the role of sediments in the fate of microcystins in aquatic ......2015 i abstract microcystins,...

The role of sediments in the fate of

microcystins in aquatic systems

Haihong Song B. Sc. North China Electric Power University

M. Sc. National Sun Yat-Sen University China

This thesis is presented for the degree of Doctor of Philosophy

of The University of Western Australia

School of Civil, Environmental and Mining Engineering

2015

i

Abstract Microcystins, potent toxins produced by cyanobacteria, occur widely in aquatic

systems across the world, and are a known environmental and public health hazard.

Therefore, it is important to understand their fate in aquatic systems. As an important

component of aquatic systems, sediments play multiple roles in the distribution and

natural attenuation of microcystins. However, systematic studies identifying the effects

of environmental variables on the variability of microcystins in lake sediments are

lacking, and the contribution of sediments to the removal of these toxins in the water body

are not yet clear. Hence, this research aimed to: (a) investigate the variability of

microcystins in lake sediments; and (b) identify the relative contribution of the

biodegradation and adsorption ability of sediments to the removal of microcystins from

the water. As research into microcystins in lake sediments has been hindered by the lack

of an effective analysis method, a further aim of this study was to develop a method to

quantify these toxins in sediment samples using supercritical carbon dioxide.

The first part of this research involved a field study, analysing microcystin

concentrations in lake sediments and their correlation with environmental variables.

Microcystins were detected in all sediment samples, even at one of the sampling sites

with negligible cyanobacterial biomass present in the water. The concentration of these

toxins in lake sediments had a weak, but significant correlation with intracellular

microcystins, total microcystins and cyanobacterial biomass in the water. Furthermore,

their variability of in lake sediments could be explained by a combination of total

microcystins in the water, cyanobacterial biomass in the water, pH and temperature.

In the second part of this research, changes in the concentration of microcystin-LR

(MCLR) in the water in the presence of sediments were quantified in a laboratory

experiment. The results of this experiment showed that each time MCLR was added to

sterile lake water in the presence of sediments, MCLR concentration decreased

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significantly following an exponential decay curve, with no observed lag phase.

Comparison between different treatment conditions implied that the adsorption and

biodegradation ability of sediments caused the MCLR removal and that biodegradation

was the dominant mechanism.

The final part of this research investigated the use of supercritical carbon dioxide in

quantifying microcystins in sediment samples. A protocol was developed which included

the optimisation of extraction conditions using supercritical carbon dioxide. This protocol

was use to quantify microcystin concentrations in natural field samples. The results

showed that for sediment samples with added MCLR, the conventional method recovered

more spiked MCLR but fewer microcystin variants. In contrast, supercritical carbon

dioxide with water as modifier extracted a higher amount of total microcystins.

Overall, this research highlights the wide occurrence of microcystins in the sediments

of the studied lake, and the biodegradation ability of sediments to remove microcystins

quantitatively from the water. This study suggests that researchers and water management

authorities should include sediments when assessing the potential hazards and fate of

microcystins in aquatic systems.

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Acknowledgements Foremost, I thank Professor Anas Ghadouani and Assistant Professor Elke Reichwaldt

for their supervision. Their scientific guidance, ongoing encouragement and continued

enthusiasm made this research possible. I also express my appreciation to the China

Scholarship Council and The University of Western Australia (UWA) for awarding me

scholarships that made this PhD study possible. My sincere thanks to UWA for providing

an excellent learning environment and research facilities.

I sincerely thank Dr Brad Berven and Professor George Koutsantonis for generously

sharing the Supercritical Fluid Extractor with me and patiently training me to use it.

Thank you to Dianne Krikke at the Environmental Research Laboratory for her technical

support of my laboratory work; to Som Cit Si Nang and Dani Barrington for their

guidance in sample analysis methods in the laboratory; to Weiyi Wang, Azra Daud, Liah

Coggins and Elke Reichwaldt for their assistance during the field sampling. Thank to Drs

Krystyna Haq and Michael Azariadis for providing wonderful workshops on writing and

their support during my writing journey; and to Laura Firth, Kevin Murray and Robin

Miline from UWA Centre for Applied Statistics for their valuable statistical advice; to Dr

Kristin Argall of Wordthreads Editing for providing editing services according to the

Institute for Professional Editors’ Guidelines for Editing Research Theses. My special

thanks go to the staff in the UWA Student Support Services Center for providing stress

management strategies during the writing process. Many thanks go to my fellow

colleagues in SESE for their scientific support and friendship.

Most importantly, I would like to thank my family, especially my beloved husband

Weiyi Wang, for their unconditional love and support. They are always the rock of my

life.

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State of candidate contribution

DECLARATION FOR THESIS CONTAINING PUBLISHED WORK AND/OR WORK

PREPARED FOR PUBLICATION

This work was the outcome of research completed during the course of my enrolment

for the degree of Doctor of Philosophy at the School of Civil, Environmental and Mining

Engineering, UWA. The main body of the thesis (Chapters 2–4) is a compilation of three

papers written for journal publication. Each chapter is a stand-alone manuscript，which

includes an abstract, an introduction, materials and methods, a presentation of results and

a discussion of results. The introductory Chapter 1 presents an overview of the basic

background, motivation and objectives of the study to provide a context for Chapters 2–

4. The major findings of this work and suggestions for future work are summarised in

Chapter 5.

Chapter 2， titled “The effects of environmental variables on the spatial and temporal

variability of microcystins in aquatic systems” by Haihong Song, Elke S. Reichwaldt,

Anas Ghadouani, is accepted by Toxins.

Chapter 3, titled “Contribution of sediments in the removal of microcystin-LR from the

water” by Haihong Song, Elke S. Reichwaldt, Anas Ghadouani, Toxicon,

10.1016/j.toxicon.2014.02.019.

Chapter 4, titled “Extraction of microcystins from lake sediments using supercritical

carbon dioxide” by Haihong Song, Brad Berven, Elke S. Reichwaldt, Anas Ghadouani,

is submitted to Toxins.

The material presented in this thesis reflects solely my original ideas and interpretations.

I completed all the work under the supervision of Professor Anas Ghadouani and

Assistant Professor Elke Reichwaldt. For Chapter 4, Dr. Brad Berven provided training

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for the use of supercritical fluid extractor and the methodology to identify the extracted

chemicals suspected to be microcystin variants.

Student signature

_______________

Coordinating supervisor signature

________________

Co-supervisor signature

________________

vi

Contents

Abstract……………………………………………………………….…………………i

Acknowledgements………………………………………………………………….…iii

State of candidate contribution………………………………………………………..iv

Contents………………………………………………………………………...…........vi

List of abbreviations………………………………………………………...………....ix

List of figures…………………………………………………………………………...xi

List of tables……………………………………………………………………....…..xiii

Publications arising from this thesis…………………………………………………xiv

Conference presentations arising from this thesis……………………..……………xv

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

1.1 Background………………………………………………………………..…….3

1.2 Cyanobacteria and cyanobacterial blooms………………….…………………...5

1.3 Cyanobacterial toxins …………………….………………………………..........8

1.4 Microcystins……………………………………………………………………10

1.5 Sediments………………………………………..……………………………..11

1.6 Fate of microcystins in aquatic systems…….……………………….…………13

1.6.1 Microcystin occurrence in aquatic systems……………………………………13

1.6.2 Biodegradation of microcystins…………………….……………………….....14

1.6.3 Adsorption of microcystins to sediments……………………………...……….16

1.7 Microcystins in the sediments ………………………………………………….19

1.7.1 Occurrences of microcystins in the sediments………………………………….19

1.7.2 Factors affecting the occurrence of microcystins in sediments……………..…21

1.7.3 Analysis of microcystins in lake sediments………………………...………….22

1.8 Research aims and hypothesises………………………..………………………26

1.9 Thesis overview……………………………………….………………………..31

Chapter 2. The effects of environmental variables on the spatial and temporal

variability of microcystins in the sediments…………………………....33

2.1 Abstract……………………………..…………………………………………..35

2.2 Introduction…………………………………………………………………….36

2.3 Materials and methods………………………………….………………………39

2.3.1 Study site………………………………………………………………..….…..39

2.3.2 Sampling and sample analyses…………………………………………………40

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2.4 Results………………………………………………………………………….46

2.4.1 Temporal and spatial variability of microcystin concentration in sediments….46

2.4.2 Temporal and spatial variability of environmental variables……..…………...49

2.4.3 Correlation between microcystins in the sediments and environmental

variables…………………………………………………..…………………….50

2.4.4 Prediction of microcystin concentrations in sediments from environmental

variables………………………………………………………………...………51

2.5 Discussion………………………………………………………………………52

2.6 Acknowledgements…………………………………………………..………...56

Chapter 3. Contribution of sediments in the removal of microcystin-LR from the

water……………………………………………………………………....57

3.1 Abstract…………………………………………………………………………59

3.2 Introduction…………………………………...………………………………..60

3.3 Material and methods…………………………………………………………..62

3.3.1 Experimental design…………………………………………………………...62

3.3.2 Sampling…………………………………….……………..………………......64

3.3.3 Experimental set-up……………………………………………….……..…….64

3.3.4 Sediment analysis……………….…………………..…..……………………..65

3.3.5 MCLR analysis………..……………………………….……………….……...66

3.3.6 Data analysis…………………………………………………...……………....66

3.4 Results…………………………………...……………………………………..67

3.4.1 Overall contribution of sediments to the removal of MCLR from the

water……………………………………………………………..…………....67

3.4.2 Adsorption ability versus biodegradation ability of sediments……….……….70

3.4.3 Contribution of water versus contribution of sediment to the removal of MCLR

from water…………………………………….………………………..…….70

3.5 Discussion…………………….……………………………..………….….….71

3.6 Acknowledgements…………………………………………………………….74

Chapter 4. Extraction of microcystins from lake sediments using supercritical

carbon dioxide…………………………………….………………….…75

4.1 Abstract……………………………………………..………………………..…77

4.2 Introduction………………………………………………………………...…..78

4.3 Materials and methods………………………….……………………………....81

4.3.1 Natural and spiked sediment samples……………………………….…………83

4.3.2 Supercritical fluid extraction…………………………….………………….….83

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4.3.3 Conventional extraction method………………………………………….…....85

4.3.4 Microcystin analysis…………………………………………..…………….…85

4.4 Results……………………………………………………………………...…..86

4.4.1 Optimisation of modifiers……………………..…………………………....…86

4.4.2 Optimisation of extraction pressure…………………….…..…………………89

4.4.3 Optimisation of equilibration time…………………….………….…….….….90

4.4.4 Comparison between the supercritical fluid extraction and conventional

extraction method…………………………………………………………..…90

4.5 Discussion………………….…………………………………………………..92

4.6 Acknowledgements…………………………………………………………….94

Chapter 5. Conclusions and recommendations……….………………………….95

5.1 Significance of contribution……………………………………………………97

5.2 Variability of microcystins in the sediments…………….……………….…....101

5.3 Contribution of sediments to the removal of microcystins from the

water………………………………………………………….…………….….102

5.4 Quantification of microcystins in the sediments using supercritical carbon

dioxide….………………………………………………………………..……104

5.5 Policy and management implications………………..………………………..105

Literature cited…………………………………………………………………........108

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List of abbreviations °C Degree Celsius

