which is in the range of estimates from fluidinclusions by Horita et al. (2002) and Zimmermann (2000).
*Steady state value calculated as a function of other known fluxes (see text).
§This is used for model intialization. Later on isotope ratio of respective seawater sulfate.
#Freox=FMSR-Fbp
**Steady state value calculated as a function of other known fluxes (see text).
Table 4.1 Model fluxes and sulfur and sulfate oxygen isotope ratiosin the steady state
This for steady state run yields δ18OFreox of 7.7‰, which is equal to
35% reoxidation via inorganic processes and 65% reoxidation via
microbially mediated processes.
4.5.6 Model forcing
All model runs start at 4.1Ma. The resulting seawater sulfate
oxygen and sulfur isotope ratios are calculated simultaneously.
Chapter 4 66
We use the sea level estimates of Miller at al. (2011) to calculate
shelf area which is used to force fluxes affected by its change:
pyrite weathering and burial, microbial sulfate reduction and
sulfide reoxidation. Sea level variations are often modified by local
signals (e.g., gravity, isostatic rebound etc). We are however only
interested in a global average and thus use the sea level data from
Miller et al. (2011) without further modifications.
First we calculate the shelf area (As) as a function of sea level at
any given point in time using model cubic polynomial fit (eq. 11 -
from Bjerrum et al. 2006) of the global mean hypsometric curve
from ETOPO5:
A s=A∗(1−0.307∗z3+0.624∗z2
+0.43∗z+0.99991) (11)
where A is the area of the ocean ~3.6*1014 m2 and z sea level (m).
Next we take the fluxes which are affected by sealevel change
(sulfate reduction, sulfide reoxidation, pyrite weathering and pyrite
burial) and divide them in two boxes, the one of which represents
background flux while the other varies in proportion to calculated
shelf area (Eq 12-15).
Global sulfate reduction is calculated from (12):
(12)
where F*MSR is calculated sulfate reduction at any point in time
FoMSR is the initial sulfate reduction – 7.5*1013 mol S/yr; Qabyss and
Qshelf is the percentage of sulfate reduction taking place in the deep
water regions (abyssal and continental slope) and shelf up to 150m
depth, respectively. We assume that both account for 50% of total
sulfate reduction. As is the shelf area at each step. Amin and Amax is
FMSR* =FMSR (abyss)+F MSR(shelf )∗
A s−Amin
Amax−Amin
Chapter 4 67
minimum and maximum extent of shelf area, respectively.
Then, pyrite weathering and burial flux are calculated from (13-
14):
(13)
where Amax is the maximum extent of shelf area and As is the shelf
area at each step; F*wp is the calculated pyrite weathering flux
corresponding to shelf change at each time step. Fowp is the
minimum pyrite weathering flux corresponding to maximum shelf
extent (Amax). We assume Fowp to be 90% of the steady state value
calculated for the modern conditions. This assumption is based on
the estimates of maximum shelf flooding area in the past 3Ma.
During times of maximum flooding the sea level may have been up
to 10 m higher than the current sea level (Miller et al. 2011),
corresponding to a 10% larger shelf area. At present some pyrite
weathering takes place on this previously inundated shelf area.
Therefore, we assume that during times of maximum extent of
shelf pyrite weathering was lower and only 90% of today, because
pyrite rich shelf sediments were inundated.
(14)
where Fbp-abyssal corresponds to the minimum pyrite burial which
takes place in slope and abyssal environments at minimum shelf
extent in this case 0.85*1012 molS/yr, Fbp-shelf is the portion of pyrite
that is buried on the shelf at the maximum shelf extent (Amax)
assumed to be 1.65*1012 mol S/yr; Amin is the minimum shelf extent
and As is the shelf area at each step. These numbers are based on
present day estimates of sulfate reduction rates and pyrite burial in
sediments at different water depth (Jørgensen 1982; D'Hondt et al.
Fwp* =Fwp
o ∗[1+ Amax−A s
A s]
Fbp =Fbp-abyssal +Fbp-shelf∗A s−Amin
Amax−Amin
Chapter 4 68
2002; Jørgensen and Kasten 2006; Thullner et al. 2009).
Finally sulfide reoxidation is calculated from (15):
(15)
The δ18O ratio of sulfate from oxidized sulfide (δ18OFreox) depends
on the pathway of reoxidation (Figure 4.2) and the oxygen isotope
composition of seawater (δ18Osw). At present day δ18Osw is on
average 0‰ (VSMOW). However, it has varied in the past in
accord with glacial-interglacial cycles. For example, the waxing of
ice sheets in glacial stages increases δ18O ratios of ocean water
(e.g. Shackleton 1967&1987) during glacials. Pore water studies
constrain the amplitude of seawater oxygen isotope variations due
to the growth of ice sheets but only for the most recent glacials.
For the last glacial maximum (LGM) the estimated change is
+1.0+/-0.1 ‰ (Schrag et al. 1996; Adkins et al. 2002). Although it
is difficult to give estimates further back in time we can simply
extrapolate LGM estimates into the rest of Quaternary, in order to
roughly assess what would be the effect of fluctuating seawater
δ18O on sulfate cycle. Thus, the δ18OFreox is calculated using the
following:
(16)
where δ18OFreox is δ18O ratio of sulfate from oxidized sulfide, δ18Osw
is estimated seawater δ18O ratio, δ18Oabyss is minimum δ18OFreox
corresponding to inorganic reoxidation taking place in abyssal
environments, CδO18
shelf is coefficient representing δ18OFreox
variations dependent on the shelf area.
Thus calculated δ18OFreox ratios represent the isotopically modified
sulfate during MMSC. The δ18OFreox depends on the relative
Freox*
=FMSR*
−Fbp*
δ OFreox18 =δ Osw
18 +δ O abyss18 +C
δOshelf18 ∗
As−Amin
Amax−Amin
Chapter 4 69
importance of abiotic and microbial processes (MSR,
disproportionation and microbial reoxidation) which have different
oxygen isotope signatures 0‰ vs up to 29‰ vs respectively. We
can than calculate the relative contribution of these two pathways.
4.6 Results and discussion
The δ18O of sulfate in our samples spanning the past 4.1 million
years varies between 4.4‰ and 7.6‰ (VSMOW) with an average
value of 6.5‰ (Fig. 4.3). Between 4.1 Ma and 1.2Ma, δ18OSO4
ratios fluctuate around 7‰ with a standard deviation (1 σ) of 0.4‰
(twice the analytical uncertainty of 0.2‰). However, from 1.2Ma
to 0.2Ma we observe a steady decline from 7‰ to 4.4‰, which is
the lowest value in our record. This minimum is followed by an
abrupt upwards trend from 4.4 ‰ to ~6‰ in the most recent
sediments. The δ18OSO4 ratios of the core top samples fall between
5.5‰ and 6.5‰, with a mean of 6‰ and 1 σ of 0.4‰. The core
top values are about 2-2.5‰ lower than modern seawater (~8.6‰,
VSMOW, see Chapter 5 for details). The exact reason for this
offset is not known, but Turchyn and Schrag (2004&2006) also
observe a similar offset of 1.5-2 ‰.
Chapter 4 70
While it is generally assumed that barite records the sulfur isotopic
ratio of dissolved seawater sulfate (e.g., Paytan et al. 1998& 2004),
it is less clear to what extent this is true for sulfate bound oxygen.
