The Role of Labile Dissolved Organic Carbon in …...decades, the importance of these processes in systems with a low concentration of labile organic carbon in the sediment and water

The Role of Labile Dissolved Organic Carbon in Influencing

Fluxes Across the Sediment-Water Interface: From Marine

Systems to Mine Lakes

Deborah J. Read

BE(Hons.), BSc

School of Environmental Systems Engineering

Faculty of Engineering, Computing and Mathematics

This thesis is presented in fulfilment of the requirements for the degree of

Doctor of Philosophy of the University of Western Australia

June 2008

i

Abstract

Sediment diagenesis in aquatic systems is usually understood to be controlled by the

concentrations of both organic carbon and the oxidant. However, the concept that sediment

respiration may be limited by the supply of organic carbon, even in systems with moderate

concentrations of organic carbon in the water column, has yet to be fully explored.

Typically we assume that a direct coupling between water column and sediment diagenesis

processes occurs and the chemical evolution of porewater and surface water are linked

through fluxes of chemical species across the sediment-water interface. While the dynamics

of supply of particulate organic carbon (POC) to the sediments via plankton deposition and

resuspension, has previously been examined, the fate of dissolved organic carbon (DOC)

once in the sediments, has rarely been investigated.

A series of experiments comprising batch tests, microcosms and sediment cores

were conducted on sediment and water from four diverse field sites in which sediment

respiration was considered to be carbon limited. Three sites were oligotrophic, acidic lakes

and the fourth an oligotrophic coastal embayment. During each experiment dissolved

organic carbon was added and measurements were undertaken of solutes that were

considered participants in diagenetic processes.

While each system differed in its chemical, biological and geological makeup, a key

commonality was the rapid onset of anoxic conditions in the sediments irrespective of the

overlying water oxygen concentrations, indicating lack of direct coupling between

biogeochemical processes in the water column and sediments. Also, similar apparent DOC

remineralisation rates were observed, measured solute fluxes after the addition of DOC

indicated adherence to the ecological redox sequence, and increased ammonium

concentrations were measured in the overlying waters of the acidic microcosms. In marine

system experiments it was noted that diagenetic respiration, as indicated by decreasing

concentrations of oxygen in the overlying water, increased rapidly after labile DOC was

added.

To explore the influence of geochemical processes on sediment respiration, a

diagenetic model was tested against the laboratory data. The model was able to capture the

rapid changes observed in the microcosms after addition of DOC in both the marine and

ii

acidic systems experiments. The model has the potential to serve as an essential tool for

quantifying sediment organic matter decomposition and dissolved chemical fluxes.

This work has focussed our attention on the control of DOC availability on

sediment respiration and thus its ultimate control on solute fluxes across the sediment water

interface. The results highlight the need to understand and quantify the supply of DOC to

the sediment (as POC or already as the dissolved form), its transport through the sediment

and its eventual remineralisation. This understanding is critical for improved management

of aquatic systems, possibly even in systems where water column organic carbon is

plentiful but sediment respiration is constrained by high organic carbon turnover rates in

the water column and a resulting low flux of organic carbon to the sediment.

iii

Statement of Originality

Contained in this Thesis are four manuscripts intended for journal publication. In all cases,

the first author performed all fieldwork, lab analysis (unless otherwise stated in the

Methods), modelling, writing and presentation. The second, third and, in some cases fourth,

authors provided supervision and review of the work. Feedback regarding the papers is yet

to be received from the Journals to which they were submitted.

iv

v

Acknowledgements

This study was supported with funding from the Western Australian Centre of

Excellence for Sustainable Mine Lakes and Australian Research Council Linkage Project

LP0454252 and the Water Corporation. I was financially supported by an Australian

Postgraduate Award

Many thanks to Carolyn Oldham and Greg Ivey for their supervision and support.

Thanks also to Ursula Salmon for valuable scientific discussions and Matthew Hipsey for

assistance with the modelling phase of this project.

My lab work would never have been able to be carried out without first getting the

sediment and water from the field sites and for assistance with this I’d like to thank Greg

Attwater, Geoff Wake, Ursula Salmon and Alicia Loveless.

Staff and students of SESE, in particular administration staff (Julia Rice, Wendy

Naubaum and Rosamund Gatt) and Laboratory Manager, Dianne Krikke.

A big thank you to those people who made the days at SESE more enjoyable, my

office mates and friends: Alicia Loveless, Dianne Krikke, Kelsey Hunt, Patricia Okely,

Ursula Salmon, Huynh Pham, and Saskia Noorduijn.

Lucky, for being a faithful friend and Bella for the burst of energy.

My brother, Andrew, for keeping everything in perspective and for providing me

with a constant stream of great music. Last but definitely not least, my parents, Len and

Anne, for giving me a great education, plenty of love and encouragement, and the certain

amount of stubbornness and independence required.

vi

vii

Table of Contents

Abstract i

Statement of Originality iii

Acknowledgements v

Table of Contents vii

1 Introduction

1.1 Motivation 1

1.2 Thesis Overview 1

1.2.1 Hypothesis 3

1.2.2 Objectives 3

1.2.3 Approach 4

1.3 Thesis Outline 4

1.4 References 6

2 Background

2.1 Organic Carbon and Sediment Diagenesis 7

2.2 Reaction-Transport Models 12

2.3 Mine Lakes 15

2.3.1 Formation 15

2.3.2 Remediation 18

2.4 Field Sites 21

2.4.1 Cockburn Sound 21

2.4.2 Lake Kepwari 21

2.4.3 Chicken Creek 22

2.4.4 Mining Lake 111 22

2.5 References 22

3 Addition of dissolved organic carbon to promote aerobic respiration in

sediments: estimation of a rate constant

3.1 Abstract 35

viii

3.2 Introduction 36

3.3 Methodology 39

3.3.1 Site Descriptions 39

3.3.2 Sediment Experiments 40

3.3.3 Chemical Analyses 43

3.3.4 Statistical Analyses 44

3.4 Results 44

3.4.1 Lake Sediment Slurry Experiments 44

3.4.2 Cockburn Sound 47

3.5 Discussion 49

3.6 Conclusion 56

3.7 Acknowledgements 57

3.8 Notation 57

3.9 References 58

4 Sediment diagenesis and porewater solute fluxes in acidic mine lakes: the

impact of organic carbon additions

4.1 Abstract 65

4.2 Introduction 66

4.3 Methodology 69

4.3.1 Study Sites 69

4.3.2 Laboratory 70

4.3.3 Chemical Analysis 72

4.3.4 Calculations 73

4.4 Results 74

4.4.1 Surface Water 74

4.4.2 Sediment Porewater 78

4.4.3 Sediment 85

4.5 Discussion 85

4.5.1 Dissolved Oxygen 87

4.5.2 Nitrogen 88

4.5.3 Phosphorus 89

4.5.4 pH 90

ix

4.5.5 Iron 90

4.5.6 Sulfide 91

4.6 Conclusion 92


4.8 References 93

5 Effect of dissolved organic carbon on dissolved oxygen, nutrient and iron

fluxes across the sediment-water interface in carbon limited marine systems

5.1 Abstract 99

5.2 Introduction 100

5.3 Methodology 102

5.3.1 Study Site 102

5.3.2 Experiment Setup 103

5.3.3 Chemical Analysis 104

5.3.4 Calculations 105

5.3.5 Model Description and Implementation 105

5.4 Results 110

5.4.1 Experimental Results 110

5.4.2 Model Results 113

5.5 Discussion 117

5.6 Conclusion 120


5.8 References 121

6 Predicting the combined impact of dissolved organic carbon loading and

geochemical processes on sediment fluxes in an acidic lake

6.1 Abstract 127

6.2 Introduction 128

6.3 Methodology 129

6.3.1 Study Site 129

6.3.2 Experiment 130

6.3.3 Modelling 131

6.4 Results 135

x

6.5 Discussion 141

6.6 Conclusion 144


6.8 References 145

7 Conclusions

7.1 Significance of Organic Carbon Limitation 151

7.2 Recommendations for Future Work 155

7.3 References 156

1

1 Introduction

1.1 Motivation

When we think about changing chemical conditions within lake and marine

systems most of us think of those associated with large scale processes. This may

include large scale fluxes of chemical species through inflows and outflows of rivers;

geochemical interactions with rocks and groundwater; or, chemical and biological

reactions within the water column itself. Most people do not consider that the chemical

and biological interactions in the top few centimetres of sediment may play an

important role in determining the chemical make up of potentially the entire water

column. These interactions in the top few centimetres occur through a series of

processes collectively known as diagenesis, which are changes in sediment through the

physical, chemical and/or biological processes upon a particle reaching the sediment-

water interface (Berner, 1980).

While much knowledge has been gained of diagenesis over the past few

decades, the importance of these processes in systems with a low concentration of labile

organic carbon in the sediment and water column has only been considered for pelagic

environments and a few neutral lake systems (e.g. Boudreau, 1996; Soetaert et al., 1996;

Jourabchi et al., 2005; Burdige, 2006). Also, most diagenetic research has been

undertaken with reference to particulate organic carbon (POC) and very little research

has been done with reference to dissolved organic carbon (DOC). Given that organic

carbon-limited systems are often, but not always, oligotrophic and are increasingly

subject to anthropogenic pressures, understanding of diagenesis in these environments is

crucial to understanding the system as a whole and thus to assigning appropriate

management strategies. With the increased interest in a global carbon budget, an

increased understanding of diagenesis in the pelagic ocean, typically low in organic

carbon, also aids in constraining this budget.

1.2 Thesis Overview

Understanding of marine and freshwater diagenesis requires joint consideration

of physical, chemical and biological processes. Quantitative descriptions of DOC and

oxidant fluxes necessitate knowledge of the controls on the rates of marine diagenesis at

2

low concentrations of DOC. This raises the issue of the necessity of increased

complexity for process description.

Traditionally rates of organic matter degradation have been modelled as either

zero-order, first-order or a Monod type rate law. It has been acknowledged that zero-

order and first-order reaction rates do not capture the dynamics of diagenesis at low

DOC concentrations. However the complexity of a Monod rate law, with its

requirements for two constants, may negate its utility. In this case it may be better to

apply a second order rate law to allow the inclusion of both DOC and oxidant

concentration, but without the increased complexity provided by Monod kinetics.

In systems with low concentrations of organic carbon, the dissolved fraction

may be more important in diagenesis than the particulate fraction as, for example, in

systems where POC is degraded before reaching the sediment. In such systems even a

slight change in organic carbon concentration can lead to a dramatic change in fluxes

across the sediment-water interface. The change in organic carbon that would produce

these flux changes and the timescale over which this may occur is unknown.

Freshwater systems with low DOC content are atypical, however there is an

increasing abundance of acidic systems with a low DOC content, such as lakes subject

to acid rain, volcanic lakes and mine lakes. Management of systems such as mine lakes

requires long term prognosis of chemical species within the water column, something

that is unable to be achieved without an understanding of diagenesis in these systems

with their characteristic low concentrations of labile DOC. Added complexity is

provided in these systems through the interaction of diagenetic processes with aqueous

geochemical processes.

At the moment it is not known whether all mine lakes operate with similar

dominant processes, regardless of pH and geology, or whether there are key differences

between systems. Incorporating this knowledge into a numerical model and application

of the model would allow some insight into long term (> 20 years) trends in chemical

evolution and responses to remediation strategies, allowing more appropriate

management strategies to be adopted.

3

1.1.1 Hypothesis

This thesis hypothesizes that:

1. Diagenetic processes in acidic sediments can be limited by the amount of

labile organic carbon and that the concentrations of both the oxidant and

labile organic carbon are important in controlling rates of diagenesis.

2. Certain diagenetic processes, including the sequence of oxidants used

and the cycling of nitrogen, sulfur and iron, are consistent across marine

and freshwater systems.

3. Solute fluxes across the sediment-water interface can rapidly respond to

changes in concentration of labile DOC.

4. A combined diagenetic-geochemical-hydrodynamic model can capture

the chemical dynamics and feedbacks between the various processes at

the sediment water interface in mine lakes.

1.1.2 Objectives

Five objectives have been determined that will help evaluate this hypothesis:

Objective 1:

Determine that the field systems in question are carbon limited i.e. that concentrations

of labile DOC besides the oxidant are important in controlling the diagenetic process.

Objective 2:

Determine the second order rate constant of the aerobic respiration applicable to these

field sites.

Objective 3:

Determine the similarities and differences in processes between mine lakes with

different chemical and geological characteristics.

Objective 4:

Determine the extent to which fluxes across the sediment water interface can be

influenced by changes in labile DOC concentration in a low DOC marine environment.

4

Objective 5:

Develop and apply a coupled diagenetic-geochemistry-hydrodynamic model and assess

its ability to capture the chemical dynamics of the systems in question.

1.1.3 Approach

To meet the previously stated objectives, a series of experiments were conducted

using sediment and water from four field sites: a mine lake batch experiment, two

marine column experiments and three mine lake column experiments. These

experiments observed the temporal and spatial changes in chemical species known to be

important in diagenesis, both in the water column and in the sediment itself. To do this,

water samples were collected and analysed at an external analytical laboratory and

chemical sensors and microsensors were used for in-lab analysis.

These experiments were followed up by a series of modelling exercises carried

out with two numerical models: a simple batch reactor model and a more complex 1-D

in the vertical diagenesis-geochemical-hydrodynamic model. Similarities and

differences between experimental data and predicted output from the models determined

the key processes in each of these systems as well as highlighting where process

understanding may be lacking in the literature.

1.3 Thesis Outline

A general background to diagenesis and dissolved and particulate organic

carbon, transport reaction models and mine lakes will be presented in Chapter 2, and a

review of literature relevant to the chapter in question is also presented within that

chapter. Chapters 3 to 6 are presented in the style of manuscripts submitted for journal

publication. As a result these chapters are self contained and may be viewed

independent of the rest of the thesis. Due to the choice of this format there may be some

repetition of site description, methodology and literature review, however, together

these papers help tell the story of sediment diagenesis in environments with low DOC

concentrations and its importance in regulating fluxes across the interface. References

for these chapters can be viewed at the end of each chapter.

Chapter 3 deals with two experiments: a batch experiment conducted on the two

mine lakes (Lake Kepwari and Chicken Creek), and a column experiment conducted

5

using cores from the marine site of Cockburn Sound. These experiments were designed

to help establish that these systems are carbon limited in their diagenetic processes and

also to obtain a second order rate constant for the oxic breakdown of organic matter.

This chapter has been submitted to Ecological Engineering for publication as an article.

Chapter 4 is a comparison of core experiments conducted using sediment and

water from three different mine lakes: two from Australia (Lake Kepwari and Chicken

Creek) and one from Germany (Lake 111). These experiments involved cores half full

of sediment and the remainder filled with water which is spiked with a DOC source

(treacle). The chemical responses of the pore water and the water column were

monitored using microsensors and surface water sampling. As the three systems are all

different in terms of defining chemical characteristics and formation, this experiment

has been used to generalize diagenesis in acidic lakes by identifying the similarities

between the systems as well as defining some of the reasons for differences in

chemistry between the lakes. This article has been submitted to Marine and Freshwater

Research.

Chapter 5 focuses on another set of column experiments conducted on cores

from Cockburn Sound. These experiments observed not only water column species

concentrations (as in the first marine column experiment), but also the pore water

concentrations through the use of microsensors. This allows further inferences about the

remaining diagenetic reactions in marine systems to be made while at the same time

considering the low labile DOC concentrations and its influence on fluxes across the

interface. The evolution of these cores are then numerically modelled using a

diagenetic-hydrodynamic model, which highlights the influence of DOC mineralisation

on the dynamics of nutrient and metals fluxes across the interface. This chapter has been

submitted to Marine and Freshwater Research for publication as an article.

Chapter 6 discusses the set of experiments from Chapter 4 on Lake Kepwari.

The feedback of geochemistry on the diagenetic process is discussed in this chapter and

analysed with the aid of a joint diagenetic-geochemistry-hydrodynamic numerical

model of the cores. This chapter has been submitted to Water, Air and Soil Pollution.

Finally, Chapter 7 summarises all findings and aims to bring the previous four

chapters together in a succinct description of diagenesis in low DOC marine and lake

environments, its control over fluxes across the interface and also the feedbacks

imposed by geochemistry and biology on these processes. Conclusions will be drawn as

to the hypothesis and recommendations for future research on diagenesis are presented.

A list of all references used in this each chapter is provided at the end of that chapter.

6

1.4 References

Berner, R. A., 1980, Early Diagenesis: A Theoretical Approach, Princeton University

Press,

Boudreau, B. P., 1996, A method-of-lines code for carbon and nutrient diagenesis in

aquatic sediments. Computers and Geosciences, 22, 479-496.

Burdige, D., 2006, Geochemistry of Marine Sediments, Princeton University Press,

Jourabchi, P., Van Cappellen, P. & Regnier, P., 2005, Quantitative interpretation of pH

distributions in aquatic sediments: a reaction-transport modelling approach.

American Journal of Science, 305, 919-956.

Soetaert, K., Herman, P. M. J. & Middelburg, J. J., 1996, Dynamic response of deep-sea

sediments to seasonal variations: A model. Limnology and Oceanography, 41,

1651-1668.

7

7

2 Background and Literature Review

2.1 Organic Carbon and Sediment Diagenesis

It is only in the last few decades, with the improvement of measurement

techniques of organic carbon and in particular of DOC, that scientists have started to

realize the importance of dissolved organic matter (DOM) and its reactivity in the water

column and sediment (Hedges, 2002). The traditional perception that the majority of the

DOM in the ocean is refractory, high molecular weight, humic like substance with little

dynamic role in biological cycling has been changing over the past few decades as

research in this area increases.

The prediction of future oil and gas deposits prompted a concerted effort by

researchers to better understand the marine carbon cycle, the least constrained

component of the global carbon budget, and particularly the processes that cause

organic matter to be preserved in the sediment (Toggweiler, 1988; Williams and

Druffel, 1988; Hedges, 1992; Hedges, 2002). In both marine and freshwater systems,

diagenesis is a key process governing not only organic carbon removal but also the

return of bio-available nutrients and dissolved inorganic carbon (DIC) to the water

column (Jørgensen, 1983).

With this understanding has also come the realization that there can be strong

coupling between the sediment and the water column (benthic-pelagic coupling; Rowe

et al., 1975; Vidal and Morgu�, 2000; Dale and Prego, 2002) and activities in the

sediment can directly affect the water column and vice versa in a continual feedback,

moderating conditions in both the water column and the sediment. Sediment diagenesis

can have feedback affects on the biology (dissolved oxygen (DO) limitation, nutrient

fluxes) and potentially even the hydrodynamics (via chemical stratification and

chemoclines) of marine and freshwater systems. However the focus was previously on

POC and little is known about the importance of DOC, in particular labile DOC, in

systems with only a small concentration of organic carbon. It has been shown that large

amounts of bioavailable DOC can be released from allochthonous organic matter

(O'Connell et al., 2000) and the implication of DOC releases such as this has yet to be

considered.

These organic carbon limited systems are such that they tend also to be

oligotrophic, so low concentrations of nutrients and carbon are contained in these

systems meaning there is a potential for even a slight change in fluxes to cause a large

8

change in the chemical and biological composition of these water bodies. This

limitation is not just limited to oligotrophic systems as organic carbon limitation may

also occur in mesotrophic systems where labile organic carbon is degraded prior to

reaching the sediment.

Organic carbon in marine and freshwater systems comes in a range of sizes from

particulate right down to low molecular weight molecules (Middelburg et al., 1993;

Burdige, 2002). Within this continuum there is also a range of reactivity (Toth and

Lerman, 1977; Westrich and Berner, 1984; Henrichs and Doyle, 1986; Emerson and

Hedges, 1988). It has been estimated that over 50% organic matter in seawater and

sediments still remains uncharacterised (Williams and Druffel, 1988; Hedges et al.,

2000) and there is much ongoing research into identifying specific organic compounds,

their origins and their breakdown pathways (e.g. Brown et al., 1972; Hatcher et al.,

1983; Henrichs and Doyle, 1986; Hamilton and Hedges, 1988; Hedges et al., 1988; Sun

et al., 1993). Adding to this chemical complexity, physical processes such as sediment

resuspension, deposition and erosion can serve to re-partition organic carbon between

the dissolved and particulate phases (Middelburg and Herman, 2007) complicating the

degradation pathway.

In most cases, the remineralization of this organic matter is mediated by the

actions of bacteria, using enzymes to speed up the reaction rate while at the same time

harnessing the energy yielded from the respiration reactions (Libes, 1992; Middelburg

et al., 1993; Stumm and Morgan, 1996; Fenchel et al., 1998; Wetzel, 2001). Bacteria

typically import small organic carbon molecules across their cell membranes (size ~600

Da) so larger organic molecules must first be hydrolysed to smaller molecules outside

of the cell (Weiss et al., 1991; Arnosti, 2004). In order to be broken down, organic

carbon must first pass through the dissolved phase where it is potentially accessible by

bacteria (Emerson and Hedges, 1988). In marine systems most organic carbon is in the

dissolved form (Emerson and Hedges, 1988). The most easily degraded or labile organic

matter is used by bacteria first and hence lability of organic matter often decreases with

depth in the water column and in the sediment (Stumm-Zollinger, 1968; Mateles and

Chian, 1969; Emerson and Hedges, 1988; Middelburg et al., 1993; Burdige, 2002).

These effects cause the more refractory organic matter to accumulate in sediment and

porewater (Westrich and Berner, 1984). However, while the sediment tends to have

lower quality (ie more refractory) organic matter than surface water microbes have

showed adaptions enabling similar reaction rates to that in the surface water (Misic and

Covazzi Harriague, 2008).

9

Four indicators of organic matter lability have been commonly used in previous

scientific studies:

1. The carbon to nitrogen (C:N) ratio of the organic molecule in question (Huston

and Deming, 2002). This ratio does not take into account any chemical features

of the organic matter and as a result it tends to overestimates the amount of

biologically available nitrogen due to the abundance of non-bioavailable

nitrogenous compounds such as humic material (Mayer 2005).

2. The chlorophyll-a content (Fabiano et al., 1995), which came into use as most

labile organic carbon is derived from the water column.

3. The ratio of proteins to carbohydrates. Proteins are more labile than refractory

carbohydrates such as cellulose and chitin remnants (Danovaro and Fabiano,

1997; Cividanes et al., 2002). However high ratios do not necessarily indicate

good quality (Covazzi Harriague et al., 2007) due to the occurrence of protein

aging (Keil and Kirchman, 1994) which may sequester proteins into refractory

pools.

4. Biomimetic analyses in which selected enzymes are applied to the organic

matter samples in controlled conditions to evaluate the degree of lability (e.g.

Gordon, 1970; Mayer et al., 1995; Dell'Anno et al., 2000).

Studies using a combination of these methods have found that approximately

10% of organic matter in sediment was labile (Manini et al., 2003; Misic and Covazzi

Harriague, 2008). Despite the physical separation and relatively long mixing timescales,

the chemical composition of the DOC pool may be relatively homogeneous throughout

the ocean (McCarthy et al., 1993). This is mostly due to the major source of DOM being

in-situ production from plankton (Lee and Wakeham, 1988) with terrestrial sources

providing less than 10% of DOM (Meyers-Schulte and Hedges, 1986). The other source

of DOM to the water column are marine sediments, which constitutes approximately 5-

10% of the total flux (Hedges, 1988).

Marine surface water samples typically contain a more labile DOM with a C:N

ratio of 13 to 15, while deep water samples have more refractory DOM with a C:N ratio

of 18 to 22, indicating that marine DOM is relatively carbon rich when compared to the

Redfield ratio of 7 for fresh plankton material (McCarthy et al., 1993). Humic

substances, which have been found to be among the most refractory organic matter,

typically have a C:N ratio of 35 to 45 (Meyers-Schulte and Hedges, 1986). The

10

difference in ratios is due to the difference in composition of DOM in surface and

deeper marine water.

The proportion of the organic matter characterised rapidly decreases with depth

and molecular size. The surface DOM contains approximately 50% carbohydrates

(McCarthy et al., 1993). Amino acids, sugars and fatty acids are among the most labile

organic compounds, being preferentially utilised during decomposition (Skoog and

Benner, 1997; Lee et al., 2000; Amon et al., 2001) and are degraded at rates orders of

magnitude greater than that of abiotic condensation (Hedges, 1988), hence they are

unlikely to undergo abiotic reactions and become refractory. Polymeric compounds are

degraded 2 to 10 times more slowly (Keil and Kirchman, 1993) hence abiotic reactions

are more likely to occur. Lability of DOM decreases when the molecules are associated

with other organic molecules (Keil and Kirchman, 1993) or mineral surfaces.

The deep DOM is primarily composed of aliphatic and carboxylic acid carbon

and is present throughout the ocean as a more refractory organic background which has

survived multiple mixing cycles (McCarthy et al., 1993). The average age of this deep

DOM is between 4000 years (Bauer et al., 1992) and 6000 years (Druffel et al., 1992).

Carboxyl-rich alicyclic molecules are the most abundant defined refractory component

of deep ocean water making up approximately 8% of DOC and are mostly comprised of

the decomposition products of biomolecules (Hertkorn et al., 2006).

The molecularly uncharacterised component (MUC) comprises approximately

75% of the marine DOM (Benner, 1998) and was once assumed to be derived from the

abiotic condensation of simple biochemicals such as amino acids, phenols, sugars

(Hedges, 1988). A shift in this paradigm occurred due to two reasons. The first of these

being that the size of the molecule seemed to be at least as important as the chemical

form in determining the reactivity of the organic matter (Hedges et al., 2000). There are

a number of studies that indicate that as the size of the molecule of DOM decreases so

does the reactivity (e.g. Amon and Benner, 1994; Amon and Benner, 1996; Mannino

and Harvey, 2000; Harvey and Mannino, 2001; Hama et al., 2004; Zou et al., 2004;

Seitzinger et al., 2005). There has also been the observation of an age-size continuum

(Loh et al., 2004).

The second reason being that extensive in-situ formation of new chemical

compounds was seldom evident from analysis (Hatcher et al., 1983) and organic matter

degradation was mostly associated with attrition, hence resistant chemicals accumulated

into MUC (Hedges et al., 2000). There is, however, some evidence for spontaneous

bond formation between molecules through photochemical formation (Harvey et al.,

11

1983; Gatellier et al., 1993) and diagenetic incorporation of protein derived molecules

into hydrolysis resistant organic matter (Zegouagh et al., 1999).

The refractory nature of some DOM is thought to be due to the inaccessibility of

different components of the molecule to enzymes and inorganic chemical reagents and

also the inability of microorganisms to transport large molecules across their membrane,

meaning that these molecules must be somewhat degraded outside the cell (Hedges et

al., 2000). However, much is still unknown about the smallest size fraction of MUC as

the current methods for isolating DOM from seawater are limited to molecules greater

than 1000 amu in size. As a result the molecules that are able to be transported across

cell membranes (<600 amu) are unable to be characterised (Hedges et al., 2000).

While labile organic matter is degraded before refractory, there is also a certain

order in which oxidants are used to mineralise the organic matter and this sequence is

expressed not only through time, but also with increasing sediment depth resulting in

zonation within the sediment (Middelburg et al., 1993; Stumm and Morgan, 1996;

Fenchel et al., 1998). Oxidants with the highest Gibbs Free Energy are used first,

subsequently progressing through the oxidants with the next highest free energy

(Middelburg et al., 1993; Stumm and Morgan, 1996; Fenchel et al., 1998). In sediments

with a neutral pH this sequence is first oxygen; then nitrate and nitrite; iron (III);

manganese (IV); sulfate; and finally organic carbon itself in a process known as

methanogenesis (Middelburg et al., 1993; Stumm and Morgan, 1996; Fenchel et al.,

1998).

Equations describing these reactions can be written in a number of ways

depending on the representation of the organic matter component. A generic description

can be found by assigning the ratio of C:N:P as A:B:C as in the following set of

equations (Boudreau 1996):

Aerobic respiration:

( ) ( ) ( ) ( ) 433224332 2 POCHBHNOACOOBAPOHNHOCH CBA ++→++ (2.1)

Denitrification:

( ) ( ) ( ) 34332234332 5554

552

54

2 NHB

POHC

HCOA

COA

NA

NOA

POHNHOCH RCBA ++�

�

��

�+��

��

�+��

��

�→��

��

�+ −− (2.2)

Manganese reduction:

( ) ( ) ( ) 43332

224332 4232 POCHBNHAHCOAMnACOAMnOPOHNHOCH CBA +++→++ −+ (2.3)

12

Iron reduction:

( ) ( ) ( ) ( ) 43332

2)(4332 8474 POCHBNHAHCOAFeACOIIIAFePOHNHOCH SCBA +++→++ −+ (2.4)

Sulfate reduction:

( ) ( ) ( ) 43332244332 22

POCHBNHAHCOSHA

SOA

POHNHOCH CBA +++ →+ −− (2.5)

Methanogenesis:

( ) ( ) ( ) 433244332 22POCHBNHCO

ACH

APOHNHOCH CBA +++ → (2.6)

Some bacteria use specific oxidants in the remineralization process, others are

capable of switching between different oxidants (Fenchel et al., 1998). While bacteria

often occupy a certain niche in an environment, performing a specific task, many also

lie dormant and can quickly become active should a suitable change in conditions occur

(Fenchel et al., 1998). Bacteria capable of taking advantage of ambient conditions are

almost always present, even in environments with extreme conditions such as low/high

pH or temperature (Fenchel et al., 1998). A lack of DOC availability may limit

respiration activity by bacteria, which may lead to either low nutrient release or to the

increased penetration of DO into the sediment.

Understanding of these processes then allows predictions to be made regarding

the cycling of DOC and the impact it has on other species concentrations within the

water column and sediment, and hence the impact on other geochemical, biological and

even physical processes. Diagenetic models can be used as a tool to aid in our

understanding allowing us to establish which chemical reactions are likely to be taking

place and, if predictions do not match measurement, then they are also able to indicate

that some key process description is incomplete or missing and may also provide some

insight as to what this might be.

2.2 Reaction-Transport Models

Mathematical descriptions of diagenesis are based on a mass balance approach

and are applied to a 1 dimensional situation through the General Diagenetic Equation

(Berner, 1980a):

13

RCwzC

Dzt

CS Σ+�

�

��

� +∂∂−

∂∂−=

∂∂ φφφφ

(2.7)

Where C is the solute concentration, DS is the molecular diffusion coefficient, w

is the porewater advection rate, R is the rate of reaction, φ is porosity and z is the depth

in the sediment.

Equation 2.7 describes the local transport processes of advection and diffusion,

with diffusion described using Fick’s First Law. Species modelled by this equation are

coupled by the R term. Diagenetic models are all derived from Berner’s diagenetic

equation, however the differences between them arise in the number of species

modelled, the mathematical description of reactions, solution method for the equations

and whether or not steady state is assumed.

The first wave of diagenetic models were relatively simple models that assumed

steady state and were solved analytically (e.g. Goldberg and Koide, 1962; Berner, 1964;

Boudreau, 1987; Boudreau and Canfield, 1988; Boudreau, 1991; Boudreau and

Canfield, 1993). A second wave of more sophisticated models emerged and these

models can be defined by the use of a numerical approach, non-linear kinetics

(discussed below), depth dependent transport parameterization and these models

typically focused on particular processes or sediment zones (e.g. Gardner and Lerche,

1987; Gardner and Lerche, 1990; Rabouille and Gaillard, 1991; Blackburn et al., 1994;

Tromp et al., 1995; Dhakar and Burdige, 1996).

During this time there was also an ongoing evolution of the kinetic description

of organic matter remineralization. This first wave of models included the 1-G model,

developed by Berner (Berner, 1964; Berner, 1980a), in which metabolizable POC as a

whole is degraded at one rate according to first order kinetics.

kGtG

R −=∂∂= (2.8)

where G is the concentration of degradable particulate organic matter and k is a

first order rate constant. Using equation 2.8, the concentration of reactive organic matter

can be calculated independently of other species such as oxidants. The formulation is

based on the theory that the rate-limiting step in the remineralization process is the

hydrolysis of large organic carbon molecules to produce smaller molecules that are

capable of being absorbed by bacteria (Gujer and Zehnder, 1983; Kristensen et al.,

1995). However, as discussed by Brüchert and Arnosti (2003) and Arnosti (2004), the

rate limiting step is not necessarily the hydrolysis of organic molecules to smaller sizes

but is more dependent on the type of organic compound and the enzymes available.

14

In the 1-G model oxidant consumption or the production of nutrients is

calculated based on stoichiometric ratios with respect to organic carbon. This

assumption may give erroneous results as the ratio of N and P release to organic carbon

remineralised may change as decomposition progresses due to the preferential

remineralisation of functional groups containing N and P in the organic matter (Toth

and Lerman, 1977; Suess and Müller, 1980; Krom and Berner, 1981; Jørgensen, 1983;

Ingall and Van Cappellen, 1990).

Later, when it was realized that there are fractions of organic carbon that decay

at different rates, this model evolved into the multi-G model (Berner, 1980b), which

also contained first order kinetic descriptions of POC remineralization. As pointed out

by Middelburg (1989) in order to apply this model one must know the number of labile

groups, their relative amounts and their reactivity.

The range of reactivity of POC was encapsulated in the power model

(Middelburg, 1989) which is based on the G model with the rate k now being a power

function, decreasing over time and is in it’s general form in equation 2.9 and 2.10

(Tarutis Jnr, 1993).

GtktG

R )(−=∂∂= (2.9)

( )qtaptk +=)( (2.10)

where p, a and q are model parameters to be determined, with a termed as the

“apparent initial age” of the organic matter (Janssen, 1984).

Recognition of the involvement of bacteria in mediating remineralization saw

the incorporation of Monod kinetics to describe POC decay, the inclusion of oxidants

and eventually the bi-products of respiration reactions. Explicit incorporation of bacteria

population dynamics into a diagenetic model has also been conducted by Schultz and

Urban (2008).

The third wave of diagenetic models saw the emergence of a handful of models

capable of depicting all redox zones with an extensive range of chemical species and

reactions included in the model. In particular are OXMEDIA (Soetaert et al., 1996b),

STEADYSED (Van Cappellen and Wang, 1996), and CANDI (Boudreau, 1996). These

models have also been applied, mostly by their creators: OXMEDIA (Soetaert et al.,

1996a; Epping et al., 2002), STEADYSED (Wang and Van Cappellen, 1996), CANDI

(Boudreau et al., 1998).

The latest generation of diagenetic models has been brought about by the need to

move away from rigid codes that are site specific and allow the user to choose the

15

required reactions and other processes to be included in the simulation (Meysman et al.,

2003a). Two such “buildable” models have been developed: the Knowledge Base

Reaction Transport Model (KB_RTM; Regnier et al., 2002; Aguilera et al., 2005) and

MEDIA (Meysman et al., 2003b). All these diagenetic models treat the sediment in

isolation from the water column and, while the user is able to prescribe fluxes, these

fluxes are not linked to a dynamic water column.