ANOVA Analysis of variance

c(CB overlying) Concentration of cyanobacterial biomass in overlying water

c(CB surface) Concentration of cyanobacterial biomass in surface water

c(tMC) Concentration of total microcystins

CB Cyanobacterial biomass

chl-a Chlorophyll-a

cm Centimetre

d.f. Degree of freedom

d.m. Dry mass

DDT Dichlorodiphenyltrichloroethane

EDTA Ethylenediaminetetraacetic acid

h Hour

HPLC High-performance liquid chromatography

MCLR Microcystin-LR

MCs Microcystins

m Meter

min Minute

mm Millimetre

MMPB 2-methyl-3-methoxy-4-phenyl-butyric acid

OM Organic matter

p Probability

PAHs Polycyclic aromatic hydrocarbons

PCBs Polychlorinated biphenyls

x

PDA Photodiode array

R2 Coefficient of determination

SFE Supercritical fluid extraction

SPE Solid phase extraction

spp. Species

SPSS Statistical package for the social sciences

TFA Trifluoroacetic acid

tMC Total microcystins

TN:TP ratio Ratio of total nitrogen to total phosphorus concentration

UWA The University of Western Australia

v/v Volume/volume

WHO World Health Organization

xi

List of figures Figure 1.1: Impact of human activities on freshwater resources (modified after

Kundzewicz et al. (2007)). .............................................................................. 4

Figure 1.2: Examples of freshwater systems impacted by proliferating cyanobacterial

blooms (Paerl and Paul, 2012). ....................................................................... 6

Figure 1.3: Structure of microcystin-LR, where L-Leu is L-leucine, D-Me-Asp is D-erythro-

β-methlaspartic acid, L-Arg is L-arginine, Adda is (2s,3s,8s,9s)-3-amino-9-

methoxy-2,6,8-trimethyl-10-phenyldeca-4(E), 6(E)-dienoic acid, D-Glu is D-

glutamic acid, Mdha is N-methyl-dehydroalanine, and D-Ala is D-alanine

(Fang et al., 2011). ........................................................................................ 10

Figure 1.4: Map of the world showing where microcystins have been detected in

freshwater (Si Nang, 2012). .......................................................................... 11

Figure 1.5: A schematic diagram of the natural routes of detoxification of microcystins in

aquatic systems. ............................................................................................. 14

Figure 1.6: Carbon dioxide pressure-temperature phase diagram (Leitner, 2000). ........ 25

Figure 1.7: Framework showing the connection between study aims and the main chapters

of the thesis ................................................................................................... 29

Figure 2.1: Study site and wind conditions. (A) Map of Lake Yangebup with sampling

sites (coordinates for sites 1-4 are 32°07'21"S, E115°49'45", 32°07'07"S,

115°49'31"E, 32°6'50"S, 115°49'43"E and 32°07'11"S, 115°50'07"E

respectively) ; (B) Wind speed and direction for the sampling days and the

two antecedent days for each month. Wind measurements were taken at 9 am

and 3 pm of each day, resulting in 4 measurements for each month, represented

by a line with either a dot or an arrow in wind direction (from: Reichwaldt et

al 2013, which is a study that was performed in parallel to the study presented

here)………………………………………………………………………...47

Figure 2.2: Microcystin concentration (MCs) in sediments ( ● ), total microcystin

concentration in water (▲) and cyanobacterial biomass (CB) (○) in water as

a function of time at sites 1 - 4 (S1 - S4). CB in water is the calculated mean

between CB in surface and overlying water. * indicates missing value for

cyanobacterial biomass. Error bars represent one standard error (with n = 3 for

MC concentration in sediments and n = 2 for CB biomass in water). .......... 47

Figure 2.3: Spatial (A) and temporal variability (B) of microcystin concentrations in lake

sediments. Boxes represent the 25th and 75th percentiles; solid lines within

xii

the boxes mark the median, dashed lines indicate the mean. Whiskers

represent the largest and smallest observed values excluding the outliers.

Filled circles represent outliers (>1.5 box lengths) (n=15). .......................... 48

Figure 3.1: Mean removal of MCLR from the water by (A) sterile lake water with (▲)

and without sediments (●); (B) sterile lake water with sediments without a

microbial inhibitor for three consecutive additions. Numbers denote the

respective additions; curves of best fit are first order exponential decay:

addition 1: c(MCLR) = c(MCLR)0 -0.057t (adj. R2 = 0.908, p < 0.05); addition

2: c(MCLR) = c(MCLR)0 -0.04t (adj. R2 = 0.997, p < 0.05); addition 3: c(MCLR)

= c(MCLR)0 -0.05t (adj. R2 = 0.939, p < 0.05); (C) sterile lake water with

sediments with (▲) and without aeration (●); (D) sterile lake water with

sediments with (▲) and without a microbial inhibitor (●); curves of best fit

are first order exponential decay for the treatment without addition of

microbial inhibitor: c(MCLR) = c(MCLR)0 -0.057t (adj. R2 = 0.908, p < 0.05)

and linear regression for the treatment with addition of microbial inhibitor:

c(MCLR) = 91.52 – 0.29t (adj. R2 = 0.838, p < 0.05); (E) non-sterile lake water

(●) and sterile lake water with sediments (▲). Error bars represent standard

errors in all panels (n = 4). Data of the following treatments are shown twice

within this figure to simplify comparisons: Treatment 3 (▲) is shown in

panels A, C and E; Treatment 5 (●) is shown in panels B and D. ............... 69

Figure 4.1: Extraction procedure for microcystins from sediments using supercritical

carbon dioxide. .............................................................................................. 83

Figure 4.2: Microcystins extracted from spiked sediment samples using supercritical

carbon dioxide with different modifiers: methanol (10%), methanol (8.7%) +

water (1.3%), and water (10%). “*” represents no detectable level of MCLR.

Error bar represent the standard errors of replicates (n = 3). ........................ 87

Figure 4.3: The first 20 min of the high performance liquid chromatogram of extracted

microcystin variants using supercritical carbon dioxide from spiked sediment

samples with different modifiers: (A) 10% water; (B) 8.7% methanol + 1.3%

water. “*” represent microcystin variants other than MCLR. ....................... 88

Figure 4.4: Total microcystins extracted from spiked sediment samples using supercritical

carbon dioxide with different extraction pressures when all other extraction

xiii

conditions are the same. Error bar represents the standard error of replicates

(n = 3). ........................................................................................................... 89

Figure 4.5: Total microcystins extracted from spiked sediment samples using supercritical

carbon dioxide with different equilibration times when all other extraction

conditions are the same. Error bar represents the standard error of replicates

(n = 3). ........................................................................................................... 90

Figure 4.6: Microcystins extracted from spiked sediment samples using the conventional

and supercritical fluid (SFE) methods. Error bar represents the standard error

of replicates (n = 3). ...................................................................................... 92

Figure 5.1: Conceptual framework of the study aims and main findings of the study. MCs

refers to microcystins; CB refers to cyanobacterial biomass ........................ 99

List of tables Table 1.1: Examples of harm caused by cyanobacterial toxins to humans and animals. . 9

Table 1.2: Particle size classification by the Wentworth Grade Scale (after Schöne et al.

(2010). ........................................................................................................... 13

Table 1.3: Reported occurrences of microcystins in the sediments. ............................... 20

Table 1.4: Summary of available methods used for the analysis of microcystins in lake

sediments. ...................................................................................................... 24

Table 1.5: Thesis structure .............................................................................................. 31

Table 2.1: Physicochemical parameter means in each sampling month in Lake Yangebup.

-indicates no measurement due to failure of the probe. DO means dissolved

oxygen………………………………………………………………………41

Table 2.2: Particle size fractions of sediment samples at each sampling site………….. 42

Table 2.3: Statistical results for differences between sites (one-way ANOVA or Kruskal-

Wallis ANOVA on ranks) and between months (repeated-measures ANOVA

or Friedman repeated-measures ANOVA on ranks) of microcystin

concentrations in the sediments and other environmental variables in the

sediments and water. * indicates p < 0.05. n.s. means not significant. ......... 49

Table 2.4: Pearson’s correlation values (R) between microcystin concentrations (MC) in

the sediments (µg/g d.m.) and environmental variables…………………….51

Table 3.1: Experimental design and incubation conditions. ........................................... 64

xiv

Publications arising from this thesis

SONG, H., Coggins, L., REICHWALDT, E. S. & GHADOUANI, A. 2015. The

importance of lake sediments as a pathway for microcystin dynamics and in

shallow eutrophic lake. Toxins, (accepted).

SONG, H., REICHWALDT, E. S. & GHADOUANI, A. 2014. Contribution of sediments

in the removal of microcystin-LR from the water. Toxicon,

10.1016/j.toxicon.2014.02.019.

SONG, H., BERVEN, B., REICHWALDT, E. S. & GHADOUANI, A. 2014. Extraction

of microcystins from lake sediments using supercritical carbon dioxide. Toxins,

(submitted).

REICHWALDT, E. S., SONG, H. & GHADOUANI, A. 2014. Effects of the distribution

of a toxic Microcystis bloom on the small scale patchiness of zooplankton. PLOS

ONE, 8, E66674.

xv

Conference presentations arising from this thesis

SONG, H., REICHWALDT, E. S. & GHADOUANI, A. 2012. The role of sediments in

the removal of microcystin-LR from the water. 2012 ASLO Aquatic Sciences

Meeting, Lake Biwa, Otsu, Shiga, Japan.

REICHWALDT, E. S., HAIHONG, S. & GHADOUANI, A. 2012. Toxic cyanobacterial

blooms as drivers for the heterogeneous distribution of zooplankton in a small,

shallow lake. 2012 ASLO Aquatic Sciences Meeting, Lake Biwa, Otsu, Shiga,

Japan.

1

Chapter 1. Introduction

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CHAPTER 1: INTRODUCTION

3

1.1 Background

Freshwater is regarded as the most essential of natural resources as it is indispensable

for all forms of life. It is required by all terrestrial life and is needed in almost all human

activities. Access to water, sanitation and a healthy environment are basic human rights

that must be respected (United Nations Committee, 2003). The right to water must

encompass both the quality and quantity of water provision. However, rapid population

growth combined with poverty, industrialisation, urbanisation and agricultural

intensification is leading to a global water crisis (Vorosmarty et al., 2010).