Part of the uncertainty may be caused by contamination from other
minerals in the sediment residue that contains the barite, which
may be affecting the measurements of barite δ18O (e.g., silicates,
zircon, rutile) which have a range of δ18O compositions up to 30‰
(e.g., Hoefs 2009 and references therein). In addition, the presence
of diagenetic barite with anomalously high δ18OSO4 ratios may
cause an offset (Griffith and Paytan 2012). Finally, the presence of
crystal lattice bound water in barite (Walton and Walden 1946a&b)
which presumably has the δ18O ratios of seawater (e.g., Schrag et
al. 1996; Adkins et al. 2002) would result in anomalously low
barite δ18O. Samples containing pyrite, could release reduced S
during the acid extraction process and subsequent oxidation to
Fig. 4.3 Sulfate δ18O results. The circles denote the averagemeasured seawater sulfate δ18O for each sample, the shaded areathe 95% confidence interval of a LOESS approximation of the“true” δ18OSO4 value (see method section)
Chapter 4 71
sulfate and reaction with Ba released from other phases during the
chemical leaching could result in the formation of barite with low
oxygen isotope ratios (DeBond et al. 2012).
We adjusted our sampling and extraction procedures to address the
above problems, i.e. we selected samples from cores with low
organic matter content and where there are no (or only small
variations) in pore water sulfate content. Furthermore, we re-
dissolved all barite to separate it from mineral phases like silicates,
oxides or TiO2 which do not dissolve with Na2CO3 (see method
section for details and Chapter 2. for more in-depth discussion).
Our isotope record differs considerably from the previously
published δ18OSO4 record by Turchyn and Schrag (2004) which
shows constant values at ~9.5‰ (VSMOW) between 10 and 6Ma
followed by a steady increase to ~14‰ (VSMOW) between 6Ma
and 3Ma (Fig. 4.4). However, in the past 3Ma, their δ18OSO4 record
shows steep decline from ~14‰ (VSMOW) at ~3Ma to ~7‰
(VSMOW) at present (Fig. 4.4). The difference between our data
and the data of Turchyn and Schrag (2004) reflects in part an
assigned δ18O value for international NBS 127 standard (9.3 ‰
VSMOW, Turchyn and Schrag 2004 vs. 8.6‰ VSMOW, this
study). However, this could have shifted their results towards more
positive values by only 0.7‰.
Chapter 4 72
The main difference between our study and Turchyn and Schrag
(2004) is likely in the separation method used to extract barite.
Turchyn and Schrag (2004) use density separation with lithium
polytungstate (LST) heavy liquid. Considering that the LST has a
density of up to 2.85 g/ml, Turchyn and Schrag (2004) method
likely fails to separate barite from minerals with densities
exceeding those of LST (many silicates, rutile, iron oxides).
Contamination of barite with these minerals may introduce an error
during O isotope measurement, because these minerals also carry
oxygen and have a range of δ18O compositions up to 30‰ (e.g.,
Hoefs 2009 and references therein).
It is also possible that Turchyn and Schrag (2004) samples are
contaminated with diagenetic barite which has anomalously high
δ18OSO4. Since diagenetic barites have anomalously high δ34S
values (Paytan et al. 2002), they can be excluded if the sulfur
Fig. 4.4 Sulfate oxygen isotope record by Turchyn and Schrag(2004). Note that circles represent individual measurements.Vertical lines connect individual measurements of the samesample.
Chapter 4 73
isotope composition of Turchyn and Schrag (2004) samples is also
analyzed.
If we accept the premise that our data is a “true” recorder of the
marine seawater δ18OSO4 signal, the observed variations imply
considerable changes in the oxygen isotopic composition and/or
flux of sulfate into the ocean. Climatic variations during the
Quaternary likely affected the weathering fluxes of sulfate/sulfide
into the ocean (see Chapter 3). However, these fluxes are small
compared to the ocean sulfate reservoir and are unlikely to have
large impact the δ18OSO4 signal (see Fig. 4.1). On the other hand,
changes to microbially mediated sulfur cycling may have
significant impact on seawater δ18OSO4 (see Fig. 4.1). All of these
processes are susceptible to changes in the submerged shelf area,
which in turn is greatly affected by glacial-interglacial sea level
variations.
During interglacials, high sea levels resulted in expanded shelf
areas that are characterized by high OM burial rates and intense
microbial carbon turnover through MSR (Jørgensen 1982; Berner
1982). Bioturbation and the abundant iron and manganese
oxyhydroxides supplied by weathering on continents, promote re-
oxidation of hydrogen sulfide to intermediate sulfur compounds,
which favors sulfur cycling through microbially mediated
disproportionation (Thamdrup et al. 1993&1994; Canfield and
Thamdrup 1994; Canfield and Thamdrup 1996). High rates of
sulfur cycling through microbially mediated processes (MSR and
disproportionation, microbial oxidation) impart a distinct δ18OSO4
enrichment in resulting pore water sulfate.
During sea level lowstands, however, shelf areas supporting the
above processes are much smaller and replaced by low-lying
coastal plains transected by rivers. This affects sedimentary sulfur
Chapter 4 74
cycling in several ways: 1) The total area supporting MSR is
reduced, which also reduces the total production of dissolved
sulfide, and its subsequent reoxidation to sulfate either via
disproportionation or microbial oxidation; 2) This in turn increases
the relative importance of deep water environments which are
dominated by abiotic sulfide oxidation processes (Jørgensen and
Nelson 2004; Jørgensen and Kasten 2006; Blake et al. 2006) which
ultimately leads to a decrease of seawater sulfate δ18O; 3)
Previously deposited sediments are being eroded and pyrite and
organic S contained in these sediments are being oxidized which
produces sulfate with δ18O ratio close to that of the ambient water,
(e.g., Balci et al. 2007) thus lowering marine δ18O sulfate as well.
Note that the conversion of pyrite to sulfate will affect the marine
sulfate concentration as well as its sulfur isotope ratio (δ34S). The
latter effect is indeed visible in the ~1‰ negative shift of sulfate
δ34S ratio in the past 1.2 Ma (see Chapter 3. and also Paytan et al.
1998).
4.7 Quantitative Interpretation
We explore the impact of sea level changes on the global sulfate
fluxes with a box model that considers the variable fluxes on the
shelf, and the constant fluxes in the pelagic environments. We first
calculate the ocean covered shelf area as function of sea level
using Miller et al. (2011) sea level estimates. Subsequently, we
calculate the benthic sulfate fluxes as a function of the calculated
shelf area (see Methods section for a detailed description).
Chapter 4 75
Fig. 4.5 The effect of sea level variation on MMSC fluxes.Microbially mediated reoxidation refers to both microbialdisproportionation and microbial oxidation (Fig. 4.2).
If we lower the sea level by e.g., 100m (typical for the glaciations
in the past 1Ma, see Miller et al. 2011) the available shelf area is
reduced by 50%, which causes a 40% decrease in sulfate reduction.
These numbers are in good agreement with present day estimates
of sulfate reduction rates in sediments at shallow water depth (0-
150m) (Jørgensen 1982&1983; Jørgensen and Kasten 2006;
Thullner et al. 2009).
The relative contribution of microbial mediated reoxidation
processes which are predominantly carried out in the shelf
sediments also decreases by ~30% (Fig. 4.5). At the same time the
areal extent of deep water environments, where abiotic oxidation
dominates, remains constant so that the relative contribution of
abiotically oxidized sulfate increases by ~50% (Fig. 4.5).
The exposure and erosion of previously water covered shelf areas,
results in the oxidation of sulfidic mineral phases, which increases
this source of δ18O low sulfate from 1.9*1012 mol S/yr to 3.9* 1012
Chapter 4 76
mol S/yr. The overall increase in pyrite weathering is constrained
by our seawater sulfate δ34S record (see Chapter 3).