The concept of using fluxes predicted by diagenetic models in ocean circulation

models has only occurred fairly recently with increased computing ability and at this

stage the full potential of this application has yet to be explored. Luff and Moll (2004)

linked CANDI to the water column to model the North Sea to investigate seasonal

dynamics. Soetaert (2000) also used a coupled model, but that is the extent of linking

diagenetic models with water column models.

Given that there can be strong feedback and interaction between chemical and

physical processes in the water column and the sediment it seems that the sophistication

level of diagenetic models cannot be extended any further without incorporating

processes from the water column, not only the physical and chemical processes but

biological as well. Biological processes can strongly influence the supply of organic

carbon to the sediment and are also strongly influenced by the release of nutrients from

the sediment as has already been established in discussions on benthic-pelagic coupling.

Viewing a process in isolation may lead to a different set of conclusions to those that

would be reached viewing a process as part of a larger system.

2.3 Mine Lakes

2.3.1 Formation In the process of mining minerals, most voids are dewatered to allow access to

the mineral in question. The dewatering often exposes sulfur containing minerals (e.g.

pyrite and marcasite) found in conjunction with economically valuable minerals such as

coal and various metal ores (Evangelou, 1998). After the completion of mining,

dewatering ceases and the void is allowed to fill with water. Many voids are

intentionally turned into artificial lakes through filling with ground water, diversion of

rivers or actively pumping water into the void. This has become increasingly prevalent

world wide as the number of former mining voids increases. In Germany, for example

160 mine lakes were formed due to the rapid closure of many mines after the

reunification of East and West Germany (Klapper, 2002). While the establishment of an

16

artificial lake may seem like the perfect solution to the problem of post mining void use,

it has its own set of associated problems.

Many mine lakes experience geogenic acidification, where acidic groundwater

flows into the void. Dewatering may expose pyrite and marcasite to the atmosphere

causing them to oxidize and leading to the production of acidic waters (Klapper and

Schultze, 1995), which can contain high concentrations of Fe, Mn, Al, SO4 and heavy

metals (Evangelou, 1998; Klapper, 2002).

The reactions governing pyrite oxidation are (Evangelou, 1998 and references

therein; Klapper, 2002): +−+ ++→++ HSOFeOHOFeS 225.3 2

42

222 (2.11)

OHFeHOFe 23

22 2141 +→++ +++ (2.12)

++ +→+ HOHFeOHFe s 3)(3 )(323 (2.13)

42423422 8158)(7 SOHFeSOOHSOFeFeS +→++ (2.14)

Equation 2.11 represents the initial weathering reaction that occurs when pyrite

first comes into contact with moisture and air (Klapper, 2002).

Equation 2.12 shows further oxidation of Fe(II) by oxygen which can occur

abiotically but may be accelerated up to 6 orders of magnitude by microorganisms such

as Thiobacillus ferrooxidans (Evangelou, 1998; Klapper, 2002). This bacteria is

ubiquitous in geologic environments and is not only able to oxidize iron(II), but also

elemental sulfur and other reduced inorganic sulfur compounds (Evangelou, 1998). The

abiotic oxidation of iron(II) is pH sensitive, occurring rapidly above pH 5 and slowly in

acidic conditions (Evangelou, 1998). At neutral and alkaline pH, Evangelou (1998)

predicts that there is probably little bacterial participation in pyrite oxidation.

Equation 2.13 is a reversible precipitation reaction and hence is a source/sink of

iron(III), taking place with pH values as low as 3 (Evangelou, 1998). It occurs more

rapidly than the oxidation of pyrite by oxygen (Evangelou, 1998) and also delivers the

highest proportion of protons to the water. In very acidic conditions (pH < 3.5) Fe(OH)3

may actually remain in solution giving the water a red/brown colour (Klapper, 2002).

This series of reactions (2.11-2.14) shows that pyrite can be oxidized by both

oxygen (Equation 2.11) and iron(III) (Equation 2.14) which is an important point to

note when considering remediation strategies.

The overall reaction can be summarised as (Klapper, 2002):

( ) +− ++→++ HSOOHFeOHOFeS 425.375.3 243222 (2.15)

17

The acidic water contained in mine lakes is strongly buffered, hence

neutralization is complicated (Klapper et al., 1998). Three buffering systems govern the

water chemistry of mine lakes (Klapper and Schultze, 1995):

- Bicarbonate (pH 6-8)

- Aluminium (pH 4-5)

- Iron (pH 2-4)

Only when the binding capacity of the buffer is saturated does alkalinisation

alter the pH to the next buffer system (Klapper, 2002). Natural lakes typically operate in

the bicarbonate buffer range, whereas mining lakes are typically in the aluminium or

iron buffer ranges.

As a result of their recent formation and the acidity generating processes in the

mine walls and overburden, mine lakes typically lack organic carbon (Klapper and

Schultze, 1995; Nixdorf and Kapfer, 1998) and have higher concentrations of sulfate

and iron (Kleeberg, 1998; Peiffer, 1998; Fyson et al., 2002) when compared to most

natural freshwater lakes meaning that any diagenesis is thought to be more like that of a

marine system, rather than a freshwater lake. Lack of inorganic carbon in mining lakes

(Klapper and Schultze, 1995; Peiffer, 1998; Fyson et al., 2002) also means that the lake

operates in more acidic buffer zones than natural lakes and are also more likely to have

higher concentrations of soluble heavy metals (Klapper, 2002). Typically these lakes are

also poor in the macronutrients P, N and Si (Kleeberg, 1998; Fyson et al., 2002;

Klapper, 2002). Phosphorus in particular is often limiting due to binding with

aluminium and iron followed by precipitation from the water column (Nixdorf and

Kapfer, 1998).

The biodiversity in mine lakes tends to correlate with pH, being extremely low

in systems with low pH and increasing with increasing pH (Kapfer, 1998; Kleeberg,

1998; Nixdorf et al., 1998; Woelfl, 2000). Although most acidic lakes have low

biodiversity some acidic lakes are still extremely productive (Nixdorf et al., 1998) as

low pH values do not necessarily mean reduced plankton biomass and high algal

densities have been observed in some acidic lakes (Kapfer, 1998; Nixdorf et al., 1998;

Woelfl, 2000). Production is usually limited by factors such as DIC and phosphate

availability rather than acidity itself (Nixdorf and Kapfer, 1998; Woelfl, 2000; Beulker

et al., 2003). Current estimates of primary production do not take into account the

contribution of benthic photosynthesis and only consider photosynthesis in the water

column (Koschorreck and Tittel, 2002). Due to the low total algal biomass these lakes

18

can often be classified as oligotrophic (Klapper and Schultze, 1995; Klapper et al.,

1998).

2.3.2 Remediation Government regulations in many countries require that some form of

remediation is carried out on mine lakes or that there is at least some kind of

demonstration that their condition will not deteriorate in the future. As a result a number

of strategies have been developed centralizing on raising the pH of the lake water and at

the same time precipitating iron and sulfur. These techniques can be classified as in-situ

or ex-situ, active or passive. and chemical or biological (Gazea et al., 1996; Totsche et

al., 2002), although as yet, no sustainable technique for the treatment of acidic mine

lakes has been demonstrated (Wendt-Potthoff et al., 2002).

Given the large buffering capacity of these lakes, chemical remediation is

usually not an option owing to the large amount of neutralizing agent required and

hence the associated large economic expense (Klapper and Schultze, 1995; Klapper et

al., 1998). This leaves biological remediation as the preferred option with the aim being

to speed up natural biological processes.

Acidity can be removed through biologically mediated sulfate reduction

(Kleeberg, 1998; Fyson et al., 2002), commonly observed in anaerobic diagenesis,

leading to the precipitation of insoluble sulfides, however this requires anoxic

conditions to be present (Anderson and Schiff, 1987). Klapper (2002) points out that in

the sediments the pH can be raised to near neutral through hypolimnetic anoxia,

enhancing microbial alkalinity production from anaerobic respiration. These reactions

can be described with the following equation (Anderson and Schiff, 1987):

OHFeSCOHSOFeOOHOCH saqaqs 2)(22)()_(24)(2 25415168415 ++→+++ +− (2.16)

Note that there is the need for a source of organic carbon in these reactions. If

there is a lack of organic carbon in the system, then this reaction is inhibited. The

organic carbon limitation of microbial respiration is suspected to occur in many mine

lakes (Brugam et al., 1995; Klapper and Schultze, 1995; Friese et al., 1998; Kleeberg,

1998; Peine et al., 2000). Increasing the supply of organic carbon is thought to

encourage alkalinity generation through this method (Brugam et al., 1995). The

degradation of organic matter in the sediment also provides DIC, N and P back to the

water column making them available for phytoplankton growth (Nixdorf and Kapfer,

1998; Woelfl, 2000). Many researchers are also advocates for controlled eutrophication

19

of acidic mine lakes to increase primary production and hence the supply of organic

carbon to the sediments (e.g. Fyson et al., 1998; Klapper et al., 1998; Fyson et al.,

2002). However, it is thought that the oxygen demand by remineralization of

sedimented algae may not be high enough for water column DO depletion (Klapper et

al., 1998), so the addition of organic matter is viewed as the favourable method to start

the alkalinity generating process.

As diagenesis occurs mainly in the sediment, the sediment-water interface is an

extremely important site when considered in relation to water chemistry within the lake.

In an unstratified lake, fluxes across this interface can affect the entire water column.

One of the main sources of protons to mine lakes is the inflow of acidic groundwater

(Koschorreck et al., 2003a), so redox conditions at the sediment-water interface can

determine the magnitude of protons fluxing into the lake.

At this stage there is no established method for predicting long term chemical

and biological evolution of mine lakes under various remediation strategies (Eary,

1999), although much research has been carried out using micro and mesocosms of

mine lake waters and sediment on the effects of organic carbon addition on pH. Focus

has primarily been on the net change of pH and the biogeochemical processes involving

iron and sulfur and there has been relatively little research on the mechanisms involving

organic carbon in acidic systems. Specifically, a gap was identified in the knowledge of

redox processes associated with sediment diagenesis in acidic systems (Eary, 1999).

Since this time there have been some microcosm and mesocosms experiments

on mine lake sediment and water where different types of organic matter have been

added to generate alkalinity e.g.

- whey (Christensen et al., 1996)

- manure, sawdust, peat, mushroom compost (Vile and Wieder, 1993)

- straw (Brugam et al., 1995)

- straw and carbokalk (Frömmichen et al., 2001; Koschorreck et al., 2002; Wendt-

Potthoff et al., 2002)

Wendt-Potthoff et al. (2002) conducted an enclosure experiment in Mining Lake

111 (ML111) where they added carbokalk to the sediment which stimulated iron(III)

and sulfate reduction. However this was unsustainable and after 5 months of the

experiment iron(II) oxidation exceeded iron(III) reduction (Wendt-Potthoff et al., 2002).

They concluded that for the process to sustainably produce alkalinity, the iron(II)

needed to be immobilized as a solid, either through precipitation with carbonate or

20

sulfide and that it was also necessary to provide an excess of organic substrate to help

maintain anoxia in the sediment (Wendt-Potthoff et al., 2002).

Koschorreck et al. (2002) conducted an enclosure experiment on ML111 using

different combinations of straw and carbokalk to determine the function of straw in the

process of alkalinity production. It was proposed that straw was a substratum for

microbial growth and also served to stabilise and deoxygenate profundal water, however

in the time of the experiment the straw did not develop a reactive biofilm, nor did it

deoxygenate the profundal water (Koschorreck et al., 2002). H2S was noted to form in

the sediment, the maximum being at the sediment surface where it was able to flux back

into the water column and be reoxidised (Koschorreck et al., 2002). The presence of

H2S also coincided with an increase in pH in the sediment pore water (Koschorreck et

al., 2002). The main function of straw was determined to be as a long term nutrient

source for alkalinity generation by bacteria in the sediment (Koschorreck et al., 2002), a

finding supported by earlier work by Blodau et al. (1998) who argued that in acidic

waters, nutrient requirements are much more important for the occurrence of certain

diagenetic reactions.

Initial research into redox chemistry in mine lakes assumed that the sequence of

oxidants used is controlled by the Gibbs Free Energy of the reactions (Berner, 1980a),

but the influence of geochemistry may make the process a little more complex and the

concept of relative energy yield leading to the formation of zones of specific redox

processes may not be very accurate (Postma and Jakobsen, 1996; Blodau and Peiffer,

2003).

In environments that are rich in iron(II), such as mine lakes, the pH of the pore

water becomes important in determining whether iron(III) or sulfate reduction is

preferred (Postma and Jakobsen, 1996). Until recently is was thought that sulfate

reduction could not occur below a certain pH (pH < 5.5) (Koschorreck et al., 2002),

however sulfate reduction has now been measured in acidic sediment (pH<3) of a

Argentinean volcanic lake and in the sediment of a mine lake, so it is possible in acidic

conditions (Küsel et al., 1999; Koschorreck et al., 2003b).

It appears that sulfate reduction competes with iron reduction, with iron(III)

reduction being more favourable at low pH (Peine et al., 2000). This is supported by

Koschorreck et al. (2003b) who observed that H2S production stopped in the sediment

of the volcanic lake when iron(III) reduction was stimulated. It has also been suggested

that the presence of more types of iron oxides in the sediment causes an increased

overlap in the boundaries between iron(III) and sulfate reduction (Postma and Jakobsen,

21

1996). While in marine diagenesis iron(III) is reduced prior to sulfate, in mine lakes

simultaneous reduction of sulfate and iron(III) is actually possible under varying

sediment conditions and sulfate reduction may even occur before iron(III) reduction

(Vile and Wieder, 1993; Postma and Jakobsen, 1996).

In the past, methods of predicting mine lake water quality have focused solely

on geochemical modelling and ignored in lake generation of organic matter through

phytoplankton growth and the remineralization of organic matter (e.g. Rolland, 2001;

Werner et al., 2001; Mazur et al., 2002). Although Eary (1998) claims that current

models may actually over estimate rates of alkalinity generating processes.

2.4 Field Sites

2.4.1 Cockburn Sound The marine system of Cockburn Sound is a semi-enclosed coastal embayment

30km south of Perth, Western Australia, which has been under anthropogenic pressure

for several decades. It has a maximum depth of 20m, a width of 7km and a length of

20km. Sediment is predominantly coarse grained carbonate sand with reportedly 8%

organic matter content (dry weight) (Department of Environmental Protection, 1996).

The hypolimnion DOC concentration is typically 1.1 - 1.3 mg L-1 and DO concentration

ranges between 4.5 and 7.0 mg L-1 (Department of Environmental Protection, 1996).

There have been recordings of algal blooms as early as 1973 and there are anecdotal

reports of fish and crab kills in the deep basin (Department of Conservation and

Environment, 1979; Department of Environmental Protection, 1996).

2.4.2 Lake Kepwari Lake Kepwari is located in the Collie Basin, Western Australia, approximately

160km southeast of Perth. It is a former open cut mine voids that has filled with water

from groundwater and diverted river flow. At the time of the experiments Lake Kepwari

had a maximum depth of 65m and a volume of 25GL. It is a monomictic lake, usually

experiencing thermal stratification from spring to autumn (October – April) and is fully

mixed from May to September. Although Lake Kepwari is relatively deep and stratified

for half the year, it remains oxic for the entire year with DO concentrations in the

hypolimnion of around 6 mg L-1. DOC concentrations are approximately 1.2 - 1.5 mg L-

1 in Lakes Kepwari (depth averaged). The oxidation of remnant pyritic material causes

the lake to be acidic with a pH of 4.8. The primary mineral phases in Lake Kepwari

22

sediment are kaolinite and quartz with a small amount of goethite and it has an organic

matter content of 2.13%.

2.4.3 Chicken Creek Chicken Creek is also located in the Collie Basin, Western Australia and at the

time of the experiment Chicken Creek had a maximum depth of 35m and a volume of

2.6GL. Like Lake Kepwari, Chicken Creek is also monomictic, with the same

stratification and mixing cycle, and it also remains oxic for the entire year with DO

concentrations around 6 mg L-1. DOC concentrations are approximately 0.5 – 1.0 mg L-1

in Chicken Creek (depth averaged). The oxidation of remnant pyritic material causes the

lake to be acidic with a pH of 2.8. As for Lake Kepwari, Chicken Creek sediment is

primarily composed of kaolinite and quartz with a small amount of goethite and has an

organic carbon content of 2.55%.

2.4.4 Mining Lake 111 ML111 is located in the Lusation Mining district in Germany (51°29`N,

13°38È). It has a surface area of 107 000m2, a mean depth of 4.5m and a maximum

depth of 10.5m. ML111 was formed after the cessation of mining in 1956 and by 1967

the lake had been completely filled with groundwater (Karakas et al., 2003). It has no

surface inflows or outflows, so water may only enter the void through groundwater

inflow and precipitation (Karakas et al., 2003). The pH of the lake is around 2.6, it

contains high sulfate and iron concentrations and low concentrations of inorganic and

organic carbon.

2.5 References

Aguilera, D. R., Jourabchi, P., Spiteri, C. & Regnier, P., 2005, A knowledge-based

reactive transport approach for the simulation of biogeochemical dynamics in

Earth systems. Geochemistry Geophysics Geosystems, 6,

Amon, R. M. W. & Benner, R., 1994, Rapid-cycling of high-molecular-weight

dissolved organic-matter in the ocean. Nature, 369, 549-552.

Amon, R. M. W. & Benner, R., 1996, Bacterial utilization of different size classes of

dissolved organic matter. Limnology and Oceanography, 41, 41-51.

23

Amon, R. M. W., Fitznar, H. P. & Benner, R., 2001, Linkages among the bioreactivity,

chemical composition, and diagenetic state of marine dissolved organic matter.

Limnology and Oceanography, 46, 287-297.

Anderson, R. F. & Schiff, S. L., 1987, Alkalinity generation and the fate of sulfur in

lake sediments. Canadian Journal of Fisheries and Aquatic Sciences, 44, 188-

193.

Arnosti, C., 2004, Speed bumps and barricades in the carbon cycle: substrate structural

effects on carbon cycling. Marine Chemistry, 92, 263-273.

Bauer, J. E., Williams, P. M. & Druffel, E. R. M., 1992, 14C activity of dissolved

organic carbon fractions in the north-central Pacific and Sargasso Sea. Nature,

357, 667-670.

Benner, R., 1998, Cycling of dissolved organic matter in the ocean. in Hessen, D. O. &

Travnik, L. J. (Eds.) Aquatic Humic Substances: Ecology and Biogeochemistry.

Springer, New York.

Berner, R. A., 1964, An idealized model of dissolved sulfate distribution in recent

sediments. Geochimica et Cosmochimica Acta, 28, 1497-1503.

Berner, R. A., 1980a, Early Diagenesis: A Theoretical Approach, Princeton University

Press,

Berner, R. A., 1980b, A rate model for organic matter decomposition during bacterial

sulfate reduction in marine sediments. Colloques Internationaux du C.N.R.S. -

Biogeochemimie de la Matiere Organique a l'Interface Eau-Sediment Marin,

293, 35-44.

Beulker, C., Lessmann, D. & Nixdorf, B., 2003, Aspects of phytoplankton succession

and spatial distribution in an acidic mining lake (Plessa 117, Germany). Acta

Oecologica, 24, S25-S31.

Blackburn, T. H., Blackburn, N. D., Jensen, K. & Risgaard-Petersen, N., 1994,

Simulation model of the coupling between nitrification and denitrification in a

freshwater sediment. Applied and Environmental Microbiology, 60, 3089-3095.

Blodau, C., Hoffmann, S., Peine, A. & Peiffer, S., 1998, Iron and sulfate reduction in

the sediments of acidic mine lake 116 (Brandenburg, Germany): Rates and

geochemical evaluation. Water, Air and Soil Pollution, 108, 249-270.

Blodau, C. & Peiffer, S., 2003, Thermodynamics and organic matter: constraints on

neutralization processes in sediments of highly acidic waters. Applied

Geochemistry, 18, 25-36.

24

Boudreau, B. P., 1987, A steady-state diagenetic model for dissolved carbonate species

and pH in the porewaters of oxic and suboxic sediments. Geochimica et

Cosmochimica Acta, 51, 1985-1996.

Boudreau, B. P., 1991, Modelling the suflide-oxygen reaction and associated pH

gradients in porewaters. Geochimica et Cosmochimica Acta, 55, 145-159.



Boudreau, B. P. & Canfield, D. E., 1988, A provisional diagenetic model for pH in

anoxic porewaters: Application to the FOAM site. Journal of Marine Research,

46, 429-455.

Boudreau, B. P. & Canfield, D. E., 1993, A comparison of closed- and open-system

models for porewater pH and calcite-saturation state. Geochimica et


Boudreau, B. P., Mucci, A., Sundby, B., Luther, G. W. & Silverberg, N., 1998,

Comparative diagenesis at three sites on the Canadian continental margin.

Journal of Marine Research, 56, 1529-1284.

Brown, F. S., Baedecker, M. J., Nissenbaum, A. & Kaplan, I. R., 1972, Early diagenesis

in a reducing fjord, Saanich Inlet, British Columbia-III. Changes in organic

constituents of sediments. Geochimica et Cosmochimica Acta, 36, 1185-1203.

Brüchert, V. & Arnosti, C., 2003, Anaerobic carbon transformation: experimental

studies with flow-through cells. Marine Chemistry, 80, 171-183.

Brugam, R. B., Gastineau, J. & Ratcliff, E., 1995, The neutralization of acidic coal mine

lakes by additions of natural organic matter: a mesocosm test. Hydrobiologia,

316, 153-159.

Burdige, D. J., 2002, Sediment pore waters. in Hansell, D. A. & Carlson, C. A. (Eds.)

Biogeochemistry of Marine Dissolved Organic Matter. Academic Press, San

Diego.

Christensen, B., Laake, M. & Lien, T., 1996, Treatment of acid mine water by sulfate-

reducing bacteria; results from a bench scale experiment. Water Research, 30,

1617-1624.

Cividanes, S., Incera, M. & López, J., 2002, Temporal variability in the biochemical

composition of sedimentary organic matter in an intertidal flat of the Galician

coast (NW Spain). Oceanologica Acta, 25, 1-12.

25

Covazzi Harriague, A., Misic, C., Petrillo, M. & Albertelli, G., 2007, Stressors affecting

the macrobenthic community in Rapallo Harbour (Ligurian Sea, Italy). Scienta

Marina, 71, 705-714.

Dale, A. W. & Prego, R., 2002, Physico-biogeochemical controls on benthic-pelagic

coupling of nutrient fluxes and recycling in a coastal upwelling system. Marine

Ecology Progress Series, 235, 15-28.

Danovaro, R. & Fabiano, M., 1997, Seasonal changes in quality and quantity of food

available for benthic suspension-feeders in the Golfo Marconi (North-Western

Mediterranean). Estuarine, Coastal and Shelf Science, 44, 723-736.

Dell'Anno, A., Fabiano, M., Mei, M. L. & Danovaro, R., 2000, Enzymatically

hydrolysed protein and carbohydrate pools in deep-sea sediments: estimates of

the potentially bioavailable fraction and methodological considerations. Marine


Department of Conservation and Environment, 1979, Cockburn Sound Environmental

Study 1976-1979. Government of Western Australia, Perth

Department of Environmental Protection, 1996, Southern Metropolitan Coastal Waters

Study (1991-1994). Government of Western Australia, Perth

Dhakar, S. P. & Burdige, D. J., 1996, A coupled, non-linear, steady state model for

early diagenetic processes in pelagic sediments. American Journal of Science,

296, 296-330.

Druffel, E. R. M., Willison, P. M., Bauer, J. E. & Ertel, J. R., 1992, Cycling of dissolved

and particulate organic matter in the open ocean. Journal of Geophysical

Research, 97, 639-659.

Eary, L. E., 1998, Predicting the effects of evapoconcentration on water quality in mine

pit lakes. Journal of Geochemical Exploration, 64, 223-236.

Eary, L. E., 1999, Geochemical and equilibrium trends in mine pit lakes. Applied


Emerson, S. & Hedges, J. I., 1988, Processes controlling the organic carbon content of

open ocean sediments. Paleoceanography, 3, 621-634.

Epping, E., van der Zee, C., Soetaert, K. & Helder, W., 2002, On the oxidation and

burial of organic carbon in sediments of the Iberian margin and Nazare Canyon

(NE Atlantic). Progress in Oceanography, 52, 399-431.

Evangelou, V. P., 1998, Environmental Soil and Water Chemistry, Principles and

Applications, John Wiley and Sons, New York.

26

Fabiano, M., Danovaro, R. & Fraschetti, S., 1995, A three-year time series of elemental

and biochemical composition of organic matter in subtidal sandy sediments of

the Ligurian Sea (Northwestern Mediterranean). Continental Shelf Research, 15,

1453-1469.

Fenchel, T., King, G. M. & Blackburn, T. H., 1998, Bacterial Biogeochemistry: The

Ecophysiology of Mineral Cycling, Academic Press, San Diego.

Friese, K., Wendt-Potthoff, K., Zachmann, D. W., Fauville, A., Mayer, B. & Veizer, J.,

1998, Biogeochemistry of iron and sulfur in sediments of an acidic mining lake

in Lusatia, Germany. Water, Air and Soil Pollution, 108, 231-247.

Frömmichen, R., Koschorreck, M., Wendt-Potthoff, K. & Friese, K., 2001,

Neutralization of acidic mining lakes via in situ stimulation of bacteria. In

Leeson, A., Peyton, B. M., Means, J. L. & Magar, V. S. (Eds.) the Sixth

International In Situ and On-Site Bioremediation Symposium. Battelle Press,

San Diego, California

Fyson, A., Deneke, R., Nixdorf, B. & Steinberg, C. E. W., 2002, Extremely acidic mine

lake ecosystems and their functioning as the basis for ecotechnological acidity

removal measures. In Schmitz, G. H. (Ed.) the Third International Conference

on Water Resources and Environment Research. Dresden University of

Technology, Germany

Fyson, A., Nixdorf, B., Kalin, M. & Steinberg, C. E. W., 1998, Mesocosm studies to

assess acidity removal from acidic mine lakes through controlled eutrophication.

Ecological Engineering, 10, 229-245.

Gardner, L. R. & Lerche, I., 1987, Simulation of sulfate-dependent sulfate reduction

using Monod kinetics. Mathematical Geology, 19, 219-239.

Gardner, L. R. & Lerche, I., 1990, Simulation of sulfur diagenesis in anoxic marine

sediments using Rickard Kinetics for FeS and FeS2 formation. Computers and

Geosciences, 16, 441-460.

Gatellier, J.-P. L. A., de Leeuw, J. W., Sinninghe-Damsté, J. S., Derenne, S., Largeau,

C. & Metzger, P., 1993, A comparative study of macromolecular substances of a

Coorongite and cell walls of the extant alga Botryococcus braunii. Geochimica

et Cosmochimica Acta, 57, 2053-2068.

Gazea, B., Adam, K. & Kontopoulos, A., 1996, A review of passive systems for the

treatment of acid mine drainage. Minerals Engineering, 9, 23-42.

Goldberg, E. D. & Koide, M., 1962, Geochronological studies of deep sea sediments by

the ionium/thorium method. Geochimica et Cosmochimica Acta, 26, 417-450.

27

Gordon, D. C. J., 1970, Some studies on the distribution and composition of particulate

organic carbon in the North Atlantic Ocean. Deep-Sea Research, 17, 233-243.

Gujer, W. & Zehnder, A. J. B., 1983, Conversion processes in anaerobic digestion.

Water Science and Technology, 15, 127-167.

Hama, T., Yanagi, K. & Hama, J., 2004, Decrease in molecular weight of

photosynthetic products of marine phytoplankton during early diagenesis.


Hamilton, S. E. & Hedges, J. I., 1988, The comparative geochemistries of lignins and

carbohydrates in an anoxic fjord. Geochimica et Cosmochimica Acta, 52, 129-

142.

Harvey, G. R., Boran, D. A., Chesal, L. A. & Tokar, J. M., 1983, The structure of

marine fulvic and humic acids. Marine Chemistry, 12, 119-132.

Harvey, H. R. & Mannino, A., 2001, The chemical composition and cycling of

particulate and macromolecular dissolved organic matter in temperate estuaries

as revealed by molecular organic tracers. Organic Geochemistry, 32, 527-542.

Hatcher, P. G., Spiker, E. C., Szeverenyi, N. M. & Maciel, G. E., 1983, Selective

preservation and origin of petroleum-forming aquatic kerogen. Nature, 305, 498-

501.

Hedges, J. I., 1988, Polymerization of humic substances in natural environments. in

Frimmel, F. H. & Christman, R. F. (Eds.) Humic Substances and Their Role in

the Environment. Wiley, New York.

Hedges, J. I., 1992, Global biogeochemical cycles: progress and problems. Marine

Chemistry, 39, 67-93.

Hedges, J. I., 2002, Why dissolved organics matter. in Hansell, D. A. & Carlson, C. A.

(Eds.) Biogeochemistry of Marine Dissolved Organic Matter. Academic Press,

San Diego.

Hedges, J. I., Clark, W. A. & Cowie, G. L., 1988, Fluxes and reactivities of organic

matter in a coastal marine bay. Limnology and Oceanography, 33, 1137-1152.

Hedges, J. I., Eglinton, G., Hatcher, P. G., Kirchman, D. L., Arnosti, C., Derenne, S.,

Evershed, R. P., Kögel-Knabner, I., de Leeuw, J. W., Littke, R., Michaelis, W.

& Rullkötter, J., 2000, The molecularly-uncharacterized component of nonliving

organic matter in natural environments. Organic Geochemistry, 31, 945-958.

Henrichs, S. M. & Doyle, A. P., 1986, Decomposition of 14 C-labeled organic

substances in marine sediments. Limnology and Oceanography, 31, 765-778.

28

Hertkorn, N., Benner, R., Frommberger, M., Schmitt-Kopplin, P., Witt, M., Kaiser, K.,

Kettrup, A. & Hedges, J. I., 2006, Characterization of a major refractory

component of marine dissolved organic matter. Geochimica et Cosmochimica

Acta, 70, 2990-3010.

Huston, A. L. & Deming, J., 2002, Relationships between microbial extracellular

enzymatic activity and suspended and sinking particulate organic matter:

seasonal transformations in the North Water. Deep-Sea Research Part II, 49,

5211-5225.

Ingall, E. D. & Van Cappellen, P., 1990, Relation between sedimentation rate and burial

of organic phosphorus and organic carbon in marine sediments. Geochimica et


Janssen, B. H., 1984, A simple method for calculating decomposition and accumulation

of 'young' soil organic matter. Plant and Soil, 76, 297-304.

Jørgensen, B. B., 1983, Processes at the sediment-water interface. in Bolin, B. & Cook,

R. B. (Eds.) The Major Biogeochemical Cycles and Their Interactions. John

Wiley & Sons, New York.

Kapfer, M., 1998, Assessment of the colonization and primary production of

microphytobenthos in the littoral of acidic mining lakes in Lusatia (Germany).

Water, Air and Soil Pollution, 108, 331-340.

Karakas, G., Brookland, I. & Boehrer, B., 2003, Physical characteristics of acidic

Mining Lake 111. Aquatic Sciences, 65, 297-307.

Keil, R. G. & Kirchman, D. L., 1993, Dissolved combined amino acids in marine

waters: chemical form and utilization by heterotrophic bacteria. Limnology and

Oceanography, 38, 1256-1270.

Keil, R. G. & Kirchman, D. L., 1994, Abiotic transformations of labile protein to

refractory protein in seawater. Marine Chemistry, 45, 187-196.

Klapper, H., 2002, Mining lakes: generation, loading and water quality control. in

Murdroch, A., Stottmeister, U., Kennedy, C. & Klapper, H. (Eds.) Remediation

of Abandoned Surface Coal Mining Sites. Springer.

Klapper, H., Friese, K., Scharf, B., Schimmele, M. & Schultze, M., 1998, Ways of

Controlling Acid by Ecotechnology. in Geller, W., Klapper, H. & Salomons, W.

(Eds.) Acidic Mining Lakes. Springer, Berlin.

Klapper, H. & Schultze, M., 1995, Geogenically acidified mining lakes - living

conditions and possibilities of restoration. Internationale Revue gesamten

Hydrobiologie, 80, 639-653.

29

Kleeberg, A., 1998, The quantification of sulfate reduction in sulfate-rich freshwater

lakes - a means for predicting the eutrophication process of acidic mining lakes?


Koschorreck, M., Brookland, I. & Matthias, A., 2003a, Biogeochemistry of the

sediment-water interface in the littoral of an acidic mining lake studied with

microsensors and gel-probes. Journal of Experimental Marine Biology and

Ecology, 285, 71-84.

Koschorreck, M., Frömmichen, R., Herzsprung, P., Tittel, J. & Wendt-Potthoff, K.,

2002, Functions of Straw for In-Situ Remediation of Acidic Mining Lakes.

Water, Air and Soil Pollution - FOCUS, 3, 137-149.

Koschorreck, M. & Tittel, J., 2002, Benthic photosynthesis in an acidic mining lake (pH

2.6). Limnology and Oceanography, 47,

Koschorreck, M., Wendt-Potthoff, K. & Geller, W., 2003b, Microbial Sulfate Reduction

at Low pH in Sediments of an Acidic Lake in Argentina. Environmental Science

and Technology, 37, 1159-1162.

Kristensen, E., Ahmed, S. I. & Devol, A. H., 1995, Aerobic and anaerobic

decomposition of organic matter in marine sediment: Which is fastest?


Krom, M. & Berner, R. A., 1981, The diagenesis of phosphorus in a nearshore marine

sediment. Geochimica et Cosmochimica Acta, 45, 207-216.

Küsel, K., Dorsch, T., Acker, G. & Stackebrandt, E., 1999, Microbial reduction of

Fe(III) in acidic sediments: isolation of Acidiphilium cryptum JF-5 capable of

coupling the reduction of Fe(III) to the oxidation of glucose. Applied and

Environmental Microbiology, 65, 3633-3640.

Lee, C. & Wakeham, S. G., 1988, Organic matter in seawater: biogeochemical

processes. Chemical Oceanography, 9, 1-41.

Lee, C., Wakeham, S. G. & Hedges, J. I., 2000, Composition and flux of particulate

amino acids and chloropigments in equatorial Pacific seawater and sediments.

Deep-Sea Research Part I, 47, 1535-1568.

Libes, S. M., 1992, An Introduction to Marine Biogeochemistry, John Wiley and Sons,

New York.