The impending water crisis will affect both developing and developed countries. It is

estimated that approximately one-third of the world’s population currently lives in

countries experiencing moderate to high water stress, and it is forecasted that by 2025 as

much as two-thirds of a much-larger world population could be under stress conditions –

that is, using more than 20% of their available resources to compensate for the rise in

population and water use (Arnell, 2004). There are three main factors influencing water

stress: (1) changes in water availability due to climate change; (2) changes in water

withdrawals due to trends in socio-economic drivers; and (3) the threat of pollution from

urban-industrial-agricultural development (Alcamo et al., 2010).

Climate change can have direct and significant impacts on water availability and

quality through high water temperatures, increased precipitation intensity and longer

periods of low flows (Reichwaldt and Ghadouani, 2012). Climate change therefore

exacerbates many problems caused by water pollution, including its impact on

ecosystems, human health, water system reliability and operating costs for water supply

and water treatment (Kundzewicz et al., 2007, Ghadouani and Coggins, 2011).

Anthropogenic climate change is only one of many pressures on freshwater systems. For

4

example, human activities can also affect freshwater resources – both quantity and quality

– in various ways (Figure 1.1).

Figure 1.1: Impact of human activities on freshwater resources (modified after Kundzewicz et al. (2007)).

The combined pressure from natural forces and human activities on surface waters may

provide a competitive advantage to cyanobacteria, leading to an increase in the spatial

and temporal extent of cyanobacterial blooms (Hudnell, 2010). As cyanobacteria biomass

is responsible for a range of amenities, water-quality and treatment problems, the mass

occurrence of cyanobacterial blooms is increasingly attracting the attention of

environment agencies, water authorities, and human and animal health organisations.

Furthermore, some species of cyanobacteria can produce toxins that are hazardous to

human and animal health.

The presence of cyanobacterial toxins in water bodies poses a serious problem.

Numerous planktonic (Takamura et al., 1984, Head et al., 1999, Pal et al., 2007, Welker

et al., 2007) and benthic cyanobacterial blooms (Brunberg and Boström, 1992, Hitzfeld

Freshwater Water use Water timing Water management Water quality & quantity

Human Activity Technology Laws & regulations Population & life style Industry & agriculture

Climate Change Temperature Solar radiation Hydrological cycle Atmospheric CO2

& O3

Anthropogenic forces

Consequences


5

et al., 2000, Latour and Giraudet, 2004, Verspagen et al., 2004, Verspagen et al., 2005,

Ma et al., 2010, Misson and Latour, 2012) can produce toxic peptides and alkaloids.

Ingestion of these cyanotoxins has been linked to liver, ingestive and skin diseases,

neurological impairment and even death of animals (Spoerke and Rumack, 1985, Fraga

et al., 1988, Edwards et al., 1992, Anderson et al., 1996, Mez et al., 1997, Codd et al.,

1999, Bretz et al., 2002, Chen and Chou, 2002, Taleb et al., 2003, Kirkpatrick et al., 2004,

Green et al., 2004, Arena et al., 2004, de Figueiredo et al., 2004, Abouabdellah et al.,

2008, Dörr et al., 2010). The tragic death of 76 patients in a haemodialysis clinic in Brazil

in 1996 was connected to the presence of cyanobacterial toxins in the water used for the

dialysis, and a high incidence of primary liver cancer in China has been attributed to

drinking water contaminated with cyanobacterial toxins (Ueno et al., 1996, Pouria et al.,

1998, Carmichael, 2001, Azevedo et al., 2002). Cyanobacterial toxins are therefore a

major threat to the use of freshwater ecosystems and reservoirs for drinking water,

irrigation and freshwater fishing and recreational purposes. In order to assess and manage

the risk to public health and potential threat to the ecosystem’s organisms, the fate of

cyanobacterial toxins in aquatic systems needs to be clearly understood.

1.2 Cyanobacteria and cyanobacterial blooms

Cyanobacteria (blue-green algae) are ubiquitous inhabitants of aquatic and terrestrial

environments, and natural populations of these organisms occur away from human

influence (Codd et al., 1994). As the earliest oxygen-producing organisms (Bartram et

al., 1999), cyanobacteria have an extensive evolutionary history. Fossil evidence indicates

that they may have emerged as early as 3.5 billon years ago and they have been abundant

for over 2.5 billion years (Summons et al., 1999, Schopf, 2000). The lengthy history and

variable environmental conditions under which cyanobacteria evolved have resulted in

the adaptation of some cyanobacterial taxa to extreme environments, and collectively they

6

are widely dispersed across the globe (Badger et al., 2006). They exist across a multitude

of hot, cold, alkaline, acidic and terrestrial environments, and can proliferate to be the

dominant primary producers in freshwater, estuarine and marine ecosystems (Mur et al.,

1999). These organisms also respond to cultural eutrophication by the development of

massive populations, including blooms, scums and mats (Sutcliffe and Jones, 1992)

(Figure 1.2).

Figure 1.2: Examples of freshwater systems impacted by proliferating cyanobacterial blooms (Paerl and Paul, 2012).

A cyanobacterial bloom results from the rapid increase in the population of

cyanobacteria in an aquatic system. Enrichment (particularly nitrogen and phosphorus)

associated with urban-industrial-agricultural development promotes accelerated

eutrophication, which in turn favours the periodic proliferation and dominance of harmful

blooms of cyanobacteria (Anderson et al., 2002, Heisler et al., 2008, Paerl and Paul, 2012,

Sinang et al., 2013, Duong et al., 2013). Some cyanobacterial blooms can cause adverse


7

effects, including oxygen depletion, reduced water quality aesthetics, clogging of fish

gills and toxicity. In addition, climate change is a potential catalyst for the extension and

intensification of cyanobacterial blooms (Huber et al., 2012). In recent years, as a result

of anthropogenic eutrophication and climate change, the frequency and intensity of

cyanobacterial blooms have apparently increased, increasing the threat to ecological and

public health as well as affecting the aesthetic appearance of the environment

(Carmichael, 1992, Carmichael, 2001, Paerl and Huisman, 2009, Paerl et al., 2011, Paerl

and Paul, 2012, Reichwaldt and Ghadouani, 2012, Lewitus et al., 2012, O’Neil et al.,

2012).

Warm weather may cause cyanobacterial bloom directly or indirectly (Paerl and

Huisman, 2009). Generally, cyanobacteria grow better at higher temperatures, although

there are exceptions at lower temperatures (Elliott, 2012). It has also been suggested that

higher temperatures will cause earlier and longer potential bloom periods and lead to

possible range expansions (Moore et al., 2008; Paerl and Paul, 2012). Indirectly, warm

weather induces many changes that is advantageous for cyanobacterial growth. The

changes include increasing stratification, enhanced nutrient loading caused by changed

rainfall pattern, the decrease of wind speed, light availability to phytoplankton which may

all enhance the probability of cyanobacterial dominance over the other phytoplankton

(Zhang et al., 2011; Reichwaldt and Ghadouani 2011).

It is possible that cyanobacteria possess many unique characteristics that may allow

them to dominate the phytoplankton communities in a changed climate. These

characteristics include: (1) the ability to grow in warmer temperatures; (2) buoyancy, due

to gas vesicles production; (3) high affinity for, and ability to store, phosphorus; (4)

nitrogen-fixation; (5) akinete production and associated life history characteristics; and

(6) light capture at low intensities and a range of wavelengths (Carey et al,. 2014).

However, cyanobacteria are a diverse group with different characteristics, and will

8

respond to different aspects of climate change. There will most likely also be regional

differences in which cyanobacterial taxa dominate.

1.3 Cyanobacterial toxins

Cyanobacterial blooms not only affect the taste, odour and appearance of the water, but

more significantly, some blooms can produce toxins which cause acute and possibly

chronic health problems in humans and a range of aquatic and terrestrial animals (Table

1.1). The majority of cyanobacterial toxins are considered to be secondary metabolites

and are classified into three groups according to their chemical structures and effect on

the human body. For example, cylindrospermopsin can cause gastrointestinal symptoms

or kidney disease; neuroxins can affect the nervous system and hepatotoxin can affect the

liver. Exposure to these toxins can either directly kill the organisms or decrease their

resistance to bacterial or viral infections. The ecological and economic impacts of

cyanobacterial toxins will most likely worsen as the frequency of cyanobacterial blooms

increases in many countries as a result of nutrient eutrophication.


9

Table 1.1: Examples of harm caused by cyanobacterial toxins to humans and animals.

Affected organisms

Routes of exposure

Signs and symptoms References

Humans Drinking water Gastrointestinal illness, Liver cancer

(Ueno et al., 1996, Chorus et al., 2000, Griffiths and Saker, 2003)

Recreational water,

Pneumonia (Van Apeldoorn et al., 2007)

Haemodialysis Acute liver failure (Pouria et al., 1998, Azevedo et al., 2002)

Contact with M. aeruginosa scum

Sore throat, headaches, blistered mouth, diarrhoea and vomiting

(Turner et al., 1990)

Microcystis scum ingestion

Skin rashes, mouth blisters and severe thirst

(Pearson, 1990)

Terrestrial Animals

Exposure to cyanobacteria

Insects killed (Hiripi et al., 1998)

Liver necrosis in birds, leading to death

(Matsunaga et al., 1999)

Exposed to MCLR

Liver damage in mice (Ito et al., 1997)

Drinking water containing microcystins

Liver and kidney damage in rats

(Heinze, 1999, Milutinovic et al., 2002)

Microcystis and Oscillatoria scum ingestion

Death (sheep, dogs) (Codd and and Beattie, 1991 Hunter and Roberts, 1991)

Aquatic Animals

Cell ingestion and exposure to toxins

Microcystin bioaccumulation in tissues of mussels, crayfish, fish et al.; behaviour changes in zooplankton communities

(Williams et al., 1997, Amorim and Vasconcelos, 1999, Vasconcelos, 2001, Liu et al., 2002, Papadimitriou et al., 2012, Gutiérrez-Praena et al., 2013, Ghadouani et al., 1998, Ghadouani and Pinel-Alloul, 2002, Ghadouani et al., 2003, Ghadouani et al., 2004, Ghadouani et al., 2006, Reichwaldt et al., 2013)

10

1.4 Microcystins

Microcystins are a group of cyanobacterial toxins produced by certain species of

cyanobacteria such as Microcystis spp., Anabaena spp., Nostoc spp., and Oscillatoria spp.

They are considered to be one of the most toxic groups of cyanobacterial toxins (Tricarico

et al., 2008, Cordeiro-Araújo and Bittencourt-Oliveira, 2013). The general structure of

microcystins is cyclo-(D-Ala1-X2-DMeAsp3-Z4-Adda5-D-Glu6-Mdha7), where X and

Y are L-amino acids that vary between microcystin variants (Carmichael, 1997) (Figure

1.3). So far, over 90 different microcystin variants have been identified and the number

continues to increase due to better instruments and detection methods (Hotto et al., 2008).

Different variants of microcystins have caused great concern due to their hepatotoxicity.