We note that the contribution of pyrite weathering decreases over
time since the pyrite reservoir in the shelf is finite. Therefore, we
introduce a defined pyrite reservoir of 6*1017 mol S (see Chapter 3
and methods section for details), equivalent to the pyrite content in
the first 200m of shelf sediments (on average 0.2%, Berner 1982).
We take this number as it corresponds well with the estimated shelf
sediment offloading during Quaternary (Hay and Southam 1977;
Davies et al. 1977; Hay 1998; Hay et al. 2002).
We start our model at 4.1 Ma (Early Pliocene) and forward the
resulting seawater sulfate oxygen and sulfur isotopic composition
as a function of the sea level estimates published by Miller et al.
(2011). The resulting seawater sulfate δ18O curve (see Fig. 4.6)
captures the shape and magnitude of the δ18OSO4 signal quite well.
Specifically the decline of δ18OSO4 values between 1.5 and 0.5 Ma
is well represented, supporting the notion that the intensification of
the Quaternary glaciation and its effect on the areal extent of shelf
areas had a considerable effect on the balance between abiotic vs
microbial sulfide reoxidation. Additionally, the resulting δ34S curve
agrees well with our δ34S record (model runs use same forcing
mechanism - see Chapter 3).
Our modeling results suggest that if we consider only increased
erosion of shelf pyrite and don't change other fluxes, the resulting
change is only ~ -0.5‰ which is not enough to reproduce the
magnitude or shape of our seawater δ18OSO4 signal (Fig. 4.6). While
changes of pyrite weathering control seawater sulfate δ34S (Chapter
3) the MMSC is order of magnitude larger (see Fig. 4.1). It is
therefore not surprising that pyrite weathering alone has modest
impact on seawater sulfate δ18O (Fig. 4.6).
Chapter 4 77
Fig. 4.6 Model output – seawater sulfate δ18O value when bothchanges of microbially mediated sulfur cycling and pyriteweathering are included (red solid line) and with only pyriteweathering (blue solid line). The circles denote the averagemeasured seawater sulfate δ18O for each sample, the shaded areathe 95% confidence interval of a LOESS approximation of the“true” δ18OSO4 (see method section).
Our modeling results suggest that the rates of microbial
disproportionation and microbial sulfide oxidation decrease up to
40% during glaciations resulting in an overall reduction of 15%
during past 2Ma. Turchyn and Schrag (2004) also found that
microbial disproportionation and microbial sulfide oxidation
decrease during past 3Ma, but they argue for considerably larger
reduction and effective cessation of these processes during
glaciations.
4.8 Conclusion
We show that the Quaternary glaciations and concomitant
reduction in shelf area are likely to have a considerable effect on
Chapter 4 78
microbial sulfur cycling. Quantitative modeling based on the sea
level estimates by Miller et al. (2011) suggests that during glacial
periods, this may have caused up to 40% decrease in the global
flux of microbial mediated processes (microbially mediated
disproportionation and sulfide oxidation), equivalent to an overall
15% decrease in the past 2Ma. Furthermore, our results suggest
that surface exposure of shelf areas resulted in a significant
increase of pyrite weathering which increases seawater sulfate
concentrations by ~1.5mM, in good agreement with estimates of
sulfate concentration based on fluid inclusions (Brennan et al.
2013) and estimates of ocean alkalinity budget based on boron
isotopes (Hoenisch et al. 2009).
Our results highlight the key role that continental shelf areas play
in modulating global biogeochemical cycles. More work is needed
to understand how shelf area changes affect other biogeochemical
cycles like carbon and phosphorous (Ozaki and Tajika 2013).
Previous workers suggested that shelf erosion results in a net
transfer of carbon and phosphorus into the deep water (Broecker
1982). Our data suggest, significantly reduced organic matter
remineralization rates through sulfate reduction pathway during
sea level lowstands.
Chapter 5 79
Chapter 5
Sulfur and Oxygen isotopiccomposition of contemporary
seawater sulfate and authigenic coretop barite
5.1 Abstract
Here we report sulfur and oxygen isotope ratios of dissolved
seawater sulfate and authigenic core top barites from selected
locations in the Southern and Equatorial Pacific. We show that
oxygen isotope ratios of seawater sulfate are uniform and
homogenous, and up to 2.5‰ higher than the values found in core
top barites. We hypothesize that this offset is caused by the
reoxidation of organic sulfur compounds during precipitation of
marine barite. Our results provide another puzzle piece in the
attempt to understand the origin of marine barite.
5.2 Introduction
While marine barite is used as a recorder of isotopic ratios of
seawater strontium (Paytan et al. 1993), sulfur (Paytan et al.
1998&2004), sulfate-oxygen (Turchyn and Schrag 2004&2006)
and calcium (Griffith et al. 2008&2011), our understanding of its
formation is fragmentary. It is thought that barite is formed in
micro-environments of decaying organic matter, e.g., faecal pellets
(e.g., Bishop 1988, Dehairs et al. 1980, see Fig. 5.1.). In these
micro-environments, bacterial degradation releases barium
Chapter 5 80
absorbed on organic matter which causes barium sulfate to become
locally supersaturated within the faecal pellet (e.g., Bishop 1988,
Dehairs et al. 1980, Ganeshram at al. 2003, Jacquet et al. 2007).
Barite precipitates in these localized micro-environments while the
rest of the seawater is under saturated in respect to barium sulfate
(Monnin et al. 1999; Rushdi et al. 2000).
Fig. 5.1 Schematic diagram of barite precipitation (after Jacquet2007)
Previous studies showed that barium originates from decaying
organic matter (Ganeshram at al. 2003, Jacquet et al. 2007, van
Beek et al. 2007) and it is generally assumed the sulfate is derived
ambient seawater. Indeed, Paytan et al. (1998&2002) found no
significant S-isotope offset between core top barite and
contemporary seawater sulfate. However, measurements of oxygen
isotope composition of barite (δ18O) (Turchyn and Schrag
2004&2006) suggest a significant difference between δ18O isotope
values of seawater sulfate and barite.
Here we expand on previous studies by Paytan et al. (1998&2002)
and Turchyn and Schrag (2004 & 2006) and report analyses of
Chapter 5 81
δ34SSO4 and δ18OSO4 isotope values of seawater samples from the
South Pacific and compare them to core top barites from the
Pacific and Atlantic oceans.
5.3 Sampling locations
Seawater sulfate sulfur and oxygen isotope composition are
affected by:
1. Rapid biological turnover of sulfur in sediments (e.g.,
Blake et al. 2006, Wortmann et al. 2007) or in the water
column (Canfield et al. 2010) which offsets sulfate δ34S by
up to 70‰ (Wortmann et al. 2001, Rudnicki et al. 2001,
Brunner and Bernasconi 2005, Sim et al. 2011) and δ18O
composition by up to 29‰ (e.g. Fritz et al. 1989, Van
Stempvoort and Krouse 1994, Böttcher and Thamdrup
2001, Böttcher et al. 2001, Wortmann et al. 2007, Balci et
al. 2012).
2. Input of sulfate from hydrothermal fluids with sulfate δ34S
signatures lower than contemporaneous seawater sulfate
(Paytan et al. 2002) and δ18O signatures generally higher
than seawater sulfate (e.g., Alt et al. 2010, Eickmann et al.
2014).
3. Incorporation of nitrate in barium sulfate precipitated from
seawater producing anomalously high δ18O signatures
(Michalski et al. 2008, Hannon et al. 2008).