Loh, A. N., Bauer, J. E. & Druffel, E. R. M., 2004, Variable ageing and storage of

dissolved organic components in the open ocean. Nature, 430, 877-881.

30

Luff, R. & Moll, A., 2004, Seasonal dynamics of the North Sea sediments using a three-

dimensional coupled sediment-water model system. Continental Shelf Research,

24, 1099-1127.

Manini, E., Fiordelmondo, C., Gambi, C., Pusceddu, A. & Danovaro, R., 2003, Benthic

microbial loop functioning in coastal lagoons: a comparative approach.

Oceanologica Acta, 26, 27-38.

Mannino, A. & Harvey, H. R., 2000, Biochemical composition of particles and

dissolved organic matter along an estuarine gradient: Sources and implications

for DOM reactivity. Limnology and Oceanography, 45, 775-788.

Mateles, R. I. & Chian, S. K., 1969, Kinetics of substrate uptake in pure and mixed

culture. Environmental Science and Technology, 3, 569-574.

Mayer, L. M., Schick, L. L., Sawyer, T., Plante, C. J., Jumars, P. A. & Self, R. L., 1995,

Bioavailable amino acids in sediments: a biomimetic, kinetics, based approach.


Mazur, K., Ehret, B., Rolland, W. & Gruenewald, U., 2002, Reservoir management of

post mining lakes - finding the balance between the needs to stabilise the water

resources and the risk to deteriorate the water quality. Third International

Conference on Water Resources and Environment Research. Dresden, Germany

McCarthy, M. D., Hedges, J. I. & Benner, R., 1993, The chemical composition of

dissolved organic matter in seawater. Chemical Geology, 107, 503-507.

Meyers-Schulte, K. J. & Hedges, J. I., 1986, Molecular evidence for a terrestrial

component of organic matter dissolved in ocean water. Nature, 321, 61-63.

Meysman, F. J. R., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R., 2003a,

Reactive transport in surface sediments. I. Model complexity and software

quality. Computers and Geosciences, 29, 291-300.

Meysman, F. J. R., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R., 2003b,

Reactive transport in surface sediments. II. Media: an object-oriented problem-

solving environment for early diagenesis. Computers and Geosciences, 29, 301-

318.

Middelburg, J. J., 1989, A simple rate model for organic matter decomposition in

marine sediments. Geochimica et Cosmochimica Acta, 53, 1577-1581.

Middelburg, J. J. & Herman, P. M. J., 2007, Organic matter processing in tidal estuaries.

Marine Chemistry, 106, 127-147.

Middelburg, J. J., Vlug, T. & van der Nat, F. J. W. A., 1993, Organic matter

mineralization in marine systems. Global and Planetary Change, 8, 47-58.

31

Misic, C. & Covazzi Harriague, A., 2008, Organic matter recycling in a shallow coastal

zone (NW Mediterranean): The influence of local and global climatic forcing

and organic matter lability on hydrolytic enzyme activity. Continental Shelf

Research, 28, 2725-2735.

Nixdorf, B. & Kapfer, M., 1998, Stimulation of Phototrophic Pelagic and Benthic

Metabolism Close to Sediments in Acidic Mining Lakes. Water, Air and Soil

Pollution, 108, 317-330.

Nixdorf, B., Mischke, U. & Lessmann, D., 1998, Chrysophytes and chlamydomonads:

Pioneer colonists in extremely acidic mining lakes (pH <3) in Lusatia

(Germany). Hydrobiologia, 369/370, 315-327.

O'Connell, M., Baldwin, D. S., Robertson, A. I. & Rees, G., 2000, Release and

bioavailability of dissolved organic matter from floodplain litter: influence of

origin and oxygen levels. Freshwater Biology, 45, 333-342.

Peiffer, S., 1998, Geochemical and microbial processes in sediments and at the

sediment-water interface of acidic mining lakes. Water, Air and Soil Pollution,

108, 227-229.

Peine, A., Tritschler, A., Kusel, K. & Peiffer, S., 2000, Electron flow in an iron-rich

acidic sediment - evidence for an acidity driven iron cycle. Limnology and


Postma, D. & Jakobsen, R., 1996, Redox zonation: Equilibrium constraints on the

Fe(III)/SO4-reduction interface. Geochimica et Cosmochimica Acta, 60, 3169-

3175.

Rabouille, C. & Gaillard, J. F., 1991, Towards the EDGE: Early diagenetic global

explanation. A model depicting the early diagenesis of organic matter, O2, NO3,

Mn and PO4. Geochimica et Cosmochimica Acta, 55, 2511-2525.

Regnier, P., O'Kane, J. P., Steefel, C. I. & Vanderborght, J. P., 2002, Modeling complex

multi-component reactive-transport systems: towards a simulation environment

based on the concept of a Knowledge Base. Applied Mathematical Modelling,

26, 913-927.

Rolland, W., Wagner, H., Chmielewski, R. and Gruenewald, U., 2001, Evaluation of the

long term groundwater pollution by the open cast lignite mine Jaenschwalde

(Germany). Journal of Geochemical Exploration, 73, 97-111.

Rowe, G. T., Clifford, C. H., Smith, K. L. & Hamilton, P. L., 1975, Benthic nutrient

regeneration and its coupling to primary productivity in coastal waters. Nature,

255, 215-217.

32

Schultz, P. & Urban, N. R., 2008, Effects of bacterial dynamics on organic matter

decomposition and nutrient release from sediments: A modeling study.

Ecological Modelling, 210, 1-14.

Seitzinger, S. P., Hartnett, H., Lauck, R., Mazurek, M., Minegishi, T., Spyres, G. &

Styles, R., 2005, Molecular-level chemical characterization and bioavailability

of dissolved organic matter in stream water using electrospray-ionization mass

spectrometry. Limnology and Oceanography, 50, 1-12.

Skoog, A. & Benner, R., 1997, Aldoses in various size fractions of marine organic

matter: Implications for carbon cycling. Limnology and Oceanography, 52, 85-

95.

Soetaert, K., Herman, P. M. J. & Middelburg, J. J., 1996a, Dynamic response of deep-

sea sediments to seasonal variations: A model. Limnology and Oceanography,

41, 1651-1668.

Soetaert, K., Herman, P. M. J. & Middelburg, J. J., 1996b, A model of early diagenetic

processes from the shelf to abyssal depths. Geochimica et Cosmochimica Acta,

60, 1019-1040.

Soetaert, K., Middelburg, J. J., Herman, P. M. J. & Buis, K., 2000, On the coupling of

benthic and pelagic biogeochemical models. Earth-Science Reviews, 51, 173-

201.

Stumm-Zollinger, E., 1968, Substrate utilization in heterogeneous bacterial

communities. Journal of Water Pollution Control Fed., 40, 213-229.

Stumm, W. & Morgan, J. J., 1996, Aquatic Chemistry: Chemical Equilibria and Rates

in Natural Waters, John Wiley & Sons,

Suess, E. & Müller, P. J., 1980, Productivity, sedimentation rate and sedimentary

organic matter in the oceans II. - Elemental fractionation. Colloques

Internationaux du C.N.R.S. - Biogeochemimie de la Matiere Organique a

l'Interface Eau-Sediment Marin, 293, 17-26.

Sun, M. Y., Lee, C. & Aller, R. C., 1993, Laboratory studies of oxic and anoxic

degradation of chlorophyll-a in Long Island Sound sediments. Geochimica et


Tarutis Jnr, W. J., 1993, On the equivialence of the power and reactive continuum

models of organic matter diagenesis. Geochimica et Cosmochimica Acta, 57,

1349-1350.

Toggweiler, J. R., 1988, Deep-sea carbon, a burning issue. Nature, 334, 468.

33

Toth, D. J. & Lerman, A., 1977, Organic matter reactivity and sedimentation rates in the

ocean. American Journal of Science, 277, 465-485.

Totsche, O., Fyson, A. & Steinberg, C. E. W., 2002, Chemical and Microbiological

Neutralization of extremely acidic mining lakes - Buffering of extreme acidic

mining lakes. In Schmitz, G. H. (Ed.) Third International Conference on Water

Resources and Environment Research. Dresden University of Technology,

Germany

Tromp, T. K., Van Cappellen, P. & Key, R. M., 1995, A global model for the early

diagenesis of organic carbon and organic phosphorus in marine sediments.

Geochimica et Cosmochimica Acta, 59, 1259-1284.

Van Cappellen, P. & Wang, Y., 1996, Cycling of iron and manganese in surface

sediments: A general theory for the coupled transport and reaction of carbon,

oxygen, nitrogen, sulfur, iron and manganese. American Journal of Science, 296,

197-243.

Vidal, M. & Morgu�, J. A., 2000, Close and delayed benthic-pelagic coupling in coastal

ecosystems: the role of physical constraints. Hydrobiologia, 429, 105-113.

Vile, M. A. & Wieder, R. K., 1993, Alkalinity generation by Fe(III) reduction versus

sulfate reduction in wetlands constructed for acid mine drainage treatment.


Wang, Y. & Van Cappellen, P., 1996, A multicomponent reactive transport model of

early diagenesis: Application to redox cycling in coastal marine sediments.


Weiss, M. S., Abele, U., Weckesser, J., Welte, W., Schiltz, E. & Schulz, G. E., 1991,

Molecular architecture and electrostatic properties of a bacterial porin. Science,

254, 1627-1630.

Wendt-Potthoff, K., Frömmichen, R., Herzsprung, P. & Koschorreck, M., 2002,

Microbial Fe(III) reduction in acidic mining lake sediments after addition of an

organic substrate and lime. Water, Air and Soil Pollution, 1-16.

Werner, F., Bilek, F. & Luckner, L., 2001, Impact of regional groundwater flow on the

water quality of an old post-mining lake. Ecological Engineering, 17, 133-142.

Westrich, J. T. & Berner, R. A., 1984, The role of sedimentary organic matter in

bacterial sulfate reduction: the G Model tested. Limnology and Oceanography,

29, 236-249.

Wetzel, R. G., 2001, Limnology, Lake and River Ecosystems, Academic Press, San

Diego.

34

Williams, P. M. & Druffel, E. R. M., 1988, Dissolved organic matter in the ocean:

comments on a controversy. Oceanography, 1, 14-17.

Woelfl, S., Tittel, J., Zippel, B. and Kringel, R., 2000, Occurrence of an algal mass

development in an acidic (pH 2.5), iron and aluminium-rich coal mining pond.

Acta Hydrochimica et Hydrobiologica, 28, 305-309.

Zegouagh, Y., Derenne, S., Largeau, C., Bertrand, P., Sicre, M.-A. & Saliot, A., 1999,

Refractory organic matter in sediments from the North-West African upwelling

system: Abundance, chemical structure and origin. Organic Geochemistry, 30,

101-117.

Zou, L., Wang, X. C., Callahan, J., Culp, R. A., Chen, R. F., Altabet, M. A. & Sun, M.

Y., 2004, Bacterial roles in the formation of high-molecular-weight dissolved

organic matter in estuarine and coastal waters: Evidence from lipids and the

compound-specific isotopic ratios. Limnology and Oceanography, 49, 297-302.

35

3 Addition of dissolved organic carbon to promote

aerobic respiration in sediments: estimation of the

rate constant

Deborah J. Read1, Carolyn E. Oldham1 and Gregory N. Ivey1

1School of Environmental Systems Engineering, University of Western Australia

35 Stirling Hwy, Crawley, Western Australia 6009, Australia

3.1 Abstract

Primary diagenesis in aquatic systems with low concentrations of organic carbon

is usually understood to be controlled by the concentrations of both organic carbon and

the oxidant. While many diagenetic models include both of these parameters in their

kinetic descriptions through the incorporation of Monod type kinetics, instead of using

either zero order or first order type kinetics, these routines are often cumbersome to use

when modelling large systems or long time frames. Simpler first and zero order kinetic

descriptions are inappropriate to use in systems where respiration may be limitation by

dissolved oxygen or dissolved organic carbon; a second order kinetic description is then

required. Dissolved organic carbon was applied to sediment samples from two

oligotrophic lakes and one oligotrophic coastal embayment, in which sediment

respiration was considered to be carbon limited. Measurement of the concentrations of

both dissolved oxygen and dissolved organic carbon in the overlying waters, before and

after the addition of the carbon substrate, allowed the estimation of a second order rate

constant as 6.6 mL mol-1 s-1. Simple mixed reactor models using first order, second

order, and Monod kinetic descriptions showed that the second order description was

better able to capture the dissolved oxygen dynamics of our experiments. These second

order kinetic descriptions provide relatively simple parameterizations that are suitable

for use in systems where limitation is temporally dynamic with oxidant and organic

carbon limitation occurring at different times of the year.

36

3.2 Introduction

Microbial aquatic food webs and biogeochemical cycles in many aquatic

systems are controlled by the availability and respiration of labile dissolved organic

carbon (DOC) and the relevant oxidants (Schindler et al., 1992; Tranvik, 1992;

Pomeroy et al., 2000). While this has long been accepted in principle for lakes, and the

role of DOC in marine sediment diagenesis is currently being investigated, modelling of

microbial respiration in both freshwater and marine systems has continued to ignore the

implications of this concept. Diagenetic models, used to estimate biogeochemical

cycling in aquatic and marine sediments assume respiration is zero order (Hensen et al.,

1997), first order with respect to DOC (Berner, 1980; Westrich and Berner, 1984) or

controlled by a Monod-type rate law (Boudreau, 1996; Herzfeld et al., 2001; Berg et al.,

2003). Current respiration rate estimations use the flux or concentration of particulate

organic carbon (POC) as the limiting reactant, assuming that all oxidants mineralise

organic carbon at the same rate (del Giorgio and Duarte, 2002), although Canavan

(2006) accounted for increased organic carbon mineralization rates through the use of

an acceleration factor when oxygen was the oxidant. When there is the possibility that

either DOC or oxidant availability may be the limiting factor in diagenesis, zero and

first order rate laws are no longer applicable and it is more appropriate to utilize second

order rate laws to describe oxidant dynamics (e.g. changes in oxygen concentration with

time) prior to moving to the complexities of a Monod type rate law.

There is a range of water bodies whose sediments are frequently considered to

be carbon limited, including alpine lakes, the pelagic ocean, volcanic lakes and mine

lakes, and many are classified as oligotrophic. In such environments, sediment

porewater fluxes, and hence sediment processes influencing these fluxes, may become

extremely important as they can be a dominating source or sink of key chemical species,

such as dissolved oxygen (DO), DOC or nutrients, at times influencing the chemistry of

the entire water column. Indeed, it is currently unknown whether the oceans are a net

source or sink of carbon (del Giorgio and Duarte, 2002) and the role of marine DOC in

the global carbon cycle is still being researched (Toggweiler, 1988; Williams and

Druffel, 1988; Hedges, 1992; Hedges, 2002), therefore detailed understanding of these

porewater fluxes through diagenetic models becomes critical for calculation of the

global carbon budget as well as for the management of smaller water bodies such as

lakes, especially when under threat from changing environmental conditions or

anthropogenic activities, such as in mining lakes.

37

The amount of DOC present, and hence the redox condition of the sediment, is a

major control on porewater fluxes. Upon reaching the sediment-water interface

particulate organic matter is converted to dissolved organic matter (Kristensen et al.,

1995; Hee et al., 2001; Arnosti, 2004) which may be taken up by bacteria and

remineralised due to its smaller particle size (Arnosti, 2004). In a neutral closed system

containing plentiful organic matter, oxidation of this organic matter will occur first by

O2, then by NO3-/NO2

-, MnO2, Fe3+, SO42- and finally organic matter itself (Stumm and

Morgan, 1996). Sediment diagenesis has frequently been modeled as an abiotic first

order chemical reaction with a focus on POC rather than DOC (Berner, 1980; Westrich

and Berner, 1984), but given that POC is hydrolyzed to DOC before being

remineralised and more than 90% of the decomposition of this DOC is mediated by

bacteria in lakes and streams (Wetzel, 1992), it seems pertinent to parameterize this

degradation of DOC itself.

More than 99% of bacteria are frequently associated with surfaces (Brüchert and

Arnosti, 2003) making the sediments a hotspot for microbial remineralization. Even in

extreme oligotrophic environments, populations of bacteria exist and mediate the

breakdown of organic matter (Horneck, 2000; Karlsson et al., 2001; Wendt-Potthoff and

Koschorreck, 2002; Vincent et al., 2004). As a result of the involvement of bacterial

communities, the rate of oxygen consumption by DOC breakdown has sometimes been

modelled as a Michaelis-Menton (Herzfeld et al., 2001) or Monod type law (Boudreau,

1996; Berg et al., 2003; Meysman et al., 2003). However, the use of Michaelis-Menton

kinetics assumes knowledge of the microbial population dynamics and the specification

of constants depicting the rate at which the population consumes the reactant(s) in

question. The paucity of such knowledge questions the appropriateness of utilizing the

more complex Michaelis-Menton kinetics for modelling diagenesis, although kinetic

rate constants are available for some reactions from reactor experiments (e.g. Pallud et

al., 2007). A Monod rate law is empirical (Monod, 1949) and hence is no more

applicable than any other rate laws with different orders but it has the added difficulty

of requiring extra constants to be specified. In such models there is a danger that

validation may become a curve fitting exercise rather than an analysis of the processes

involved.

Before moving to the complexities of Monod type kinetics, there are a number

of environments in which it may be more applicable to utilize second order rate kinetics

that do not assume that DOC is in excess. Low concentrations of labile DOC may be the

cause of low productivity in some lakes and ocean areas due to the lack of nutrient

38

release from organic matter breakdown (Wetzel, 1992; Peine et al., 2000; Wendt-

Potthoff and Koschorreck, 2002; Nixdorf et al., 2003). This situation can be modeled

using a first order rate approximation, however such a rate parameterization cannot

capture a scenario where respiration is dynamically limited by alternating low DO and

DOC concentrations, as is frequently the case in low DOC environments, nor can a

constant first order rate coefficient be assigned that is appropriate for a range of sites.

The type of DOC, as well as its concentration may ultimately limit respiration in

freshwater and marine systems as DOC itself is a heterogeneous range of organic

compounds with some being more susceptible to degradation than others (Middelburg,

1989; Burdige, 2002; Arnosti, 2004). Much DOC in sediment pore water is thought to

be refractory (Schindler et al., 1992; Burdige, 2002), however biologically active

fractions of DOC have been identified and this labile fraction can be intensively cycled

(Hobbie, 1992; Arnosti, 2004). The absence of this labile DOC can lead to carbon

limitation of microbial growth, for example in pelagic regions of natural waters

(Wetzel, 1992) even though there may be refractory carbon present (e.g. Blodau et al.,

2000).

Organic carbon is also thought to play an important role in the buffering of pH

within lakes (Brugam et al., 1995; Blodau et al., 2000; Fauville et al., 2004). This is

particularly important for acidic lakes, such as mine lakes, and several microcosm and

mesocosm studies have been conducted primarily focusing on the effect of adding

different types of organic matter (with varying lability) on pH, and iron and sulfate

concentrations (Christensen et al., 1996; Fyson et al., 1998; Castro et al., 1999). Many

of these microcosm experiments show only initial and final chemical concentrations

(Frömmichen et al., 2003; Fauville et al., 2004; Frömmichen et al., 2004) and no

knowledge can be gained on how the oxidants are used to break down the organic

matter during the course of the experiment. These studies did not address whether or not

the systems are indeed limited by labile carbon or how this limitation affects the rate of

oxidant consumption and therefore do not allow for the calculation of second order rate

constants.

There is a paucity of experimental data that can be used to establish the second

order rate constants required in diagenetic models of labile DOC limited respiration.

This paper presents the results from organic carbon addition experiments designed

specifically to provide such data. To parameterize the dynamic control of dissolved

oxygen and DOC on aerobic respiration, we conducted experiments investigating the

effect of DOC addition on water column dissolved oxygen concentration. Two

39

oligotrophic freshwater systems and one oligotrophic marine system were compared

and the data was used to estimate a second order rate constant for aerobic respiration.

The data was also used to estimate the corresponding first order and Monod constants,

for comparison with literature values. Finally we compared the robustness of these three

rate laws through the application of a simple mixed reactor model to our experimental

data.

3.3 Methodology

3.3.1 Site Descriptions

Our two freshwater lakes, Kepwari (LK) and Chicken Creek (CC), are located

approximately 160 km south southeast of Perth, Western Australia, in the Collie Basin.

Both of these lakes are former open-cut coal mines that have since filled with water.

Since 1999, Lake Kepwari has been progressively filled during winter (the high flow

period) with water from the diverted Collie River South, and at the time of the

experiment it had a maximum depth of 65 m and a volume of 25 GL. Chicken Creek

has been slowly filling from groundwater inflow alone, and at the time of the

experiment it had a maximum depth of 41.2 m and a volume of 6.8 GL. Sediment from

both Lake Kepwari and Chicken Creek is primarily composed of goethite, kaolinite and

quartz with Lake Kepwari sediment having an organic carbon content of 2.4%, while

Chicken Creek sediment has an organic carbon content of 2%.

Lake Kepwari and Chicken Creek Lake are both monomictic, typically

experiencing thermal stratification from spring to autumn (October – April) and full

mixing from May – September. Despite their depths and stratification dynamics both

lakes remain oxic throughout the water column for the whole year, with minimum

dissolved oxygen concentrations in the bottom waters of around 6 mg L-1. Depth-

averaged DOC concentrations were 1.2 – 2.5 mg C L-1 and 0.5 – 1.0 mg C L-1 in Lakes

Kepwari and Chicken Creek respectively. Both lakes are acidic due to oxidation of

remnant pyritic material; Lake Kepwari has a pH of 4.8, and Chicken Creek Lake has a

pH of 2.8.

Cockburn Sound is a semi-enclosed marine basin located 30 km south of Perth

with a maximum depth of 20 m, a length of 20 km and a width of 7 km. Sediment in

this area is coarse grained sand of primarily carbonate (>65% of dry weight) with low

organic carbon content (0.9% of dry weight). The concentration of DOC in the

40

hypolimnion is around 1.1-1.3 mg L-1 and DO concentrations are typically of the range

of 4.5-7.0 mg L-1 (Department of Environmental Protection, 1996). Records of algal

blooms in the Sound reach as far back as 1973 (Department of Conservation and

Environment, 1979) and there have been anecdotal reports of fish and crab kills in the

deep basin at the southern end of Cockburn Sound (Department of Environmental

Protection, 1996).

3.3.2 Sediment Experiments

Two types of experiments were conducted: slurry experiments using sediment

samples collected from the deepest point of the freshwater lakes, and column

experiments using sediment cores collected from the deep basin of the marine site. The

sediment slurry experiments were conducted because collection of sediment cores was

problematic in the lakes due to water depths (> 50 m), steep littoral zones, highly acidic

waters (pH 2.7 at one site) and the unconsolidated nature of the sediments. After

collection, the sediments were covered with hypolimnetic lake water to a depth of 20-

30cm to prevent warming and stored in the dark for the journey back to the laboratory,

where the sediments were exposed to air and allowed to equilibrate overnight in a dark,

constant temperature (19°C) room. The subsequent sediment experiments were

conducted in the same constant temperature room.

The dissolved organic carbon source used in the experiments was treacle

(CSR brand) and/or POC was provided in the form of straw (bedding hay). Highly

specific carbon sources, such as acetate, have been used in many microbiological

nutrient addition studies, but for these experiments a more realistic, complex carbon

source (treacle) was considered to be appropriate as it does not target one specific

bacterial group. Frömmichen et al. (2004) showed that the addition of straw, as well

as a labile carbon source such as treacle, provided better initial conditions for

respiration under extremely low pH conditions. Straw was not added to the marine

sediment core experiments, where the overlying waters were around pH 7.

Lake sediment slurry experiments

Lake sediment (50.5 ± 0.5 g) was added to lake water (1.124 ± 0.015 L) and was

subjected to one of four treatments: no DOC/POC addition; sterilization plus no

DOC/POC addition; DOC/POC addition; sterilization plus DOC/POC addition.

41

Sterilization consisted of jars, sediments and water being autoclaved for 20 min at

121 °C.

For the Lake Kepwari DOC/POC additions, 10.5 g (± 0.5 g) of straw and 20 mL

of a treacle feed solution (a stock solution contained approximately 10 g of treacle

dissolved in 500 mL of lake water, then diluted 1:50 for the feed solution) were added

to the sediment water mixture. Chicken Creek Lake DOC/POC additions were the same

except 5.0g (± 0.5 g) of straw was added to facilitate easier measurement. Ratios of

water to straw (1 L:7.5 g) were similar to those used previously (see for example:

Brugam et al., 1995; Fauville et al., 2004; Frömmichen et al., 2004). The sediment to

water ratio was decreased by a factor of 2-3 from that of Fauville et al. (2004) and

Frömmichen et al. (2004) to account for the greater volume to sediment area ratio of our

lakes. Each sample was shaken twice daily to minimise the establishment of

concentration and temperature gradients in the mixtures.

A further control was conducted with straw (10.5 ± 0.5 g for LK; 5.25 ± 0.5 g

for CC) added to water (1.124 L) to assess the influence of the straw alone on dissolved

nutrient and DOC concentrations. DOC, ammonium (NH4+), nitrate and nitrite (NOx)

and filterable reactive phosphorous (FRP) was monitored over time in the overlying

waters of all treatments (Table 3.1). Four replicates were used for each of Lake Kepwari

and Lake Chicken Creek, and water samples were collected on days 1, 2, 4 and 7.

Table 3.1 Species sampled and day of sampling for the sediment slurry and core experiments.

Experiment Day of Sampling

Species for Analysis Source of Sample

LK – slurry 0 NOx, NH4+, FRP, Fe, Mn,

SO42-, TOC, DOC

Site Water

17 NOx, NH4+, FRP, Fe, Mn,

SO42-, TOC, DOC

Slurry

CC – slurry 0 NOx, NH4+, FRP, Fe, Mn,

SO42-, TOC, DOC

Site Water

2,7 NOx, NH4+, FRP, Fe, Mn,

SO42-, TOC, DOC

Slurry

CS – column 1 NOx, NH4+, TN, Fe All cores, all site water

2,3,4,6 NOx, NH4+, TN, Fe All cores

20 20

NOx, NH4+, Fe

DOC All cores, treacle stock

solution 5 cores, treacle stock

solution 22 NOx, NH4

+, Fe, DOC 5 cores

42

Marine sediment core experiments

Perspex corers (ID 93 mm and height 243 mm) were used to collect 18 sediment

cores (approx. 100 mm deep) together with overlying water (approx. 150 mm deep)

from three sites within the deep basin of Cockburn Sound and stored upright and intact

for subsequent DOC addition experiments. Dissolved oxygen concentrations in the

overlying waters were measured every 1 or 2 days (Figure 3.1) until steady state

sediment oxygen demand was achieved (Figure 3.2). Samples were then collected from

the overlying water for measurement of initial DOC concentrations and then 20 mL of a

treacle feed solution (a stock solution contained approximately 10 g of treacle dissolved

in 250 mL of lake water, then diluted 1:100 for the feed solution) was added to five

sediment cores. DO concentrations in the overlying water of the columns were

measured over the following two days and sediment oxygen demand (SOD, g m-2 day-1)

was determined by multiplying the change in DO concentration by the water depth.

Figure 3.1 The average DO concentration (mg L-1) for the duration of the Cockburn Sound

sediment core experiment. Note the sharp decrease in the average DO concentration for the five

cores that were dosed with DOC.

43

Figure 3.2 The average SOD (g m-2 day-1) of the cores during the sediment core experiment. Note

the very low SOD in the last week of the experiment.

3.3.3 Chemical Analyses

Measurements of DO, pH and temperature were made in the overlying waters

periodically throughout our experiments using a TPS pH and temperature probes and a

TPS Aqua-D DO meter with a TPS ED1 sensor. Water samples for total iron were

filtered through a 0.45 �m cellulose acetate filter, acidified using concentrated nitric

acid and refrigerated until analysed using ICP-AES (Varian Vista AX). Water samples

for analyses of dissolved nutrient species were filtered through a 0.45 �m cellulose

acetate filter, then frozen until analyses for NH4+, NO3

- and NO2- (NOx ), and FRP

(Lachat Automated Flow Injection Analyser). DOC samples were kept refrigerated until

analysis using automated combustion (Shimadzu TOC 5000A). Owing to the relatively

small volumes used in these experiments when compared to sample size no duplicate

samples were take for analysis from the batches or the cores themselves. Duplicate

samples were taken for analysis of all site water, DOC solutions and a blank comprising

of deionised water. The nature of the experiments allowed for replicates of the controls

and treatments to negate the need for replicate sampling.

44

3.3.4 Statistical Analyses

All statistical analysis was conducted using either Single Factor ANOVA for endpoint

data or Repeated Measures ANOVA for time series data, both with a 95% confidence

interval. Error estimates for each treatment and sampling day were determined using the

t-distribution and a 95% confidence interval according to the equation:

n

sTError ×= (1)

where s is the standard deviation of the results for the treatment in question, n is the

degrees of freedom and t is the t-distribution value for (n-1) degrees of freedom.

3.4 Results

3.4.1 Lake Sediment Slurry Experiments

There was a significant difference in the oxygen consumption rate between those

sediment samples treated with DOC and the control sediments (LK df = 5, p < 0.01; CC

df = 4 p < 0.01). In the samples receiving DOC treatments, decreases in DO

concentrations were observed after just one day, and all DO was consumed within three

days. The sediments that had no DOC addition were still oxic after one week (Figure

3.3).

Sterilization had a significant affect on oxygen consumption rate (LK df = 5, P <

0.01; CC df = 4, P < 0.01) (Figure 3.3). Note that sterilization by autoclave temporally

decreases DO concentrations in the overlying water due to changes in saturation, and

this was observed in the experiments; by the end of the experiments, the DO

concentrations in the sterilized and non-sterilized samples were equal. However oxygen

consumption rate is estimated from the change in DO concentrations with time and

there was a significant difference between sterilized and non-sterilized samples.

45

Figure 3.3 A) Average DO concentration (mg L-1) for each sediment slurry experiment from Lake Kepwari.

Note that three days after the addition of a carbon source LK2 and LK4 are already anoxic. B) Average DO

concentration for each sediment slurry experiment from Chicken Creek. In both cases, the sediment treated

with treacle and straw become anoxic first. C) The change in DO concentration (�DO; mg L-1 day-1) for the

Lake Kepwari sediment slurry experiment. D) The change in DO concentration (�DO; mg L-1 day-1).for the

Chicken Creek sediment slurry experiment. In all figures, LK1, LK3, CC1 and CC3 were control slurries.

LK2, LK4, CC2 and CC4 were the treated slurries.

46

Addition of the treacle stock solution added zero NH4+, 0-0.2 �g N L-1 in the

form of NOx, 0.3 - 0.4 �g P L-1 of FRP and between 1.7 and 2.3 mg C L-1 DOC to each

sediment sample. The straw itself contained 2.2 mg N g-1 total kjeldahl nitrogen, 0.18

mg P g-1 total phosphorous and 42% total organic carbon. The experiments with

sediment and straw only, showed rapid increase in overlying water DOC concentrations

within the first day (to a maximum value of 125 ± 26 mg C L-1 in CC and 250 ± 24 mg

C L-1 in LK) and then DOC concentrations remained constant (Figure 3.4). Note that

contrary to our expectations, the straw rapidly supplied a large amount of DOC relative

to the treacle feed solution and this raised the question of whether the treacle additions

were actually required as a source of DOC. From the “straw only” experiments, it is

likely that during the DOC supplementation experiments, the straw was the source of all

the nitrogen and FRP released into the water column.

Figure 3.4 Release of DOC (A) and FRP (B) in the straw-only control. After day 1, concentrations

of DOC in Chicken Creek and in Lake Kepwari remained constant for the remainder of the week.

FRP concentrations observed in the straw-only control were greater than those observed at the

start and finish of the treacle-added experiments (LK < 20 �g L-1; CC < 5 �g L-1).

47

Analysis of FRP, NH4+ and NOx concentrations revealed that both field sites can

be classified as oligotrophic with respect to phosphorous (TP < 25 �g L-1) and Lake

Chicken Creek was oligotrophic with respect to nitrogen (TN < 700 �g L-1), according

to the definition given by Wetzel (2001).

To ensure that the observed increase in aerobic respiration was in fact due to the

addition of organic carbon, and not due to the alleviation of nutrient limitation of the

responsible microbial populations, simple nutrient ratios were calculated at the

beginning and at the end of the experiments (Figure 3.5). All treatments exhibited

phosphorous limitation at the beginning and end of the experiment according to C:P

ratios; treatments involving the addition of treacle were nitrogen limited by the end of

the experiment; none of the controls exhibited nitrogen limitation. It is interesting to

note that N:P ratios in 7 out the 8 carbon supplemented experiments did not imply

phosphorous limitation. The addition of the source of DOC moved these systems

towards a nitrogen limited state in combination with the already existing phosphorous

limitation.

3.4.2 Cockburn Sound

Steady state zero sediment oxygen demand, was reached after 2 weeks (Figure

3.1) while the overlying water still contained 4.2 mg L-1 of dissolved oxygen. Maximum

SOD occurred when the water column was nearly saturated with DO (Figure 3.2). The

addition of treacle solution on day 20 increased the overlying waters DOC

concentration within the five cores, compared to the controls (P < 0.01), but did not

change NH4+, NOx or FRP concentrations (P > 0.05).

The addition of treacle solution increased SOD to near initial levels and there

was a marked decrease in DO concentrations (down to 3.2 mg L-1). The average of the

molar ratio of DO consumed to DOC consumed, after the addition of treacle, was 4:1,

which is close to the stoichiometric ratio of aerobic respiration (1:1).

Ratios of C:P were at all times greater than 100, indicating that the treated cores

were phosphorous limited throughout the experiment, with N:P ratios also indicating P

limitation (Figure 3.5). C:N ratios of four of the five treatment replicates were at all

times less than 5. These results indicate that there was no change in nitrogen or

phosphorus status after the addition of DOC to the cores.

48

Figure 3.5 The degree of nitrogen limitation can be ascertained from plots of DOC concentration (�g L-1) vs NOx and NH4+

concentration (�g L-1) for Lake Kepwari (A), Chicken Creek (B) and Cockburn Sound (C) at the beginning and the end of

the experiments. Similarly, phosphorous limitation can be determined from plots of DOC concentration (�g L-1) vs the FRP

concentration (�g L-1) for Lake Kepwari (D), Chicken Creek (E) and Cockburn Sound (F). Points above the solid line

indicate severe limitation by nitrogen or phosphorous according to the mass ratio of 12.5:1 and 100:1 respectively. Points

above the dotted line, but below the solid line, indicate moderate limitation according to the mass ratios 7.1:1 and 51.6:1

respectively. Points below the dotted line do not indicate any nitrogen or phosphorous limitation relative to DOC

concentration. Limitation of phosphorous relative to nitrogen can be observed for Lake Kepwari (G), Chicken Creek (H)

and Cockburn Sound (I), with points lying above the solid line being phosphorous limited according to the mass ratio of

10.4:1. Ratios were obtained from (Wetzel, 2001)

49

3.5 Discussion

The data collected in these experiments indicated that sediment respiration in all

three systems was initially limited by the availability of labile dissolved organic carbon;

respiration limitation by DOC continued in the control experiments, where no additional

organic carbon was added. C:N:P ratios indicated that the increased oxygen

consumption rates observed in the carbon supplemented experiments were a function of

DOC and DO concentrations alone and not due to the supply of a limiting nutrient

(phosphorous) for microbial respiration. In the DOC supplemented batch experiments,

aerobic respiration ultimately became limited by the availability of DO.