Figure 1.3: Structure of microcystin-LR, where L-Leu is L-leucine, D-Me-Asp is D-erythro-β-methlaspartic acid, L-Arg is L-arginine, Adda is (2s,3s,8s,9s)-3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4(E), 6(E)-dienoic acid, D-Glu is D-glutamic acid, Mdha is N-methyl-dehydroalanine, and D-Ala is D-alanine (Fang et al., 2011).

Microcystins have been widely reported across the world (Figure 1.4) and many more

occurrences are reported daily by water resources authorities. They have caused livestock,

pet and wildlife deaths and have been implicated in human injury and death through

drinking water and dialysis fluid (Codd et al., 1999, Chorus et al., 2000, Palikova et al.,

2013, Tian et al., 2013). Due to the potential for chronic toxicity from microcystins, the


11

World Health Organization (WHO) has established a guideline value of 1 µg/L and 20

µg/L for total microcystin-LR (MCLR) in drinking water (WHO, 1998) and recreation

water (WHO, 2003) respectively. Therefore, to avoid the potential health risk to humans

and animals, scientists and water authorities should understand the natural degradation

and elimination pathways of microcystins with the aim of reducing the risks associated

with these pollutants.

Figure 1.4: Map of the world showing where microcystins have been detected in freshwater (Si Nang, 2012).

1.5 Sediments

Following their formation, lakes accumulate sediments in a continual process that in

many cases has spanned several thousand years or longer. Sediments are heterogeneous

mixtures that include mineral phases and detrital organic matter (Misson et al., 2012).

Most sediments in surface waters derive from surface erosion and comprise a mineral

component, arising from the erosion of bedrock, and an organic component, arising

during soil-forming processes including biological and microbiological production and

decomposition. An additional organic component may be added by biological activity

within the water body (Schöne et al., 2010).

Canada

USA

South America

Africa

Europe

China

Australia

New Zealand

12

Sediments play a complex role in ecosystem functioning and the provision of

ecosystem services. At the sediment-water interface, they form an integral part of the

ecosystem because they are affected by a continuous flux of physical, chemical and

biological components between the sediment, interstitial water and the overlying water.

The role of sediment as a sink for pollutants and a source of contaminants that potentially

influence the quality of receiving waters has been well recognised (Rodríguez et al., 2007).

Particle size, settling speed and mineralogy are essential characteristics of individual

sedimentary particles. Finer sedimentary particles generally do not exist in isolation, and

in the overlying water they are usually present as aggregates of particles, that is, as flocs.

In contrast, coarser-grained particles usually do not aggregate and are present as

individual particles. Properties of a floc, such as size and density, are often significantly

different from those of the individual particles making up the floc (Sharma et al., 2012).

The most obvious property of sediment is the size of its particles. There is no

universally accepted scale for the classification of particles according to their sizes. In

North America, the Wentworth Grade Scale (Table 1.2) is commonly used; elsewhere,

the International Grade Scale is preferred (Schöne et al., 2010). There are minor

differences between the two scales, and it is therefore important to note which scale has

been selected and to use it consistently (Schöne et al., 2010).


13

Table 1.2: Particle size classification by the Wentworth Grade Scale (after Schöne et al. (2010).

Particle description Particle size (mm) Cobble 256-64 Gravel 64-2 Very coarse sand 2-1 Coarse sand 1-0.5 Medium sand 0.5-0.25 Fine sand 0.125-0.063 Silt 0.062-0.004 Clay 0.004-0.00024

Sediment properties, including particle size, often vary greatly throughout a sediment

bed in the horizontal and vertical directions. Sediment types can change rapidly in the

horizontal direction from coarse sands (where currents and wave action are strong) to

fine-grained muds (where currents and wave action are small). They also can change

rapidly in the vertical direction. For example, during strong storms, layers of coarse sands

may be deposited on top of finer sediments. During quiescent conditions, this coarse layer

may then be covered by fine sediments (Sharma et al., 2012).

The properties of sediments can influence their capacity to adsorb and degrade

microcystins in aquatic systems. These properties include organic matter content (living

biomass of microorganisms, fresh and partially decomposed residues, and humus) and

particle size fraction (sand, slit and clay) (Miller et al., 2001, Mohamed et al., 2006, Chen

et al., 2008, Munusamy et al., 2012).

1.6 Fate of microcystins in aquatic systems

1.6.1 Microcystin occurrence in aquatic systems

Microcystins occur in four parts of the aquatic system: within the cyanobacterial cells

(Jones and Orr, 1994), in the water (Carmichael, 2001), in aquatic organisms (Chen and

Xie, 2005, Song et al., 2007a) and in sediments (Klitzke et al., 2010). They are normally

confined within the cyanobacterial cells and enter the surrounding water after lysis and

cell death (Watanabe et al., 1992a). The amount of intracellular toxins produced within

the cells is influenced by various environmental factors like temperature (Davis et al.,

14

2009), light intensity (Kaebernick et al., 2000), phosphorus (Rinta-Kanto et al., 2009a)

and nitrogen (Long et al., 2001) concentration, TN:TP ratio (Vezie et al., 2002), iron level

(Sevilla et al., 2010) and the presence of other competing phytoplankton (Engström-Öst

et al., 2011). Dissolved microcystins are not very stable under natural conditions (Welker

et al., 2001). There are several processes that can affect the persistence of the dissolved

microcystins in aquatic systems. In the water, these include photolysis, uptake by aquatic

animals leading to bioaccumulation, adsorption to suspended particles and biodegradation

by bacterial communities in the water (Corbel et al., 2014) (Figure 1.5).

Figure 1.5: A schematic diagram of the natural routes of detoxification of microcystins in aquatic systems.

1.6.2 Biodegradation of microcystins

Biodegradation can affect the fate of microcystins in aquatic systems (Dziga et al., 2013)

and it has been reported in lake water (Jones and Orr, 1994, Lahti et al., 1997, Edwards

et al., 2008, Ho et al., 2012b, Ho et al., 2012c), lake sediments (Cousins et al., 1996, Holst

et al., 2003, Song et al., 2014), estuaries (Lemes et al., 2008), sand filters (Bourne et al.,

Microcystins Biodegradation Bioaccumulation

Photolysis Bloom

Bacteria Biodegradation Adsorption

Sediments

Bacteria


15

2006), various biofilms (Saito et al., 2003, Babica et al., 2005, Wu et al., 2010) and

artificial media (Ji et al., 2009). Bacteria capable of degrading cyanobacterial toxins have

been isolated from water, sediments, sand filters, coastal lagoons and even an operating

anthracite bio-filter (Jones et al., 1994, Bourne et al., 1996, Takenaka and Watanabe,

1997, Park et al., 2001, Holst et al., 2003, Chen et al., 2007, Eleuterio and Batista, 2010,

Ho et al., 2012b). Therefore, the biodegradation of microcystins should be of great

importance when studying the natural attenuation of microcystins, particularly its

detoxification mechanism.

It has been suggested that exposure to microcystins drives changes in the structure and

physiology of bacterial communities, which in turn degrade microcystins (Giaramida et

al., 2013). Studies have shown that a history of pre-exposure to cyanobacterial bloom and

microcystins is closely related to the lag phase of biodegradation (Edwards et al., 2008,

Jones and Orr, 1994, Lam et al., 1995a, Christoffersen et al., 2002, Li et al., 2012).

Generally, the biodegradation of microcystins is characterised by a prolonged lag phase

that ranges from a few days to several weeks followed by a rapid decomposition of toxins.

Other factors influencing the ability of bacteria to biodegrade microcystins include

nutrient conditions and temperature. For example, the degradation capability of a biofilm

was shown to be stimulated by nitrate but inhibited by ammonium and glucose (Babica

et al., 2005). Park et al (2001) reported that isolated microcystin-degrading bacteria were

strongly dependent on temperature, with the maximum rate of degradation occurring at

30°C (Park et al., 2001). In one Australian study, it was observed that biodegradation of

microcystin-LR in source water only occurred at 24 °C in spring, while in summer it only

occurred at 14°C (Ho et al., 2012b). It has also been shown that microcystin removal in a

saturated sediment column was higher in temperatures ranging between 16 °C and 32 °C

than below 11°C (Klitzke and Fastner, 2012). Wang et al. (2007) and Ho et al. (2007)

also reported that higher temperature increased the ability of sediments to biodegrade

16

microcystins. It may be that higher temperatures increase the metabolic activity, and by

extension the biodegradation ability, of bacteria.

Additionally, clay and organic matter in the sediments can increase the capacity of

sediments to degrade microcystins. Lam et al. (1995b) reported that in soils with sandy

material, microcystins showed no degradation over 20 days while in soils with higher clay

or higher organic content, their concentration did show a decrease that could be attributed

to biodegradation. Chen et al. (2010) found that glucose addition had no statistically

significant effect on the anoxic biodegradation of microcystins, which implied that

organic carbon content is not a major factor affecting anoxic biodegradation of MCs in

aquatic sediments. The study of Park et al. (2001) suggested that added organic nutrients

competed with microcystins as a source of carbon and energy and therefore inhibited the

degradation of microcystins. In contrast, Holst et al. (2003) found that glucose addition

could stimulate the anoxic biodegradation of microcystins in sediment slurries. The

contradictory results may be due to the bacteria capable of degrading microcystins are

different. It may also because carbon can stimulate bacterial activity by providing an

essential source of carbon and energy for microcystin degrading bacteria while an extra

carbon source may prevent bacteria from degrading microcystin (Chen et al., 2010). The

effect of nitrogen on the degradation rate of microcystins are also contradictory. Chen et

al., 2010 found that microcystin degradation was not coupled to de-nitrification in their

experiments. Differently, Holst et al. (2003) found that the addition of NO3-N

significantly stimulated microcystin degradation under anoxic conditions, and suggested

that this process was coupled to denitrification.

1.6.3 Adsorption of microcystins to sediments

The capability of sediments to adsorb microcystins has been confirmed in batch

adsorption experiments in the laboratory (Mohamed et al., 2007, Munusamy et al., 2012,


17

Song et al., 2014). For example, Rivasseau et al. (1998) reported that as little as 10% of

microcystins were adsorbed on suspended articles and 7% on sandy sediments during a

three-day experiment. However, although these studies clearly indicated that adsorption

by sediments contributes to the detoxification of microcystins under natural conditions,

the significance of adsorption compared to other detoxification mechanisms is not yet

clear.

Adsorption of microcystins to sediments occurs when microcystins bind to the surface

of a sediment, usually at a specific site. However, the mechanisms for the adsorption of

microcystins onto lake sediment are still unclear. Wu et al (2011) observed that the

adsorption of microcystins onto sediments usually involved relatively weak

intermolecular forces and had small energy requirements for hydrogen bonding. They

concluded that the adsorption of microcystins onto sediments was a physisorption process.

However, according to Chen et al. (2006b), the adsorption mechanism of microcystins in

soils and sediments involves not only physical adsorption, but also chemical binding with

the metal ions on the surfaces of the soil and sediment particles. For instance, due to the

nitrogen and oxygen atoms present in their structure, microcystins can chelate with the

metal ions in clay.