In order to address these concerns, we selected samples from sta-
tion 19 of cruise RV Kilo Moana (KM) 703 (20°S, 170°W) located
in the western part of South Pacific Gyre region. In this region
primary productivity is very low (Behrenfeld and Falkowski,
Chapter 5 82
1997), particulate organic carbon flux is the lowest in the world
(Jahnke 1996), concentrations of dissolved oxygen in the water
column are fairly constant and the seawater profile has low
nitrate/sulfate ratios (Suzuki et al. 2013). Therefore, it is unlikely
that our samples are affected by nitrate coprecipitation or have an-
omalous δ34S and δ18OSO4 signatures. Furthermore, the site is far
from active hydrothermal vents in the region of old crust of Creta-
ceous age (~100Ma, Expedition 329 Scientists 2011) and therefore
is likely unaffected by hydrothermal fluids.
Marine barite which forms in the water column is thought to record
seawater sulfate S and O isotope ratios (Griffith and Paytan 2012).
In sediments it is stable except in environments with high rates of
sulfate reduction where sulfate in pore waters is exhausted
(e.g.,Torres et al. 1996, Griffith and Paytan 2012). In these
environments, barite is soluble releasing barium to solution. This
barium will diffuse and barite will reprecipitate forming diagenetic
barite with typically anomalously high δ34S and δ18O signatures
(Paytan et al. 2002, Turchyn and Schrag 2004&2006).
We separate our barite from core top samples which are collected
at variety of locations in abyssal regions of Equatorial Pacific and
Atlantic and Pacific sections of Southern ocean (listed in Data
tables at the end of chapter). In these deep water environments
sulfate reduction occurs at depths of several m or more and rates
are generally very low (Jørgensen and Kasten 2006; Blake et al.
2006). Although we don't have pore water sulfate profiles at our
sites, shallow core depth (up to 15cm) and abyssal locations of our
samples suggest that sulfate reduction was not prevalent. Thus our
barite samples in sediments at these sites are not likely to have
been affected by barite dissolution and/or reprecipitation and
originate form sinking particles in the water column (e.g. marine
Chapter 5 83
barite).
5.4 Methods
Filtered and acidified seawater samples were initially stored in
trace metal grade Teflon bottles. About 30-40mL was transferred to
50ml Falcon centrifuge tubes. Each sample was filtered through
0.2µm Millex syringe filter and split into a working and an archive
half. The working half was acidified to pH 0-1 using trace metal
grade hydrochloric acid. To this solution is added 5-7 mL of 10%
solution of BaCl2 (99.99% Sigma-Aldrich). The solution is then left
to react overnight. Next day samples were centrifuged and liquid
was decanted. Following this step, miliQ water (resistivity >
18MΩ) is added to the tube, the remaining precipitate is shaken,
centrifuged and decanted again. This “washing” step is repeated 5-
7 times, to obtain a precipitate free from BaCl2 and HCl residues.
After this step, the barium sulfate precipitate is left in oven to dry
overnight. In the final step, dry precipitate is heated at 700oC for
one hour, to eliminate hydration water, which cannot be eliminated
by drying at low temperature (Walton and Walden 1946a&b) and
causes anomalously low barium sulfate δ18OSO4 ratios (Hannon et
al. 2008).
5.4.1 Core top barite separation
We separate barites following the sequential dissolution method by
Paytan et al. (1996). Samples are treated with: (I) acetic acid to
remove carbonates; (II) sodium hypochlorite to oxidize organic
matter; (III) hydroxylamine hydrochloride to remove iron and
manganese oxyhydroxides; (IV) concentrated HF-HNO3 mixtures
with ratios 1:2, 1:1, 2:1 to remove silicates; (V) aluminum chloride
in 1M HNO3 to remove fluorides; (VI) heated at 750oC in the
furnace for 1h to oxidize highly refractory organic matter and
Chapter 5 84
remove water sorbed on or trapped in barite crystalline lattice.
After steps I-V we centrifuge the samples, decant the supernatant
and wash the residue three times with ultrapure deionized water.
We examine the purity of the extracted barite with X-ray
diffraction. Furthermore we check for presence of diagenetic barite
using SEM imaging/EDS analysis.
To avoid contamination with residual mineral phases containing
oxygen (e.g., silicates, zircon, rutile), which have a range of δ18O
compositions up to 30‰ (e.g., Hoefs 2009 and references therein),
we redissolved the extracted barite with sodium carbonate and
subsequently reprecipitated pure BaSO4 (see Chapter 4 for details).
This reprecipitated BaSO4 is heated at 700oC for one hour, to
eliminate hydration water (Walton and Walden 1946a&b, Hannon
et al. 2008). Lastly, we use control samples with known sulfur and
oxygen isotope ratios to ensure that the sample preparation did not
alter isotopic composition of original barite (see Chapter 4 for
details).
5.4.2 Isotope analysis
Sulfur and oxygen isotope measurements are conducted separately
on a continuous flow isotope ratio mass spectrometry (CF-IRMS)
system. For sulfur isotope analysis solid barite samples (200µg)
are mixed in a tin cup with ~600µg of V2O5 powder and introduced
into Eurovector Elemental Analyzer (EA) where BaSO4 is
converted to SO2 by combustion in a flush of oxygen. For oxygen
isotope analysis barite samples (~200µg) are weighed into a silver
capsule and introduced into Hekatech high temperature pyrolysis
furnace where BaSO4 is converted to CO gas at 1350o C under
helium atmosphere. The resulting gas (SO2 or CO) is swept with a
He carrier gas and introduced in a continuous flow mode into a
Chapter 5 85
Finnigan MAT 253 mass spectrometer via a Finnigan Conflo III
open split interface.
Measurements are calibrated using international sulfate standards
NBS 127 (+21.1 ‰,Vienna Canyon Diablo Troilite – VCDT;
+8.6‰, Vienna Standard Mean Oceanic Water - VSMOW), IAEA
SO5 (+0.49 ‰,VCDT; +12.13‰, VSMOW), IAEA SO6 (–34.05
‰, VCDT; 11.35‰, VSMOW), USGS 32 (+25.4‰,VSMOW)
(Coplen et al. 2001; Böhlke et al. 2003; Brand et al. 2009) and an
in-house synthetic BaSO4 (Sigma-Aldrich) standard (8.6 ‰,
VCDT; 11.9+/-0.2‰,VSMOW).
Repeated measurements of the in-house standard (typically >10
measurements per run) and international standards (3-4
measurements per standard per run) yield reproducibility of 0.15‰
(1 standard deviation –1σ) for sulfur and 0.2‰ (1σ) for oxygen
isotope measurements.
5.4.3 Statistical evaluation
Both our seawater sulfate and core top barite samples carry
unknown errors which are associated with either in situ
biogeochemical sulfur transformations or sampling and handling
procedures (see previous chapters for detailed discussion).
We adjusted our sampling and extraction procedures to address
these problems, i.e. we selected seawater samples from region of
low productivity, far from active hydrothermal vents, with low
nitrate/sulfate ratios which are therefore unlikely to be affected by
nitrate coprecipitation and to have anomalous δ34S and δ18OSO4
signatures. Core top barite samples were redissolved to separate
pure barium sulfate from mineral phases like TiO2 which do not
dissolve with Na2CO3. Furthermore, to eliminate crystalline water
we heated all our samples at 700 C for 1hr. Therefore, we are
Chapter 5 86
reasonably certain that our samples reliably report the sulfur and
oxygen isotope composition of average seawater sulfate and core
top barite.