Aerobic respiration has frequently been modelled using first order kinetics,

however such a rate parameterization cannot capture the shifting controls observed in

our experiments, where respiration is dynamically limited by alternating low DO and

DOC concentrations, nor can a constant first order rate coefficient be assigned that is

appropriate across a range of environments. We propose that second order kinetics may

provide a simple model for the prediction of aerobic respiration.

If we plot �DO versus DO for the lake sediment slurry experiments (Figure 3.6),

the slope of the best fit line (units of day-1) divided by the average DOC concentration

(mol L-1) gives the second order rate constants, k (L mol-1 day-1) for each of the

experiments, with an average of 6.6 mL mol-1 s-1 (Table 3.2). The first order rate

constant with respect to DO, k'DO, can be obtained directly from the slope of the line and

the average k'DO for Lake Kepwari and Lake Chicken Creek was 4.7 × 10-6 s-1. It should

be noted that the best fit lines were forced to pass through the origin as theoretically

there should be no oxygen consumption when the DO concentration is 0 mg L-1. As a

result of this, the best fit lines have a lower R2 value than would have been achieved if

the line were not forced through the origin. It should also be noted that an average of

initial and final DOC concentrations was used for each batch, as the concentration

during the experiments was unknown. A similar plot (not shown) can be constructed for

�DO versus DOC with the gradient giving the first order rate constant with respect to

DOC, k'DOC, which was determined to be 4.8 × 10-7 s-1 from the batch tests.

50

Figure 3.6 Change in oxygen concentration (�DO, mmol L-1 day-1) vs the DO concentration

(mmol L-1) for Lake Kepwari (A) and Chicken Creek (B). Note that the data points fall on two

distinct lines depending on whether or not the batches were treated with DOC.

The rate constants for the control batches were slightly larger than those for

carbon treated batches. This is counter intuitive, as it was expected that the rate

constants should be equivalent for different amounts of DOC, however, the authors

hypothesize that with the extreme excess of carbon, the DOC concentration was far

beyond that required for unlimited aerobic respiration. Therefore there must be some

DOC concentration (Climit) at which the consumption of oxygen is no longer dependent

on the DOC concentration. The concentration can be derived from the batch test

experiment by using the value of k obtained from the control batch tests and the

gradient for supplemented batches as shown in Figure 3.6. The limiting value above

which DOC concentration becomes less important is 3.2 mg L-1 and 2.3 mg L-1 for CC

and LK respectively. This result implies that only at very low DOC concentrations does

respiration become carbon limited and the ambient concentrations in the water column

of Lake Kepwari, Chicken Creek and Cockburn Sound were all below this level.

51

Table 3.2 Second order rate constants, k (L mol-1 s-1), calculated from zero order, first order and

Monod literature rates and also the constants calculated from these experiments. The wide range in

K for some literature calculations is due to the uncertainty surrounding the in situ DO

concentration.

Source Order of

Cited Rate

Cited Rates Measured or converted k (L mol-1 s-1)

This Experiment:

LK1 & LK3 2nd 5.44 x 10-3 LK2 & LK4 2nd 6.48 x 10-4 CC1 & CC3 2nd 1.95 x 10-2 CC2 & CC4 2nd 9.45 x 10-4

Average 2nd 6.6 x 10-3 Murphy & Shramke

(1998) 0th 6.84×10-17 - 2.07×10-14 mol L-1 s-1 2.19×10-9 – 4.15×10-3

Marinelli & Woodin (2002) 0th 8.33×10-13 mol L-1 s-1 2.67×10-5 – 1.67×10-1

Reimers & Suess (1983) 1st wrt OC 4.10×10-10 – 9.70×10-12 s-1 5.17×10-8 – 1.37×10-2

Baird (2001) 1st wrt OC 2.04×10-8 s-1 1.09×10-4 – 6.79×10-1 Goloway & Bender

(1982) 1st wrt O 5.00×10-7 – 2.30×10-4 s-1 3.00×10-3 – 1.38

Farias (2003) 1st wrt OC 2.54×10-8 – 4.12×10-8 s-1 1.35×10-4 – 1.37 Murray & Kuivila

(1990) 1st wrt OC 2.63×10-12 – 1.58×10-10 s-1 2.63×10-8 – 5.25×10-3

Jahnke et al. (1982) 1st wrt OC 4.20×10-11 – 7.30×10-11 s-1 2.80×10-7 - 4.87×10-7 Westrich & Berner

(1984) 1st wrt OC 9.51×10-9 – 1.05×10-6 s-1 5.07×10-5 – 5.58×10-3

Rabouille & Gaillard (1991) Monod Rmax = 1.50×10-9 s-1

Km = 3.10×10-6 mol L-1 9.80×10-6

Boudreau (1996) Monod Rmax = 3.17×10-8 s-1 Km = 8.0×10-6 mol L-1 2.01×10-4 – 3.95×10-3

Park & Jaffe (1996) Monod Rmax = 3.17×10-8 s-1 Km = 2.00×10-5 mol L-1 8.03×10-5 - 1.58×10-3

Environments where DOC concentrations vary around this limiting value may

be limited by DOC availability at one point in time, but by oxidant availability at

another point in time, requiring the ability to capture both types of limitation. Hence a

second order parameterization may prove to be extremely useful in providing a simple

method for predicting changes in DO and DOC concentration or in predicting SOD.

While DOC limitation on respiration has been noted in several aquatic and

marine ecosystems, no second order rate constants have been published for lake or

marine sediments, which would allow the incorporation of DO limitation. There has

been recognition that organic matter varies in lability and hence in degradation rates in

the multi-G model proposed by Berner (1980) and Westrich and Berner (1984), and also

in the Power model proposed by Middelburg (1989). However none of these models

52

incorporate the limitation that may be imposed by lack of DO. If the interest is in

predicting both DOC and DO concentrations the one must be able to deal with both

types of limitation.

It is useful to compare our second order rate constants with other data derived

from the literature. Several microcosm and mesocosm studies have focused on the effect

of organic matter lability on pH, and iron and sulfate concentrations (Christensen et al.,

1996; Fyson et al., 1998; Castro et al., 1999). While these experiments imply DOC

limitation of respiration, there was no quantification of the rate of oxidant consumption

as a function of DOC availability; no second order rate constants can be extracted from

the published data. Some second order rate constants can be derived from zero order,

first order, and Monod rate constants found in literature where additional DOC, or DO,

concentration data has been supplied (Table 3.2). Almost all of this data comes from

marine sites and one value for groundwater has also been included for comparison

(Murphy and Shranmke, 1998).

To convert the published rate constants to second order it was assumed that they

were pseudo first order or Monod constants as appropriate, allowing them to be equated

to a second order rate constant according to the following equations:

[ ] [ ][ ]COkOkDO 22' = for conversion of a first order constant with respect to DO (2)

[ ] [ ][ ]COkCkDOC 2' = for conversion of a first order with respect to DOC (3)

[ ] mkORk

+−=

2max

1 for conversion of Monod constants (4)

where k' is the first order rate constant, Rmax is the maximum rate, km is the half

saturation constant, k is the second order rate constant, [O2] is the dissolved oxygen

concentration and [C] is dissolved organic carbon concentration. Values of [O2] and [C]

were taken as typical concentrations for the environment from which the literature

constant was derived.

The resulting second order rate constants show a large amount of variability,

spanning nine orders of magnitude in the range 2.19×10-9 to1.38 L mol-1 s-1 (Table 3.2).

This huge range of values highlights the need for careful description of experimental

conditions; minimal data is provided concerning in-situ temperatures, DOC and/or DO

concentrations and also minimal explanation of biological or chemical limitation on

respiration. As more data become available from sites where respiration is limited by

DOC availability, it is essential that the required data be provided to narrow the range.

When comparing rates obtained in this experiment to those in the literature it is

important to note that our rates are based on concentration of DOC, rather than the

53

concentration of particulate organic carbon (POC). The authors feel it is more

appropriate to use DOC as all POC must first be degraded to DOC prior to

remineralisation and DOC is of a more accessible molecule size to bacteria, although

that does not necessarily equate with increased lability (Arnosti, 2004). There may be a

correlation between POC and the rate of DO consumption (Murray and Kuivila, 1990)

but not all POC is able to be converted to DOC to be utilized by bacteria, hence using

POC concentration may give erroneous results.

In situ aerobic respiration may also be limited by physical and chemical barriers

such as concentration gradients, non-optimal temperatures and nutrient limitation.

Laboratory measurement of aerobic respiration in batch tests and sediment columns

remove or reduce some of these obstacles to respiration and by doing so allow a more

accurate determination of k, one made without the imposition of other processes. It is

anticipated that in-situ measurements allowing the calculation of k would produce a

lower value due to the affect of previously discussed processes, however the laboratory

experiments provide useful information of the upper limit of k. Assuming all other key

processes are accounted for such as physical transport and key chemical reactions, this

value of k may be used in diagenetic models to replace a first order model of

respiration.

To compare the versatility and accuracy of the first and second order rate

constants derived in this experiment a simple mixed reactor model was developed based

on the following equations. For the second order model:

[ ] [ ][ ]COk

tO

22 −=

∂∂

(5)

[ ] [ ]

tO

tC

∂∂=

∂∂ 2 (6)

For the first order models:

[ ] [ ]2

2 Okt

ODO′−=

∂∂

(7)

[ ] [ ]Ck

tO

DOC′−=∂

∂ 2 (8)

For the Monod model:

[ ] [ ] [ ]

[ ] mm kO

OCR

tO

+−=

∂∂

2

22 (9)

Note again that in equations 5, 8 and 9 [C] is given by DOC concentration. The model

was constructed with the assumptions that the solution was fully mixed, there was no

54

import or export of reactants, and DO and DOC react with the molar ratio of 1:1. We

also assumed there was no oxidation of dissolved metals or any other kind of byproduct.

Boundary conditions were taken as the initial DO and DOC concentrations, a value of

0.0066L mol-1 s-1 was used for k, 4.7 ×10-6 s-1 for k'DO and 4.8 ×10-7 s-1 for k'DOC as

obtained from the mine lake sediment slurry experiments. Rmax was assumed to be 1

year-1 as used by (Boudreau, 1996) and km was assumed to be 2 × 10-5 mol L-1 (Park and

Jaffe, 1996). The model was applied to the marine core experiment by using the

relevant boundary conditions and was also used to calculate SOD for a core containing

the average sediment height. While the marine sediment cores are not mixed batch

reactors, this exercise serves to highlight that even without the inclusion of physical

transport processes; a more appropriate rate law can improve predictions.

The results from this model application were then compared to those from the

column experimental data to assess the validity of applying second order reaction

kinetics and the applicability of the calculated k, k'DO and k'DOC (Figure 3.7). The

experimental DO concentration is better predicted by second order kinetics, with the

first order description with respect to DOC also coming close to experimental results,

however the experimental error in the SOD calculations is such that both first with

respect to DO and second order approximations fall within the error bounds. Monod

kinetics neither predicts DO concentration nor SOD very accurately when compared to

the other two methods; however this may be a reflection of the literature values chosen.

Although both first and second order equally represent the experimental SOD the

authors think it is more advantageous to use second order kinetics to obtain the more

accurate prediction of DO concentration. SOD is often used as an indirect method to

calculate the water column DO concentration and being able to predict DO directly

bypasses many error producing calculations.

Before a second order parameterization of aerobic respiration can be

implemented in a diagenetic model, further work would be required, possibly in the

form of further sediment slurry and column experiments, to determine and refine the

value of k for various environments. It may be useful in these experiments to add treacle

periodically rather than adding straw to the batches as there is less ambiguity in the

addition of treacle when compared to the release of chemicals, in particular DOC and

phosphorous, from the straw. Also, to improve our determination of the range of k

values, the role of bacteria in the degradation of carbon and their influence of the value

of k should be defined. Studies have already been undertaken investigating the

parameterization of the effect of bacteria in dual limitation reactions (Borden and

55

Bedient, 1986; Bae and Rittmann, 1996) however at present the vast majority of sites do

not have enough data available to elicit the required parameters and an abiotic second

order approximation can be used.

Figure 3.7 A simple mixed reactor model used to predict (A) the DO concentration (mg L-1) and (B)

SOD (g m-2 day-1) for Cockburn Sound using a first order (with respect to DO and DOC

respectively), second order and Monod kinetics approach, and rate constants determined from the

mine lakes sediment slurry experiments.

The importance of bacteria in respiration for these systems was ascertained

through observing the impact of sterilisation on aerobic respiration. Sterilisation can

have two impacts on DO concentration: by reducing the amount dissolved in the water

column; and also by decreasing DO consumption through the respiration process by

reducing or even eliminating mediating bacteria present in the jars. A combination of

56

both these effects is thought to have occurred in these experiments, but it is unlikely that

the entire bacterial population was eliminated.

The importance of the availability of labile DOC to aerobic respiration by

bacteria, as ascertained by these experiments, implies that DOC concentration rather

than POC may also be important in the parameterization of the kinetics of other redox

reactions. The relationship between particulate organic carbon (POC) mineralization

and oxidant species has been parameterized (Jahnke et al., 1982; Jahnke et al., 1997),

however the parameterization of the relationship between the rates of DOC

mineralization and the reduction of species such as nitrate, iron and sulfate has yet to be

researched. The relationship between POC supply, breakdown and DOC formation is

also yet to be fully understood, let alone parameterized.

Before application to the management of a marine or lake system it may be

useful to include physical processes, in particular diffusion and advection, to better

account for concentration gradients that may limit reactions. This may best be achieved

through incorporating sediment diagenesis, complete with second order respiration, into

a hydrodynamic model such as DYRESM (Gal et al., 2003), allowing investigation of

the interaction of physical, chemical and biological processes across the sediment-water

interface. Such interactions may be critical for marine and lake ecosystem health.

3.6 Conclusions

Modelling of microbial respiration has usually ignored the implications of labile

organic carbon limitation. Through laboratory based lake sediment slurry tests, the

availability of DOC as well as DO was found to influence the rate of aerobic respiration

and a second order rate constant k was determined to be 6.6 mL mol-1 s-1. In the lake

sediment slurry tests the effect of sterilization on respiration was found to be

inconclusive, most likely due to the difficulty in fully sterilizing sediment without

breaking down the sediment itself. There is strong evidence to suggest carbon limitation

is being experienced by Lake Kepwari, Chicken Creek and the deep basin of Cockburn

Sound. The application of a simple first order, second order and Monod kinetics model

to CS cores showed that first and second order kinetics over and under predicted,

respectively, the DO concentration in a typical core equally, while the Monod model

was less accurate. SOD for a typical core was better predicted by second order kinetics,

making second order kinetics the ideal compromise between increased accuracy of

57

process description without the need for determining extra parameters. Future research

in this area will extend this approach to other diagenetic reactions, the re-oxidation of

oxidation byproducts such as Fe(II), the inclusion of physical transport processes such

as diffusion and advection and finally incorporation into a hydrodynamic model to

allow modelling of whole lake/ocean scenarios and for use as a tool in management of

carbon limited systems.

Carbon limitation may prevent anoxia in the hypolimnion and hence may

prevent the establishment of processes that require anoxia to continue, such as

denitrification or the release of phosphorous from the sediment through iron reduction:

Both processes are of importance in marine and lake systems. In the more specific

example of mine lakes where bacterial remediation is being targeted to remediate the

more acidic of these lakes, iron and sulfate reduction processes only occur in the

absence of oxygen. Hence the inclusion of both DOC and DO limitation in the

parameterization of aerobic respiration can be extremely useful in predicting the

biogeochemical evolution of such environments.

3.7 Acknowledgements

This project was supported financially by the Western Australian Centre of

Excellence for Sustainable Mine Lakes, the Water Corporation of Western Australia

and Australian Research Council Linkage Project LP0454252. Financial support for DJ

Read was provided by an Australian Postgraduate Award. Thanks to Matthias

Koschorreck and Anas Ghadouani for valuable comments on the manuscript. This

manuscript is School of Environmental Systems Engineering Publication SESE-044-

DR.

3.8 Notation

The following notation is used in this paper:

[ ]C DOC concentration

k second order rate constant

DOk ' first order rate constant with respect to DO

DOCk ' first order rate constant with respect to DOC

58

mk half saturation constant

n degrees of freedom

[ ]2O DO concentration

maxR maximum rate

s standard deviation

t time

T t-distribution value for (n-1) degrees of freedom

3.9 References



Bae, W. & Rittmann, B. E., 1996, A structured model of dual-limitation kinetics.

Biotechnology and Bioengineering, 49, 683-689.

Baird, M., 2001, CSIRO Simple Estuarine Model: Technical Description of the

Ecological Model. CSIRO Land and Water,

Berg, P., Rysgaard, S. & Thamdrup, B., 2003, Dynamic modelling of early diagenesis

and nutrient cycling. A case study in an artic marine sediment. American

Journal of Science, 303, 905-955.

Berner, R. A., 1980, Early Diagenesis: A Theoretical Approach, Princeton University

Press,

Blodau, C., Peine, A., Hoffmann, S. & Peiffer, S., 2000, Organic matter diagenesis in

acidic mine lakes. Acta Hydrochimica et Hydrobiologica, 28, 123-125.

Borden, R. C. & Bedient, P. B., 1986, Transport of dissolved hydrocarbons influenced

by oxygen-limited biodegradation 1. Theoretical development. Water Resources

Research, 22, 1973-1982.





59



316, 153-159.

Burdige, D. J., 2002, Sediment pore waters. in Hansell, D. A. & Carlson, C. A. (Eds.)

Biogeochemistry of Marine Dissolved Organic Matter. Academic Press, San

Diego.

Canavan, R. W., Slomp, C. P., Jourabchi, P., Van Cappellen, P., Laverman, A. M. &

van den Berg, G. A., 2006, Organic matter mineralization in sediment of a

coastal freshwater lake and response to salinization. Geochimica et


Castro, J. M., Wielinga, B. W., Gannon, J. E. & Moore, J. N., 1999, Stimulation of

sulfate-reducing bacteria in lake water from a former open-pit mine through

addition of organic wastes. Water Environment Research, 71, 218-223.



1617-1624.

del Giorgio, P. A. & Duarte, C. M., 2002, Respiration in the open ocean. Nature, 420,

379-384.





Farias, L., 2003, Remineralization and accumulation of organic carbon and nitrogen in

marine sediments of eutrophic bays: the case of the Bay of Conception, Chile.

Estuarine, Coastal and Shelf Science, 57, 829-841.

Fauville, A., Mayer, B., Frömmichen, R., Friese, K. & Veizer, J., 2004, Chemical and

isotopic evidence for accelerated bacterial sulphate reduction in acid mining

lakes after addition of organic carbon: laboratory batch experiments. Chemical

Geology, 204, 325-344.

Frömmichen, R., Kellner, S. & Friese, K., 2003, Sediment Conditioning with Organic

and/or Inorganic Carbon Sources as a First Step in Alkalinity Generation of

Acid Mine Pit Lake Water (pH 2-3). Environmental Science and Technology,

37, 1414-1421.

60

Frömmichen, R., Wendt-Potthoff, K., Friese, K. & Fischer, R., 2004, Microcosm studies

for neutralization of hypolimnic acid mine pit lake water (pH 2.6).

Environmental Science and Technology, 38, 1877-1887.

Fyson, A., Nixdorf, B. & Steinberg, C. E. W., 1998, Manipulation of the sediment-

water interface of extremely acidic mining lakes with potatoes: Laboratory

studies with intact sediment cores. Water, Air and Soil Pollution, 108, 353-363.

Gal, G., Imberger, J., Zohary, T., Antenucci, J., Anis, A. & Rosenberg, T., 2003,

Simulating the thermal dynamics of Lake Kinneret. Ecological Modelling, 162,

69-86.

Goloway, F. & Bender, M., 1982, Diagenetic models of interstitial nitrate profiles in

deep sea suboxic sediments. Limnology and Oceanography, 27, 624-638.

Hedges, J. I., 1992, Global biogeochemical cycles: progress and problems. Marine

Chemistry, 39, 67-93.

Hedges, J. I., 2002, Why dissolved organics matter. in Hansell, D. A. & Carlson, C. A.

(Eds.) Biogeochemistry of Marine Dissolved Organic Matter. Academic Press,

San Diego.

Hee, C. A., Pease, T. K., Alperin, M. J. & Martens, C. S., 2001, Dissolved organic

carbon production and consumption in anoxic marine sediments: A pulse-tracer

experiment. Limnology and Oceanography, 46, 1908-1920.

Hensen, C., Landenberger, H., Zabel, M., Gundersen, J. K., Glud, R. N. & Schulz, H.

D., 1997, Simulation of early diagenetic processes in continental slope

sediments off southwest Africa: the computer model CoTAM tested. Marine

Geology, 144, 191-210.

Herzfeld, M., Hamilton, D. P. & Douglas, G. B., 2001, Comparison of a mechanistic

sediment model and a water column model for hindcasting oxygen decay in

benthic chambers. Ecological Modelling, 136, 255-267.

Hobbie, J. E., 1992, Microbial control of dissolved organic carbon in lakes: research for

the future. Hydrobiologia, 229, 169-180.

Horneck, G., 2000, The microbial world and the case for Mars. Planetary and Space

Science, 48, 1053-1063.

Jahnke, R., Heggie, D., Emerson, S. & Grundmanis, V., 1982, Pore waters of the central

Pacific Ocean: nutrient results. Earth and Planetary Science Letters, 61, 233-

256.

61

Jahnke, R. A., Craven, D. B., McCorkle, D. C. & Reimers, C. E., 1997, CaCO3

dissolution in California continental margin sediments: The influence of organic

matter remineralization. Geochimica et Cosmochimica Acta, 61, 3587-3604.

Karlsson, S., Jonsson, A. & Jansson, M., 2001, Bacterioplankton production in lakes

along an altitude gradient in the subarctic north of Sweden. Microbial Ecology,

42, 372-382.




Marinelli, R. L. & Woodin, S. A., 2002, Experimental evidence for linkages between

infaunal recruitment, disturbance, and sediment surface chemistry. Limnology

and Oceanography, 47, 221-229.

Meysman, F. J. R., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R., 2003, Reactive

transport in surface sediments. II. Media: an object-oriented problem-solving

environment for early diagenesis. Computers and Geosciences, 29, 301-318.



Monod, J., 1949, The growth of bacterial cultures. Annual Review of Microbiology, 3,

371-394.

Murphy, E. M. & Shranmke, J. A., 1998, Estimation of microbial respiration rates in

groundwater by geochemical modelling constrained with stable isotopes.


Murray, J. W. & Kuivila, K. M., 1990, Organic matter diagenesis in the northeast

Pacific: transition from aerobic red clay to suboxic hemipelagic sediments.

Deep-Sea Research, 37, 59-80.

Nixdorf, B., Krumbeck, H., Jander, J. & Beulker, C., 2003, Comparison of bacterial and

phytoplankton productivity in extremely acidic mining lakes and eutrophic hard

water lakes. Acta Oecologica, 24, S281-S288.

Pallud, C., Meile, C., Laverman, A. M., Abell, J. & Van Cappellen, P., 2007, The use of

flow-through sediment reactors in biogeochemical kinetics: Methodology and

examples of applications. Marine Chemistry, 106, 256-271.

Park, S. S. & Jaffe, P. R., 1996, Development of a sediment redox potential model for

the assessment of postdepositional metal mobility. Ecological Modelling, 91,

169-181.

62




Pomeroy, L. R., Sheldon, J. E., Sheldon, W. M. J., Blanton, J. O., Amft, J. & Peters, F.,

2000, Seasonal changes in microbial processes in estuarine and continental shelf

waters of the south-eastern USA. Estuarine, Coastal and Shelf Science, 51, 415-

428.

Rabouille, C. & Gaillard, J. F., 1991, Towards the EDGE: Early diagenetic global

explanation. A model depicting the early diagenesis of organic matter, O2, NO3,

Mn and PO4. Geochimica et Cosmochimica Acta, 55, 2511-2525.

Reimers, C. E. & Suess, E., 1983, The partitioning of organic carbon fluxes and

sedimentary organic matter decomposition rates in the ocean. Marine Chemistry,

13, 141-168.

Schindler, D. W., Bayley, S. E., Curtis, P. J., Parker, B. R., Stainton, M. P. & Kelly, C.

A., 1992, Natural and man-caused factors affecting the abundance and cycling

of dissolved organic substances in precambrian shield lakes. Hydrobiologia,

229, 1-21.



Toggweiler, J. R., 1988, Deep-sea carbon, a burning issue. Nature, 334, 468.

Tranvik, L. J., 1992, Allochthonous dissolved organic matter as an energy source for

pelagic bacteria and the concept of the microbial loop. Hydrobiologia, 229, 107-

114.

Vincent, W. F., Mueller, D. R. & Bonilla, S., 2004, Ecosystems on ice: the microbial

ecology of Markham Ice Shelf in the high Arctic. Cryobiology, 48, 103-112.

Wendt-Potthoff, K. & Koschorreck, M., 2002, Functional groups and activities of

bacteria in a highly acidic volcanic mountain stream and lake in Patagonia,

Argentina. Microbial Ecology, 43, 92-106.

Westrich, J. T. & Berner, R. A., 1984, The role of sedimentary organic matter in

bacterial sulfate reduction: the G Model tested. Limnology and Oceanography,

29, 236-249.

Wetzel, R. G., 1992, Gradient-dominated ecosystems: sources and regulatory functions

of dissolved organic matter in freshwater ecosystems. Hydrobiologia, 229, 181-

198.

63

Wetzel, R. G., 2001, Limnology, Lake and River Ecosystems, Academic Press, San

Diego.

Williams, P. M. & Druffel, E. R. M., 1988, Dissolved organic matter in the ocean:

comments on a controversy. Oceanography, 1, 14-17.

64

65

4 Sediment diagenesis and porewater solute fluxes in acidic mine lakes: the impact of organic carbon additions

Deborah J. Read1, Tiina Myllymäki2, Carolyn E. Oldham1 and Matthias Koschorreck2



2UFZ-Helmholtz Centre for Environmental Research

Department of Lake Research

4.1 Abstract

Sediment diagenesis through microbial sulfate reduction is considered an important

process in the pH amelioration of acidic mine lakes, but is often limited by the

availability of organic carbon. Prediction of the effectiveness of remediation strategies

requires a detailed knowledge of sediment diagenesis under acidic systems. This study

aims to further the understanding of functional similarities and differences in diagenetic

processes in acidic lakes through column experiments using sediment and water from

three very different (formation method, bathymetry and chemical concentrations) mine

lakes, 2 from Australia and 1 from Germany. Sediment microcosms were made using

sediment and hypolimnetic water retrieved from each lake, with 2 microcosms used as

controls, 2 receiving a low dose of a dissolved organic carbon (DOC) solution and 2

receiving a high dose of DOC solution (Treacle). The porewater and surface water of

the microcosms were then monitored in a laboratory environment over the following 7

weeks. Results indicate that there is a marked difference between the German and

Australian lakes in porewater dissolved oxygen (DO), sulfide and pH responses.

Comparisons showed that increased H2S production coincided with lower iron

concentration, higher pH and higher DOC dose. The sequence of chemical species

removed from and released to the water column indicated that all sets of microcosms

followed, but with differing magnitude of response, the classic ecological redox

sequence when degrading organic matter. The microcosms from the most productive

lake exhibited a large release of ammonium attributed to a higher proportion of labile

66

particulate organic carbon in the sediment resulting from the higher primary

productivity in the lake as a whole. While each lake differs in its chemical, biological

and geological makeup, some similarities in processes were noted, including the

adherence to the ecological redox sequence; the prevalence of nitrate reduction; similar

DOC remineralisation rates regardless of oxidants used; and, anoxia in the porewater of

all sets of microcosms less than one week into the experiment.

4.2 Introduction

After the cessation of open cut mining, dewatering of groundwater is no longer

required and the remaining voids may subsequently fill with water. Many voids are

intentionally turned into artificial lakes through filling with ground water infiltration,

diversion of rivers or actively pumping of water into the void. This has become

increasingly prevalent world wide as the number of former mining voids increases.

However, many mine voids experience geogenic acidification, where acidic

groundwater and surface water flows into the void. Dewatering exposes iron sulfides

(e.g. pyrite and marcasite) to the atmosphere causing them to oxidize and leading to the

production of acidic waters (Klapper and Schultze, 1995), which can contain high

concentrations of Fe, Mn, Al, SO4 and heavy metals (Evangelou, 1998; Klapper, 2002).

As a result of their recent formation and the acidity generating processes in the

mine walls and overburden, mine lakes typically contain high concentrations of sulfate

and iron (Kleeberg, 1998; Peiffer, 1998; Fyson et al., 2002) when compared to most

natural freshwater lakes. Many mine lakes lack inorganic carbon and as a result are

usually dominated by acidic buffer systems, such as aluminium or iron, rather than the

carbonate buffer system normally encountered in natural lakes (Klapper and Schultze,

1995; Peiffer, 1998; Fyson et al., 2002). Typically these lakes are poor in the

macronutrients P, N and Si (Kleeberg, 1998; Fyson et al., 2002; Klapper, 2002).

Phosphorus in particular is often limiting due to binding by Al and Fe complexes and

precipitation from the water column (Nixdorf and Kapfer, 1998). The lakes usually have

low levels of primary production and organic carbon (Nixdorf and Kapfer, 1998).

The large buffering capacity (Fe and Al) and high acidity of these lakes, means

that chemical remediation is very costly since large amounts of neutralizing agents are

required and treatment is not sustainable (Klapper and Schultze, 1995; Klapper et al.,

1998). This leaves biological remediation as the preferred option with the aim being to

67

accelerate natural biological processes involving the production of inorganic and

organic carbon and the generation of alkalinity.

Specifically, acidity can be removed through the biologically mediated reduction

of sulfate and iron (III) leading to the precipitation of insoluble sulfides, however this

requires anoxic conditions to be present (Anderson and Schiff, 1987). In stratified

systems sediment porewater pH can be raised to near neutral through hypolimnetic

anoxia enhancing microbial alkalinity production via anaerobic respiration (Klapper,

2002). This reaction can be described with the following equation (Anderson and

Schiff, 1987):

OHFeSCOHSOFeOOHOCH saqaqs 2)(22)()_(24)(2 25415168415 ++→+++ +− (1)

If there is a lack of organic carbon in the system, then this reaction is inhibited

(Laskov et al., 2002). This is known as organic carbon limitation of microbial

respiration, and is suspected to occur in many mine lakes (Brugam et al., 1995; Klapper

and Schultze, 1995; Friese et al., 1998; Kleeberg, 1998; Peine et al., 2000). To alleviate

this limitation it has been suggested that organic carbon substrates be added to mine

lakes to encourage alkalinity generation (Brugam et al., 1995). It has also been

suggested that pH below 5.5 can limit sulfate reduction due to the reduced

competitiveness of sulfate reducing bacteria compared to iron reducing bacteria at low

pH (Koschorreck et al., 2002), however low pH alone does not preclude sulfate

reduction and it has been observed in pH<3 (Küsel et al., 1999; Koschorreck et al.,

2003b).

As organic matter remineralisation and alkalinity generation occur mainly in the

sediment, the sediment-water interface is an extremely important site when considered

in relation to water chemistry within these lakes. One of the main ongoing sources of

protons to mine lakes is the inflow of acidic groundwater (Blodau, 2006), so redox

conditions at the sediment-water interface can mediate the proton flux into the lake.

In working towards a method to predict the long term chemical and biological

evolution of mine lakes much research has been carried out on the effects of organic

carbon addition (both labile and refractory) on pH, using micro and mesocosms of mine

lake waters and sediment. To date, the focus has primarily been on the net change of pH

and the biogeochemical processes involving iron and sulfur (e.g. Blodau et al., 1998;

Fyson et al., 1998a; Fyson et al., 1998b; Frömmichen et al., 2003; Küsel, 2003;

Frömmichen et al., 2004; Meier et al., 2004; Koschorreck et al., 2007). Much of this

research focuses on finding specific types of organic matter that achieve the greatest pH

change; see for example Christensen et al. (1996; whey), Vile and Wieder (1993;

68

manure, sawdust, peat, mushroom compost ) and Brugam et al. (1995; straw). In

particular, a series of experiments added straw and carbokalk, a by-product of the sugar

industry which contains lime (Frömmichen et al., 2001; Koschorreck et al., 2002;

Wendt-Potthoff et al., 2002). The lime in the carbokalk temporarily increased alkalinity

in the mesocosms and stimulated sulfate reduction. Experiments have also been carried

out using other readily available sources of organic carbon such as ethanol

(Frömmichen et al., 2003) and acetate and potatoes (Fyson et al., 2006). A key point

arising from this research seems to be that if the pH can be raised slightly then sulfate

reducing bacteria are more competitive with iron reducers, provided there is a readily

available organic substrate.

Research has also been carried out on organic carbon cycling in acidic systems

(e.g. Blodau et al., 2000; Blodau and Peiffer, 2003a; Blodau and Peiffer, 2003b),

however, as yet there has been little to no research into the dependence of iron and

sulfur cycling on varying concentrations of organic carbon. There is also limited

generalisation of findings across multiple lakes, with most research being specific to

one mine lake. This understanding is key if these acidic lakes are to be optimally

managed or remediated. This paper specifically investigates the dependence of iron and

sulfur cycling on labile dissolved organic carbon dosage in microcosms from multiple

lakes and is a step towards that process understanding.

To increase our process understanding of sediment diagenesis we dosed

microcosms with labile DOC rather than the labile or refractory POC that has been

traditionally used. Bacteria can typically uptake DOC with molecular sizes ~600Da

(Arnosti, 2004). The use of DOC as opposed to POC in these experiments allows us to

focus directly on the interplay between diagenesis and geochemistry. We conducted

column experiments using sediment and water from three very different coal mine

lakes, one in Germany and two in Australia with a view to understanding the functional

similarities and differences in the diagenetic process across these three lakes. These

lakes have a range of pH levels (2.6 – 4.8) and ages, and have different morphometry

and filling techniques therefore they can be considered to be representative of a broad

spectrum of acidic mine lakes.