The presence of clay and organic matter enhances the capacity of sediments to adsorb

microcystins (Mohamed et al., 2007, Grut macher et al., 2010). It has been reported that

during the sediment passage of microcystins, the adsorption coefficients of microcystins

for fine-grained aquifer materials containing clay and organic matter was much higher

than that for filter sand (Grut macher et al., 2010). The important contribution of clay and

organic matter in sediments to the adsorption of microcystins was further confirmed in

the study by Mohamed et al. (2007). In this study of the Nile River, Egypt, the capacity

of sediments to adsorb microcystins was significantly correlated to their clay and organic

matter, but not their sand or silt content. Other studies have also demonstrated the link

18

between a sediment’s ability to adsorb microcystins and its organic matter and clay

content (Donati et al., 1994, Rapala et al., 1994, Lambert et al., 1996, Morris et al., 2000,

Miller et al., 2001). Additionally, Wu et al (2011) have shown that organic matter in

sediments has a two-fold effect on the adsorption process. For sediments with lower levels

of organic matter (i.e. less than 8%), the adsorption of microcystins decreased with

increasing organic matter and was dominated by the competition for adsorption sites

between the toxins and the organic matter. In contrast, microcystin adsorption to organic-

rich (i.e. more than 8%) sediments increased with increasing organic matter and was

dominated by the interaction between the toxins and the adsorbed organic matter.

Other factors influencing the capacity of sediments to adsorb microcystins include pH,

temperature and salinity. Miller et al. (2001) observed increased adsorption of

microcystins at lower pH values in buffered systems. This result was supported by Liu et

al. (2008), who reported that the adsorption of microcystin-LR and microcystin-LW

decreased significantly with increased pH. Wu et al. (2011) and Munusamy et al. (2012)

also confirmed that the adsorption of microcystin-LR onto sediments and clay was pH

dependent (i.e. adsorption decreased with increasing pH). Wu et al. (2011) suggested that

pH influenced the surface charge heterogeneity of the sorbate (microcystin-LR) and

sorbents (sediments and clay). Wu et al. (2011) also found that temperature influenced

the adsorption capacity of sediments differently and depended on their organic matter

content nts. Sediments with higher levels of organic matter (i.e. more than 8%) a stronger

adsorption capacity at higher temperatures, while sediments with lower levels of organic

matter (i.e. less than 8%) showed the opposite tendency. With regards to salinity, it is

reported that increasing salinity may enhance the adsorption ability of sediments and soils

to cyanobacterial toxins by increasing a solution’s ionic strength (Miller et al., 2001).


19

1.7 Microcystins in the sediments

1.7.1 Occurrences of microcystins in the sediments

Microcystins have been found in a variety of sediments, particularly those in eutrophic

lakes with a history of toxic cyanobacterial blooms (Table 1.3). The occurrence of

microcystins in sediments has multiple consequences. For example, they have been

shown to affect benthic organisms living in these sediments (Montagnolli et al., 2004,

Mohamed et al., 2007). Hazards caused by microcystins in lake sediments should be of

particular concern in shallow aquatic systems where the interaction between water and

sediment is greater.

Sediments may play an important role in the fate of microcystins. On the one hand,

sediments can act as a sink for microcystins. For example, cyanobacterial cells from the

water can settle on the sediments and release microcystins when they lyse (Wörmer et al.,

2011). Microcystins can also accumulate in sediments through their adsorption from

water (Tsuji et al., 2001) and the sinking of fecal pellets from aquatic life (Sivonen and

Jones, 1999). On the other hand, sediments can be a source of cyanobacterial cells and

microcystins in water via reinvasion (Verspagen et al., 2004). In addition, sediments can

also facilitate the biodegradation of microcystins (Chen et al., 2010). Therefore,

sediments should not be neglected when considering the fate of microcystins in aquatic

systems.

Understanding the mechanisms by which sediments remove microcystins in the water

is important. This includes a quantification of the relative contribution of biodegradation

and adsorption by sediments. Hence, one aim of the present study is to quantify the

sediment’s contribution to the removal of microcystins from the water and compare the

relative contribution of the biodegradation and adsorption abilities of sediments in the

removal of microcystins from the water.

20

Table 1.3: Reported occurrences of microcystins in the sediments.

Sediment depth (cm)

Microcystins concentration (µg/g)

Place References

0-5 0.017-0.35 Brno reservoir, Czech Republic

(Babica et al., 2006)

0-5 0-1.95 Dianchi Lake, China (Wu et al., 2012)

0-10 25-300* Quitzdorf Reservoir, Germany

(Ihle et al., 2005)

0-10 0.039-0.092 Nile River, Egypt (Mohamed et al., 2007)

0-20 20.4-168.1 Taihu Lake, China (Chen et al., 2008)

0-30 0.08-2.33 Tsukui Lake, Japan (Tsuji et al., 2001)

0-30 0-0.12 Donghu Lake, China (Chen et al., 2006a) * µg/L

Although previous studies have reported the temporal and spatial variability of

microcystins in sediments, there has little agreement between researchers. Babica et al.

(2006) observed high microcystin concentrations in sediments during spring, followed by

a decrease until the summer months when cyanobacterial biomass in the water was at a

maximum. This finding agreed with Ihle et al. (2005). In contrast, Chen et al. (2008)

found that the highest microcystin concentrations in sediments usually occurred during

summers, when cyanobacterial biomass and microcystin production were at maximum.

For vertical distribution, Latour et al. (2007) found high concentrations of microcystins

not only at the sediment’s surface but also at depths of 25–35 cm and 70 cm. Chen et al.

(2008) investigated the distribution and annual variations of microcystins in sediments in

Lake Taihu, China, and found that microcystins were mainly distributed on the surface of

sediments, and their concentration decreased with increasing sediment depth.


21

1.7.2 Factors affecting the occurrence of microcystins in sediments

Many factors can contribute to the variability of microcystins in lake sediments. First,

the occurrence of microcystins in lake sediments can be attributed to the presence of

cyanobacterial cells in these sediments, and microcystins have been reported in the

presence of toxic cyanobacteria in a variety of sediments (Mez et al., 1997, Ihle et al.,

2005, Mohamed et al., 2007, Kim et al., 2010, Rinta-Kanto et al., 2009b). Similarly,

microcystin concentrations in the Nile River and irrigation canal sediments correlated to

the total count of cyanobacteria in the water, particularly Microcystis, and microcystins

within phytoplankton cells at most sampling sites (Mohamed et al., 2007). Second, the

occurrence of microcystins in sediments can be caused by the sedimentation of

microcystin-containing particles in the water. Such a process was studied in three

reservoirs located in Central Spain and the result identified sedimentation as a major

destination of the toxins (Wörmer et al., 2011). Third, the adsorption of dissolved

microcystins can be affected by the physicochemical properties of sediments and

microcystin concentrations in the water (Miller et al., 2001, Chen et al., 2006b, Mohamed

et al., 2007, Wu et al., 2011, Munusamy et al., 2012).

In summary, in addition to cyanobacterial biomass in sediments, other environmental

variables – such as cyanobacterial biomass and microcystins in the water – could

contribute to the variability of microcystins in the sediments. Although it is important to

understand the correlation between the variability of microcystins in lake sediments and

potential contributing environmental factors in order to better understand the fate of

microcystins in aquatic systems, studies in this area are lacking. Hence, another aim of

the current study was to examine the temporal and spatial variability of microcystins in

sediments to detect any correlation between their variability and other environmental

factors such as the concentration of cyanobacterial biomass and microcystins in the water.

22

1.7.3 Analysis of microcystins in lake sediments

1.7.3.1 Conventional solvent methods

While understanding the occurrence of microcystins in lake sediments is important, the

process was hindered by the lack of a suitable quantification method to measure

microcystins in sediment samples. Conventional methods to extract microcystins from

sediments usually involve the use of chemical solvents (Table 1.4). Babica et al. (2006)

evaluated different extraction approaches and investigated recoveries for a range of

sediment types, toxin variants, and extraction solvents. They found that extraction with

5% acetic acid in 0.2% trifluoroacetic acid (TFA)-methanol was the most appropriate for

sediments with different organic material contents. Chen and co-workers (2006a)

introduced an improved extraction method based on aqueous solutions of strong chelator,

EDTA-sodium, which are thought to displace adsorbed toxins from metal ions on

inorganic particle surfaces. Extraction efficiencies were reported to be around 90% for a

range of sediment and soil samples. Recovery rates using these solvent-based extraction

methods seem to be satisfactory, but they are time consuming and a clean-up step is

usually needed which is costly and may result in the loss of microcystins.

1.7.3.2 MMPB method

The MMPB (2-methyl-3-methoxy-4-phenylbutyric acid) method is widely considered

to be an effective analytical procedure for the total quantification of microcystins in lake

sediments. This technique involves cleavage of the chemically unique C20 amino acid-

amino acid (2S, 3S, 8S, 9S)-3-amino-9-methoxy-2, 6, 8-trimethyl-10-phenyldeca-4, 6-

dienoic acid (Adda) to form MMPB for indirect quantification of the total microcystins.

The MMPB method was applied to sediment samples collected from Japanese lakes and

microcystins were detected in six of the eleven samples (Tsuji et al., 2001). However,

there is still a significant problem with this method, because the necessary very low


23

reaction temperature of -78°C is difficult to achieve and involves oxidation of the

microcystins to form MMPB by ozonolysis. In addition, as this method involves the

conversion of microcystins, only total toxin concentrations can be detected, which is

impractical when information about the concentration of microcystin variants is required.

1.7.3.3 Lemieux oxidation method

Free/extractable MCs refers to the microcystin fractions which are readily extracted

without changing their molecular structures. They are usually loosely adsorbed onto

sediments and can be easily redissolved in the aqueous phase (Wu et al., 2012). For the

determination of free/extractable and total microcystins in lake sediment, an analytical

procedure based on a combination of solvent extraction and Lemieux oxidation was

developed (Wu et al., 2012). In this study, a sequential solvent extraction using 50% (v/v)

methanol and EDTA-sodium pyrophosphate was selected as the optimal extraction

solvent for free microcystin analysis. After the extraction, the purified extracts and

sediment residuals underwent optimised Lemieux oxidation to determine the total

microcystins in lake sediments. Unfortunately, information regarding the effects of the

primary factors such as reaction conditions and sediment characteristics (i.e. organic

matter) on the oxidation efficiency was not provided by Wu et al. (2012). As for the

MMPB method, this method only applies to the measurement of total microcystin.

Therefore, the development of an effective, time-saving and more selective detection

method was urgently needed to reliably quantify microcystin concentrations in sediment

samples.

24

Table 1.4: Summary of available methods used for the analysis of microcystins in lake sediments.