In addition to uncertainties which are related to sample selection
and processing, our core top marine barite isotope data also
includes uncertainties in the time domain i.e., although our samples
are “true” core top they actually represent sediments from an
unknown age span. For most of our core top samples
sedimentation rates were previously constrained using 230Thex for
NBP 9802 and PS 1509 cores (Chase 2001, Walter et al. 2000), C14
TT013 (and K7905) samples (DeMaster and Pope 1994) or
detailed δ18O age for VNTRO1 (Paytan et al. 1996). Sedimentation
rates obtained for those samples vary between 0.46 cm/ky for PS
1509 (Walter et al. 2000) and ~2-2.5cm/ky (DeMaster and Pope
1994) which gives the ages of our core top barites between 1ka and
10ka. For the rest of the core top samples we do not have
sedimentation rates, however, if we assume the average deep sea
sedimentation rates of 0.5 to 5 cm/kyr (Hüneke and Rüdiger,
2011), the age span of those samples is between 1ka and 20ka.
In order to visualize the underlying distribution of our S- and O-
isotope data we use kernel density estimation (KDE) in R software
package (R Core Team 2012). This procedure enables visualization
of the underlying distribution of oxygen isotope data using a non-
parametric method i.e. without assumptions as to what the
underlying distribution should be. We use a Gaussian kernel
density estimator with a default Silverman's ‘rule of thumb’
bandwidth (Silverman 1986). This allows us to compare oxygen
isotope ratios of dissolved sulfate and barite relative to each other.
Chapter 5 87
5.5 Results and Discussion
Seawater sulfate δ34SSO4 isotope values are on average 21.2‰
VCDT with 1 σ of 0.1‰ (Fig. 5.2). The δ18OSO4 values of the same
samples show the average of 8.1‰ (VSMOW) with 1 σ of 0.25‰
(Fig. 5.3).
Fig. 5.2 δ34S composition of seawater sulfate at KM703 station 11
(black), IAPSO seawater standard (blue) and mean with 1σ spread
of results for core top barite (red). Note: error bars for seawater
sulfate δ34S represent calculated 1σ reproducibility of sulfur
isotope results based on measurements of standards (see Methodsection).
Chapter 5 88
Since sulfate oxygen isotope measurements show relatively large
analytical uncertainties (e.g., Sakai and Krouse 1971, Boschetti
and Iacumin 2005) we analyzed all samples at least in duplicate.
In order to better constrain seawater sulfate δ18OSO4 some of the
samples were run up to 20 times and results are shown in Fig. 5.3.
The overall reproducibility of results for all samples except KM
100 is less than reproducibility of standards (1σ = 0.2‰). Sample
KM 100 was analyzed 20 times and overall reproducibility is
0.26‰.
The δ34S isotope values of selected core top barite samples are on
average 21.1‰ (VCDT) with 1σ of 0.2‰ (Fig. 5.2). The oxygen
isotope composition of the same samples vary between 5.2 ‰ and
6.9 ‰ (VSMOW) with the average of 5.9‰ (VSMOW) (Fig. 5.3).
The overall variability of measured δ18O compositions of core top
Fig. 5.3 δ18O composition of seawater sulfate at KM703 station 11
(black), IAPSO seawater standard (blue) and mean with 1σspread of results for core top barite (red). Note: error bars for
seawater sulfate δ18O composition represent calculated 1σ spread
of results for samples run 10 times or more.
Chapter 5 89
barites is 0.3‰ (1σ), which is slightly higher than the
reproducibility of standards with our measurement technique
(1σ=0.2‰).
5.5.1 Comparison with previously published records
A wide range of seawater sulfate δ34S and δ18O values is published
(Table 5.1&5.2). Most of these earlier studies didn't use currently
available international isotope standards, and therefore, it is not
possible to directly compare our results with previously published
data. The lack of standards is likely the main reason for poor inter-
laboratory comparability of results, along with other factors, such
as memory effect during offline conversion of BaSO4 to CO2 for
oxygen isotope measurements (Sakai and Krouse 1971) or memory
effect and oxygen isotope interference for sulfur isotope
measurements (Rees et al. 1978).
In order to compare our results with previously published data we
need to normalize published data. In one study, authors report
values used for international isotope standard – NBS 127 (Böttcher
et al. 2000, Table 5.1). We normalize their results using the
difference between their reported standard value and the newly
calibrated one (Table 5.1). For the rest of the studies we cannot do
that because standard values are not reported. However, we can
normalize this data by adding 1‰ to published δ34S values and
subtracting 0.7‰ from published δ18O values (Table 5.1&5.2). The
correction of 1‰ for δ34S data is an estimate to account for
memory effect and oxygen isotope interference associated with
previously used technique of dual injection of offline prepared SO2
(Rees et al. 1978, see also discussion in Leone et al. 1987,
Longinelli 1989, Coplen et al. 2001). The correction of -0.7‰ for
δ18O data is to account for recent recalibration of NBS 127
international standard from 9.3+/-0.4‰ SMOW (Gonfiantini et al.
Chapter 5 90
1995) to 8.6+/-0.2‰ VSMOW Böhlke et al. 2003, Brand et al.
2009). The NBS 127 standard was first calibrated using the off-line
conversion technique (Gonfiantini et al. 1995) which is the same
method used in all previous oxygen isotope studies. Although
earlier studies did not use the NBS 127 standard we can use this
difference of -0.7‰ as a crude correction for normalizing the older
data because NBS 127 is derived from modern seawater.
Following, this normalization, most of published data falls within
2σ range of our measured δ34SSO4 and δ18OSO4 values (21.2+/-0.2
VCDT and 8.1+/-0.5‰ VSMOW, respectively).
While there is a relatively large number of studies of sulfur and
oxygen isotopic composition of seawater sulfate, much less is
known about S and O isotope composition of marine barite from
core tops. Paytan et al. (1998&2002) found that core top barite S
isotope composition is on average ~21‰ CDT, which is in good
agreement with our study. On the other hand, a previously
published 10Myr record of marine barite oxygen isotope
composition includes three samples from the past 100ky with
reported δ18OSO4 values between 7.6‰ and 8.4‰ VSMOW
(1σ=0.3‰) (Turchyn and Schrag 2004). This study assumed value
for NBS 127 standard of 9.3‰ SMOW. Since recent inter-
laboratory calibration determined new value of 8.6+/-0.2‰
VSMOW for this standard (Böhlke et al. 2003, Brand et al. 2009)
we can re-evaluate Turchyn and Schrag (2004) data as 6.9-7.7‰
(VSMOW) which is in close agreement with our record.
Chapter 5 91
Reference Locationssampled
d34SSO4
[CDT]
Corrected d34SSO4 [VCDT]
Rees et al. (1978)* Worldwide 20.74-21.12 20.74-21.12
Ault and Kulp(1959)
Atlantic,Pacific, Gulfof Mexico
18.9 -20.7 19.9-21.7
Thode et al. (1961) Worldwide 19.3 -20.8 20.3-21.8
Sasaki (1972) Pacific 19.62-20.32 20.62-21.32
Leone et al. 1987&Longinelli 1989
Worldwide 20+/-0.25 21+/-0.25
Cortecci 1975 Pacific 20+/-0.3 21+/-0.3
Böttcher et al.(2000)*
Arabian Sea,North Atlantic
20.49+/-0.08(Arabian Sea),20.57+/-0.06 (NorthAtlantic)
21.1+/-0.08 (ArabianSea), 21.2+/-0.06 (NorthAtlantic)
Table 5.1 Published range of sulfur isotope ratios of seawatersulfate. Note the use of Canyon Diablo Troilite (CDT) scale whichis now obsolete. Original CDT standards were found to beisotopically inhomogeneous with variation of up to 0.4‰(Beaudoin et al. 1994) *The δ34SSO4 value not corrected. **The
δ34SSO4 value Böttcher et al. (2000) assign to NBS 127 is
+20.59±0.08‰ (VCDT). Correction is done by adding 0.6‰.