69

4.3 Methodology

4.3.1 Study Sites Lake Kepwari (LK) and Lake Chicken Creek (CC) are located in the Collie

Basin, Western Australia, approximately 160km southeast of Perth. Both lakes are

former open cut mine voids that have filled with groundwater and diverted river flow.

Both lakes are monomictic, usually experiencing thermal stratification from spring to

autumn (October – April) and are fully mixed from May to September. Although

Chicken Creek and Lake Kepwari are relatively deep and are stratified for half the year,

both lakes remain oxic for the entire year with DO concentrations of around 6 mg L-1

observed in the hypolimnion. Depth averaged DOC concentrations in Lakes Kepwari

and Chicken Creek are approximately 1.2 - 1.5 mg L-1 and 0.5 – 1.0 mg L-1

respectively. The oxidation of pyritic material prior to filling causes both lakes to be

acidic (Table 4.1). The primary mineral phases in Lake Kepwari and Chicken Creek

sediment are kaolinite and quartz with a small amount of goethite.

Table 4.1 Physical and chemical characteristics of Chicken Creek, Lake Kepwari and Mining Lake

111.

Chicken Creek Lake Kepwari Mining Lake 111 Mean Depth (m) - - 4.6 1 Maximum Depth (m) 35 65 10.2 1 Surface Area (m2) 6.79 x 105 1.04 x 106 1.07 x 105 1 Volume (m3) 2.6 × 106 25 × 106 0.5 × 106 pH 2.8 4.8 2.6 1 Sulfate (mmol/L) 3.4 - 4.9 1.0 - 1.2 11 - 16 2 Dissolved Iron (mmol/L) 0.13 – 0.23 0.0005 - 0.004 2.1 - 3.4 [Fe(II)] 2 DOC (mg/L) 0.5 – 1.0 1.2 - 1.5 1.4

1 Karakas et al. (2003), 2 Tittel and Kamjunke (2004)

Mining Lake 111 (ML) is located in the Lusatian lignite mining district in

eastern Germany (51º29Ń, 13º38É). The lake is stratified during summer but remains

oxic for the entire year with the exception of a small monimolimnion at the deepest

point (Karakas et al., 2003).The water contains high amount of SO42- and Fe (Table 4.1)

but low concentrations of organic and inorganic carbon (DOC: 1.4mg L-1; Koschorreck

et al., 2003a; Koschorreck et al., 2007). The sediment is dominated by iron minerals

(Table 4.2).

70

Table 4.2 Sediment mineralogical data and porosity for sediment used in each set of microcosms

LK CC ML Si (%) 27.7 22.4 17.7 Al (%) 8.82 11.0 4.56 Fe (%) 0.89 1.72 11.2 Ca (%) 0.02 0.03 0.12 K (%) 0.34 0.17 0.79

Mg (%) 0.05 0.08 0.31 Na (%) 0.03 0.04 0.05 Ti (%) - - 0.44 S (%) 0.09 0.25 - Cl (%) 0.04 0.07 -

Total Carbon (%) 2.27 2.66 5.4 Total Organic Carbon (%) 2.13 2.55 5.3

Porosity 0.90 0.90 0.88

Approximately 5L of sediment was retrieved from each lake using a grab

sampler from depths of approximately 30m (LK), 10-15m (CC) and 6m (ML). A 10cm

layer of hypolimnetic lake water was kept over each sediment sample to minimise

oxygen penetration into the sediment. A further 25L of hypolimnetic water was also

retrieved from each lake to be used in the establishment of the microcosms. Sediment

and water were transported back to the laboratory in the dark.

4.3.2 Laboratory Upon reaching the laboratory the layer of water covering the sediment was

drained and each sediment sample was stirred to create a homogeneous mixture and

reduce differences between microcosms. Sediment was then transferred into 6 Perspex

cores for each lake (9cm internal diameter, 20cm height) to a depth of 10-12 cm. The

cores were then topped up with hypolimnetic water and the CC and LK cores were then

left open to the atmosphere to equilibrate. ML cores were incubated in an aquarium

containing 20L of ML hypolimnetic water which was continuously bubbled to create a

more oxic and stable environment within the microcosms. The dissolved oxygen (DO)

was monitored in the pore water of one to two microcosms from each set for the

following four to five days (days -4 to 0) until the microcosms appeared to have reached

equilibrium at which point all microcosms were sealed using Parafilm to exclude direct

contact with air and PVC caps. During the experiment all microcosms were stored in

the dark at a constant temperature room (15°C).

Once equilibrium was attained, initial DO and pH sediment porewater profiles

were recorded for each microcosm and H2S profiles were recorded for all CC and LK

microcosms (day 0). For all CC and LK microcosms, profile measurements were taken

71

every 0.5mm for the upper 5mm of the sediment and then every 1mm up until a depth

of 40mm for DO profiles and 30mm for pH and H2S profiles. ML profile measurements

(DO, pH, H2S) were taken from about 2mm above the sediment to 6mm into the

sediment at 0.1mm intervals. All sensors were calibrated prior to and after profiling of

each set of microcosms according to manufacturer instructions and the sediment-water

interface was located optically. Sampling and profiling days can be seen in Table 4.3.

Table 4.3 Days of profiling and surface water sampling for each set of microcosms

Profile/Sample CC & LK ML DO 0, 7, 14, 22, 29, 38, 49 0, 1, 2, 3, 6

pH & H2S 1, 8, 15, 39 50 0*, 2, 6, 9, 15 Surface water

samples 0, 1, 2, 3, 4, 6, 7, 8, 14, 15, 22,

29, 38, 49 50 0, 1, 2, 3, 6, 9,

15, 41 * No H2S profile taken

After the initial profiles were taken, a low dose (1mL) of a DOC stock solution

was added to two microcosms from each set (labelled L1 and L2) and a high dose

(10mL) was added to another two microcosms from each set (labelled H1 and H2). The

remaining two microcosms in each set (labelled C1 and C2) were kept as controls

(Table 4.4). The DOC stock solution was made by dissolving approximately 10g of

CSR brand treacle in 250mL of the relevant lake’s water, then performing a 1:10

dilution of this solution.

Table 4.4 Microcosm name and associated treatment.

Control DOC Low Dose DOC High Dose

Lake Kepwari LKC1 LKC2

LKL1 LKL2

LKH1 LKH2

Chicken Creek CCC1 CCC2

CCL1 CCL2

CCH1 CCH2

Mining Lake 111 MLC2 MLC2

MLL1 MLL2

MLH1 MLH2

During the experiment, the surface water in each microcosm was sampled for

DOC, nitrate/nitrite (NOx), ammonium, filterable reactive phosphorus (FRP), filterable

iron and filterable manganese. The volume of water removed for sampling (~40 mL)

was replaced using stored hypolimnetic water and the change in solute concentration

caused by this addition was calculated. DO was measured in the surface water before

and after the sampling procedure to quantify the amount of oxygen introduced to the

water during replenishment.

72

After profiling on day 39, each treated CC and LK microcosm received an

additional dose of DOC equivalent to the initial dose, as it was anticipated that the

initial DOC dose had already been respired.

At the conclusion of the experiment (day 50 for CC and LK microcosms, and

day 41 for ML microcosms), the surface water in all microcosms was sampled for total

nitrogen, total Phosphorus, filterable organic carbon, total organic carbon, sulfate and

total sulfur as well as the previously monitored nutrients and metals. ML microcosms

were also sampled for total inorganic carbon (TIC). The remaining hypolimnetic water

taken from each of the lakes, the DOC stock solution and a blank comprising deionised

water were also sampled in triplicate for all of the above chemical species.

Pore water samples were taken from the upper 5 cm of the sediment from each

sediment microcosm by centrifuging the sediment and decanting the supernatant, and

were then analysed for DOC. The porosity of the sediment in all microcosms was also

determined. Sediment samples were taken from the upper 5 cm from each sediment

microcosm for X-ray fluorescence (XRF) analysis.

Stored ML water was sampled in the end of experiment for all of the above

species allowing for comparison with initial and final water composition in the ML

microcosms and also observation of microbiological activity in the water column in the

absence of sediment. The DOC solution was also sampled for all the above species at

the time of microcosm treatment. From this sampling it was determined that the treacle

itself contained the species concentrations given in Table 4.5.

Table 4.5 Concentrations of soluble nutrients, organic carbon, manganese, iron and sulfate in the

treacle.

Species Concentration (mg/g) NH4

+ 2.85 x 10-2 FRP 7.17 x 10-2 NOx 1.67 x 10-2 TP 1.46 x 10-1 TN 1.15 Soluble Organic Carbon 3.13 x 102 Soluble Mn 1.5 x 10-2 Soluble Fe 8.1 x 10-2 SO4

2- 1.57 x 101

4.3.3 Chemical Analysis DO measurements in the surface water were made using a TPSTM Aqua-D DO

meter with a TPSTM ED1 sensor. Sediment porewater profiles were conducted using

microsensors, reference electrode, 2 channel picoammeter, pH meter and

73

micromanipulator (UNISENSETM, Denmark). Oxygen (Clark type), H2S and pH

microsensors all had a tip diameter of 50�m. pH microsensors were used in conjunction

with an Ag-AgCl reference electrode of 5000 �m tip diameter.

All water samples collected for analyses of dissolved species were filtered

through a 0.45 �m (LK and CC) or a 0.2 µm (ML) cellulose acetate filter. Filtered

samples for analysis of NH4+, FRP and NOx were frozen (LK and CC) or refrigerated

(ML) until analysis using Flow Injection Analysis. Filtered samples for analysis of total

iron and manganese were acidified using concentrated nitric acid and stored in a fridge

until analysis using ICP-AES. DOC samples were acidified with one drop of

concentrated sulfuric acid and kept refrigerated until analysis using automated

combustion. No duplicate samples were taken owing to the relatively small water

volumes used in these experiments when compared to sample size.

4.3.4 Calculations The diffusive flux for DO was determined using either the concentration

gradient in the diffusive boundary layer or the concentration gradient in the sediment

corrected for porosity. The DO concentrations and fluxes were corrected for additional

DO contained in the replenishing water added to the sediment microcosms after

sampling. A negative flux denotes uptake by sediment and a positive flux denotes

release from sediment.

Porosity was estimated as the ratio of the volume of water evaporated to the

volume of wet sediment sample. The average porosity of the sediment was determined

to be 0.61, 0.73 and 0.88 for LK, CC and ML respectively (Table 4.2).

Total dissolved sulfide [S-tot] was calculated using the measured H2S

concentration [H2S] and the pH according to the equation (Jeroschewski et al., 1996):

[ ] [ ] [ ]��

��

�+= +

−

OHK

SSH tot3

12 1/ for pH < 9

Where 1101pKK −= and pK1 was determined from (Millero et al., 1988):

ςς 0135.0157.0ln04555.15/4.576508.98 5.01 +−++−= TTpK

and where ζ is salinity and T is temperature in degrees Kelvin.

74

4.4 Results

Results are presented in three sections: those for the overlying water, those for

the sediment porewater, and those for the sediment itself.

4.4.1 Surface water DOC (Figure 4.1) decreased in the overlying water of all treated microcosms

over the experiment, however in the first two days of the experiment there was an

unexpected rapid increase in DOC concentration in 5 of the high dose microcosms and

4 of the low dose microcosms. This rapid increase in DOC was also noted in 3 high

dose microcosms after the second dose of DOC on day 39. The increase appeared to be

proportional to the amount of DOC added. The rate of decrease in DOC concentration

was greater for microcosms receiving the high dose of DOC and by the end of the

experiment DOC concentrations in treated microcosms were approximately the same as

in control microcosms. DOC concentrations in the control microcosms remained

constant throughout the experiment indicative of low reactivity of in-situ DOC over the

time scale of the experiment.

Surface water DO concentrations decreased in all microcosms during the

experiment, although the timescale and magnitude of the decrease differed depending

on the lake (Figure 4.2). All Mining Lake 111 microcosms were anoxic in the overlying

water within 10 days of starting the experiment, despite starting with higher initial DO

concentrations, whilst some Lake Kepwari and Chicken Creek microcosms were still

oxic after 7 weeks. The remnant DO in some of these microcosms can be explained by

air leaks through the top cap of the core system, meaning results from these microcosms

had to be interpreted with caution.

There was a general increase in ammonium concentration in the surface waters

of most microcosms, although much greater increases were noted in the surface water of

Chicken Creek microcosms (~200 �mol L-1; Figure 4.3, a-c). Lake Kepwari

microcosms, which had the lowest initial ammonium concentrations, showed a decrease

in ammonium concentrations over the first 1-2 weeks (greatest in high dose

microcosms) after which concentrations increased again. The greatest increases in

ammonium concentrations across Lake Kepwari and Chicken Creek microcosms were

observed in the control and low dose microcosms. In Mining Lake 111 microcosms

DOC treatment did not significantly affect ammonium flux.

75

Figure 4.1 Concentrations of DOC (�mol L-1) in the surface water of each set of microcosms during

the experiment: Lake Kepwari (a), Chicken Creek (b) and Mining Lake 111 (c). Solid, dashed and

dotted lines denote control, low dose and high dose microcosms respectively.

76

Figure 4.2 Concentrations of DO (�mol L-1) in the surface water of each set of microcosms during

the experiment: Lake Kepwari (a), Chicken Creek (b) and Mining Lake 111 (c). Solid, dashed and

dotted lines denote control, low dose and high dose microcosms respectively.

77

Figure 4.3 Concentrations (�mol L-1) of ammonium, nitrate and nitrite (NOx), total dissolved iron

and filterable reactive Phosphorus (FRP) in the surface water for each set of microcosms: Lake

Kepwari (a, d, g, j), Chicken Creek (b, e, h), Mining Lake 111 (c, f, i, k). Solid, dashed and dotted

lines denote control, low dose and high dose microcosms respectively. Note the different scales on

the y axis.

78

NOx concentration decreased in the surface water of all microcosms, with the

magnitude of the decrease differing across sites depending on the initial NOx

concentration (Figure 4.3, d-f). In all microcosms, the NOx concentration decreased to

approximately a quarter of the initial concentration after 6-7 weeks. There was no

discernible difference between NOx concentrations in microcosms based on treatment

type. A decrease in the nitrate concentration was also observed in the Mining Lake 111

water which was incubated without sediment (data not shown). Thus, the observed

decrease of nitrate in Mining Lake 111 microcosms does not necessarily mean that there

was a flux of nitrate into the sediment.

Initial concentrations of total dissolved iron in the overlying water varied greatly

between lakes (Figure 4.3, g-i). The Mining Lake 111 microcosms, which contained the

highest initial concentrations of iron, showed an increase in concentration as soon as

they were anoxic after 4 days. Mining Lake 111 and Lake Kepwari microcosms that

were treated with a high dose of DOC showed a greater and earlier increase in dissolved

iron concentrations in the surface water compared to other microcosms.

FRP concentrations in the overlying water of microcosms were generally low

with any FRP added through treacle addition (between an additional 9.04 x 10-3 and

1.19 x 10-2 �mol for low doses; and, 8.98 x 10-2 and 1.20 x 10-1 �mol for high doses in an

overlying water volume of approximately 600mL) disappearing rapidly from the

microcosms after the first dose (<3 days; Figure 4.3, j-k). The low FRP concentration in

Lake Kepwari microcosms made FRP added through the treacle potentially more

significant, although one microcosm (LKL2) had an abnormally high initial FRP

concentration indicating there could have been some heterogeneity associated with FRP

within the sediment itself. FRP concentrations in the Mining Lake 111 microcosm

surface waters were similar and were not distinguishable based on treatment type. The

FRP concentration in the surface waters of Chicken Creek microcosms was unavailable

due to analytical problems.

Total dissolved manganese concentrations changed very little in the overlying

waters across all sites (typically 0.2-0.4 mg L-1; data not shown) hence manganese will

not be discussed further.

4.4.2 Sediment Porewater The maximum DO penetration depth in Lake Kepwari microcosms was around

5 mm, although generally it was less than 2 mm (Figure 4.4). Chicken Creek

microcosms had a smaller maximum penetration depth of 2 mm. The initial DO

79

concentrations at the interface of the Chicken Creek and Lake Kepwari microcosms

were more variable than the Mining Lake 111 microcosms due to not being subject to

the water bath treatment prior to the commencement of the experiment. The DO

concentration in the porewater reduced in all microcosms during the experiment;

Mining Lake 111 microcosms became anoxic whereas Lake Kepwari and Chicken

Creek microcosms did not; all Mining Lake 111 microcosms, except ML L2, were

anoxic by day six matching the water column decrease in DO. Some Chicken Creek and

Lake Kepwari microcosms had a higher concentration of DO in the porewater on day 38

than on day 0, indicating diffusion of DO from the water columns that remained anoxic.

No distinction was able to be made in the DO profiles between treated and control

microcosms.

The DO fluxes across the interface predicted using sediment porewater DO

concentration gradients and Fick’s first law (Boudreau, 1997) differ significantly to

those estimated using the change of DO in the water column itself. A plot of fluxes

calculated from sediment porewater data against that from water column data does not

indicate any correlation (a 1:1 line would indicate matching fluxes) and the Fickian flux

tends to be greater than the observed flux (Figure 4.5).

There was a marked difference between the Chicken Creek and Lake Kepwari

porewater pH profiles and the Mining Lake 111 porewater pH profiles (Figure 4.6). ML

profiles were constant over the measured depth of 7 mm with pH between 2.5 and 3. In

the Chicken Creek and Lake Kepwari pH profiles there was a sharp increase (up to 2.5

units) in porewater pH with depth below the sediment-water interface. In Chicken

Creek microcosms this gradient occurred in the 5 mm below the interface, regardless of

treatment and in Lake Kepwari microcosms the gradient occurred at around 5-10 mm

depth.

In the Chicken Creek microcosms, the porewater pH at the interface correlated

with treatment; high dose microcosms displayed the highest final pH (~ 5.5) and the

control microcosms displayed the lowest interface pH (~ 3.9).

80

Figure 4.4 DO concentration profiles (�mol/L) in the porewater of some of the microcosms during

the experiments. LK (a, d, g), CC (b, e, h), ML (c, f, i). The top row of plots are profiles from

control microcosms (C1), the middle row are low dose microcosms (L1) and the bottom row are

high dose microcosms (H1).

81

Despite the porewater being anoxic there was no H2S production detected in the

upper 7 mm of the Mining Lake 111 microcosms. Some H2S production was detected in

the Chicken Creek and Lake Kepwari microcosms, with more sulfide formed in the

Chicken Creek microcosms than Lake Kepwari microcosms (Figure 4.7). The

maximum sulfide concentration was generally between 5 and 10mm depth, although

high dose Chicken Creek microcosms showed a peak closer to the surface at ~3 mm

(Figure 4.7, f) coinciding with the observed gradient in pH. These microcosms also had

greater sulfide concentrations (~8-10 �mol L-1), whereas control and low dose

microcosms showed similar maximum concentrations (~5 �mol L-1). The difference

between control, low and high dose Lake Kepwari microcosms was not as distinct due

to the lower sulfide concentrations however treated microcosms all showed a peak in

sulfide concentration of between 1 and 3 �mol L-1 correlating with the pH gradient,

whereas the control microcosms had mostly constant profiles with concentrations less

than 1 �mol L-1. After the second dose of treacle the progression to sulfide production

was much quicker with the same concentrations being achieved in Chicken Creek after

one week instead of the three weeks taken after the first dose. Greater sulfide

concentrations were observed in the Lake Kepwari microcosms after the second DOC

addition.

Porewater concentrations of DOC at the end of the experiment were

significantly different between Lake Kepwari (40 mg L-1) and Chicken Creek

(9 mg L-1), however within these sets there was not a significant difference between

treatment types.

82

Figure 4.5 DO flux (mmol m-2 day-1) calculated from the change in surface water DO concentration

vs that calculated from the DO porewater profiles using Fick’s First Law for the CC microcosms

(a), LK microcosms (b) and ML microcosms (c). The dotted line indicates the 1:1 line of matching

Fickian predictions and observed flux.

83

Figure 4.6 pH profiles in the porewater of some of the microcosms during the experiments. LK (a,

d, g), CC (b, e, h), ML (c, f, i). The top row of plots are profiles from control microcosms (C1), the

middle row are low dose microcosms (L1) and the bottom row are high dose microcosms (H1).

84

Figure 4.7 Sulfide concentration (�mol/L) profiles in the porewater of some of the microcosms

during the experiments. LK (a, c, e) and CC (b, d, f). The top row of plots are profiles from control

microcosms, the middle row are low dose microcosms and the bottom row are high dose

microcosms.

85

4.4.3 Sediment Results from the XRF analysis of the sediment can be seen in Table 4.2. The

dominant form were SiO2 and Al2O3 in Lake Kepwari, Chicken Creek and Mining Lake

111 microcosms, with Mining Lake 111 also having a large concentration of Fe2O3.

There was very little manganese present in the sediment with concentrations of

0.11 �mol g-1, 0.15 �mol g-1 and 1.4 �mol g-1 for Lake Kepwari, Chicken Creek and

Mining Lake 111 microcosms respectively.

While this experiment provided an interesting insight into the chemical

behaviour of mine lake sediments when exposed to a labile DOC source, there are a few

experimental artefacts that should be heeded when interpreting the results. In creating a

homogenous sediment mixture to use in the microcosms, and thus provide a uniform

initial condition, the stirring may have disturbed bacterial activity. This would have

occurred through the mixing of microniches, disturbing gradients and exposing bacteria

to chemicals that they otherwise might not have come into contact with (Findlay et al.,

1990; Langezaal et al., 2003; Stocum and Plante, 2006). This may have benefited some

species of bacteria while hindering others.

It is also suspected that some microcosms absorbed oxygen from air trapped in

the headspace of the microcosms, which may have delayed the use of other oxidants

and also provided additional DO for the reoxidation of by-products. While microcosms

were stored in the dark, profiling in Lake Kepwari and CC was necessarily undertaken

in the light and it is thought that this may have stimulated some benthic photosynthesis

in the Lake Kepwari and Chicken Creek microcosms.

4.5 Discussion

While the method of formation of Lake Kepwari, Chicken Creek and Mining

Lake 111 are similar, there were differences in experimental results, particularly with

respect to DO, nitrogen, pH and sulfide. By observing the solute fluxes and temporal

dynamics of the porewater profiles, it can be seen that all microcosms followed the

ecological redox sequence with respect to the order of oxidants used to degrade DOC,

with the decrease in DO concentration followed by a decrease in NOx concentration and

then an increase in dissolved iron. This is not unexpected given that Friese et al. (1998)

noted that Mining Lake 111 exhibited redox gradients similar to a natural lake.

86

In Lake Kepwari and Chicken Creek microcosms the greatest porewater profile

changes occurred in the upper 1-2cm of the sediment microcosms, which corresponded

to the distance that DOC diffused before it reacted completely. An indication of the

scale of DOC diffusion can be given through the equations:

etD=δ (2)

2ϕDOCe DD = (3)

where t is the timescale of interest, DDOC is the diffusion coefficient of the DOC,

De is the effective diffusion coefficient defined using Archie’s Law with m = 2

(Boudreau, 1997) and the sediment porosity (�). Using a porosity of 0.7 and a DDOC of

0.67×10-5 cm2s-1 (diffusion coefficient for glucose; Boudreau, 1997) we estimate a

diffusion length scale of 0.64 cm after 1 day. This provides an indication that the

majority of DOC was being respired in the upper few centimetres of the sediment.

Not unexpectedly, within each set of microcosms the higher remineralisation

rates were observed in the high dose microcosms and the lowest in the control

microcosms. DOC consumption in the control and low dose microcosms was limited by

DOC with the average maximum remineralisation rates for these microcosms being

7.6 (± 4.4) mmol m-2 day-1, 8.9 (± 4.9) mmol m-2 day-1 and 5.0 (± 2.4) mmol m-2 day-1

for Lake Kepwari, Chicken Creek and Mining Lake 111 microcosms respectively.

Assuming that the majority of DOC reacted in the upper few cm of sediment and that

diffusion into this area did not appreciably change the DOC concentration in the surface

water and that the reduction in DOC concentration is due to remineralisation alone and

not due to adsorption to sediment surfaces, then the maximum mineralisation rate of

DOC was estimated to be 37.3 mmol m-2 day-1, 63.6 mmol m-2 day-1 and

90.8 mmol m-2 day-1 for the Lake Kepwari, Chicken Creek and Mining Lake 111 high

dose microcosms respectively; these rates occurred when the DOC concentration was

at its maximum. All high dose microcosms had maximum DOC concentrations between

3.5 and 4 mmol L-1, however this similarity in maximum concentrations is not reflected

in the maximum remineralisation rates. This indicates that for the high dose mesocosms,

something other than the DOC concentration was limiting the initial hydrolytic step of

DOC remineralisation; possible limitations are oxidant availability, the number or

species of active bacteria, or the concentration of exoenzymes used to degrade

macromolecules.

While it has been previously noted that the upper 3 cm of Mining Lake 111 is a

highly reactive zone of biogeochemical transformation (Friese et al., 1998; Meier et al.,

87

2004), the nature of the setup of this experiment mixed deeper, less reactive sediment

with more reactive surface sediment. Despite this the Mining Lake 111 sediment was

still highly reactive, as evidenced by the DO data.

4.5.1 Dissolved Oxygen A notable difference between microcosm experimental results was that the

Mining Lake 111 microcosms became anoxic very quickly while Lake Kepwari and

Chicken Creek microcosms did not, and though it is possible this was due to lower

dissolved iron (II) concentrations in Lake Kepwari and Chicken Creek, there is little

evidence to suggest this. If all observed decreases in total dissolved iron concentration

were attributed to oxidation by oxygen and precipitation, then approximately the same

amount of oxygen (within 1 standard deviation) was used by Mining Lake 111 and

Chicken Creek microcosms (ML: 91 ± 53 �mol L-1; CC: 82 ± 36 �mol L-1). Lake

Kepwari microcosms would have used less oxygen in the oxidation of dissolved iron

(22 ± 10 �mol L-1). This calculation is highly simplistic as it is unable to account for

oxidation of iron by oxygen and then subsequent reduction of iron by another reductant,

however it does provide an indication that the rapid anoxia onset in Mining Lake 111

microcosms was not due to iron oxidation alone.

The Mining Lake 111 microcosms showed a stronger correlation between DO

concentration and DOC concentration than Lake Kepwari and Chicken Creek

microcosms, however this could be due to the reduced number of data points owing to

the earlier onset of anoxia (four points for each Mining Lake 111 microcosm; data not

shown). The ongoing presence of DO in the water column made it difficult to calculate

the amount of DOC aerobically respired versus that anaerobically respired in the

sediment of Lake Kepwari and Chicken Creek microcosms however the overall

decrease in DOC in high dose microcosms (2.57 x 103 µmol and 2.77 x 103 µmol

respectively) is approximately 10 times the consumption of DO (239 µmol and

242 µmol respectively). In the high dose Mining Lake 111 microcosms the amount of

DOC respired over the first six days (oxic surface water) was approximately five times

greater than the amount of DO consumed (DOC: 465 µmol, DO:85 µmol). So even with

an oxic water column, only a small amount of DOC was removed through oxic

respiration. DOC concentrations in all three sets of microcosms decreased at the same

rate so the difference in oxic conditions was not due to differences in respiration rates

unless DOC was being adsorbed from the water column onto mineral surfaces.

88

While the temporal dynamics of DO concentrations in the water column were

very different, there was no correlation between predicted porewater Fickian fluxes and

measured fluxes for any of the sites. This can be attributed to the reaction of DO in the

surface water being misinterpreted as a flux or to disequilibrium in the microcosms.

Fluxes predicted using porewater gradients are only applicable to the point in time at

which the profile was taken, this assumes the system is in quasi-equilibrium. If

equilibrium is not maintained between the time surface water DO was measured and the

time of porewater profiling, attempts to match flux estimates will fail. It may be that the

DO in the microcosms was never actually in equilibrium therefore fluxes predicted by

the porewater gradients do not match those observed. Epilimnetic water from Mining

Lake 111 consumed oxygen with a rate of 3 �mol L-1 d-1 at 19°C and this rate was only

slightly stimulated by the addition of various dissolved carbon sources (unpublished

data). Thus, water column respiration can be neglected at least in the Mining Lake 111

incubations. It is possible that bacteria were growing on the walls of the microcosms,

facilitating better access to the water column and the DOC.

4.5.2 Nitrogen The large increase in ammonium concentrations observed in CC microcosms

can only be accounted for through sediment POC degradation. It cannot be accounted

for by dissimilatory nitrate reduction to ammonium (DNRA) as there was not a

corresponding decrease in NOx concentrations. The increases cannot result from the

decomposition of the treacle solution alone as the increase is of the same magnitude in

control, low dose and high dose microcosms. Also, the ratio of DOC:TKN in the treacle

is too high (333:1) for the ammonium to have resulted entirely from the amount of

treacle added. TOC in the water column of Chicken Creek is only 2.7 mg L-1, which

cannot entirely account for the increase in ammonium concentration observed in the

microcosms (assuming a Redfield ratio of C:N = 106:16; Redfield et al., 1963). Thus,

we suggest that respiration of existing POC within the sediment resulted in the

increased ammonium concentration; recall that the sediment had an organic carbon

content of 2.4%.

The increase in ammonium in the water column was used to calculate the POC

remineralisation rates assuming that only POC contributed to ammonium concentrations

according to the Redfield ratio (Redfield et al., 1963). Chicken Creek microcosms had

the highest remineralisation rates (21.5-38.1 mg m-2 day-1), followed by Lake Kepwari

89

microcosms (3.16-6.69 mg m-2 day-1) and Mining Lake 111 microcosms

(0.65-0.88 mg m-2 day-1). In Lake Kepwari and Mining Lake 111 microcosms there is

no clear correlation between POC remineralisation rates and DO fluxes or DOC

consumption. However, POC remineralisation rates were much lower for the high dose

Chicken Creek microcosms than the low and control microcosms suggesting that the

POC in the sediment was not as labile as the added treacle.

The Lake Kepwari and Mining Lake 111 microcosms had a much smaller

increase in water column ammonium attributed to a higher C:N ratio in the organic

matter being remineralised in the microcosms. While Lake Kepwari has a similar

organic carbon content in the sediment (2.0%) to Chicken Creek, it has lower primary

production (unpublished data) hence the sediment probably had less labile organic

matter due to a reduced contribution from phytoplankton and more refractory organic

matter tends to have a higher C:N ratio. Mining Lake 111 is also a well established

mine lake with little primary production, so the organic carbon content of at least the

deeper sediment layers is also likely to be of a more refractory nature (Friese et al.,

1998).

The reduction of NOx is observed in all three sets of microcosms, although there

is not a substantial difference between treated and control microcosms in terms of

reduction rates. While the concentration of nitrogen in mine lakes is usually low

(Kleeberg, 1998; Fyson et al., 2002; Klapper, 2002) it is interesting to note that nitrate

reduction can still occur in acidic water. There may also have been some adsorption of

DOC onto the surface of iron minerals in the sediment which may have contributed to

decrease in DOC concentrations in the water column, however such an adsorption is

likely to reduce the reactivity of these surfaces (Blodau et al., 1998).

4.5.3 Phosphorus It is apparent from the results showing FRP concentration in the water column

of the cores that FRP was not released due to iron reduction in the sediments. If so, it

was immediately utilised by sediment bacteria. If any FRP was released through such a

process in the sediment from Mining Lake 111 then the signal was so small it was lost

in the FRP that was already available in the water column.

90

4.5.4 pH A notable difference between the sites was also in the porewater pH profiles,

with those from the Mining Lake 111 microcosms being constant with depth while Lake

Kepwari and Chicken Creek microcosms showed a sharp gradient of increasing pH at 5-

10mm depth. Measurements in intact sediment microcosms from Mining Lake 111 had

shown the same pattern with pH < 3 down to 30 cm sediment depth (Koschorreck et al.,

2007). The porewater pH profiles observed in the Lake Kepwari and Chicken Creek

microcosms could be due to a couple of reasons: the sediment was originally neutral

and the surface sediment layer was acidified during the experiment, or the sediment was

acidic and the deeper sediment was neutralised by microbial processes.

While these profiles did not actually represent in-situ lake sediment profiles (as

the sediment was mixed prior to the formation of the microcosms), similar pH profiles

have been found in other mine lake sediments (e.g. Blodau et al., 1998; Bachmann et

al., 2001; Herzsprung et al., 2002; Koschorreck et al., 2002; Blodau and Peiffer, 2003a;

Blodau and Peiffer, 2003b; Koschorreck et al., 2007), indicating similar processes at

work.

The porewater pH was similar in both Lake Kepwari and Chicken Creek pore

water, despite the in-situ lake water being at different pH’s (4.8 and 2.8 respectively).

The sediment characteristics and processes in Lake Kepwari and Chicken Creek are

dominated by their geological setting which is very similar; the sediments of both lakes

were dominated by kaolinite, quartz and goethite. However the lake water chemistry is

strongly influenced by filling regime which was quite different; Lake Kepwari was

filled mostly by river water and Chicken Creek was filled by groundwater. These

differences in water column origins may result in differences in lake buffering

capacities.

4.5.5 Iron Initial decreases in total dissolved iron concentrations in the overlying waters of

the microcosms were probably due to the oxidation of any iron (II) in the water column

and/or precipitation of iron (III). Timescales of the iron flux out of the water column

varied between the three sets of microcosms, from three weeks in the CC microcosms to

one week in the LK microcosms to a few days in the ML microcosms. Later fluxes of

iron out of the sediment were assigned to iron reduction in the sediment and in ML

microcosms were also likely due to the lower pH maintaining more iron in solution.

91

Addition of POC to an enclosure in ML111 also led to a high flux of dissolved

iron out of the sediment (Koschorreck et al., 2007). Maximum iron reduction rates for

this experiment were calculated from the flux of iron into the surface water and ranged

over an order of magnitude for each set but showed no correlation with treatment type

(CC: 5.4-30.5 mmol m-2 day-1; LK: 0.2-14.3 mmol m-2 day-1; ML: 4.8-24.2 mmol m-2

day-1). Interestingly the highest flux rates were seen in CC microcosms rather than ML

microcosms which had the greater net change in dissolved iron concentration.

Koschorreck et al. (2003a) used in-situ porewater profiles to conclude that there

was no evidence of iron reduction or oxidation in the top 6cm of littoral sediment of ML

111. This is consistent with our porewater pH profiles from ML, which were relatively

constant once the microcosms become anoxic. However, the change in species

concentration in the overlying water indicates that there was indeed diagenetic activity

in the sediment. In contrast to the ML porewater pH, DO and H2S profiles, evidence of

diagenetic activity in the surface sediments was observed in the porewater profiles of

LK and CC. Changes in the pH, sulfide and DO profiles indicate that the diagenetic

activity was occurring in the upper 2 cm of sediments of LK and CC microcosms. This

activity may have been allowed by or caused the higher pH below the sediment surface.