Method

Solvent/Pathway

Shortcomings

References

Conventional solvent methods

5% (v/v) acetic acid in methanol containing 0.1% or 0.2% (v/v) trifluoroacetic acid (TFA)

Use of large amount of chemical solvents, time consuming, clean-up procedure is required

(Tsuji et al., 2001, Babica et al., 2006, Mohamed et al., 2007)

5% (v/v) acetic acid (Ihle et al., 2005)

0.1 M EDTA–0.1 M Na4P2O7

(Chen et al., 2006a, Chen et al., 2006b)

MMPB method

Ozonolysis of the Adda moiety to form MMPB for indirect quantification of the total microcystins

Toxin structure is destroyed and only total toxin concentrations can be detected

(Tsuji et al., 2001)

Lemieux oxidation method

Sequential solvent extraction using 50% (v/v) methanol and 0.1 M EDTA–0.1 M Na4P2O7 followed by optimised Lemieux oxidation for determination of total microcystins

Only total toxin concentrations can be detected

(Wu et al., 2012)

1.7.3.4 Supercritical fluid extraction – a potential method

Extraction of organic compounds using supercritical fluids as solvents is gaining

increased attention. Supercritical fluid extraction (SFE) is based on the use of unique

physicochemical features of supercritical fluids that are in the state between liquid and

gas (Figure 1.6). The density of a supercritical fluid is typically 102–103 times that of the

gas. Consequently, molecular interactions increase due to shorter intermolecular

distances. However, the diffusion coefficients and viscosity of the fluid, although density

dependent, remain more like that of a gas. The ‘liquid-like’ behaviour of a supercritical

fluid results in greatly enhanced solubilising capabilities when compared to the

corresponding liquid. These properties allow for similar solvent strengths to liquids but


25

greatly improved mass-transfer properties that facilitate rapid extraction rates and more

efficient extraction due to a better penetration of the matrix. Supercritical fluid extraction

technology has been applied in extracting organic compounds from solid matrices for

analytical work (Anitescu and Tavlarides, 2006). In general, SFE has a number of

potential advantages including more rapid extraction rates, more efficient extractions,

increased selectivity, and the potential to quantify a combined analyte fractionation.

Figure 1.6: Carbon dioxide pressure-temperature phase diagram (Leitner, 2000).

Among various supercritical fluids used for extraction, supercritical carbon dioxide is

the most widely used since it is non-toxic, non-flammable, non-corrosive, easy to handle

and allows supercritical operation at low pressures and near room temperature. It is

therefore is widely applied in the analysis of compounds in environmental samples

(Sunarso and Ismadji, 2009). Supercritical carbon dioxide has been used on various soils

and sediments to analyse contamination by a plethora of chemicals like PCBs, PAHs,

26

DDT, dioxins and furans, and pesticides in order to extract contaminants (Anitescu and

Tavlarides, 2006). Application of this technique has been also reported for the

determination of pesticides in complex matrices such as soils, sediment and water

samples (after a solid-phase extraction), plant materials, animal tissues and food items

(Camel, 1998).

Pyo and co-workers used the SFE technique to extract microcystins from lysed algal

bloom samples in a series of studies which proved the advantages and practical use of

supercritical carbon dioxide in the extraction of microcystins (Pyo and Shin, 1999, Pyo,

1999, Pyo and Shin, 2001, Pyo et al., 2004). However, so far, to our best knowledge, no

study has attempted to use SFE to extract microcystins from sediments and soils.

Therefore, the third aim of the present study was to evaluate the applicability and

efficiency of SFE for the extraction of microcystins from lake sediments.

1.8 Research aims and hypothesises

Overall, the objective of this study was to identify the role of sediments in the fate of

microcystins in shallow aquatic systems (Figure 1.7). More specifically, this study aimed

to:

1. identify the temporal and spatial variability of microcystins in the sediments of

a shallow lake and determine any correlations between this variability and

environmental variables. This part of research was based on the hypothesis that

the variability of microcystins in lake sediments is influenced by a combination

of environmental variables

2. quantify the significance of the sediment’s contribution to the removal of

microcystins from the water and compare the relative contribution of the

biodegradation and adsorption abilities of sediments to the removal of

microcystins from the water. This part of study was based on the hypothesis


27

that sediments can significantly contribute to the natural attenuation of

microcystins in water

3. evaluate the applicability and efficiency of SFE as a method for

quantifying microcystin concentrations in lake sediments. This part of study

was based on the hypothesis that supercritical carbon dioxide has the potential

to be applied in the quantification of microcystin in these sediments.

29

Figure 1.7: Framework showing the connection between study aims and the main chapters of the thesis

CB in sediments

MCs in sediments

Sediments

Chapter 2: To identify the temporal and spatial variability of microcystins in the sediments of a shallow lake and determine the correlation of the variability with environmental variables (Aim 1).

CB in water

MCs in water

Water

Chapter 4: To evaluate the applicability and efficiency of supercritical carbon dioxide in the quantification of microcystins from lake sediments (Aim 3).

Chapter 3: To quantify the significance of the sediment’s contribution to the removal of microcystins from the water column and compare the relative contribution of the biodegradation and adsorption abilities of sediments to the removal of microcystins from the water column (Aim 2).

CH

APT

ER

1: INTR

OD

UC

TION


31

1.9 Thesis overview

The body of work presented in this thesis is composed of three experimental chapters:

Chapters 2–4. Each of these chapters was written as a paper intended for publication in a

journal. Each has its own introduction, materials and methods, results, discussion and

conclusion. Table 1.5 provides an outline of the thesis structure, including the research

aims for each section.

Table 1.5: Thesis structure

Research aims Chapter 1 Introduction

Chapter 2 Spatial and temporal variability of microcystins in lake sediments and correlation of the variability with environmental variables

Quantification of temporal and spatial variability of microcystins in the sediments of an urban lake.

Determination of the correlation between microcystin variability in sediments and environmental variables.

Chapter 3 Contribution of sediments in the fast removal of microcystin-LR from the water

Quantification of the sediment’s contribution to the removal of microcystin-LR in water.

Comparison of the biodegradation ability and adsorption ability of sediments to microcystin-LR in water.

Chapter 4 Extraction of microcystins from lake sediments using supercritical carbon dioxide

Evaluation of the applicability and efficiency of SFE of microcystins from lake sediments.

Comparison of the SFE and conventional methods for both natural and spiked sediment samples.

Chapter 5 Conclusions and recommendations

Chapter 2. The effects of environmental variables on the spatial and temporal variability of microcystins in the sediments

SONG, H., REICHWALDT, E. S. & GHADOUANI, A. 2014. The effects of

environmental variables on the spatial and temporal variability of microcystins in

lake sediments. Toxions, (submitted).

CHAPTER 2: VARIABLITY OF MICROCYSTINS IN LAKE SEDIMENTS

35

2.1 Abstract

Microcystins are toxins produced by cyanobacteria. They occur in aquatic systems

across the world and their occurrence is expected to increase in frequency and magnitude.

As microcystins are hazardous to humans and animals, it is essential to understand their

fate in aquatic systems in order to control the health risks. While the occurrence of

microcystins in sediments has been widely reported, the factors influencing their

occurrence and variability are not yet well understood. In the present study, microcystin

concentrations in lake sediments and their correlation with environmental variables were

analysed. Microcystins were detected in all sediment samples, with concentrations

ranging from 0.06 to 0.78 µg equivalent microcystin-LR/g sediments (dry mass). The

concentration of microcystins in the sediments had weak but significant correlation with

the concentration of intracellular microcystins, total microcystins and cyanobacterial

biomass in the surface water. The combination of total microcystins in surface water,

cyanobacterial biomass in the water, pH and temperature accounted for 46.7% of the

variability of microcystins in sediments. The present study highlights the importance of

the interaction between water and sediments in the distribution of microcystins in aquatic

systems.

Keywords: Cyanobacterial bloom; Microcystins; Cyanobacterial biomass; Sediments;

Spatial variability; Temporal variability


36

2.2 Introduction

Microcystins are produced by certain species of cyanobacteria and are hazardous to

humans and animals. Considered to be the most toxic group of cyanobacterial toxins,

microcystins are inhibitors of specific protein phosphatases and can cause skin irritations,

allergic reactions and gastroenteritis (Žegura et al., 2011). Humans are primarily exposed

to microcystins via drinking water consumption and accidental ingestion of recreational

water (Chorus et al., 2000, Funari and Testai, 2008). Recreational exposure by skin

contact or inhalation to microcystins is now a recognised cause of a wide range of acute

illnesses which can be life-threatening (de la Cruz et al., 2011). While microcystins have

been reported across the world (Zurawell et al., 2005), the frequency of their occurrence

in aquatic systems is expected to rise as a result of climate change (Moore et al., 2008,

Paerl and Huisman, 2009, El-Shehawy et al., 2012, Reichwaldt and Ghadouani, 2012,

Paerl and Paul, 2012). To control their associated health risks, it is therefore essential to

understand their fate in aquatic systems.

In aquatic systems, microcystins can be present not only in the water but also in the

sediments. They have been reported in a variety of sediments, with or without the

occurrence of benthic cyanobacteria (Mez et al., 1997). Microcystins were found in six

of eleven sediment samples collected from four Japanese eutrophic lakes, at

concentrations ranging from 0.08 to 2.33 µg/g d.m. (Tsuji et al., 2001). Similarly,

sediment samples collected from Brno reservoir (Czech Republic) showed concentrations

between 0.016 and 0.474 µg/g d.m. (Babica et al., 2006). The occurrence of microcystins

in lake sediments indicates that sediments might play an important role in the fate of these

toxins in aquatic systems (Corbel et al., 2014, Song et al., 2014). As their occurrence in

lake sediments is also a potential hazard to benthic organisms – for example, by altering


37

the organisms’ metabolism (Montagnolli et al., 2004, Mohamed et al., 2006) – their wide

occurrence in lake sediments should be of concern.

Studies on the distribution of microcystins in sediments of aquatic systems are limited

because it is hard to quantify the amount of microcystins present in sediments (Tsuji et

al., 2001). Earlier study shows that, with conventional solvents for toxin extraction, the

extraction efficiency strongly depends on the extraction solvent and was affected by the

type of sediment and the structures of the microcystins (Babica et al., 2006). The MMPB

(2-methyl-3-methoxy-4-phenylbutyric acid) method is widely considered to be an

effective analytical procedure for the total quantification of microcystins in lake

sediments. However, the necessary very low reaction temperature of -78°C is hard to

achieve. EDTA- sodium pyrophosphate solution as an extraction solvent proved to be

highly efficient and achieved over 90% recovery with the sediment samples used in the

study of Chen et al (2006). The easily extractable microcystins in the upper layer of lake

sediments should be area requiring more focus due to the frequent interaction with the

water column and the easy access for aquatic animals.