Chapter 5 92
Reference Locationssampled
d18OSO4 [SMOW] Normalized d18OSO4
[VSMOW]
Lloyd 1967 Atlantic, Gulf ofMexico,Pacific, PersianGulf
9.3-10.1 8.6-9.4
Longineli andCraig 1967
Worldwide 8.44-9.83 7.74-9.13
Rafter andMizutani 1967
Pacific Ocean 9.9 9.2
Cortecci 1975 South Pacificdeep water
9.5+/-0.2 8.8+/-0.2
Holser et al. 1979* Worldwide 8.6 8.6
Claypool et al.1980**
Worldwide 8.6 8.6
Leone et al. 1987&
Longinelli 1989
Worldwide low latitudes: 9.45+/-0.15 high latitudes9.1+/-0.3
8.75+/-0.15(lowlatitudes), 8.4+/-0.3(high latitudes)
Table 5.2 Published range of seawater sulfate oxygen isotoperatios. Note: All of these previous studies do not use internationalstandards for oxygen in sulfate as they did not exist at the time.Note: *This data was not normalized because reported value forseawater sulfate δ18OSO4 is the same as recalibrated NBS 127
standard. **Claypool et al. (1980) measured 8.1 corrected to 8.6 tobring in line with Holser et al. 1979.
5.5.2 Statistical analysis
If we accept the premise that our samples represent “true” seawater
sulfate and barite sulfur and oxygen isotope composition we can
use kernel density estimation (KDE) in R software package to
present our results in a statistically meaningful way. We use this
procedure to visualize the underlying distribution of sulfur and
oxygen isotope data and to compare sulfur and oxygen isotope
Chapter 5 93
ratios of dissolved sulfate and core top marine barite relative to
each other.
The KDE distribution of our seawater sulfate δ34SSO4 values
suggest highest frequency around 20.8-21.5‰ (VCDT) (Fig. 5.4)
which is close to currently accepted δ34SSO4 value of NBS 127
standard (21.1 ‰ VCDT, Coplen et al. 2001). Core top barite δ34S
have essentially the same calculated KDE distribution between
20.5-21.8‰ (VCDT) (Fig. 5.4).
The KDE distribution of our seawater sulfate δ18OSO4 ratios suggest
highest frequency around 8-8.5‰ (VSMOW) (Fig. 5.5) which is
close to currently accepted δ18OSO4 value of NBS 127 standard
(seawater sulfate). However, calculated KDE distribution of core
top barite suggest significantly different δ18Obarite composition
Fig. 5.4 Calculated kernel density distribution (Gaussian) of theseawater sulfate δ34S (blue-sharp peak) compared to that of of
core top barite δ34S ratios (red-wide peak)
Chapter 5 94
between 5.5-6.5‰ (VSMOW) (Fig. 5.5) which is 2-2.5‰ lower
than δ18OSO4 ratio of seawater sulfate.
It is possible that ages of our core top barite are not well
constrained. However, based on sedimentation rates on our sites,
most of which are established using 230Thex , C14and δ18O (Chase
2001, Walter et al. 2000, DeMaster and Pope 1994, Paytan et al.
1996), the maximum age of our samples is 20ka. Since this period
is an order of magnitude shorter than residence time of oxygen in
seawater sulfate (Jørgensen and Kasten 2006) it is unlikely that
δ18O offset between core top barite and seawater sulfate is due to
poor age control. Therefore we suggest that a fractionation process
of some sort is responsible for this offset.
We suggest that the δ18O offset between barite and seawater is
result of oxidation of organic S compounds during barite
Fig. 5.5 Calculated kernel density distribution (Gaussian) of theseawater sulfate δ18OSO4 (blue-right) compared to that of of core
top barite δ18O ratios (red-left)
Chapter 5 95
precipitation in microenvironments of sinking organic matter (Fig.
5.6). Barite is thought to be formed in such microenvironments
which are supersaturated in respect to barium sulfate (e.g., Bishop
1988, Dehairs et al. 1980, Ganeshram at al. 2003, Jacquet et al.
2007) while the rest of the water column is under saturated
(Monnin et al. 1999; Rushdi et al. 2000). If the source of sulfate is
organic S compounds in those microenvironments, then this sulfate
is expected to have S isotope ratios close to seawater because
assimilatory sulfate reduction does not produce S isotope
fractionation (Canfield 2001). If this interpretation is correct, the
oxidation of these S compounds would produce sulfate with S
isotope values close to seawater sulfate (~21‰), while the O
isotope values of marine barite, would be shifted towards seawater
oxygen isotope composition (0‰) which is in line with our results
(Fig. 5.4&5.5).
Fig. 5.6 Schematic diagram of barite precipitation showingdifferent sources of sulfate (modified from Jacquet 2007)
Chapter 5 96
This finding implies that the marine barite δ18O signature
represents oxygen isotope composition from two sources with end
members being seawater (0‰ VSMOW) and seawater sulfate
(8.6‰ VSMOW). We can than calculate contribution of each end
member using:
δ18Ocore top=δ18OSO4*ASW-SO4+ δ18Oreox-S*Breox-S
where ASW-SO4 and Breox-S are the fractions of O atoms coming from
seawater sulfate and seawater respectively. Solving this equation
gives us ~0.70 for oxygen coming from seawater sulfate and 0.30
for sulfate originating from oxidized organic S with oxygen
coming from seawater. This implies that if δ18O of sulfate from
reoxidized organic S is close to seawater (0‰ VSMOW), the
relative contribution of this sulfate is ~30%. However, we don't
know the isotopic composition of reoxidized organic S which may
be close to seawater δ18O or higher depending on the pathway of S
oxidation (see Chapter 4 for details). Therefore, this estimate is
likely the minimum contribution of reoxidized organic S.
5.6 Conclusions
This study shows that oxygen isotope composition of core top
marine barite is different from seawater sulfate δ18O composition
by ~-2 to -2.5‰. More work is needed to elucidate the origin of
this offset. We hypothesize that this offset is caused by
incorporation of isotopically anomalous sulfate from reoxidized
organic S compounds during barite precipitation in
microenvironments of sinking organic matter. This hypothesis can
be tested by δ18O analysis of barite from organic matter decay
experiments similar to Ganeshram at al. (2003) in which ambient
seawater has variable δ18O composition. Furthermore, since there is
Chapter 5 97
a possibility of mixing of barite of different ages in our core tops
(albeit on a relatively short age span of 20ky), future work should
constrain δ18O variability in currently forming barite crystals from
sediment traps in highly productive areas like Eastern Equatorial
Pacific.