4.5.6 Sulfide While small amounts of sulfide production were seen in the CC and LK

microcosms, none was observed in the ML microcosms. As the sulfide concentrations

likely resulted from sulfate reduction or reduction of solid iron sulfides, the reasons

behind the lack of sulfide peaks in ML porewater are most probably associated with

high iron concentrations and low pH. In CC and LK microcosms the peak in sulfide

coincides with the sharp increase in pH in the sediment, to levels above pH 5. Similar

peaks of H2S associated with a steep pH gradient, were observed in an enclosure in ML

after addition of organic substrate (Koschorreck et al., 2007). At low pH, H2S does not

precipitate therefore can diffuse into the overlying water where it is re-oxidised, or into

deeper, neutral sediment, where it is precipitated as iron sulfides.

It has been proposed in the past that sulfate reduction will not occur below pH

5.5 due to limitations imposed on the mediating microbes (Koschorreck et al., 2002).

However it has also been noted that sulfate reduction is possible below pH 5.5 when

iron concentration is sufficiently low (Koschorreck et al., 2002; Wendt-Potthoff and

Koschorreck, 2002). Given that the iron concentrations in the LK and CC microcosms

92

are much lower than those in ML microcosms, the data also support the suggestion that

higher iron concentrations delay the onset of sulfate reduction to a certain extent.

There is added difficulty in defining what processes are actually occurring in the

sediment due to the high reactivity of sulfide, much may have already reacted and

precipitated in forms such as iron sulfide or may have been reoxidised. In the enclosure

experiment of Koschorreck et al. (2007) net sulfide reduction, as measured by iron

sulfide accumulation, was only about 10% of the gross rate as measured by 35S core

injection. Thus the low concentrations of free sulfide in LK and CC microcosms do not

necessarily preclude higher sulfide production rates. In ML several attempts to quantify

sulfate reduction by 35S-tracer techniques have been unsuccessful. Sulfate reduction was

only observed in the sediment of the local monimolimnion (e.g. Meier et al., 2004;

Koschorreck et al., 2007). Preclusion of DO from the sediment porewater and also the

presence of enough organic carbon to reduce competition between iron and sulfate

reducing bacteria are common factors among these microcosms.

4.6 Conclusion

The comparison of the chemical evolution of sediment from three different mine

lakes leads to a number of key points about sediment diagenesis in acid mine lakes with

a view to remediation. The DO concentration in the water column was not overly

important for mine lake sediment diagenesis. In contrast the presence of organic carbon

was essential to allow the sediment to become anoxic. However the concentration of

DOC was not overly important as long as there was enough to move beyond oxic

respiration and nitrate reduction. The concentration of DOC alone did not control

maximum remineralisation rates, with higher rates observed in ML than CC and LK

microcosms respectively.

Once the pore water was anoxic the interaction between iron and sulfate

determined whether or not alkalinity was generated. Small amounts of H2S production

where observed in CC and LK microcosms, coinciding with a pH gradient. No H2S was

observed in ML microcosms indicating the possibility of iron reducing bacteria out-

competing sulfate reducing bacteria in the ML microcosms.

Diagenesis in all lakes followed the ecological redox sequence previously

observed in neutral freshwater and marine systems. All lakes showed signs of nitrate

reduction occurring as part of this redox sequence, even in the more acidic microcosms.

93

The experiment serves to highlight that whilst these lakes are unique systems

there are functional similarities in sediment diagenesis between the systems and that

process understanding gained from one lake may be applied to another. This

understanding may also be applicable to atmospherically acidified lakes where

concentrations of organic carbon are very low.


This project was supported financially by the Western Australian Centre of Excellence

for Sustainable Mine Lakes and Australian Research Council Linkage Project

LP0454252. Financial support for DJ Read was provided by an Australian Postgraduate

Award and for T Myllymäki by a Leonardo da Vinci grant from the European

Community. Thanks to Gregory Ivey for valuable comments on the manuscript. This

manuscript is School of Environmental Systems Engineering Publication SESE 083.

4.8 References

Anderson, R. F. & Schiff, S. L., 1987, Alkalinity generation and the fate of sulfur in

lake sediments. Canadian Journal of Fisheries and Aquatic Sciences, 44, 188-

193.



Bachmann, T. M., Friese, K. & Zachmann, D. W., 2001, Redox and pH conditions in

the water column and in the sediments of an acidic mining lake. Journal of

Geochemical Exploration, 73, 75-86.

Blodau, C., 2006, A review of lake acidity generation and consumption in acidic coal

mine lakes and their watersheds. Science of the Total Environment,

Blodau, C., Hoffmann, S., Peine, A. & Peiffer, S., 1998, Iron and sulfate reduction in

the sediments of acidic mine lake 116 (Brandenburg, Germany): Rates and

geochemical evaluation. Water, Air and Soil Pollution, 108, 249-270.

Blodau, C. & Peiffer, S., 2003a, Deposition of organic matter and schwertmannite

controls neutralization rates in sediments of acidic mine lakes. in Schulz, H. D.

& Hadeler, A. (Eds.) Geochemical Processes in Soil and Groundwater:

Measurement-Modelling-Upscaling. Wiley, Weinheim.

94

Blodau, C. & Peiffer, S., 2003b, Thermodynamics and organic matter: constraints on



Blodau, C., Peine, A., Hoffmann, S. & Peiffer, S., 2000, Organic matter diagenesis in

acidic mine lakes. Acta Hydrochimica et Hydrobiologica, 28, 123-125.

Boudreau, B. P., 1997, Diagenetic Models and Their Implementation, Modelling

Transport and Reactions in Aquatic Sediments, Springer, Berlin.



316, 153-159.



1617-1624.

Evangelou, V. P., 1998, Environmental Soil and Water Chemistry, Principles and

Applications, John Wiley and Sons, New York.

Findlay, R. H., Trexler, M. B., Guckert, J. B. & White, D. C., 1990, Laboratory study of

disturbance in marine sediments: response of a microbial community. Marine


Friese, K., Wendt-Potthoff, K., Zachmann, D. W., Fauville, A., Mayer, B. & Veizer, J.,

1998, Biogeochemistry of iron and sulfur in sediments of an acidic mining lake

in Lusatia, Germany. Water, Air and Soil Pollution, 108, 231-247.

Frömmichen, R., Kellner, S. & Friese, K., 2003, Sediment Conditioning with Organic

and/or Inorganic Carbon Sources as a First Step in Alkalinity Generation of

Acid Mine Pit Lake Water (pH 2-3). Environmental Science and Technology,

37, 1414-1421.

Frömmichen, R., Koschorreck, M., Wendt-Potthoff, K. & Friese, K., 2001,

Neutralization of acidic mining lakes via in situ stimulation of bacteria. In

Leeson, A., Peyton, B. M., Means, J. L. & Magar, V. S. (Eds.) the Sixth

International In Situ and On-Site Bioremediation Symposium. Battelle Press,

San Diego, California

Frömmichen, R., Wendt-Potthoff, K., Friese, K. & Fischer, R., 2004, Microcosm

studies for neutralization of hypolimnic acid mine pit lake water (pH 2.6).




95



Technology, Germany

Fyson, A., Nixdorf, B. & Kalin, M., 2006, The acidic lignite pit lakes of Germany -

Microcosm experiments on acidity removal through controlled eutrophication.


Fyson, A., Nixdorf, B., Kalin, M. & Steinberg, C. E. W., 1998a, Mesocosm studies to



Fyson, A., Nixdorf, B. & Steinberg, C. E. W., 1998b, Manipulation of the sediment-

water interface of extremely acidic mining lakes with potatoes: Laboratory

studies with intact sediment cores. Water, Air and Soil Pollution, 108, 353-363.

Herzsprung, P., Friese, K., Frömmichen, R., Goettlicher, J., Koschorreck, M.,

Tuempling, W. V. J. & Wendt-Potthoff, K., 2002, Chemical changes in

sediment pore-waters of an acidic mining lake after addition of organic substrate

and lime for stimulating lake remediation. Water, Air and Soil Pollution -

FOCUS, 3, 123-140.

Jeroschewski, P., Steuckart, C. & Kühl, M., 1996, An amperometric microsensor for the

determination of H2S in aquatic environments. Analytical Chemistry, 68, 4351-

4357.

Karakas, G., Brookland, I. & Boehrer, B., 2003, Physical characteristics of acidic

Mining Lake 111. Aquatic Sciences, 65, 297-307.




Klapper, H., Friese, K., Scharf, B., Schimmele, M. & Schultze, M., 1998, Ways of

Controlling Acid by Ecotechnology. in Geller, W., Klapper, H. & Salomons, W.

(Eds.) Acidic Mining Lakes. Springer, Berlin.







96

Koschorreck, M., Bozau, E., Frömmichen, R., Geller, W., Herzsprung, P. & Wendt-

Potthoff, K., 2007, Processes at the sediment water interface after addition of

organic matter and lime to an acid mine pit lake mesocosm. Environmental

Science and Technology, 41, 1608-1614.

Koschorreck, M., Brookland, I. & Matthias, A., 2003a, Biogeochemistry of the

sediment-water interface in the littoral of an acidic mining lake studied with

microsensors and gel-probes. Journal of Experimental Marine Biology and

Ecology, 285, 71-84.




Koschorreck, M., Wendt-Potthoff, K. & Geller, W., 2003b, Microbial Sulfate Reduction

at Low pH in Sediments of an Acidic Lake in Argentina. Environmental Science

and Technology, 37, 1159-1162.

Küsel, K., 2003, Microbial cycling of iron and sulfur in acidic coal mining lake

sediments. Water, Air and Soil Pollution, 3, 67-90.

Küsel, K., Dorsch, T., Acker, G. & Stackebrandt, E., 1999, Microbial reduction of

Fe(III) in acidic sediments: isolation of Acidiphilium cryptum JF-5 capable of

coupling the reduction of Fe(III) to the oxidation of glucose. Applied and

Environmental Microbiology, 65, 3633-3640.

Langezaal, A. M., Ernst, S. R., Haese, R. R., van Bergen, P. F. & van der Zwaan, G. J.,

2003, Disturbance of intertidal sediments: the response of bacteria and

foraminifera. Estuarine, Coastal and Shelf Science, 58, 249-264.

Laskov, C., Amelung, W. & Peiffer, S., 2002, Organic matter preservation in the

sediment of an acidic mining lake. Environmental Science and Technology, 36,

4218-4223.

Meier, J., Babenzien, H.-D. & Wendt-Potthoff, K., 2004, Microbial cycling of iron and

sulfur in sediments of acidic and pH-neutral mining lakes in Lusatia

(Brandenburg, Germany). Biogeochemistry, 67, 135-156.

Millero, F. J., Plese, T. & Fernandez, M., 1988, The dissociation of hydrogen sulfide in

seawater. Limnology and Oceanography, 33, 269-274.

Nixdorf, B. & Kapfer, M., 1998, Stimulation of Phototrophic Pelagic and Benthic

Metabolism Close to Sediments in Acidic Mining Lakes. Water, Air and Soil

Pollution, 108, 317-330.

97



108, 227-229.




Redfield, A. C., Ketchum, B. H. & Richards, F. A., 1963, The influence of organisms on

the composition of seawater, Wiley-Interscience, New York.

Stocum, E. T. & Plante, C. J., 2006, The effect of artificial defaunation on bacterial

assemblages of intertidal sediments. Journal of Experimental Marine Biology

and Ecology, 337, 147-158.

Tittel, J. & Kamjunke, N., 2004, Metabolism of dissolved organic carbon by planktonic

bacteria and mixotrophic algae in lake neutralisation experiments. Freshwater

Biology, 49, 1062-1071.

Vile, M. A. & Wieder, R. K., 1993, Alkalinity generation by Fe(III) reduction versus

sulfate reduction in wetlands constructed for acid mine drainage treatment.








98

99

5 Effect of dissolved organic carbon on dissolved

oxygen, nutrient and iron fluxes across the

sediment-water interface in carbon limited marine

systems

Deborah J. Read1, Carolyn E. Oldham1, Matthew R. Hipsey2 and Gregory N. Ivey1

1 School of Environmental Systems Engineering, University of Western Australia


2 Centre for Water Research, University of Western Australia


5.1 Abstract

The chemical evolution of porewater and surface water in coastal marine

systems are linked through fluxes of chemical species across the sediment-water

interface, particularly when these systems are limited by organic carbon or nutrient

availability. For example, sediment diagenesis can be limited by the supply of labile

organic matter to the sediment due to a lack of surface water production, which can in

turn be limited by the supply of nutrients back to the water column. Chemical fluxes are

often only measured over short time scales, of the order of days, but may be influenced

by hydrodynamic and biological variabilities that occur over longer time scales such as

weeks, months or seasons. Combined with this, it is often only the role of particulate

organic carbon (POC) that is considered when determining these fluxes, however in

systems where organic carbon availability limits sediment respiration the role of

dissolved organic carbon (DOC) becomes more significant.

In this study, experiments were conducted where a form of labile DOC (treacle)

was added to sediment cores taken from a semi-enclosed, organic carbon limited,

coastal embayment. Chemical constituents within the pore and surface waters were

monitored for 3 weeks and at times there appeared to be an uncoupling between the

surface water DO concentration and fluxes of other chemical species across the

100

interface, an indication of excessive consumption in the sediment with the fluxes of

oxidants not able to match consumption in the sediment. To examine the observed

phenomena in more detail, a numerical model of the experimental cores was developed

to simulate the hydrodynamic, geochemical and diagenetic processes. Unlike other

models of early diagenesis, the model included parameterization of labile and refractory

DOC, as well as POC. The model was able to capture the rapid changes observed in the

sediment cores, and has the potential to serve as a valuable tool for quantifying

sediment organic matter decomposition and dissolved chemical fluxes.

5.2 Introduction

It has long been acknowledged that the chemical evolution of both the sediment

porewater and the overlying water column in coastal marine systems are intimately

linked through fluxes across the interface (benthic-pelagic coupling; Rowe et al., 1975;

Berner, 1980; Vidal and Morgu�, 2000; Dale and Prego, 2002). These fluxes are

important during early diagenesis as they supply oxidants for the breakdown of organic

matter (OM) and remove the byproducts of this process. Early diagenesis is key in

governing not only organic carbon breakdown but also the return of bio-available

nutrients and dissolved inorganic carbon (DIC) to the water column (Berner, 1980;

Jørgensen, 1983).

The need to quantify fluxes in a more dynamic manner has prompted interest in

the evolution of porewater chemical profiles, particularly with respect to organic

carbon, oxidizing species and nutrients. The flux due to diffusion is determined by the

shape of profiles near the sediment-water interface according to Fick’s Second Law

(Berner, 1964):

2

2

zC

DtC

∂∂=

∂∂

(1)

where C is the concentration of the chemical of interested (mg L-1), z is depth into the

sediment (m), t is time (s) and D is the diffusion coefficient for the chemical in

sediment (m s-1).

Measurement of chemical fluxes across the sediment-water interface is usually

undertaken using either benthic chambers or sediment porewater profiles (e.g. Güss,

1998; Lavery et al., 2001; Wijsman et al., 2002; Janssen et al., 2005; Belias et al.,

2006). Many of these types of experiments have been conducted over a period of a day

or two (e.g. Baric et al., 2002; Berelson et al., 2003), however fluxes and porewater

101

profiles may vary on a scale of days to weeks, on top of the seasonal variation often

seen in many systems due to biological and hydrodynamic variabilities. To the authors’

knowledge there are only few experiments that have been simultaneously monitored

marine porewater and water column chemistry over a timescale of weeks (Van

Raaphorst et al., 1988; Van Raaphorst et al., 1990; Van Raaphorst et al., 1992). Over

this time scale bacteria are able to adapt to changes in chemical conditions in the water

column and in the sediment porewater which may have ramifications for diagenetic

processes (Arnosti, 2004), and therefore chemical fluxes.

It is known that bacteria play an important role in mediating diagenesis

(Oppenheimer, 1960) and for bacteria to utilize the organic carbon it must first be

processed into low molecular weight (LMW) dissolved molecules that may be absorbed

across their membranes (Weiss et al., 1991). This involves any particulate or high

molecular weight (HMW) dissolved organic carbon compounds undergoing

extracellular enzymatic hydrolysis through a stepwise degradation (Arnosti, 2004). So

all organic carbon, be it particulate or HMW dissolved, must degrade through the LMW

DOC state, unless it is being preserved (Alperin et al., 1994; Kristensen et al., 1995;

Hee et al., 2001). The rate of hydrolysis of large molecular weight compounds to small

molecular weight compounds may vary depending on the type of substrate (Arnosti et

al., 1994; Brüchert and Arnosti, 2003; Arnosti, 2004) and these studies highlight that

the assumption of a single rate-limiting step within the breakdown sequence is

questionable. It may therefore be appropriate to quantify the roles of both DOC and

POC, as well as their labile and refractory components, in regulating interfacial fluxes

(Alperin et al., 1999).

The concentrations of DOC and nutrients in marine systems are usually low

compared to lakes, in both the surface water and the sediment. The lower organic

carbon concentration is largely due to the reduced input from detritus, particularly low

in pelagic regions (Emerson et al., 1985). Consequently, surficial marine sediments may

be found to be oxic despite relatively low oxygen concentrations in the deep waters, due

to the low consumption of oxygen within the sediment. Oxic sediments also have low

fluxes of ammonia, phosphate, iron, manganese and hydrogen sulfide. As a result it may

only require a small change in these fluxes for there to be a substantial shift in the

chemical dynamics of the system. Given that microbes can respond very quickly to

changes in substrates (Arnosti, 2004) there is potential for large changes in chemical

fluxes to occur over short timescales.

102

Increasingly, diagenetic models are being used to help understand nutrient

cycling in near shore environments and quantify interfacial fluxes (Boudreau, 1996;

Hensen et al., 1997; Haeckel et al., 2001; Epping et al., 2002; Wijsman et al., 2002).

These models typically do not explicitly model DOC dynamics and given that in marine

water columns most organic carbon is in the dissolved form (Emerson and Hedges,

1988) and it is a key middle step in the degradation of POC in the sediment, this could

have ramifications for the accuracy of model output.

A stand-alone sediment diagenetic model does not incorporate the water column

feedback mechanisms that may be crucial in these systems. The current practice of

describing diagenesis in isolation from the water column may result in inaccurate

predictions of the decay of organic matter or of fluxes of nutrients and metals across the

interface, as is discussed for numerical models in Soetaert (2000).

In order to examine the feedback between chemistry in the water column and

sediment diagenesis, this study focused on a series of sediment cores which had

different amounts of DOC added to the water column. The use of sediment cores, as

opposed to a field experiment, allowed for a defined control volume and also for well

defined hydrodynamics given that there was no advection and transport was primarily

by diffusion. The surface and pore waters were monitored over three weeks to elucidate

the chemical responses during this time.

To complement the experimental data, a new model of the water column and

sediment processes was developed and included explicit incorporation of the OM

degradation pathway. It included specification of labile and refractory DOC and POC,

and was capable of simulating the interactions between the water column and sediment

porewater over a wide range of time scales (hours to years). The model was validated

against the column data and used to highlight the interactions and feedback between

diagenetic processes and fluxes across the interface, as well as the extremely responsive

nature of the system.

5.3 Methodology

5.3.1 Study Site The chosen site was a near shore marine system, Cockburn Sound, which has

been under anthropogenic pressure for several decades. Cockburn Sound is a semi

enclosed coastal embayment 30 km south of Perth, Western Australia with a maximum

depth of 20m, a width of 7km and a length of 20km. Sediment is primarily coarse

103

grained carbonate sand with reportedly 8% organic content (dry weight), the

hypolimnion DOC concentration is typically 1.1-1.3 mg L-1 and DO concentration

ranges between 4.5 – 7.0 mg L-1 (Department of Environmental Protection, 1996).

There have been recordings of algal blooms as early as 1973 and anecdotal reports of

fish and crab kills in the deep basin (Department of Conservation and Environment,

1979; Department of Environmental Protection, 1996).

5.3.2 Experiment Setup Two field trips were conducted to two sites in the deep basin of Cockburn

Sound: site B (32° 11.000’ S, 115° 42.600’ E; depth 20.2m) and, site C (32° 14.980’ S,

115° 43.670’ E; 19.7), with 6 cores being selected for incubation. Cores had an internal

diameter of 9cm, a height of 20cm and were approximately half filled with sediment.

Cores were stored in the dark on ice for transport back to the laboratory and where they

were then allowed to equilibrate in a dark constant temperature room (17 ± 1°C) for 1

day with the lids off the cores. Cores remained in this room for the duration of the

experiment. Twenty five L of site bottom water was also collected and transported back

to the laboratory for top-up use during the experiment.

Initial profiles of dissolved oxygen (DO) concentration, oxidation reduction

potential (ORP), H2S, and pH were taken (day 0 and day 1), and then profiling was

conducted weekly for the incubation period of 3 weeks. Sediment profiles were between

3 and 4cm in depth with measurements made every 1mm. Microsensors were calibrated

according to manufacturer instructions prior to the measurement of profiles. The surface

water was sampled for nitrate/nitrite (NOx), ammonium, filterable reactive phosphorus

(FRP), filterable organic carbon, total filterable iron and total filterable manganese.

Samples were taken daily in the first week and then simultaneously with porewater

profiling in the following weeks. Water removed in the sampling procedure was

replaced with the same volume of site bottom water, and DO was measured before and

after sampling to allow quantification of DO introduced through refilling.

After the initial profiling and sampling, cores were divided into groups with 3

cores becoming control cores (C1-C3), with the other three cores (L1, M1 and H1)

receiving a low (1 mL), medium (20 mL) and high (50 mL) dose, respectively, of a

dissolved treacle stock solution. The treacle solution was made by dissolving 10g of

CSR brand treacle into 250 mL of site water, followed by a 1:10 dilution.

On completion of the experiment the top 5 cm of the core were sliced at 1 cm

intervals, centrifuged at 4000 rpm for 15 min and the porewater analysed for DOC. The

104

sediment was dried at 40°C until there was no more change in sample weight. Samples

of the dried sediment were then analysed for total organic carbon using automated

combustion.

The surface water was sampled for total phosphorous, total nitrogen, total

organic carbon, sulfate and total sulfur, as well as those species sampled during the

experiment. The site water, treacle stock solution and a blank comprising of Milli-Q

deionised water were also sampled in triplicate for these chemical species. No duplicate

samples were taken owing to the relatively small volumes used in these experiments

when compared to sample size.

5.3.3 Chemical Analysis Water samples for analyses of dissolved nutrient species were filtered through a

0.45 �m cellulose acetate filter into nutrient tubes, and then frozen until analyses for

ammonium, FRP and NOx by ion chromatography (Lachat Automated Flow Injection

Analyser). Water samples for total iron and total manganese were filtered through a

0.45 �m cellulose acetate filter into nutrient tubes, acidified using p.a. grade

concentrated nitric acid to pH<2 and stored in fridge until they were analysed by ICP-

AES (Varian Vista AX). Filterable organic carbon samples were filtered through 0.45

�m cellulose acetate filter into amber glass bottles after first rinsing the filter with 60mL

deionised water. Samples were then acidified with one drop of concentrated sulfuric

acid and kept refrigerated until analysis using automated combustion, NDIR (Shimadzu

TOC 5000A). Samples for total dissolved N and P were filtered through 0.45 �m

cellulose acetate filters into HDPE bottles for analysis by ion chromatography following

autoclave digestion.

In the laboratory analysis, duplicate measurements were made of approximately

10% of the DOC samples and results of the second measurement were always within

10% of the first. Duplicate samples were also submitted of blanks (Milli-Q deionised

water) and site water with blanks registering below the detection limit and site water

results being within 1mg/L of each other. For all other water samples, duplicate

measurements were made of 10% of the samples and 5% of all samples analysed by ion

chromatography or ICP-AES were checked with a matrix matched internal standard.

A TPS Aqua-D DO meter with a TPS ED1 sensor was used to measure DO in

the surface water. Sediment porewater profiles were conducted using Unisense

microsensors, reference electrode, 2 channel picoammeter, pH meter and

105

micromanipulator. Oxygen (Clark type), H2S, pH and ORP microsensors; all had a tip

diameter of 50�m. pH and ORP microsensors were used in conjunction with an Ag-

AgCl reference electrode of 5000 �m tip diameter.

5.3.4 Calculations Porosity was estimated as the ratio of the volume of water evaporated to the

volume of wet sediment sample. The average porosity of the sediment was determined

to be 0.52.

ORP was corrected for Ag-AgCl reference electrode, pH and temperature

(Meier, 2001). The surface water DO concentrations were corrected for additional DO

contained in the replenishing water added to the sediment cores after sampling.

Total dissolved sulfide [S-tot] was calculated using the measured H2S

concentration [H2S] and the pH according to the equation (Jeroschewski et al., 1996)

[ ] [ ] [ ]��

��

�+= +

−

OHK

SSH tot3

12 1/ for pH < 9 (2)

where 1101pKK −= , pK1 was determined from(Millero et al., 1988)

SSTTpK 0135.0157.0ln04555.15/4.576508.98 5.01 +−++−= (3)

where S is salinity (psu) and T is temperature (°K).

5.3.5 Model Description and Implementation To discern the prominent organic matter breakdown pathways in the system, a

coupled hydrodynamic-biogeochemical model able to resolve the early diagenetic

processes was developed. The model was based on two models that have been widely

used and published. The first is DYRESM-CAEDYM, a hydrodynamic, geochemical

and biological model, typically used to model the water column of lakes and reservoirs

(Romero et al., 2004). A new module describing the early diagenetic processes within

sediment was added to CAEDYM, based on CANDI, a diagenetic model detailing the

breakdown of organic matter within sediment (Boudreau, 1996). CANDI has been

applied to numerous marine sediments (e.g. Haeckel et al., 2001; König et al., 2001;

Luff and Moll, 2004). Fluxes across the interface couples the sediment and water

column components and allows feedback between the systems. The newly developed

model system is hereafter referred to simply as CAEDYM, since the DYRESM

hydrodynamic model was used only as static column of water. The use of sediment

106

cores to test the new diagenetic component of CAEDYM allowed focus on specific

interfacial processes and fluxes; this would not have been possible had the model been

applied to a whole water body.

CAEDYM solves two types of processes: slow kinetically controlled reactions

and equilibrium reactions that are solved to determine pH, aqueous speciation and

solubility equilibrium control. The implemented early diagenesis code differed from

CANDI in that it included labile and refractory DOC components (DOCL and DOCR

respectively). The OM breakdown pathway described by CAEDYM is conceptually

summarised in Figure 5.1. Reactions in the kinetic component of CAEDYM included

the hydrolysis of the complex OM pools (POCVR POCR, DOCR, POCL) and

transformation of LMW DOCL by oxidants (O2, MnO2, Fe(III) and SO42- - the so-called

terminal metabolism), and the release of resulting nutrients (NO3-, NH4

+, PO42-) and

reduced by-products (Mn2+, Fe2+, NH4+, H2S, CH4). Oxidants, nutrients and by-products

were all capable of interacting. For a complete list of the diagenetic and secondary

oxidation reactions included, refer to Boudreau (1996); they were implemented

identically as in CANDI, but the generic OM term was replaced with DOCL in the

breakdown equations, and the POCVR, POCR, POCL and DOCR breakdown steps were

included maintaining the existing reactions rate constants for all cases, except

nitrification where the rate 0.05 day-1 was maintained from CAEDYM. The rate

constants for the degradation of organic carbon are presented in Table 5.1.

Table 5.1 Reaction rate constants used in CAEDYM for the conversion of organic carbon.

Reaction Rate Constant (yr-1) DOCL consumption 1000 Conversion of POCL to DOCL 1.100 Conversion of DOCR to DOCL 0.200 Conversion of POCR to DOCR 0.100

Aqueous speciation and solubility equilibrium control was accounted for by

including Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), Mn(II), Mn(IV), SiO2, Cl-, DIC, SO42-,

PO42-, NO3

-, NH4+, CH4 and H2S, as simulated components and solving the associated

mass-action expressions according to the numerical method of Barrodale and Roberts

(1980) as discussed in Parkhurst and Appelo (1999) and in the CAEDYM

documentation (Hipsey et al., 2007). Mineral phases were limited to those which were

significant in the sediment and which were expected to interact with the diagenetic

processes: calcite, iron hydroxide and iron sulfide. The mass-action constants used for

107

speciation were from the WATEQ4F database (Nordstrom et al., 1990). Dissolved

phase geochemical variables were subject to diffusion as presented in Boudreau (1996).

As we wished to focus on the diagenetic component of CAEDYM we did not

include a biological component so there was no bioturbation, bioirrigation,

phytoplankton or any higher order biology. Any animals capable of mixing the

sediment were removed prior to the experiment commencing and those that were

capable of irrigating it would have become apparent during the experiment when the

dissolved oxygen concentration decreased in the water column. As none appeared we

have assumed that all irrigating and mixing animals were successfully removed.

Temperature was set at a constant 17°C, as in the experiment, and porosity was a

constant 0.52 over the entire depth of the cores. Transport in the water column and core

was achieved by diffusion and the water column was mixed on sampling days by

forcing water column turnover. The time step for all calculations was 3 hours.

Figure 5.1 Chemical species and transformations depicted in the diagenetic component of CAEDYM.

108

In the sediment, the grid thickness increased exponentially from the surface into

the sediment. Upper and lower boundary concentrations (Tables 5.2 and 5.3) were

prescribed for all modeled species with the upper boundary condition applied to the

water column (WC) and the first layer of sediment and the lower boundary condition

applied to the remaining sediment layers (S). Measured concentrations in the surface

water of the cores were used to set the initial concentrations for DO, PO42-, NH4

+, NOx,

Fe(II) and Mn(II). Initial concentrations of dissolved inorganic carbon (DIC) and SO42-

were set to measured concentrations in the site water and concentrations of Na+, Cl-,

Ca2+, K+ and SiO2 were set to typical marine values (Stumm and Morgan, 1996).

Table 5.2 Initial simulation concentrations (mg L-1) used for cores C2, M1 and H1 obtained from experimental data.

Variable Boundary* C2 M1 H1

DOCL WC I

0.25 0.25

52.4 0.25

117 0.25

DOCR WC I

0.25 0.25

0.25 0.25

0.25 0.25

POCL WC I

0 0.0023

0 0.0023

0 0.00046

POCR WC I

0 4.31 x 10-4

0 4.31 x 10-4

0 0

DONL WC I

0.0257 0.0257

0.0103 0.0103

1.80 0.026

DONR WC I

0 0

0 0

0 0

PONL WC I

0 0.000237

0 0.000127

0 0.000047

PONR WC I

0 0

0 0

0 0

DOPL WC I

0.00250 0.00250

0.0200 0.0200

0.585 0.0025

DOPR WC I

0 0

0 0

0 0

POPL WC I

0 0.000023

0 0.000023

0 0.0000046

POPR WC I

0 0

0 0

0 0

DO WC S

4.4 0.1

3.0 0

3.1 0

PO42- WC

S 0.015 0.015

0.0165 0.0165

0.042 0.042

NH4+ WC

S 0.087 0.087

0.06 0.06

0.0147 0.0147

NOx WC S

0.018 0.018

0.019 0.019

0.015 0.015

Fe(II) WC S

0.003 0.003

0.016 0.016

0.09 0.09

Mn(II) WC S

0.0039 0.0039

0.0045 0.0045

0.016 0.016

* WC = water column, I = sediment-water interface, S = sediment porewater

109

Table 5.3 Component concentrations (mg L-1) and pH values used in simulation of all cores.

Variable All cores

Na+ 10768 Cl- 19353

Ca2+ 412.3 K+ 399.1

SO42- 2650

DIC 27 Fe(III) 0

Mn(IV)* 0 (WC), 0.6 (S) SiO2 1.4 pH 7.6

* WC = water column, S = sediment porewater

Initial organic matter profiles for the simulated variables (POCL, POCR, DOCL

and DOCR) were configured to exponentially decrease from the sediment surface

according to:

( ) ( ) [ ]zkOMzOM xxx −= exp0 (4)

where x is a generic OM group identifier, z is depth below the sediment-water interface

(cm) and k is a user defined constant describing the shape of the OM profile. Since little

information on the natural OM profile was known, kx was set to 0.5, for each of the OM

species. The C:N:P ratio of the sediment DOCL and POCL groups varied depending on

the core, but was between 260-320:11-23:1. It was assumed that refractory organic

matter contained negligible nitrogen or phosphorous. It was also assumed there was no

particulate organic matter in the water column able to recharge the sediment store.

110

5.4 Results

5.4.1 Experimental Results In general, the DO concentration in the water column decreased with time across

all cores, as did DOC concentrations (Figure 5.2). A more rapid decrease in water

column DO concentration was observed in treated cores. Control cores showed an

initial increase in NOx concentrations followed by decreasing concentrations after

2-4 days, while all treated cores showed a decrease over the first couple of days. FRP

concentration increased after 2-5 days, with treated cores experiencing greater

increases. Whilst the increase in Fe concentration was similar for control and treated

cores, the timing of the release from the sediments was different, with control cores

lagging treated cores by 5-7 days. Concentrations of dissolved Fe then decreased again

1-2 weeks after release from the sediment. The concentrations of dissolved manganese

in the water column were the same for control and treated cores (Figure 5.2).

With the exception of M1, all cores started with the upper 2-5 mm of the

sediment being oxic, with a maximum DO concentration at the interface of 5 mg/L

(data not shown). After 1 week all cores, except one of the controls (C1), were anoxic

and remained anoxic despite water column DO concentrations reaching up to 2 mg/L in

some cores (due to refilling).

Porewater pH varied between 7.2 and 8.3, with the general trend being

increasing pH with depth (Figure 5.3). Profiles of H2S concentration show that there

was a small amount of H2S and sulfide formation in the porewater (Figure 5.4). There

were only profiles for C1 and L1 due to probe malfunction, however, it was noted that

over the course of the experiment black layers formed in the surface of treated cores,

likely to be sulfide precipitation.

In contrast to previously reported values of up to 8% organic carbon content

(Department of Environmental Protection, 1996) in the sediment, analysis showed an

average of only 2.4% by weight organic carbon.

111

Figure 5.2 Concentration of DO, DOC, NH4+, FRP, NOx, Fe and Mn in the overlying water column for all cores throughout the experiment. Dotted lines denote treated cores and solid lines refer to control cores.

112

Figure 5.3 Porewater pH over the course of the experiment.

113

Figure 5.4 H2S (A and B) and Sulfide (C and D) concentrations (�mol L-1) in the porewater of cores C1 and L1. The chemical form of sulfide indicated in figures C and D was the total concentration of S2- , HS- and H2S.