Microcystin concentrations in sediments have been shown to be highly variable on a

temporal and spatial scale. High microcystin concentrations have been found in sediments

during spring, followed by a decrease until the summer months when cyanobacterial

biomass in the water was at a maximum (Babica et al., 2007; Ihle et al., 2005). In contrast,

Chen et al. (2008) have found that the highest microcystin concentrations in sediments

usually occur during summers, when cyanobacterial biomass and microcystin production

are at a maximum. Microcystins have been reported at different depths of sediments

(Latour et al. 2007; Tsuji et al. 2001) – for example, their concentration decreased with

increased depth of sediment in Lake Taihu, China (Chen et al. 2008). However, no studies

have been carried out into the spatial variability of microcystins in sediments across a

lake.


38

Many factors have the potential to contribute to the variability of microcystins in lake

sediments. Ihle et al. (2005) reported that microcystin concentrations in the sediments of

a shallow lake were correlated to Microcystis biomass in the sediments. Similarly,

microcystin concentrations in the sediments of the Nile River and irrigation canal

sediments were correlated to the total count of cyanobacteria, particularly Microcystis

spp., and intracellular microcystins in the water (Mohamed et al., 2007). The

concentration of microcystins in sediments can also be influenced by the sedimentation

of suspended particles with absorbed microcystins (Wörmer et al., 2011) and the

adsorption of dissolved microcystins from the water (Tsuji et al., 2001, Babica et al., 2006,

Mohamed et al., 2007, Chen et al., 2008, Munusamy et al., 2012, Song et al., 2014).

Furthermore, the properties of sediments can influence their capacity to adsorb and

degrade microcystins in aquatic systems. These properties include organic matter content

and particle size fraction (sand, slit and clay) (Miller et al., 2001, Mohamed et al., 2006,

Chen et al., 2008, Munusamy et al., 2012). Additionally, physicochemical parameters of

lake water such as temperature, salinity and pH can influence the capacity of sediments

to adsorb and degrade microcystins (Miller et al., 2001, Wang et al., 2007, Liu et al., 2008,

Wu et al., 2011). To date, however, systematic studies on the correlation between the

variability of microcystins in lake sediments and potential contributing environmental

factors are lacking.

The present study aims to identify the spatial and temporal variability of microcystins

in the upper layer of sediments of a shallow, eutrophic lake, and to determine the

environmental variables contributing to the variability. We hypothesized that the

variability of microcystin concentration in lake sediments can be predicted using a

combination of environmental variables such as cyanobacterial biomass in the sediments,

cyanobacterial biomass and microcystins in the water, physicochemical parameters of

lake water and sediment characteristics.


39

2.3 Materials and methods

2.3.1 Study site

This study was conducted at Lake Yangebup in Western Australia (32°6'40"S,

115°50'00"E). Lake Yangebup is a shallow, eutrophic, permanent lake with about 68.4

ha of open water and a mean water depth of 2.5 m (Arnold and Oldham, 1997). In a

previous study in 2008-2010, the range of total phosphorus and total nitrogen in the water

column of Lake Yangebup was 0.49-6.98 µM and 0.14-0.37 mM respectively (Si Nang,

2012). Toxic cyanobacterial blooms dominated by Microcystis spp. frequently occur in

this lake and microcystins are present in the water most of the year (Kemp and John,

2006, Reichwaldt et al., 2013, Sinang et al., 2013). The range of concentration of

microcystins in the water column was 1-66 µg equivalent microcystin-LR/L in a previous

study in 2008-2009 (Si Nang, 2012). Lake Yangebup is a groundwater through-flow lake

that receives groundwater from the east and discharges it to the west. The lake is usually

mixed and stratification only occurs on days that receive high insolation with wind speeds

< 6 m/s (Arnold and Oldham, 1997). It is surrounded by dense fringing vegetation and an

earlier investigation has reported the presence of a flocculate sediment layer covering a

more consolidated sediment (Arnold and Oldham, 1997).


40

Figure 2.1: Study site and wind conditions. (A) Map of Lake Yangebup with sampling sites (coordinates for sites 1-4 are 32°07'21"S, E115°49'45", 32°07'07"S, 115°49'31"E, 32°6'50"S, 115°49'43"E and 32°07'11"S, 115°50'07"E respectively) ; (B) Wind speed and direction for the sampling days and the two antecedent days for each month. Wind measurements were taken at 9 am and 3 pm of each day, resulting in 4 measurements for each month, represented by a line with either a dot or an arrow in wind direction (from: Reichwaldt et al 2013, which is a study that was performed in parallel to the study presented here).

2.3.2 Sampling and sample analyses

Samples were taken monthly between August and December 2010 from four shore sites

of Lake Yangebup (Figure 2.1A). Sampling sites were chosen because they could

surround the whole lake and they were accessible. All samples, taken between 7:30 am

and 1:30 pm from water at a depth of 0.6-0.7 m from the surface, were stored on ice in

the dark for transport to the laboratory. Temperature, pH, salinity and dissolved oxygen

were taken in-situ from 10 cm below the surface water (referred to as ‘surface water’) and

directly above the sediments (referred to as ‘overlying water’) with probes (WP-81; TPS-

DO2). There was no significant difference for these parameters (ANOVA, p > 0.05)

between sites and the average values of these parameters are given in Table 2.1. At each


41

site, measurements for both surface water and overlying water were taken above the

points where sediment samples were collected. Dissolved and intracellular toxins in

surface water samples as well as cyanobacterial biomass in surface water and overlying

water samples were quantified in the laboratory.

Table 2.1: Physicochemical parameter means in each sampling month in Lake Yangebup. - indicates no measurement due to failure of the probe. DO means dissolved oxygen.

Temperature (°C)

Salinity (mg/L)

DO (%)

DO (mg/L)

pH

August 15.8 945 54.6 5.6 8.4 September 18.4 950 55.6 5.2 8.7 October 21.1 1113 64.0 5.8 8.3 November 22.9 1204 - - 8.5 December 21.9 1239 - - 9.3

For each sampling site, three sediment samples (0–4 cm) were collected with a

transparent, polycarbonate sediment corer with a stainless-steel cutter (50 mm in

diameter). The detailed design of this sediment corer and the details of the sediment

sampling operation were given in Linge and Oldham (2002). In brief, to avoid disturbing

the sediment inside the corer, the corer was manually pushed into the lake sediments with

the handle removed, the tube filled with water and the top sealed with a plastic screw cap.

The sediments were then gently pushed out after siphoning off the overlying water

column. Sediments in each sample were thoroughly mixed for quantification of

cyanobacterial biomass, microcystin concentrations, and sediment characteristics. Wind

direction and speed data came from the Australian Bureau of Meteorology monitoring

station at Jandakot Airport, which is 3km from Lake Yangebup (Figure 2.1B).

Mean daily air temperature of 14 days before the sampling day, recorded by weather

stations located nearest to Lake Yangebup, was used as a substitute for surface water

temperature in the data analysis, because water temperature depends strongly on the time

of day when sampling takes place. Earlier studies reported a good correlation between

water temperature in shallow lakes and air temperature, and found that microcystin


42

concentration in the water could be best predicted by mean daily air temperature of the

preceding 14 days (Yen et al., 2007).

The analysis of particle size was adapted from Bowman and Hutka (2002). . The

adaptions included heating the samples for a quicker digestion of the organic matter and

removing the hydrogen peroxide by sequential dilution instead of evaporation. The

particle size distribution analyser used was the Malvern Mastersizer 2000 particle size

analyser with a Malvern Hydro 2000G automated wet dispersion accessory. Organic

matter, measured by loss-on-ignition, was analysed according to Heiri et al. (2001) but

with samples dried in an oven at 110°C overnight instead of freeze drying. The organic

matter content of sediments in Lake Yangebup ranged from 0.55% to 6.17%. With respect

to particle size distribution, all sediment samples were sand, consisting of coarse and fine

sand particles with a coarse sand content. of 88 ± 10.12%. The particle size distribution

for sediment samples at each sampling site are given at Table 2.2.

Table 2.2: Particle size fractions of sediment samples at each sampling site

Sampling sites

Clay % (< 2 µm)

Silt % (2-20 µm)

Fine sand % (20-200 µm)

Coarse sand % (200-2000 µm)

Total sand %

Site 1 0 0 13.12±6.45 86.88±6.45 100 Site 2 0 0 21.05±2.71 78.95±2.72 100 Site 3 0 0 10.66±2.88 89.40±2.88 100 Site 4 0 0 3.22±0.87 96.78±0.87 100

2.3.2.1 Analysis of dissolved microcystins in the water

Surface water samples, 900–1000 ml for each water sample, were first filtered through

pre-combusted GF/C filters (Whatman). Each filtrate was then applied to a solid phase

extraction (SPE) cartridge (Oasis HLB 6cc/500mg, Waters, Australia) with a flow rate <

10 ml per minute for cleaning and concentration. Afterwards, the cartridge was washed

with 5 ml of 10% and 20% (v/v) methanol in sequence before blowing a constant air flow

through the cartridge for drying. Microcystins were eluted from the cartridge with 5 ml

of 100% methanol + 0.1% trifluoroacetic acid (v/v). The elutes were dried with nitrogen


43

gas at 45°C before being re-dissolved in 1 ml of 30% acetonitrile (v/v) and analysed by

high-performance liquid chromatography (HPLC) with a photodiode detector (1.2 nm

resolution) (Waters Alliance 2695) and an Atlantis® T3 separation column (4.6x150mm

i.d, 3 µm, 100 Å). The HPLC gradient was identical to Lawton et al. (1994), but with a

maximum of 100% acetonitrile and therefore a longer run time of 37 min. Column

temperature was 37.5 ± 2.5 °C and the limit of detection was 1.12 ng. Our limit of

quantification was 3x the limit of detection. Peaks that showed a typical microcystin

absorption spectrum with a maximum at 238.8 nm were quantified by comparing the peak

area with the area of a known standard (microcystin-LR; Sapphire, Australia).

2.3.2.2 Analysis of intracellular microcystins in the water

Water samples were filtered on pre-combusted and pre-weighed GF/C filters

(Whatman). Filters were dried at 60°C for 24 hours (h) before being re-weighed to

quantify biomass and stored at -21°C until microcystin extraction. Before the extraction

of microcystins, the filters were thawed and re-frozen three times. The extraction was

achieved with 6 ml of 75% methanol (v/v) per filter. Filters were sonicated on ice for 25

min, followed by gentle shaking for another 25 min. The extracts were then centrifuged

at 3,273×g (Beckman and Coulter, Allegra X-12 Series) for 10 min. at room temperature.

Extracts were collected and filters were extracted two more times. After the three

extractions, the supernatants were combined and diluted with Milli-Q water to 20%

methanol (v/v) before they were applied to SPE cartridges (Oasis HLB 6cc/500mg,

Waters, Australia). The subsequent analysis procedure was identical to that used for the

dissolved microcystin analysis.