5.7 Data Tables
Sample IDCore depth
[cm]Latitude Longitude
δ18OSO4
[VSMOW]
δ34S
[VSMOW]
635_PLDS 0-5 1.058 -107.215 5.7 21.2638_PLDS 0-5 1.06 -119.93 6.0 21.2
650_VNTR 7-9 0.14 -95.335 5.2
659_TTN 0 0-5 0.112 -139.723 6.2
662_TTN0 5-7 -0.866 -139.832 6.3 20.7
663_TTN0 7-9 0.112 -139.723 5.7
664_TTN0 6-8 0.815 -139.917 5.8 20.8
665_TTN0 7-9 4.041 -139.851 5.5
668_3S-27 0-5 -2.885 -139.832 5.5
NBP 9802 sta3 0-1 -66 -169 5.8 21.3
NBP 9802 sta3 2-3 -66 -169 5.7 21.1
NBP 9802 sta3 3-4 -66 -169 5.9 21.3
PS 1509-1 0-5 -65 -42 5.8 21.2
PS 1474-1 0-5 -66 -169 5.7 21.2
VNTRO1-2PC 0-5 7 -109.8 5.6 21.3
VNTRO1-4GC 0-5 5.3 -110 5.7 21.2
TN013 MC88 11-13 1 -140 6.5 21.4
TN013 MC88 13-15 1 -140 5.9 21.4
TN013 MC63 0-1 -1 -140 5.3 21
TN013 MC27 6-7 -3 -140 6.9 21.2
TN013 MC113 2-3 4 -140 6.5 21
TN13 MC113 4-5 4 -140 6.3 21
TN057-15PC 2-4 -51.9 4.5 5.8 21
K7905 42BC 10-15 1 -138 5.2 21.1
mean 5.8
Table 5.3 δ18OSO4 ratios of core top samples
Chapter 5 98
Sample IDDepth
[m]
δ18OSO4
[VSMOW]
Number ofmeasurements
StandardDeviation (1σ)
KM_A 0 8.2 2
KM_B 0 8.3 20 0.15
KM_C 0 7.8 2
KM_D 0 8.5 2
KM_90 90 8.2 2
KM_100 100 7.8 20 0.26
KM_120 120 8.1 2
KM_150 150 8.3 2
KM_2000 2000 7.8 10 0.20
KM_2700 2700 8.2 2
KM_3245 3245 8.3 2
KM_3743 3743 8.3 10 0.15
KM_4246 4246 7.9 2
KM_4997 4997 8.1 10 0.19
mean 8.13 88
Table 5.4 δ18OSO4 ratios of dissolved seawater sulfate. Samples
taken at station 19, KM 0703 cruise.
Chapter 6 99
Chapter 6
Final remarks
6.1 Conclusions
In this study I investigated the effects of Quaternary sealevel
variations on sulfur fluxes and microbial sulfur cycling in marine
sediments. The main contributions of this research are:
Methodological Contributions (Chapter 2)
Here, I assess the sodium carbonate digestion method for
obtaining the pure barium sulfate from barite contaminated with
silicates and oxides. This method is suitable for purification of
barite for the purpose of oxygen isotope measurements and highly
efficient in separating BaSO4 from other minerals with recovery of
the original barite larger than 90%. Special care needs to be taken
with regards to the presence of hydration water, which is readily
incorporated in barium sulfate precipitate. This water offsets re-
precipitated sulfate δ18O by a certain value, which depends on the
difference between δ18O of solution water and original sulfate. I
show that heating samples at 700oC for 1hr is sufficient to remove
this offset and hydration water.
Constraining Pleistocene shelf sediment offloading (Chapter 3)
In this chapter I show that the intensification of Quaternary
glaciations in the past 1.5Ma and concomitant periodic changes in
shelf area, likely changed the balance of weathering fluxes of
sulfate/sulfide and the burial of pyrite. The declining seawater
sulfate δ34S supports the idea that the transition to the climate
driven 100kyr sea level variations resulted in a net reduction of
Chapter 6 100
shelf sediment volume. This is a consequence of increased erosion
of shelf sediments during sea level low stands, which was only
partly compensated by increased sedimentation during times of
rising sea level and sea level high stands. I find that a large
increase of pyrite weathering in the past 1.5Ma is not sustained but
effectively stops by ~700ka. This suggests that shelf systems
reached a new equilibrium state about 700 kyr ago.
Shelf area fluctuations and related impacts on microbial sulfur
cycling (Chapter 4)
Here I present a revised δ18O sulfate record for the last ~4Ma and
explore the effects of Quaternary sea level variations on microbial
sulfur cycling. My results show a 1-1.5‰ drop in the marine
sulfate δ18O in the past 2Ma. This drop of the seawater sulfate δ18O
primarily reflects the balance between microbially mediated and
abiotic sulfur oxidation in the so called oxic sulfur cycle. The
increased duration and amplitude of glacially driven sea level
lowstands favors the abiotic oxidation of reduced sulfur.
Quantitative modeling of seawater sulfate δ18O data shows that the
reduction in shelf area during Quaternary glaciations resulted in up
to 40% decrease in the global flux of microbial sulfur cycling
(microbially mediated disproportionation and sulfide reoxidation),
equivalent to an overall 15% decrease in the past 2Ma.
Sulfur and oxygen isotopic composition of contemporary
seawater sulfate and authigenic core top barite (Chapter 5)
Here I constrain sulfur and oxygen isotope composition of
dissolved seawater sulfate and core top barites. My results show
that seawater sulfate and core top barite have the same δ34S, while
oxygen isotopic composition of core top marine barite is ~2 to
2.5‰ lower than seawater sulfate δ18O. While more work is needed
to elucidate its origin, I hypothesize that oxygen isotope offset
Chapter 6 101
between seawater sulfate and core top barite is caused by the
reoxidation of organic sulfur compounds during precipitation of
marine barite.
6.2 Outlook
This research highlights the key role that the continental shelf
plays in modulating global biogeochemical cycle of sulfur. Future
work is needed to understand how shelf area changes affect the
cycling of carbon, phosphorous and other elements. For example,
shelf sediment offloading and associated pyrite weathering may
have important implications on the carbon cycle. Namely, pyrite
weathering produces very strong sulfuric acid which dissolves
carbonates (e.g., Spence and Telmer 2005; Calmels et al. 2007).
Since continental shelf sediments are rich in carbonates (de Haas et
al. 2002) the production of sulfuric acid is likely balanced by
carbonate dissolution, which delivers dissolved inorganic carbon
(DIC) into the ocean–atmosphere system. Per each mole of sulfate
two moles of CO2 are transferred to the ocean (Berner and Berner
1996) (Equation 1).
2CaCO3+H2 SO4→2Ca2++2HCO3
-+SO4
2- (1)
Therefore pyrite weathering effectively increases inorganic carbon
storage in the ocean. If integrated over the entire period of the δ34S
shift, pyrite oxidation results in a net transfer of ~ 0.018PgC/yr on
average in the past 1.5Ma, which amounts to a total addition of
~14000PgC or ~1/3 of deep ocean carbon storage. This build-up of
ocean DIC storage might have contributed to the unexplained
jumps in atmospheric CO2 concentrations first at 600ky and then at
400ky observed in the ice core record (EPICA community
members 2004; Luthi et al. 2008).
Chapter 6 102
As discussed in the Chapter 5, there is a significant oxygen isotope
offset between core top barite and contemporary seawater sulfate.
Here I suggest that this offset is caused by mixing of sulfate from
two sources during barite precipitation. This would have serious
implications for the use of barite as proxy for seawater sulfate δ18O
and therefore should be further investigated. To test my hypothesis
that sulfate in barite comes from two sources, future studies should
analyze δ18O of barite from organic matter decay experiments
similar to Ganeshram at al. (2003) in which ambient seawater has
variable δ18O composition.
Future work should concentrate on sulfur and oxygen isotopic
compositions of seawater sulfate across the Eocene-Oligocene
transition. The Eocene-Oligocene transition as the first among the
Cenozoic cooling events offers an exciting opportunity to test how
glaciation and especially sea level changes affect the sulfur cycle.
The sulfur isotope record (Paytan et al. 1998) does not have
enough data points to conclusively say whether or not there is a
significant negative isotope shift. On the other hand, the oxygen
isotope record of seawater sulfate by Turchyn and Schrag (2006)
does not indicate any change in this period. Therefore, a new high-
resolution (~100kyr) marine barite sulfur and oxygen isotope
record could help resolve whether or not Eocene-Oligocene
cooling affected sulfur cycling.