5.4.2 Model Results CAEDYM was reasonably able to predict changes in water column and

sediment porewater species concentrations and hence the change in fluxes for all

Cockburn Sound sediment cores (Figures 5.5, 5.6 and 5.7). Unlike CANDI and other

diagenesis models, CAEDYM incorporated labile and refractory DOC components as

well as feedback from the water column to sediment diagenesis and fluxes across the

interface. For example the consumption of DO and nitrate via sediment diagenesis

resulted in a flux of DO and nitrate from the water column into the sediment (Figures

5.5, 5.6 and 5.7). The impact of remineralization of organic matter in the sediment was

also evident by the increase in ammonium and FRP concentrations in the water column.

114

Figure 5.5 CS-C2 simulated (solid line) and experimental (circles) data for DO, FRP, NOx, NH4+,

DOC and Fe with R2 values.

115

Figure 5.6 CS-M1 simulated (solid line) and experimental (circles) data for DO, FRP, NOx, NH4+,


116

Figure 5.7 CS-H1 simulated (solid line) and experimental (circles) data for DO, FRP, NOx, NH4+,


This was also reflected in the simulations of sediment porewater concentrations

(an example of which is shown in Figure 5.8); distinct chemical zones were predicted

within the sediment due to different diagenetic processes occurring at different sediment

depths. Porewater concentrations of DO were largely comparable with those simulated

by the model, given that the porewater became anoxic early in the experiment.

Simulated H2S concentrations were slightly larger than those observed, however this

can be accounted for by the extremely reactive nature of H2S; it may have reacted

before measurements were made. Despite this difference in concentration, the shape of

the simulated profile matched that observed in the sediment.

117

Figure 5.8 Simulated data for sediment porewater DO, DOCL, iron(II), NO3, H2S and NH4 in core M1.

The high concentration of NO3 at depth as depicted in Figure 5.8 is an artefact

of the boundary conditions used in the model. A constant concentration was specified

for nitrate throughout the soil profile. As a result nitrate was only reduced if it was near

organic matter which was not present at depth in the simulated core. As a result nitrate

remained in this area throughout the simulation.

5.5 Discussion

The experimental results were complex and while there was considerable scatter

between the control replicates, some key points can be extracted. The DO concentration

in the overlying water was highly variable in time and between cores and, after the first

few days, was not always simply dependent upon the DOC treatment. Despite this, the

porewaters of all the cores were anoxic within one week. Thus there was an apparent

uncoupling between water column DO concentrations and fluxes of solutes from the

118

sediments. As fluxes are driven by solute concentration gradients in the pore water and

hence the redox conditions in the sediment, this likely indicated an excess DOC

consumption in the sediment, with the fluxes of oxidants (DO and NOx) from the water

column unable to match the reaction rate in the sediment. Fluxes of dissolved iron and

NOx appear to be a function of DOC addition, however in the case of NOx it appeared

not to be dependent on the dose of DOC.

Our results showed that the release of FRP and ammonium into the overlying

water column was coupled to the release of iron. Phosphorous can be bound to iron in

the solid phase as ferric phosphate, and FRP can be released when this iron is reduced

and becomes aqueous (Krom and Berner, 1981; Sundby et al., 1992; Rozan et al.,

2002). It is also known that organic matter can be sorbed to various minerals and

consequently released when the mineral structure is disturbed through processes such as

iron reduction (Lorenz and Wackernagel, 1987; Mayer, 1994a; Mayer, 1994b; Mayer,

1999; Arnarson and Keil, 2001; Satterberg et al., 2003). This experiment highlights that

it doesn’t necessarily require a large dose of carbon for this to occur. In fact, two of the

three significant FRP fluxes from the sediment occurred in untreated cores. While there

is very little iron in Cockburn Sound sediments (~3.3g/kg dry sediment) it’s reduction

can have a significant effect on the release of bound organic matter and subsequent FRP

and ammonium fluxes, as well as also affecting other dissolved species such as DO and

sulfide.

There was an experimentally observed increase in FRP flux after 5 days and a

corresponding increase in ammonium flux (Figures 5.5 and 5.6). Through the

consideration of processes and inherent assumptions included in CAEDYM, the role of

organic matter remineralization and iron on the experimentally observed FRP release

was clarified. The simultaneous release of FRP and ammonium at a mass ratio of

between 1:10 and 1:30, similar to the Redfield ratio, suggested that the source was

organic. The delay in the appearance of FRP and ammonium in the water column

indicated that this type of organic matter was not at the surface but rather was some

distance below the surface, hence remineralization products would take time to diffuse

out. Alternatively the organic matter could have been initially protected from

remineralization but once remineralization began it progressed rapidly. This would

occur if organic matter was bound to minerals in the sediment (Mayer, 1994a; Mayer,

1994b; Hedges and Keil, 1995; Mayer, 1999; Arnarson and Keil, 2001; Satterberg et al.,

2003), a process that is not explicitly included in CAEDYM. This latter hypothesis was

supported by experimental results showing the simultaneous release of iron, in C1 and

119

C2, suggesting that the organic matter was released and remineralised once the iron

mineral was dissolved, most likely through reduction from Fe(III) to Fe(II).

Towards the end of the experiment dissolved iron concentrations decreased and

this was most probably due to precipitation with free sulfide fluxing out of the

sediment. This hypothesis was supported by the observed blackening of the upper cm of

sediment over the second and third week of the experiment.

CAEDYM was used to explore the causes of the H2S peak observed near the

sediment surface. A combination of water column DOC diffusing into the sediment and

user defined maximum concentrations of POC near the interface caused a sulfate

reduction zone to appear close to the sediment surface; the majority of the DOC was

remineralising in this region rather than diffusing further into the sediment. A

sensitivity analysis showed that the simulation of a H2S peak near the sediment surface

was controlled by the exponential POCL and POCR profiles, the specific inclusion of

DOC in the model parameterization and the POC-DOC transformations. The modeled

H2S peak was of higher magnitude than the measured H2S peak, most likely due to the

inherent heterogeneity of the sediment cores and relatively poor knowledge of sediment

composition, with unknown quantities of iron sulfides in the sediment.

The initial concentrations of DOC and POC used in the simulations left a

portion of organic matter observed in the sediment and water column unaccounted for

(e.g. in core M1 a total of 0.5 mg/L is assigned for the porewater DOC concentration,

however a value of 1.1-1.3 was typically observed in the surface water and the

concentration was almost certainly higher for the sediment porewater). This fraction

was indicative of the amount of organic matter unavailable for degradation due to either

being bound to sediment minerals and/or being very refractory organic matter that

degrades slowly over longer time scales.

Despite this, the model captured the general chemical dynamics and

discrepancies between simulated and laboratory data can be explained. The dilution

associated with sampling was not incorporated in the model which may account for

some differences, such as oxidation of iron(II) to iron(III), caused by the reintroduction

of oxygen to the surface water of the cores.

What is also apparent from both the experimental results and model simulations

is that even over the course of three weeks the system was extremely dynamic. It should

of course be noted that the size of the sediment cores relative to the overlying water

column would accentuate sediment-water feedback mechanisms.

120

It appears that dynamics of this system operated on a sub-seasonal timescale and

may be heavily influenced by episodic events such as algal blooms or stratification

onset. If changes occurred over a shorter timescale, this also means that the cycling of

nutrients can be much quicker and although the system is classified as oligotrophic it

still has the potential to be extremely productive for some periods due to a potentially

high turnover rate of nutrients. The high activity also means that opportunities presented

by the sudden influx of organic matter, particularly labile organic matter, can be seized

upon quickly. The rapid incorporation and degradation of organic matter will also be

apparent in the sediment porewater and as a result, the water column.

The sediment diagenesis component of CAEDYM was developed with a view to

further development and application to an entire marine or lake water column, through

coupling to a hydrodynamic model such as DYRESM and use of the biological unit in

CAEDYM, making it ideally suited to investigate such intensive nutrient cycling. While

Luff and Moll (2004) have already linked CANDI with water column model to look at

seasonal dynamics of the North Sea, only seasonal variations were analysed and the

model also lacked a biological component, ignoring a possibly key feedback mechanism

to the sediment. Surprisingly there have been few other attempts to link a diagenetic

model with a hydrodynamic model and none that also include a chemical/biological

component in the water column. Qualitative assessment of the feedbacks between

sediment and water column; and biological, chemical and physical processes can only

be achieved through such a model, providing a holistic understanding of a system.

5.6 Conclusion

From the comparison of water column DO and DOC results, it is likely that

respiration and diagenesis in Cockburn Sound sediment was limited by the availability

of DOC. Once a source of labile carbon was available to the sediment, remineralization

occurred extremely quickly, effectively forcing a decoupling of sediment and water

column chemical processes, and the effects on fluxes across the interface also changed

rapidly as indicated by core surface water concentrations. Through simulations using

CAEDYM, we were able to discern that the decay of organic matter within the

sediment, rather than the iron cycling through redox processes, played an important role

in the timing of the release of FRP to the surface water. It also became apparent that the

sediment processes were able to rapidly adjust to the input of labile organic matter

121

resulting in large changes in solute fluxes between the sediment porewater and the

water column.

In Cockburn Sound, as with many other oligotrophic or low organic matter

marine systems, sediments can be a dominant source or sink of key chemical species,

such as DO or nutrients, to the water column. Understanding of these fluxes becomes

critical for management of such water bodies especially when under threat from

changing environmental conditions as a result of anthropogenic activities.

The incorporation of sediment processes into a water quality model allowed the

interaction of many different processes and this tool was used to aid in the

determination of dominant processes under varying DOC conditions. The incorporation

of sediment diagenetic and geochemical processes into water quality predictive models

of marine cores has, to our knowledge, never been done before. The planned inclusion

of sediment diagenesis in a water quality model that already includes hydrodynamics

and water column chemistry and biology would allow for a whole system perspective

rather than merely modelling individual processes and would also allow for

incorporation of feedback mechanisms that might otherwise be missed.


This project was supported financially by the Water Corporation, the Western

Australian Centre of Excellence for Sustainable Mine Lakes and Australian Research

Council Linkage Project LP0454252. Financial support for D.J. Read was provided by

an Australian Postgraduate Award. This manuscript is School of Environmental

Systems Engineering Publication SESE-049.

5.8 References

Alperin, M. J., Albert, D. B. & Martens, C. S., 1994, Seasonal variations in production

and consumption rates of dissolved organic carbon in an organic-rich coastal


Alperin, M. J., Martens, C. S., Albert, D. B., Suayah, I. B., Benninger, L. K., Blair, N.

E. & Jahnke, R. A., 1999, Benthic fluxes and porewater concentration profiles of

dissolved organic carbon in sediments from the North Carolina continental

slope. Geochimica et Cosmochimica Acta, 63, 427-448.

122

Arnarson, T. S. & Keil, R. G., 2001, Organic-mineral interactions in marine sediments

studied using density fractionation and X-ray photoelectron spectroscopy.

Organic Geochemistry, 32, 1401-1415.



Arnosti, C., Repeta, D. J. & Blough, N. V., 1994, Rapid bacterial degradation of

polysaccharides in anoxic marine systems. Geochimica et Cosmochimica Acta,

58, 2639-2652.

Baric, A., Kuspilic, G. & Matijevic, S., 2002, Nutrient (N, P, Si) fluxes between marine

sediments and water column in coastal and open Adriatic. Hydrobiologia,

475/476, 151-159.

Barrodale, I. & Roberts, F. D. K., 1980, L1 solution to linear equations subject to linear

equality and inequality constraints. ACM Transactions on Mathematical

Software, 6, 231-235.

Belias, C., Dassenakis, M. & Scoullos, M., 2006, Study of the N, P and Si fluxes

between fish farm sediment and seawater. Results of simulation experiments

employing a benthic chamber under various redox conditions. Marine

Chemistry, 103, 266-275.

Berelson, W., McManus, J., Coale, K., Johnson, K., Burdige, D., Kilgore, T., Colodner,

D., Chavez, F., Kudela, R. & Boucher, J., 2003, A time series of benthic flux

measurements from Monterey Bay, CA. Continental Shelf Research, 23, 457-

481.

Berner, R. A., 1964, An idealized model of dissolved sulfate distribution in recent


Berner, R. A., 1980, A rate model for organic matter decomposition during bacterial

sulfate reduction in marine sediments. Colloques Internationaux du C.N.R.S. -

Biogeochemimie de la Matiere Organique a l'Interface Eau-Sediment Marin,

293, 35-44.





Dale, A. W. & Prego, R., 2002, Physico-biogeochemical controls on benthic-pelagic

coupling of nutrient fluxes and recycling in a coastal upwelling system. Marine


123





Emerson, S., Fischer, K., Reimers, C. & Heggie, D., 1985, Organic carbon dynamics

and preservation in deep-sea sediments. Deep-Sea Research, 32, 1-21.

Emerson, S. & Hedges, J. I., 1988, Processes controlling the organic carbon content of

open ocean sediments. Paleoceanography, 3, 621-634.

Epping, E., van der Zee, C., Soetaert, K. & Helder, W., 2002, On the oxidation and

burial of organic carbon in sediments of the Iberian margin and Nazare Canyon

(NE Atlantic). Progress in Oceanography, 52, 399-431.

Güss, S., 1998, Oxygen uptake at the sediment-water interface simultaneously measured

using a flux chamber method and microelectrodes: Must a diffusive boundary

layer exist? Estuarine, Coastal and Shelf Science, 46, 143-156.

Haeckel, M., König, I., Reiech, V., Weber, M. E. & Suess, E., 2001, Pore water profiles

and numerical modelling of biogeochemical processes in Peru Basin deep-sea

sediments. Deep-Sea Research Part II, 48, 3713-3736.

Hedges, J. I. & Keil, R. G., 1995, Sedimentary organic matter preservation: an

assessment and speculative synthesis. Marine Chemistry, 49, 81-115.

Hee, C. A., Pease, T. K., Alperin, M. J. & Martens, C. S., 2001, Dissolved organic

carbon production and consumption in anoxic marine sediments: A pulse-tracer

experiment. Limnology and Oceanography, 46, 1908-1920.

Hensen, C., Landenberger, H., Zabel, M., Gundersen, J. K., Glud, R. N. & Schulz, H.

D., 1997, Simulation of early diagenetic processes in continental slope

sediments off southwest Africa: the computer model CoTAM tested. Marine

Geology, 144, 191-210.

Hipsey, M. R., Romero, J. R., Antenucci, J. P. & Hamilton, D., 2007, The

Computational Aquatic Ecosystem Dynamics Model (CAEDYM): v3.1 Science

Manual, Centre for Water Research, Perth, Australia.

Janssen, F., Huettel, M. & Witte, U., 2005, Pore-water advection and solute fluxes in

permeable marine sediments (II): Benthic respiration at three sandy sites with

different permeabilities (German Bight, North Sea). Limnology and


124

Jeroschewski, P., Steuckart, C. & Kühl, M., 1996, An amperometric microsensor for the

determination of H2S in aquatic environments. Analytical Chemistry, 68, 4351-

4357.

Jørgensen, B. B., 1983, Processes at the sediment-water interface. in Bolin, B. & Cook,

R. B. (Eds.) The Major Biogeochemical Cycles and Their Interactions. John

Wiley & Sons, New York.

König, I., Haeckel, M., Lougear, A., Suess, E. & Trautwein, A. X., 2001, A

geochemical model of the Peru Basin deep-sea floor - and the response of the

system to technical impacts. Deep-Sea Research Part II, 48, 3737-3756.




Krom, M. & Berner, R. A., 1981, The diagenesis of phosphorus in a nearshore marine


Lavery, P. S., Oldham, C. E. & Ghisalberti, M., 2001, The use of Fick's First Law for

predicting porewater nutrient fluxes under diffusive conditions. Hydrological

Processes, 15, 2435-2451.

Lorenz, M. G. & Wackernagel, W., 1987, Adsorption of DNA to sand and variable

degradation rates of adsorbed DNA. Applied and Environmental Microbiology,

53, 2948-2952.



24, 1099-1127.

Mayer, L. M., 1994a, Relationships between mineral surfaces and organic carbon

concentrations in soils and sediments. Chemical Geology, 114, 347-363.

Mayer, L. M., 1994b, Surface area control of organic carbon accumulation in

continental shelf sediments. Geochimica et Cosmochimica Acta, 58, 1271-1284.

Mayer, L. M., 1999, Extent of coverage of mineral surfaces by organic matter in marine


Meier, J., 2001, Untersuchungen zum mikrobiellen Schwefelkreislauf in sauren

Tagebau-Restseen der Niederlausitz (Brandenburg). PhD Thesis. UFZ-

Umwelforschungszentrum Leipzig-Halle GmbH

Millero, F. J., Plese, T. & Fernandez, M., 1988, The dissociation of hydrogen sulfide in

seawater. Limnology and Oceanography, 33, 269-274.

125

Nordstrom, D. K., Plummer, L. N., Langmuir, D., Busenberg, E., May, H. M., Jones, B.

F. & Parkhurst, D. L., 1990, Revised chemical equilibrium data for major water-

mineral reactions and their limitations. in Bassett, R. L. & Melchior, D. (Eds.)

Chemical modeling in aqueous systems II. American Chemical Society

Symposium Series, Washington D.C.

Oppenheimer, C. H., 1960, Bacterial activity in sediments of shallow marine bays.


Parkhurst, D. L. & Appelo, C. A. J., 1999, User's guide to PHREEQC (Version 2) - A

computer program for speciation, batch reaction, one dimensional transport and

inverse geochemical calculations. Water-Resourses Investigations Report 99-

4259. U.S. Geological Survey, Denver, Colorado

Romero, J. R., Antenucci, J. P. & Imberger, J., 2004, One- and three- dimensional

biogeochemical simulations of two differing reservoirs. Ecological Modelling,

174, 143-160.

Rowe, G. T., Clifford, C. H., Smith, K. L. & Hamilton, P. L., 1975, Benthic nutrient

regeneration and its coupling to primary productivity in coastal waters. Nature,

255, 215-217.

Rozan, T. F., Taillefert, M., Trouwborst, R. E., Glazer, B. T., Ma, S., Herszage, J.,

Valdes, L. M., Price, K. S. & Luther, G. W. I., 2002, Iron-sulfur-phosphorus

cycling in the sediments of a shallow coastal bay: Implications for sediment

nutrient release and benthic macroalgal blooms. Limnology and Oceanography,

47, 1346-1354.

Satterberg, J., Arnarson, T. S., Lessard, E. J. & Keil, R. G., 2003, Sorption of organic

matter from four phytoplankton species to montmorillonite, chlorite and

kaolinite in seawater. Marine Chemistry, 81, 11-18.

Soetaert, K., Middelburg, J. J., Herman, P. M. J. & Buis, K., 2000, On the coupling of

benthic and pelagic biogeochemical models. Earth-Science Reviews, 51, 173-

201.



Sundby, B., Gobeil, C., Silverberg, N. & Mucci, A., 1992, The phosphorus cycle in

coastal marine sediments. Limnology and Oceanography, 37, 1129-1145.

Van Raaphorst, W., Kloosterhuis, H. T., Berghuis, E. M., Gieles, A. J. M., Malschaert,

J. F. P. & Van Noort, G. J., 1992, Nitrogen Cycling in two types of sediments of

126

the southern North Sea (Frisian Front, Broad Fourteens): Field data and

mesocosm results. Netherlands Journal of Sea Research, 28, 293-316.

Van Raaphorst, W., Kloosterhuis, H. T., Cramer, A. & Bakker, K. J. M., 1990, Nutrient

early diagenesis in the sandy sediments of the Dogger Bank area, North Sea:

Pore water results. Netherlands Journal of Sea Research, 26, 25-52.

Van Raaphorst, W., Ruardij, P. & Brinkman, A. G., 1988, The assessment of benthic

phosphorus regeneration in an estuarine ecosystem model. Netherlands Journal

of Sea Research, 22, 22-36.

Vidal, M. & Morgu�, J. A., 2000, Close and delayed benthic-pelagic coupling in coastal

ecosystems: the role of physical constraints. Hydrobiologia, 429, 105-113.

Weiss, M. S., Abele, U., Weckesser, J., Welte, W., Schiltz, E. & Schulz, G. E., 1991,

Molecular architecture and electrostatic properties of a bacterial porin. Science,

254, 1627-1630.

Wijsman, J. W. M., Herman, P. M. J., Middelburg, J. J. & Soetaert, K., 2002, A model

for early diagenetic processes in sediments of the continental shelf of the Black

Sea. Estuarine, Coastal and Shelf Science, 54, 403-421.

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6 Predicting the combined impact of dissolved organic carbon loading and geochemical processes on sediment fluxes in an acidic lake

Deborah J. Read1, Carolyn E. Oldham1 and Matthew R. Hipsey2



2Centre for Water Research, University of Western Australia


6.1 Abstract

Acidic mine lakes typically have high concentrations of sulfate and iron and

remediation strategies often centred on the encouragement of bacterial reduction of

these solutes, requiring anoxic sediment conditions. Prediction of water column

remediation due to these processes is difficult due to their interaction with other

physical, geochemical and biological processes. The majority of numerical modelling

has focused on groundwater inflow into the mine lakes and geochemical reactions of the

lake water itself. The role of sediment diagenesis in acidic lakes, particularly in the

transition from an acidic to neutral lake has yet to be fully explored. Low levels of

organic carbon and elevated sulfate levels imply a certain amount of similarity between

diagenesis in mine lakes and marine systems, so this study investigated the application

of many of the kinetic parameterizations used in the modelling of marine diagenesis to a

mine lake environment. We focused on parameterization and numerical modelling of

key sediment fluxes observed in a mine lake microcosm experiment that involved

treatment of cores with a labile source of DOC. The numerical model CAEDYM, a

geochemical, diagenetic and biological model, was used to simulate diagenetic

processes in the sediment cores.

Modelled predictions of dissolved organic carbon, dissolved oxygen, nitrate/nitrite

and dissolved iron largely followed the concentrations observed in the experiment

without an alteration of the kinetic rate constants developed from marine studies. Many

of the discrepancies between simulated and observed results could be explained through

128

experimental artifacts. The inclusion of solubility controls allowed the integration of

equilibrium and kinetically controlled reactions. In this case, the solubility of the

mineral gibbsite was shown to have some effect on porewater pH and fluxes of nitrate

and ammonium, even over the relatively short time frame simulated. From this

investigation it has become apparent that diagenetic processes in acidic sediments can

be simulated using kinetic descriptions traditionally applied to marine sediments. The

result may serve as a valuable tool to aid in determination of important chemical

reactions involved in controlling fluxes across the interface, and thus the prediction of

the water quality of mine lakes and other acidic lakes.

6.2 Introduction

Mine lakes are formed when voids left by mining are no longer dewatered and

consequently fill with water, either from groundwater or surface water. These lakes

typically have high concentrations of sulfate and iron (Kleeberg, 1998; Peiffer, 1998;

Fyson et al., 2002) and low concentrations of organic carbon, DIC and also in nutrients

(Klapper and Schultze, 1995; Kleeberg, 1998; Peiffer, 1998; Fyson et al., 2002;

Klapper, 2002) when compared to natural lakes. Remediation strategies centre on the

encouragement of biological sulfate and iron reduction (Kleeberg, 1998; Fyson et al.,

2002), requiring anoxic sediment conditions and hence, a low DO flux across the

interface. It has been shown that low availability of labile DOC limits respiration in

these systems (Read et al., submitted), and hence the movement of sediment diagenesis

towards sulfate reduction.

Prediction of the onset of sulfate reducing conditions without the use of a

numerical model is difficult due to the combination of processes acting within both the

sediment and the water column: diagenetic, geochemical, biological and physical

processes all impact on solute fluxes across the sediment-water interface. The ability to

predict solute fluxes across the sediment-water interface is crucial in predicting the

transition of an acidic mine lake to a neutral oligotrophic, mesotrophic or even

eutrophic lake. However, there is limited literature relating to the prediction of mine

lake chemistry evolution (Davis et al., 2006).

Literature that is available generally focuses on a simplified mass balance

approach, which aims to quantify: the amount and quality of groundwater and surface

water flowing into the lake in question; the geochemical processes occurring at the

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sediment/rock-water interface; and the chemical evolution of the lake water itself. These

chemical fluxes have thus far been determined using laboratory experiments (e.g.

Werner et al., 2001b; Davis, 2003) or numerically using a series of models dealing with

each component of the mass balance (e.g. Davis et al., 2006; Werner et al., 2006).

However both methods fail to take into account the feedback mechanisms that exist

between sediment and water column chemical processes.

Past methods of predicting mine lake water quality have focused almost solely

on geochemical modelling and ignored in-lake generation of organic matter through

phytoplankton growth and the remineralization of organic matter (e.g. Eary, 1998;

Rolland, 2001; Werner et al., 2001a; Mazur et al., 2002). At this stage, the literature

available is restricted to geochemical models based on an equilibrium approach, which

exclude the kinetic reactions of organic carbon and its associated feedbacks. Recently

there have been attempts to include limnophysical characteristics of the lakes in

simulations (Bozau et al., 2007) however these attempts still lack the explicit

parameterization of organic carbon and its role in regulating redox processes in the

sediment porewater, otherwise known as diagenesis.

It is suggested that there may be similarity between diagenesis in mine lakes and

marine systems, due to low levels of organic carbon and elevated sulfate levels, so this

study investigated the application of many of the kinetic parameterizations used in the

modelling of marine diagenesis (e.g. Boudreau, 1996; Boudreau, 1997) to a mine lake

environment. In particular, we focused on parameterization and numerical modelling of

key sediment fluxes observed in a mine lake microcosm experiment involving treatment

of sediment cores with a labile source of DOC followed by monitoring the responses of

the sediment porewater and water column. This experiment was simulated using a

numerical model incorporating parameterizations of aqueous speciation and solubility

equilibrium controls as well as diagenetic processes, typically applied to marine

systems. Simulation results were compared to our experimental results and allowed the

exploration of controls on diagenetic processes in acidic systems.

6.3 Methodology

6.3.1 Study Site Lake Kepwari is located in the Collie Basin, Western Australia, approximately

160km southeast of Perth. It is a former open cut mine void that has filled with water

from groundwater and diverted river flow. At the time of the experiment on which

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simulations were based, Lake Kepwari had a maximum depth of 65m and a volume of

25GL. It is a monomictic lake, usually experiencing thermal stratification from spring to

autumn (October – April) and is fully mixed from May to September. Although

relatively deep and are stratified for half the year, Lake Kepwari remain oxic for the

entire year with DO concentrations in the hypolimnion of around 6 mg L-1. Depth

averaged DOC concentrations were approximately 1.2 - 1.5 mg C L-1 and it was acidic,

with a pH of approximately 4.8. The primary mineral phases in Lake Kepwari sediment

are kaolinite and quartz with a small amount of goethite.

6.3.2 Experiment Sediment samples from 30 m depth and hypolimnetic water were collected from

Lake Kepwari and were used in the establishment of six microcosms, established in

Perspex cores (9 cm internal diameter, 20 cm height). Microcosms contained sediment

to a depth of 10 cm with the remaining volume filled with hypolimnetic water. The

cores were left open to the atmosphere and the dissolved oxygen (DO) was monitored in

the pore water of two cores for the following four days until the cores reached an

equilibrium.

Once equilibrium was attained initial DO, pH and H2S sediment porewater

profiles were recorded for each core (day 0). For all DO profiles, measurements were

taken every 0.5 mm for the upper 5mm of the sediment and then every 1 mm up until a

depth of 40 mm was reached. Similarly, for all pH and H2S profiles measurements were

recorded every 0.5 mm for the first 5 mm and then every 1 mm until a depth of 30 mm

was reached.

After the initial profiles were taken, a low dose (1.6 mg) of a DOC stock

solution was added to two cores (L1, L2) and a high dose (16.2 mg) was added to

another two cores (L1, L2). The remaining two cores (C1, C2) were kept as controls.

During the experiment, the surface water in each core was filtered through

0.45 �m cellulose acetate filters and analysed for DOC, nitrate/nitrite (NOx),

ammonium, filterable reactive phosphorus (FRP), filterable iron (TFFe) and filterable

manganese (TFMn). The volume of water removed for sampling was replaced by

hypolimnetic water from the same sample that was initially used to make up the cores.

DO was measured in the surface water before and after the sampling procedure to

quantify the amount of oxygen introduced to the water when replacing the removed

volume.

131

Surface water sampling was conducted initially on a daily basis, reducing to

weekly one week into the experiment. DO and pH profiles were taken approximately

weekly. Data presented in this manuscript is from the first 38 days of the experiment.

At the conclusion of the experiment, the surface water in all cores was sampled

for total nitrogen, total phosphorous, filterable organic carbon, total organic carbon,

sulfate and total sulfur as well as those nutrients and metals sampled for during the

experiment. The hypolimnetic water taken from the lake and the DOC stock solution

were also sampled in triplicate for all these chemical species.

6.3.3 Modelling The numerical model CAEDYM was applied to the sediment cores from this

experiment to simulate diagenesis in acidic sediments allowing exploration of the

processes involved in producing the experimental results and to confirm the conclusions

drawn regarding key processes. The conclusions drawn from the experiment are

summarised in the following section.

CAEDYM is a geochemical and biological model typically used in conjunction

with a hydrodynamic model to simulate the water column of lakes and reservoirs

(Romero et al., 2004). CAEDYM was further developed to include a sediment

diagenesis module (Read et al., submitted) based on CANDI, a diagenetic model

describing the breakdown of organic matter with the sediment (Boudreau, 1996)

typically applied to marine systems (e.g. Haeckel et al., 2001; König et al., 2001; Luff

and Moll, 2004).

CAEDYM utilises both slow kinetically controlled reactions and equilibrium

reactions that are solved to determine pH, aqueous speciation and solubility equilibrium

controls. Unlike other diagenetic models such as CANDI, the implemented code

included both labile and refractory DOC (DOCL and DOCR respectively) as well as

labile, refractory and very refractory POC (POCL, POCR and POCVR respectively). The

OM breakdown pathway is conceptually summarised in Figure 6.1. The kinetic

component of CAEDYM includes the hydrolysis of the complex organic matter pools

(POCVR, POCR, DOCR and POCL) and terminal metabolism of low molecular weight

DOCL by oxidants (O2, MnO2, Fe(III) and SO42-), the release and transformation of

nutrients (NH4+, PO4

2-, NO3-) and reduced byproducts (Mn2+, Fe(II), NH4

+, H2S, CH4,

FeS). Oxidants, byproducts and nutrients were all capable of interacting. A complete list

of reactions is available in Boudreau (1996); they were implemented identically to

CANDI, but the generic OM term was replaced by DOCL in the breakdown equations,

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and the POCVR, POCR, POCL and DOCR breakdown steps were included using the same

reaction rates for all cases except nitrification where the rate of 0.05 day-1 was kept

from CAEDYM and no denitrification was able to occur below pH 5 as acidity has been

found to limit denitrification (Devlin et al., 2000; Edwards et al., 2007).

Figure 6.1 Chemical species and transformations depicted in the diagenetic component of CAEDYM.

Aqueous speciation and solubility equilibrium control was accounted for by

solving the mass-action expressions for the simulated components which included Al3+,

Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), Mn(II), Mn(IV), SiO2, Cl-, DIC, SO42-, PO4

2-, NO3-,

NH4+, CH4 and H2S. The mass-action expressions were solved according to the

numerical method of Barrodale and Roberts (1980) as discussed in Parkhurst and

Appelo (1999) and in the CAEDYM documentation (Hipsey et al., 2007). Mineral

phases were limited to those that were significant in the sediment and which were

expected to interact with diagenetic processes: gibbsite (Al(OH)3), iron hydroxide

(Fe(OH)3) and iron sulfide (FeS). The mass-action constants from the WATEQ4F

133

database (Nordstrom et al., 1990) were used for speciation and all dissolved phase

geochemical variables were subject to diffusion as in Boudreau (1996).

As the focus of this study was on an acidic system with very low biomass and

productivity and microcosms were stored in the dark, we did not include bioturbation,

bioirrigation or phytoplankton in the simulations. Temperature was set at a constant

15°C and porosity was a constant 0.61 over the entire depth of the cores. Transport in

the water column and core was achieve by diffusion and the water column was mixed

on sampling days by forcing water column turnover. The time step for all calculations

was 3hrs.

In the sediment, the grid thickness increased exponentially from the surface into

the sediment. Upper and lower boundary concentrations (Table 6.1) were prescribed for

all modeled species with the upper boundary condition applied to the water column

(WC) and first layer of sediment and the lower boundary condition applied to the

remaining sediment layers (S). Measured concentrations in the surface water of the

cores were used to set the initial concentrations for DO, PO42-, NH4

+ and NOx. As the

surface water of the cores was initially oxic, Fe(II) and Mn(II) were assumed to be

equivalent to TFFe and TFMn concentrations. Initial concentrations of DIC, Na+, Cl-,

Ca2+, K+, SiO2 were set to measured concentrations of the site water (Table 6.2).

Initial organic matter profiles for the simulated variables (POCL, POCR, DOCL

and DOCR) were set as constant profiles as sediment was mixed prior to the

establishment of the microcosms. The C:N:P ratio of the sediment DOCL and POCL

groups used in the simulation was approximately 2500:200:1, as it was assumed that N

and P would be quickly stripped from the organic matter. It was assumed that refractory

organic matter contained no nitrogen or phosphorous. It was also assumed that there

was no particulate organic matter in the water column able to recharge the sediment

core. Sampling of the cores was simulated by having an outflow equivalent to the

sample volume (30-40 mL) on the sampling days, followed by an inflow of water with

concentrations equivalent to the initial conditions of the control core.

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Table 6.1 Initial simulation concentrations (mg/L) used for cores C2, L1 and H1 obtained from experimental data.

Variable Boundary* C2 L1 H1

DOCL WC S

5.0 x 10-2 5.0 x 10-2

4.0 0

23.6 0

DOCR WC S

0.80 0.80

0.80 0.80

0.60 0.60

POCL WC S

0 5.0 x 10-5

0 5.0 x 10-5

0 1.0 x 10-5

POCR WC S

0 1.0 x 10-4

0 1.0 x 10-4

0 1.0 x 10-4

DONL WC S

1.0 x 10-2

1.0 x 10-2 0 0

0 0

PONL WC S

0 5.0 x 10-6

0 5.0 x 10-6

0 1.0 x 10-6

DOPL WC S

5.0 x 10-5 5.0 x 10-5

0 0

0 0

POPL WC S

0 5.0 x 10-8

0 5.0 x 10-8

0 1.0 x 10-8

POPR, DOPR PONR, DONR WC, S 0 0 0

DO WC S

5.4 0

5.9 0

4.4 0

PO42- WC

S 1.0 x 10-3 1.0 x 10-3

2.6 x 10-3 2.6 x 10-3

6.3 x 10-3 6.3 x 10-3

NH4+ WC

S 0.3.40 0.3.40

0.3.72 0.3.72

0.3.22 0.3.22

NO3- WC

S 0.420 0.420

0.500 0.500

0.511 0.511

Fe(II)^ WC S

3.4 3.4

6.5 6.5

2.1 2.1

Mn(II)^ WC S

0.21 0.21

0.21 0.21

0.14 0.14

SO42- WC, S 120 120 110

pH WC, S 3.92 3.97 4.02 * WC = water column, S = sediment porewater ^ Fe(II) and Mn(II) assumed to be equivalent to TFFe and TFMn respectively

Table 6.2 Initial solute concentrations (mg/L) used in simulation of all cores.