References 103
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Appendix 119
AppendixTable 1. Sample list with sulfur isotope results (Chapter 3)
Leg Site H Cor Sc Top(cm) Bot(cm) Age d34S138 851 B 1 1 41 42 0.41 0.0195 20.7138 851 B 1 1 55 57 0.55 0.0261585 20.7138 851 B 1 1 106 108 1.06 0.0490645 20.6138 851 B 1 1 146 148 1.46 0.06615 20.7138 851 B 1 2 17 19 1.67 0.078225 20.8138 851 B 1 2 137 139 2.87 0.15835 20.7138 851 B 1 2 147 149 2.97 0.16985 20.8138 851 B 1 3 8 10 3.08 0.178222 21138 851 B 1 3 33 35 3.33 0.193 20.9138 851 B 1 3 101 103 4.01 0.240269 21.1138 849 B 2 1 100 104 7.7 0.30468 20.9138 849 B 2 3 42.5 46.5 10.125 0.37506 20.8138 851 B 1 5 85 90 6.85 0.41775 21.1138 849 B 2 5 5 10 12.75 0.475 20.8138 851 B 2 1 75 80 8.25 0.5275 21.1138 851 B 2 2 22 24 9.22 0.608531 21.1138 851 B 2 2 22 24 9.22 0.608531 20.9138 851 B 2 2 34 36 9.34 0.619444 20.9138 851 B 2 2 130 132 10.3 0.658652 20.9138 851 B 2 2 144 146 10.44 0.6645 20.9138 851 B 2 3 48 50 10.98 0.686094 20.7138 851 B 2 3 48 50 10.98 0.686094 21138 851 B 2 3 56 58 11.06 0.688811 20.9138 851 B 2 3 104 106 11.54 0.705113 21138 851 B 2 3 144 146 11.94 0.720333 21.1138 851 B 2 4 18 20 12.18 0.736333 20.9138 851 B 2 4 66 68 12.66 0.765447 21.1138 851 B 2 4 84 86 12.84 0.776342 21.1138 851 B 2 4 116 118 13.16 0.79455 21.2138 851 B 2 4 138 140 13.38 0.80785 21.3138 851 B 2 4 146 148 13.46 0.81545 21.3138 851 B 2 5 13 15 13.63 0.828457 21.3138 851 B 2 5 56 58 14.06 0.852414 21.4138 851 B 2 6 38 40 15.38 0.915367 21.3138 851 B 2 6 49 51 15.49 0.9216 21.2138 851 B 2 6 122 124 16.22 0.964456 21.2138 851 B 2 6 145 147 16.45 0.978 21.2138 851 B 3 1 55 57 17.55 1.124353 21.3138 851 B 3 1 122 124 18.22 1.164269 21.4
138 849 D 4 1 54 56 33.04 1.373 21.8138 851 B 3 4 130 135 22.8 1.4006 21.7138 851 B 3 6 90 95 25.4 1.54779 21.8
138 849 D 4 4 68 70 37.68 1.58 21.8138 851 B 3 7 28 30 26.28 1.606722 21.8138 851 B 4 1 97 99 27.47 1.754256 22
138 849 C 5 2 103 105 41.53 1.798 21.8
138 849 D 5 3 61 63 45.61 1.928 21.8138 851 B 4 3 96 98 30.46 1.949912 21.9138 851 B 4 4 75 77 31.75 2.018833 22.1138 851 B 4 5 87 89 33.37 2.101846 22
138 849 D 6 1 108 110 52.58 2.143 21.9
138 849 D 6 3 63 65 55.13 2.261 21.9
138 849 D 6 4 108 110 57.08 2.34 22
138 849 D 7 5 16 18 67.16 2.736 22.1
138 849 D 8 1 112 114 71.62 2.976 21.9
Depth(mbsf)
Appendix 120
Table 2. Sample list with oxygen isotope results (Chapter 4)
sample site hole core section top (cm) bot (cm) Age
A6 849 A 1 2 121 123 2.71 0.078 6.1
A6 849 A 1 2 121 123 2.71 0.078 6.4
GIG EO8 849 C 1 2 66 68 3.02 0.12 6.5
GIG D12 849 C 1 4 71 73 6.07 0.2 4.4
GIG D12 849 C 1 4 71 73 6.07 0.2 4.7
SA 09 849 D 1 2 78 80 6.28 0.309 5.6
Si-6 849 B 2 3 81 83 10.1 0.388 5.4
Si-6 849 B 2 3 81 83 10.1 0.388 5.1
Si10 849 B 2 5 5 7 12.75 0.475 5.5
Si10 849 B 2 5 5 7 12.75 0.475 5.0
Si10 849 B 2 5 5 7 12.75 0.477 5.4
Sc01 849 D 2 1 78 80 14.28 0.61 5.6
Sc09 849 D 2 2 128 130 16.28 0.679 5.6
SD07 849 D 2 4 51 53 18.51 0.758 5.8
SD06 849 D 2 4 28 30 18.28 0.761 5.5
SF2 849 D 2 6 130 132 22.3 0.91 6.3
Sj7 849 B 3 6 112 114 24.82 1.025 5.9
G10 849 D 3 4 113 115 28.37 1.143 6.2
D5 849 D 3 5 114 116 29.88 1.214 6.6
D5 849 D 3 5 114 116 29.88 1.216 7.2
H5 849 D 4 1 52 54 33.02 1.373 7.4
H9 849 D 4 4 60 62 37.6 1.58 6.2
H9 849 D 4 4 60 62 37.6 1.58 6.3
I1 849 D 4 5 60 62 39.1 1.646 6.6
B1 849 C 5 1 47 49 39.47 1.708 6.7
E4 849 C 5 2 111 113 41.61 1.798 6.7
I8 849 D 5 3 59 61 45.59 1.922 7.3
I8 849 D 5 3 59 61 45.59 1.922 7.1
I8 849 D 5 3 59 61 45.59 1.928 7.0
J5 849 D 5 5 109 111 49.09 2.012 7.2
K2 849 D 6 1 113 115 52.63 2.143 6.8
K7 849 D 6 3 58 60 55.08 2.257 7.0
K7 849 D 6 3 58 60 55.08 2.261 7.0
K7 849 D 6 3 58 60 55.08 2.261 6.4
L1 849 D 6 4 110 112 57.1 2.34 7.3L8 849 D 7 1 10 12 61.1 2.498 8.1L8 849 D 7 1 10 12 61.1 2.498 7.5
l10 849 D 7 1 110 112 62.10 2.536 6.7
M5 849 D 7 3 58 60 64.58 2.635 7.2
M10 849 D 7 5 10 12 67.1 2.734 7.5
M10 849 D 7 5 10 12 67.1 2.736 7.2
N3 849 D 7 6 9 11 68.59 2.78 6.7
N7 849 D 7 7 60 62 70.6 2.872 7.0
N9 849 D 8 1 108 110 71.58 2.976 6.4
O3 849 D 8 3 11 13 73.61 3.051 6.7
O5 849 D 8 3 110 112 74.6 3.09 7.7Q1 849 D 8 5 93 95 77.43 3.194 7.4Q1 849 D 8 5 93 95 77.43 3.194 6.9
B6 849 C 9 2 58 60 79.08 3.297 6.9P6 849 D 9 1 110 112 81.1 3.391 8.0P6 849 D 9 1 110 112 81.1 3.391 7.4
Q5 849 D 9 4 110 112 85.6 3.556 7.0Q10 849 D 9 6 63 65 88.13 3.645 7.5Q10 849 D 9 6 63 65 88.13 3.645 6.8
B9 849 C 10 2 100 102 89 3.723 7.2
R8 849 D 10 2 114 116 92.14 3.83 6.8S3 849 D 10 4 50 52 94.5 3.92 6.7S3 849 D 10 4 50 52 94.5 3.92 6.3
S7 849 D 10 6 9 11 97.09 4.016 6.9T2 849 D 11 1 54 56 99.54 4.131 6.8
mbsf depth
δ18OSO4
[VSMOW]