Variable All cores Na+ 330 Cl- 780

Ca2+ 28 K+ 4.85

DIC 1.6 Fe(III) 0 Mn(IV) 0

SiO2 3.8

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6.4 Results

Due to the sampling regime, surface water in all cores remained oxic for the

duration of the experiment. Despite this DO was rapidly removed from the sediment

porewaters of all cores (treated and control), which were anoxic within one week. The

estimated diffusion of DO from the overlying waters in the experimental cores was

unable to match the consumption in the sediments.

During the experiment NOx concentrations decreased in the surface water,

hypothesized as being due to diffusion into the sediment porewater followed by

denitrification. Contrary to what was expected, ammonium concentrations decreased in

the surface water, most likely caused by oxidation to NOx and uptake by bacteria.

pH increased sharply within 1 to 2 cm of the sediment-water interface in all

cores, with the treated cores showing an increase closer to the surface. Surface water pH

did not change substantially in the water column of any treated or control cores.

Dissolved iron concentration initially decreased in the surface waters of the cores,

hypothesized to be oxidation of iron (II) to iron (III) and subsequent precipitation.

Dissolved iron concentration increased again, after the decrease in NOx concentration.

This was thought to be caused by iron reduction from iron (III) to iron (II) in the

sediment, dissolution and diffusion out of the porewater.

No H2S was observed in the surface water of the cores. However, peaks of H2S

were observed close to the sediment-water interface, corresponding to the pH gradients.

The maximum observed H2S concentrations corresponded to the core treatment, with

high concentrations observed in those receiving more DOC.

The simulations largely predicted the change in solute concentrations in the

water column of the experimental cores without any alteration of the kinetic rates in the

model. For ease of discussion, results from one control core (C2), one low dose core

(L1) and one high dose core (H1) will be presented. There are two sets of comparisons

that were made in order to assess the application of the diagenetic module to acidic

sediments: the comparison between the simulated results and the experimental results;

and, the comparison between equivalent simulations where the different three minerals

were included as solubility controls.

Model predictions of surface water DOC, NOx, total dissolved Fe and pH follow

that observed in the water column during the experiment, with the slight deviations

noted in the prediction of DO and NH4+ concentrations (Figures 6.2, 6.3 and 6.4). The

discrepancy between experimental and simulated DO can be explained by the

136

introduction of oxygen into the cores through water replenishment after sampling,

which was not included in the model. The difference between the observed and

simulated DO concentrations is greatest for the high dosed core (up to 4.5 mg L-1). This

difference may be due to the increase in potential flux of DO across the air-water

interface during sampling. An order of magnitude estimation, using Henry’s Law and

Fick’s first law, for a 15 min sampling event reveals a potential change in DO

concentration within the surface water of 1 to 2 mg L-1 when the initial concentration of

the surface water is 1 and 5 mg L-1. This reflects the difference observed in surface

water DO concentration before and after sampling events. While the introduction of

additional DO through surface water replenishment was accounted for in the simulation,

the transfer of oxygen across the air-water interface was not and serves to explain the

discrepancies between the experimental and simulated data.

Figure 6.2 Experimental results (circles) and simulated results (line) for the surface water of control core C2.

137

Figure 6.3 Experimental results (circles) and simulated results (line) for the surface water of the low dose core L1.

138

Figure 6.4 Experimental results (circles) and simulated results (line) for the surface water of the high dose core H1.

Contrary to the decrease in ammonium observed in all cores during the

experiment, CAEDYM simulations predicted an increase in ammonium concentration,

and this difference is discussed further in the following section.

The inclusion of the relevant mineral phases (iron sulfide, gibbsite and iron

hydroxide) (Figures 6.5 and 6.6) as solubility controls did not noticeably increase the

accuracy of predictions for nutrient, DO and DOC concentrations during the

experiment, however, it should be noted that dissolved aluminium was not one of the

solutes measured. The activation of the iron sulfide mineral in the simulation changed

the partitioning of iron between oxidation states and is also discussed further in the

following section.

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Figure 6.5 Simulated (dotted, dashed and solid lines) and experimental (circles and crosses) results for sediment porewater concentrations of labile DOC, nitrate, ammonium, iron II, hydrogen sulfide and pH. Simulations only included the mandatory iron hydroxide solubility controls. As DO was not observed in the porewater, it is not plotted here.

140

Figure 6.6 Simulated (dotted, dashed and solid lines) and experimental (circles and crosses) results for sediment porewater concentrations of labile DOC, nitrate, ammonium, iron II, hydrogen sulfide and pH. Simulations included gibbsite, iron sulfide and iron hydroxide solubility controls. As DO was not observed in the porewater, it is not plotted here.

Similarly with the porewater profiles (examples of which are shown for core H1

in Figures 6.5 and 6.6), the solutes most affected by the activation of iron sulfide

solubility controls were iron, hydrogen sulfide and aluminium as these are the ones that

participate directly in the equilibrium process. The introduction of gibbsite as a

solubility control also has an influence on the pH, buffering the increase of pH in the

sediment porewater.

There was also a 20% difference in the simulated maximum nitrate and

ammonium fluxes between simulations including gibbsite and iron sulfide as well as

iron hydroxide versus simulations that only included iron hydroxide (Figure 6.7). The

141

flux of hydrogen sulfide was reduced by the simulation of more mineral species and the

inclusion of the mineral gibbsite also resulted in fluctuation of the flux across the

interface of dissolved aluminium.

Figure 6.7 Simulated results for sediment porewater fluxes of hydrogen sulfide, nitrate, dissolved aluminium and ammonium with the dashed line representing simulations involving only iron hydroxide solubility control and the dotted line showing simulations including iron hydroxide, iron sulfide and gibbsite solubility controls. A positive flux indicates a flux out of the sediment into the water column.

6.5 Discussion

A number of notable points arose from comparing the simulation data to the

experimental results, particularly relating to nitrogen cycling, iron and sulfur cycling

and pH control. The kinetics of processes at low pH has been the subject of little

research compared to neutral systems and this was particularly relevant to nitrogen

cycling the mine lake microcosms.

It was apparent from comparing experimental and simulation results that in the

experimental cores there was either a lower production of ammonium or a process

acting in the surface water removing ammonium that was not captured in the

simulation, which predicted an increase in ammonium in the surface water. Reduced

ammonium production could be accounted for through reduced N content in the in-situ

organic matter, however this content was already very small when compared to organic

142

matter in neutral sediments. Greater ammonium removal from the cores could be

accounted for in two ways: oxidation to nitrate/nitrite and subsequent removal through

denitrification; or, removal of ammonium through biological uptake, which was not

accounted for in CAEDYM. This uptake of ammonium by bacteria is considered to be

extremely small in comparison to the former option. Introduced DO across the air-water

interface was not accounted for in the model, so whilst the decrease in ammonium

concentration in the experimental cores was not matched by a corresponding increase in

NOx concentration, this is still the most likely process. The ramification of this is that

there was ultimately more nitrification and subsequent denitrification occurring in the

experimental sediments than was predicted by the model.

When oxygen is lacking and nitrate is abundant, denitrification can be limited by

carbon availability and type (Groffman and Tiedje, 1989; Henrich and Haselwandter,

1991; Devlin et al., 2000; Megonigal et al., 2004). In addition to this, traditional views

of nitrogen cycling in acidic systems hold that denitrification is limited by acidic water

particularly when the pH is less than 5 (Devlin et al., 2000). In the case of this

experiment, simulation and experimental results suggest that while denitrification was

limited in all cores by pH less than 5 for most of the experiment, there was still a

decrease in surface water NOx concentrations observed in the experiment and also

predicted by the model, indicating that sufficient sediment existed with pH greater than

5 to reduce the nitrate concentration in the core surface water.

Low pH has repeatedly been shown to increase the proportion of N2O or NO as

an end products during denitrification relative to N2 (van Cleemput and Baert, 1984;

Martikainen and de Boer, 1993). Due to the pH sensitivity of denitrifying bacteria,

abiotic denitrification of nitrite is favoured at low pH (van Cleemput and Baert, 1984)

and coupling with Fe(II) oxidation has been shown to occur at low rates (Postma, 1990).

Acid tolerant nitrifiers have been reported (Hankinson and Schmidt, 1988; de

Boer and Laanbroek, 1989; de Boer et al., 1990; Martikainen et al., 1993; Perrson and

Wirén, 1995). These bacteria are not ubiquitous and in some acidic conditions no

nitrification is detected due to the presence of acid sensitive nitrifiers (de Boer et al.,

1990; Perrson and Wirén, 1995). The decrease in ammonium observed in the

experiment indicates the possible presence of these acid tolerant nitrifiers in the

sediment of Lake Kepwari.

It has also been shown that denitrification and nitrification enzymes are

inhibited by H2S (Joye and Hollibaugh, 1995). Due to the spatial separation of these

processes, it is likely that most H2S is oxidized by iron prior to reaching the

143

denitrification zone, or oxygen prior to reaching the nitrification zone. This would result

in a peak of H2S concentration below the nitrification zone. Such a peak in

concentration is observed in the experimental results in the high and low dose cores,

and also predicted by the simulations, however the simulations had a tendency to over

predict the concentration of H2S.

The inclusion of mineral speciation in the simulations only slightly affected the

dynamics of nitrogen in the microcosms with differing magnitudes of ammonium and

NOx flux being predicted with the inclusion of gibbsite and iron sulfide as well as the

mandatory iron hydroxide. While this only had a relatively small affect over the

simulation period, over larger timeframes the differences would be more pronounced.

The simulation of iron and sulfur minerals alone is complex. Simulation of iron

hydroxide solubility led to over prediction of iron release from the sediment in all

simulated cores. The inclusion of iron sulfide in the simulations, as well as the

mandatory iron hydroxide, led to the prediction of no iron release from the sediment to

due its precipitation with sulfur. This indicated that iron mineral cycling within the

sediments of the Lake Kepwari cores did not just involve one or two minerals phases

and is much more complex. It also serves to illustrate that the diagenetic cycle can

potentially be influenced by the solubility controls on minerals, which in turn are highly

pH dependent (Blodau, 2006).

The inhibition of sulfate reducing bacteria by iron reducing bacteria appears not

to have been so important in these acidic sediments as has been suggested of acidic

sediments by Meier et al (2004). It has previously been stated that the time scales to

establish iron sulfide forming conditions are of the order of weeks to months and this is

largely dependent on the supply of electron donors to the sediment (Fyson et al., 1998;

Herzsprung et al., 2002; Koschorreck et al., 2002; Wendt-Potthoff et al., 2002;

Frömmichen et al., 2004). As pH 4.5 to 5 is the critical threshold for iron sulfide

accumulation to occur (Blodau, 2006), it is not surprising that both the experiment and

subsequent simulations indicated that these conditions were established within days of

the addition of labile DOC to the cores. Once electron acceptors are available to the

sediment, in the form of labile carbon, and the supply of reactive iron decreases, the

neutralization process is accelerated by a positive feedback mechanism (Blodau and

Peiffer, 2003). However, as has already been observed in experiments, the depletion of

labile DOC in the sediment can result in the re-establishment of low pH and iron

reducing conditions within similar time frames (Wendt-Potthoff et al., 2002) as the

regeneration of sulfate from sulfide prevents long term sequestration of acidity, which is

144

thought to have occurred in the experiment. Also, acidity is only sequestered long term

if the iron sulfides are buried in the sediment, below the oxic zone (Blodau, 2006).

Over prediction of sulfide and dissolved iron production in the simulation of the

cores may be due to the lack of inclusion in the model of the preservation of organic

matter in the sediment by adsorption to iron and aluminium minerals, which has been

known to occur (e.g. Keil et al., 1994; e.g. Laskov et al., 2002). This would have

reduced the amount of simulated labile DOC available as an electron donor to the

sulfate and iron(III).

Accurate prediction of pH is dependent on the accurate prediction of other

solutes, which in turn is dependent on accurate process description, including the

specification of the relevant solubility controls. Adding to this complexity are the

feedbacks that exist between iron and sulfur cycling, and pH.

Simulation of mineral dissolution and precipitation did, however, ultimately

affect the pH within the porewater. Specifically, the inclusion of gibbsite, known to be

solubility controlled around pH 5 lead to buffering of the pH. Over a longer time scale

this would cause feedback affects on mineral dissolution and in particular would affect

the solubility of iron hydroxides which in turn affects the other diagenetic processes.

In the experiment the buffering of pH to a higher level was seen below the

sediment surface, however there was a notable decrease in pH at the surface of the

experimental cores which was only partially simulated by the model. This indicated that

there was an additional process or solubility control that was active in this region of the

experimental cores that was not included in the simulation. The increase of pH in the

experimental cores is likely to have increased the relative competitiveness of sulfate

reducers and iron reducers (Koschorreck et al., 2002; Wendt-Potthoff and Koschorreck,

2002).

6.6 Conclusion

Diagenetic processes in acidic sediments can be simulated using the kinetic

descriptions traditionally applied to marine sediments. Incorporation of solubility

controls allows simulation of many key species used to monitor mine lake remediation

such as iron, aluminium and sulfate, and also parameters such as pH. While the

simulations covered only the relatively short timescale of the experiment, solubility

controls had influence directly on iron and aluminium concentrations. Over longer

145

timescales this would have ramifications on other diagenetic processes such as sulfate

reduction as well as on the sorption of phosphate and organic carbon to iron minerals.

To predict the transition of mine lakes from an acidic lake with little organic

matter to a neutral mesotrophic system it is necessary to include both processes that will

dominate early in the life of the lake (solubility controls) and the processes more often

associated with dominating neutral lakes that will take on more significance later in the

life of the lake, key processes being diagenetic processes. CAEDYM allows this

integration of geochemical and diagenetic processes provides the potential to simulate

water column activity and allows the intricate processes such as the cycling of nitrogen,

sulfur and iron in acidic sediments to be investigated.


This project was supported financially by the Western Australian Centre of

Excellence for Sustainable Mine Lakes and Australian Research Council Linkage

Project LP0454252. Financial support for DJ Read was provided by an Australian

Postgraduate Award. This manuscript is School of Environmental Systems Engineering

Publication SESE-050-DR.

6.8 References

Barrodale, I. & Roberts, F. D. K., 1980, L1 solution to linear equations subject to linear

equality and inequality constraints. ACM Transactions on Mathematical

Software, 6, 231-235.

Blodau, C., 2006, A review of lake acidity generation and consumption in acidic coal

mine lakes and their watersheds. Science of the Total Environment,

Blodau, C. & Peiffer, S., 2003, Thermodynamics and organic matter: constraints on





Boudreau, B. P., 1997, Diagenetic Models and Their Implementation, Modelling

Transport and Reactions in Aquatic Sediments, Springer, Berlin.

146

Bozau, E., Bechstedt, T., Friese, K., Frömmichen, R., Herzsprung, P., Koschorreck, M.,

Meier, J., Völkner, C., Wendt-Potthoff, K., Wieprecht, M. & Geller, W., 2007,

Biotechnological remediation of an acidic pit lake: Modelling the basic

processes in a mesocosm experiment. Journal of Geochemical Exploration, 92,

212-221.

Davis, A., 2003, A screening-level laboratory method to estimate pit lake chemistry.

Mine Water and the Environment, 22, 194-205.

Davis, A., Bellehumeur, T., Hunter, P., Hanna, B., Fennemore, G. G., Moomaw, C. &

Schoen, S., 2006, The nexus between groundwater modeling, pit lake

chemogenesis and ecological risk from arsenic in the Getchell Main Pit, Nevada,

U.S.A. Chemical Geology, 228, 175-196.

de Boer, W., Klein Gunnewiek, P. J. A. & Troelstra, S. R., 1990, Nitrification in Dutch

heathland soils II. Characteristics of nitrate production. Plant and Soil, 127, 193-

200.

de Boer, W. & Laanbroek, H. J., 1989, Ureolytic nitrification at low pH by Nitrosospira

spec. Archives of Microbiology, 152, 178-181.

Devlin, J. F., Eedy, R. & Butler, B. J., 2000, The effects of electron donor and granular

iron on nitrate transformation rates in sediments from a municipal water supply

aquifer. Journal of Contaminant Hydrology, 46, 81-97.

Eary, L. E., 1998, Predicting the effects of evapoconcentration on water quality in mine

pit lakes. Journal of Geochemical Exploration, 64, 223-236.

Edwards, L., Küsel, K., Drake, H. & Kostka, J. E., 2007, Electron flow in acidic

subsurface sediments co-contaminated with nitrate and uranium. Geochimica et


Frömmichen, R., Wendt-Potthoff, K., Friese, K. & Fischer, R., 2004, Microcosm

studies for neutralization of hypolimnic acid mine pit lake water (pH 2.6).






Technology, Germany

Fyson, A., Nixdorf, B., Kalin, M. & Steinberg, C. E. W., 1998, Mesocosm studies to



147

Groffman, P. & Tiedje, J. M., 1989, Denitrification in north temperate forest soils:

Relationships between denitrification and environmental factors at the landscape

scale. Soil Biology and Biochemistry, 21, 621-626.

Haeckel, M., König, I., Reiech, V., Weber, M. E. & Suess, E., 2001, Pore water profiles

and numerical modelling of biogeochemical processes in Peru Basin deep-sea

sediments. Deep-Sea Research Part II, 48, 3713-3736.

Hankinson, T. R. & Schmidt, E. L., 1988, An acidophilic and a neutrophilic Nitrobacter

strain isolated from the numerically predominant nitrite-oxidizing population of

an acid forest soil. Applied and Environmental Microbiology, 54, 1536-1540.

Henrich, M. & Haselwandter, K., 1991, Denitrifying potential and enzyme activity in a

Norway spruce forest. Forest Ecology and Management, 44, 63-68.

Herzsprung, P., Friese, K., Frömmichen, R., Goettlicher, J., Koschorreck, M.,

Tuempling, W. V. J. & Wendt-Potthoff, K., 2002, Chemical changes in

sediment pore-waters of an acidic mining lake after addition of organic substrate

and lime for stimulating lake remediation. Water, Air and Soil Pollution -

FOCUS, 3, 123-140.

Hipsey, M. R., Romero, J. R., Antenucci, J. P. & Hamilton, D., 2007, The

Computational Aquatic Ecosystem Dynamics Model (CAEDYM): v3.1 Science

Manual, Centre for Water Research, Perth, Australia.

Joye, S. B. & Hollibaugh, J. T., 1995, Influence of sulfide inhibition of nitrification on

nitrogen regeneration in sediments. Science, 270, 623-625.

Keil, R. G., Montlucon, D. B., Prahl, F. G. & Hedges, J. I., 1994, Letter. Nature, 370,

549-551.










König, I., Haeckel, M., Lougear, A., Suess, E. & Trautwein, A. X., 2001, A

geochemical model of the Peru Basin deep-sea floor - and the response of the

system to technical impacts. Deep-Sea Research Part II, 48, 3737-3756.

148




Laskov, C., Amelung, W. & Peiffer, S., 2002, Organic matter preservation in the

sediment of an acidic mining lake. Environmental Science and Technology, 36,

4218-4223.



24, 1099-1127.

Martikainen, P., Lehtonen, M., Lång, K., De Boer, W. & Ferm, A., 1993, Nitrification

and nitrous oxide production potentials in aerobic soil samples from the soil

profile of a Finnish coniferous site receiving high ammonium deposition. FEMS

Microbiology Ecology, 13, 113-121.

Martikainen, P. J. & de Boer, W., 1993, Nitrous oxide production and nitrification in

acidic soil from a Dutch coniferous forest. Soil Biology and Biochemistry, 25,

343-347.

Mazur, K., Ehret, B., Rolland, W. & Gruenewald, U., 2002, Reservoir management of

post mining lakes - finding the balance between the needs to stabilise the water

resources and the risk to deteriorate the water quality. Third International

Conference on Water Resources and Environment Research. Dresden, Germany

Megonigal, J. P., Hines, M. E. & Visscher, P. T., 2004, Anaerobic metabolism:

Linkages to trace gases and aerobic processes. in Schlesinger, W. H. (Ed.)

Biogeochemistry. Elsevier-Pergamon, Oxford.

Meier, J., Babenzien, H.-D. & Wendt-Potthoff, K., 2004, Microbial cycling of iron and

sulfur in sediments of acidic and pH-neutral mining lakes in Lusatia

(Brandenburg, Germany). Biogeochemistry, 67, 135-156.

Nordstrom, D. K., Plummer, L. N., Langmuir, D., Busenberg, E., May, H. M., Jones, B.

F. & Parkhurst, D. L., 1990, Revised chemical equilibrium data for major water-

mineral reactions and their limitations. in Bassett, R. L. & Melchior, D. (Eds.)

Chemical modeling in aqueous systems II. American Chemical Society

Symposium Series, Washington D.C.

Parkhurst, D. L. & Appelo, C. A. J., 1999, User's guide to PHREEQC (Version 2) - A

computer program for speciation, batch reaction, one dimensional transport and

inverse geochemical calculations. Water-Resourses Investigations Report 99-

4259. U.S. Geological Survey, Denver, Colorado

149



108, 227-229.

Perrson, T. & Wirén, A., 1995, Nitrogen mineralization and potential nitrification at

different depths in acid forest soils. Plant and Soil, 168-169, 55-65.

Postma, D., 1990, Kinetics of nitrate reduction by detrital Fe(II)-silicates. Geochimica

et Cosmochimica Acta, 54, 903-908.

Rolland, W., Wagner, H., Chmielewski, R. and Gruenewald, U., 2001, Evaluation of the

long term groundwater pollution by the open cast lignite mine Jaenschwalde

(Germany). Journal of Geochemical Exploration, 73, 97-111.

Romero, J. R., Antenucci, J. P. & Imberger, J., 2004, One- and three- dimensional

biogeochemical simulations of two differing reservoirs. Ecological Modelling,

174, 143-160.

van Cleemput, O. & Baert, L., 1984, Nitrite: a key compound in N loss processes under

acid conditions? Plant and Soil, 76, 233-241.







Werner, F., Bilek, F. & Luckner, L., 2001a, Impact of regional groundwater flow on the

water quality of an old post-mining lake. Ecological Engineering, 17, 133-142.

Werner, F., Bilek, F. & Luckner, L., 2001b, Implications of predicted hydrologic

changes on Lake Senftenberg as calculated using water and reactive mass

budgets. Mine Water and the Environment, 20, 129-139.

Werner, F., Müller, M. & Graupner, B., 2006, Predicted and observed water quality data

from the coal mine pit lake Bärwalde, Laustz, Germany. 7th International

Conference on Acid Rock Drainage (ICARD). American Society of Mining and

Reclamation (ASMR), St. Louis MO

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151

7 Conclusions

7.1 Significance of Organic Carbon Limitation

The work presented in this thesis constitutes a significant step forward in linking

sediment and surface water processes, recognising the importance of the feedback

effects that can occur between them and also recognising the important role that DOC

can play in this interaction. Through combining fundamental knowledge gained over the

last few decades with insights gained from laboratory experimentation the importance

of DOC in the link between sediment and surface water processes was highlighted and

extension of the diagenetic parameterisations was able to be made to acidic sediment.

Previous literature has mainly focused on the importance of POC in sediment

diagenesis, however in some systems (e.g. oligotrophic lakes) dissolved organic carbon

can constitute a higher proportion of the total labile organic carbon in the sediment and

hence can potentially take on greater significance in sediment diagenetic processes.

While total POC concentration in sediment is usually several orders of magnitude

greater than DOC, it is the role of DOC in the degradation process and it’s ability to be

more readily transported to/from the water column that make it of interest when

considering the water column and sediment system combined.

This thesis began by providing a better understanding as to how important labile

forms of dissolved organic carbon can be in the sediment of systems that have low

overall concentrations of total labile organic carbon (Chapter 3). This translated into the

concept of carbon limitation, where respiration in a sediment system can be limited by

the availability of labile organic carbon rather than by the more traditionally recognised

oxidant and nutrient limitations.

The experimentation conducted as part of this study showed this type of carbon

limitation was experienced by the field sites Lake Kepwari, Chicken Creek and

Cockburn Sound. The experiments also showed that carbon limitation may prevent

anoxia in the water column and hence may prevent the establishment of processes that

require anoxia to continue, such as denitrification or the release of phosphorous from

the sediment through iron reduction: Both processes are of importance in marine and

lake systems.

This chapter also estimated a second order rate constant as 6.6 mL mol-1 s-1 and

compared simple mixed reactor models using first order, second order, and Monod

kinetic descriptions: the second order description was better able to capture the

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dissolved oxygen dynamics of our experiments. These second order kinetic descriptions

provide relatively simple parameterizations that are suitable for use in systems where

limitation is temporally dynamic, and where oxidant and organic carbon limitation can

occur at different times of the year. This provides an option for inclusion into surface

water models as a relatively simple compromise between full descriptions of diagenetic

processes and a constant/empirically based sediment oxygen demand.

Acidic mine lakes exemplify a system that experiences limitation of sediment

respiration by labile organic carbon. Chapter 4 went on to examine sediment diagenesis

in such systems in more detail as diagenesis, through sulfate reduction, is considered to

play an important role in the pH amelioration of these lakes. As the prediction of the

effectiveness of remediation strategies requires a detailed knowledge of sediment

diagenesis under acidic systems, this chapter aimed to further the understanding of

functional similarities and differences in diagenetic processes in acidic sediments

through a series of column experiments on three different mine lakes.

The results of these experiments indicated that there was a marked difference

between the German and Australian lakes in porewater DO, sulfide and pH responses.

Comparisons showed that increased H2S production coincided with lower iron

concentration, higher pH and higher DOC dose. The sequence of chemical species

removed from and released to the water column indicated that, despite differing

magnitudes of response, all sets of microcosms followed the classic ecological redox

sequence when degrading organic matter. The microcosms from the most biologically

productive lake exhibited a large release of ammonium attributed to a higher proportion

of labile particulate organic carbon in the sediment resulting from the higher primary

productivity in the lake as a whole.

While each lake was distinctive in its chemical, biological and geological

makeup, some similarities in processes were noted, including the adherence to the

ecological redox sequence; the prevalence of nitrate reduction; similar DOC

remineralisation rates; and, anoxia in the porewater of all sets of microcosms less than

one week into the experiment.

Chapter 5 discussed the chemical evolution of porewater and surface water in

coastal marine systems and their linkage through fluxes of chemical species across the

sediment-water interface, particularly when these systems are limited by labile organic

carbon or nutrient availability. Sediment diagenesis can be limited by the supply of

labile organic matter to the sediment due to a lack of surface water production, which

can in turn be limited by the supply of nutrients back to the water column. However,

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chemical fluxes are often only measured over short time scales, of the order of days,

even though they may be influenced by hydrodynamic and biological variables that

evolve over longer time scales such as weeks, months or seasons. The role of POC is

often only considered when determining these fluxes, however in systems where

organic carbon availability limits sediment respiration the role of DOC becomes more

significant.

In this third study, experiments were conducted where a form of labile DOC

(treacle) was added to sediment cores taken from a semi-enclosed, organic carbon

limited, coastal embayment. Chemical constituents within the pore and surface waters

were monitored for 3 weeks and at times there appeared to be a de-coupling between

the surface water DO concentration and fluxes of other chemical species across the

interface, an indication of excessive consumption in the sediment with the fluxes of

oxidants not able to match consumption in the sediment. In Cockburn Sound, as with

many other oligotrophic or low organic matter marine systems, sediment porewater

fluxes can be a dominant source or sink of key chemical species, such as DO or

nutrients, to the water column. Understanding of these fluxes becomes critical for

management of such water bodies especially when under threat from changing

environmental conditions as a result of anthropogenic activities.

To examine the observed phenomena in more detail, a numerical model of the

experimental cores was developed to simulate the hydrodynamic, geochemical and

diagenetic processes. Unlike other models of early diagenesis, the model included

parameterization of labile and refractory DOC, as well as POC. The incorporation of

sediment diagenetic and geochemical processes into water quality predictive models of

marine cores has, to our knowledge, never been done before. The model was able to

capture the rapid changes observed in the sediment cores, and has the potential to serve

as a valuable tool for quantifying sediment organic matter decomposition and dissolved

chemical fluxes, allowing a perspective of the whole system rather than merely

modelling individual processes and would also allow for incorporation of feedback

mechanisms that might otherwise be missed.

Low levels of organic carbon and elevated sulfate levels imply a certain amount

of similarity through diagenetic processes in mine lakes and marine systems. Chapter 6

investigated the application of kinetic parameterizations used in the marine diagenetic

modelling to a mine lake environment.

As has previously been mentioned, acidic mine lakes typically have high

concentrations of sulfate and iron and remediation strategies often centre on the

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encouragement of bacterial reduction of these solutes, diagenetic processes. The lack of

suitable methods means the prediction of water column remediation due to these

processes is difficult due to interaction with other physical, geochemical and biological

processes.

The majority of numerical modelling of these systems has focused on

groundwater inflow to the lake and geochemical reactions within the lake water itself.

The role of sediment diagenesis in acidic lakes, particularly in the transition from an

acidic to neutral lake has yet to be fully explored. We focused on parameterization and

numerical modelling of key sediment fluxes observed in the mine lake microcosm

experiment discussed in Chapter 4. The numerical model CAEDYM, a geochemical,

diagenetic and biological model, was used to simulate diagenetic processes in the

sediment cores.

Modelled predictions of DOC, DO, nitrate/nitrite and dissolved iron largely

followed the concentrations observed in the experiment without an alteration of the

kinetic rates derived from marine systems. Discrepancies between simulated and

observed results could be explained through experimental artefacts. The inclusion of

solubility controls allowed the integration of equilibrium controlled reactions and

kinetically controlled reactions and therefore the simulation of many key species used to

monitor mine lake remediation such as iron, aluminium and sulfate. While the

simulations covered only the relatively short timescale of the experiment, solubility

controls had direct influence on iron and aluminium concentrations. Over longer

timescales this would have ramifications for other diagenetic processes such as sulfate

reduction as well as on the sorption of phosphate and organic carbon to iron minerals.

From this investigation it has become apparent that diagenetic processes in

acidic sediments can be simulated using kinetic descriptions traditionally applied to

marine sediments. The result may serve as a valuable tool to aid in determination of

important chemical reactions involved in controlling fluxes across the interface, and

thus the prediction of the water quality of mine lakes and other acidic lakes. To predict

the transition of mine lakes from an acidic lake with little organic matter to a neutral

mesotrophic lake it is necessary to include both processes that will dominate early in the

life of the lake (geochemical solubility controls) and the processes more often

associated with neutral lakes that will take on more significance later in the life of the

lake, such as diagenetic processes. CAEDYM allows this integration of geochemical

and diagenetic processes as well as providing the potential to simulate water column

chemical and biological activity.

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7.2 Recommendations for Future Work

The conclusions from the work described in this thesis have opened up new

challenges for sediment diagenesis and aquatic water quality prediction that will require

further attention in future research. Firstly, the concept of organic carbon limitation of

sediment respiration needs to be further explored. Existing and new datasets should be

analysed to verify that this type of limitation is far more common than previously

thought. It may be that respiration in the majority of sediment systems are limited by the

availability of labile organic carbon. The concept of lability itself also needs further

exploration. There is a large proportion of DOM that remains to be characterised and

this in turn will affect our definition of lability and also the partitioning of DOC into

labile and refractory compartments. With increased knowledge of this unknown DOC

component there will hopefully come increased knowledge of how the DOC decays, in

turn allowing better understanding of associated processes. Increased understanding of

the role of microbial communities will also aid in the understanding of diagenetic

processes. As with DOC, the distribution, ecological function and compositions of

microorganisms within the sediment has yet to be characterised (Hedges et al., 2000).

These concepts will have repercussions on the interpretation of aquatic

processes as the sediment system will no longer be able to be considered as “constant”.

This in turn will affect the management of some systems, such as mine lakes, as it will

be more accurate to include consideration of sediment processes when making

predictions about future water quality.

Ongoing data collection for mine lakes and other acidic lakes will be required to

document the transition from an acidic lake to a neutral lake, if it occurs. Specifically,

data collected should include concentrations of sediment POC and DOC, to enable

modelling of the sediment diagenetic processes. This should accompany the more

traditional approach of geochemical modelling of the surface and groundwater to

provide a more complete understanding of lake processes. This data can also be used to

provide verification of the applicability of marine diagenesis kinetics to acidic

sediments. This understanding may also be applicable to atmospherically acidified lakes

and volcanic lakes where concentrations of organic carbon are very low.

Of particular importance in discerning the role of cycling organic matter

between the surface water and the sediment is the ability to distinguish between labile

156

and refractory organic matter and improvement in techniques for doing so would

facilitate clearer understanding of this cycling and also of the concept of organic carbon

limitation. The reality is that there is a continuum of reactivity of organic carbon

(Middelburg, 1989; Middelburg et al., 1993) and this could possibly be incorporated

into an all-encompassing sediment and lake model such as CAEDYM, however the

benefits of doing so would need to be carefully weighed against the cost of additional

model complexity and computing time.

In terms of modelling, the incorporation and application of the diagenetic

module in CAEDYM constitutes an initial but important step forward; however there

should be further testing of this new diagenesis module, both in neutral and acidic

sediments. Further to this there should be application of the model to the lakes/marine

systems with inclusion of the appropriate solubility controls. This should be conducted

for a range of lake and marine situations including lakes ranging in pH from extremely

acidic to neutral and also marine systems with ranging organic content in both the

sediment and the water column. Accurate predictions in a range of scenarios would

provide increased confidence in the process description that was incorporated into the

model.

7.3 References

Hedges, J. I., Eglinton, G., Hatcher, P. G., Kirchman, D. L., Arnosti, C., Derenne, S.,

Evershed, R. P., Kögel-Knabner, I., de Leeuw, J. W., Littke, R., Michaelis, W.

& Rullkötter, J., 2000, The molecularly-uncharacterized component of nonliving

organic matter in natural environments. Organic Geochemistry, 31, 945-958.



Middelburg, J. J., Vlug, T. & van der Nat, F. J. W. A., 1993, Organic matter

mineralization in marine systems. Global and Planetary Change, 8, 47-58.

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