2.3.2.3 Analysis of total microcystins in the sediments

The method used for quantification of microcystins in the sediments was adapted from

Babica et al., 2006. In short, each freeze-dried sediment sample, 2.0-2.2 g (dry weight),


44

was extracted twice with 20 ml of 5% acetic acid in 0.2% trifluoroacetic acid - methanol

using an ultrasonic bath for 30 min. After each extraction, the centrifuge tube was

centrifuged for 10 min at 3,273×g (Allegra X-12, Beckman Coulter) and the supernatants

from both extractions were combined, diluted with Milli-Q water to 20% methanol (v/v)

and then applied to SPE cartridges (Oasis HLB 6cc/500mg, Waters, Australia). The

subsequent analysis procedures – such as the cleaning and concentration of microcystins

using SPE cartridges and the quantification of microcystins with HPLC – were identical

to those used for the dissolved microcystin analysis.

In a preliminary laboratory test, the recovery rate for microcystin-LR in the sediment

samples using the method described above is 87%, while a low recovery rate of 32% was

achieved using the EDTA method as described in Chen et al. (2006). Therefore, in the

current study, 5% acetic acid in 0.2% trifluoroacetic acid-methanol was adopted to extract

microcystins from the sediment samples. However, due to the limitation of this extraction

method, the microcystins in the sediments in this study represent the easily extractable

microcystins.

Throughout this study, we refer to the total concentration of microcystin variants per

sample as microcystin concentration. Total microcystin concentration (intracellular +

dissolved) is expressed as micrograms (microcystin-LR mass equivalents) per litre water,

intracellular microcystin concentration is expressed as micrograms (microcystin-LR mass

equivalents) per gram cyanobacterial dry mass and the total concentration of microcystin

variants per sediment (easily extractable microcystins) is micrograms (microcystin-LR

mass equivalents) per gram dry sediments.

2.3.2.4 Cyanobacterial biomass in the sediments and water

The cyanobacterial biomass of each water sample was measured in the laboratory with

a bench top version of the FluoroProbe (bbe Moldaenke, Germany) as µg chl-a/L (Beutler


45

et al., 2002, Ghadouani and Smith, 2005). A strong linear correlation was found between

the chlorophyll-a concentration measured with FluoroProbe and the chlorophyll-a

concentration measured with the standard method (R2 = 0.94, N = 32, p < 0.05) in an

earlier study (Si Nang, 2012). Sediment samples were washed with tap water until no

cyanobacteria colonies in the sediments could be observed microscopically. The tap water

was filtered through gauze (63 µm) to capture these colonies, which were then transferred

with 30 ml of deionized water into a 100 ml glass bottle. This washing procedure was

repeated twice and the cyanobacterial colonies were combined in a total of 100 ml

deionized water. The concentration of cyanobacterial biomass in this bottle was

quantified using the bench top version of the FluoroProbe (bbe Moldaenke, Germany) as

µg chl-a/L (Beutler et al., 2002, Ghadouani and Smith, 2005).

2.3.2.5 Statistical analyses

Differences between sites were analysed using a one-way analysis of variance

(ANOVA) when the data was normally distributed, and a Kruskal-Wallis ANOVA on

ranks with a Bonferroni t-test when the data failed the normality test. Differences between

months were analysed with a repeated-measures ANOVA with a Bonferroni t-test when

the data was normally distributed, and a Friedman repeated-measures ANOVA on ranks

with a Bonferroni t-test when the data failed the normality test (Table 2.1). Pearson’s

correlations were calculated to identify the correlation between the concentration of

microcystins in lake sediments and environmental variables (Table 2.2)

Multiple linear regression (stepwise) were carried out to identify the model that best

explained the variability in the concentration of microcystins in the sediments, with 0.05

as the probability of F-to-remove. Prior to the statistical analyses, the linearity of the

microcystin concentration in sediments and the environmental variables hypothesised to

control microcystin variability were tested by scatterplots. Both dependent and


46

independent variables were log-transformed and checked for normality by means of the

Shapiro-Wilk test. Partial correlation coefficients and part (semi-partial) correlation

coefficients were calculated to determine the specific portion of variance explained by a

given environmental variable in the multiple linear regression analysis. Statistical

analyses were conducted in SigmaPlot 11.0 (Systat Software Int.) and statistical package

for the social sciences (SPSS ) 17.0 (SPSS Inc.). Significance levels were set as p < 0.05

unless stated otherwise.

2.4 Results

2.4.1 Temporal and spatial variability of microcystin concentration in sediments

Microcystins were detected in all sediment samples, with their concentration ranging

from 0.06 (S2; Sept.) to 0.78 µg/g d.m (S2; Aug.) (Figure 2.2). The average microcystin

concentration in sediments decreased from August to September but increased from

September to December (Figure 2.3A). The lowest average concentration was observed

in September (0.13 µg/g d.m.) and the maximum average concentration was observed in

August (0.46 µg/g d.m.).


47

0

50

100

150

200

250

Aug Sep Oct Nov Dec

CB

in w

ater

(µg

chl-a

/L)

0

50

100

150

200

250S4

0.0

0.2

0.4

0.6

0.8

1.0

02468

1012

S1

Months

Aug Sep Oct Nov Dec

MC

s in

sed

imen

ts (µ

g/g

d.m

.)

0.0

0.2

0.4

0.6

0.8

1.0

MC

s in

wat

er (µ

g/L)

02468

1012

S3

*

S2

Figure 2.2: Microcystin concentration (MCs) in sediments (●), total microcystin concentration in water (▲) and cyanobacterial biomass (CB) (○) in water as a function of time at sites 1 - 4 (S1 - S4). CB in water is the calculated mean between CB in surface and overlying water. * indicates a very high value of 1113.2 µg chl-a/ L for cyanobacterial biomass in November. Error bars represent one standard error (with n = 3 for MC concentration in sediments and n = 2 for CB biomass in water).

The temporal variability of microcystin concentrations in sediments was different for

each sampling site (Figure 2.2). There was a significant difference in microcystin

concentration between months in the sediments at sites 1–3 but not at site 4 (Table 2.3)

where the smallest temporal variability of microcystins in sediments occurred (Figure

2.3B). Furthermore, the changes in microcystin concentrations in the sediment were

similar at sites 1 and 2 (Figure 2.2), where they decreased from August to September

followed by an increase in October and a decrease until December. In contrast, the

microcystin concentration in the sediments at site 3 decreased from August to September

but then increased until December. The maximum concentration of microcystin at site 3

was observed in December (Figure 2.2).


48

MonthAug Sep Oct Nov Dec

MC

s in

sed

imen

ts

(µg/

g d.

m.)

0.0

0.2

0.4

0.6

0.8

1.0A

SiteSite1 Site2 Site3 Site4

B

Figure 2.3: Spatial (A) and temporal variability (B) of microcystin concentrations in lake sediments. Boxes represent the 25th and 75th percentiles; solid lines within the boxes mark the median, dashed lines indicate the mean. Whiskers represent the largest and smallest observed values excluding the outliers. Filled circles represent outliers (>1.5 box lengths) (n=15).

The spatial variability of microcystin concentrations in sediments was different in each

month. In August, microcystin concentration in sediments at site 4 was significantly lower

than at any other site; in November and December, microcystin concentration was

significantly higher at site 3 than at any other sites (Table 2.3; Figure 2.2); and in

September and October, no significant differences in microcystin concentration between

sites were observed. The largest spatial variability of microcystins in sediments occurred

in August, while the smallest occurred in September (Figure 2.3A). On average, the

microcystin concentration in sediments was highest at site 3 and lowest at site 4 (Figure

2.3B).


49

Table 2.3: Statistical results for differences between sites (one-way ANOVA or Kruskal-Wallis ANOVA on ranks) and between months (repeated-measures ANOVA or Friedman repeated-measures ANOVA on ranks) of microcystin concentrations in the sediments and other environmental variables in the sediments and water. * indicates p < 0.05. n.s. means not significant.

Environmental variables Differences between months

Differences between sites

Microcystins in sediments (µg/g d.m.)

F(4,14) = 9.3 (site 1) * F(4,14) = 36.2 (site 2) * F(4,14) = 41.1 (site 3) * n.s. (site 4)

H=10.5 d.f. = 3 (Aug)*

n.s. (Sep) n.s. (Oct)

F(3,11) = 21.5 (Nov) * F(3,11) = 21.4 (Dec) *

Cyanobacterial biomass in sediments (µg chl-a/g d.m.)

F(4,14) = 9.9 (site 1) * n.s. (site 2) n.s. (site 3) F(4,14) = 3.9 (site 4) *

H = 8.8, d.f. = 3 (Aug)* F(3,11) = 8.6 (Sep) * n.s. (Oct) n.s. (Nov) H = 9.5, d.f. = 3 (Dec)*

Cyanobacterial biomass in surface water (µg chl-a/ L)

F(4,18) = 19.7 * H=11.2, d.f. = 3 *

Cyanobacterial biomass in overlying water (µg chl-a/L)

F(4,18) = 4.8 * n.s.

Intracellular microcystin concentration in water (µg/L)

F(4,19) = 10.9 * n.s.

Dissolved microcystin concentration in waters(µg/L)

χ2 = 11.8, d.f. = 4 * n.s.

Organic matter (%) F (4, 19) = 10.9* F (3,19) = 8.3*

2.4.2 Temporal and spatial variability of environmental variables

Cyanobacterial biomass in sediments varied significantly at sites 1 and 4 during the

study (Table 2.3), and was significantly different between sites in August (biomass at site

3 was significantly higher than at site 4), September (biomass at site 3 was significantly

higher than at sites 2 and 4) and December (biomass at site 2 was significantly higher

than at site 4) (Table 2.3).

Cyanobacterial biomass averaged over the water column (surface + overlying samples)

showed a similar temporal trend at all sites and was higher at site 3 compared to at all

other sites (Figure 2.2). On average, there were a significant difference in cyanobacterial

biomass in surface water between sites, with site 3 having a higher biomass than site 4


50

(Table 2.3), and between months, with a higher cyanobacterial biomass in December

compared to September, October and November (Table 2.3). While cyanobacterial

biomass in the overlying water was on average also significantly different between

months (higher in December than in September), there was no difference in

cyanobacterial biomass in the overlying water between the sites (Table 2.3).

Total microcystin concentration (intracellular + dissolved) in surface water showed a

similar trend to cyanobacterial biomass in the water (Figure 2.2). On average, intracellular

and dissolved microcystin concentrations in surface water were both significantly

different between months (Table 2.3), with December concentrations being higher

compared to all other months. On average, no difference was observed in intracellular

and dissolved microcystin concentration between sites (Table 2.3).

Organic matter content in sediment samples was different between sites, with site 3

having a higher content than sites 2 and 4, and between months, with the content being

significantly higher in December compared to all other months (Table 2.3).

2.4.3 Correlation between microcystins in the sediments and environmental variables

Linear correlations (Pearson’s correlations) were calculated between microcystin

concentration in sediments and other environmental variables (Table 2.4). Microcystin

concentration in the sediments had a weak but significant linear correlation with the

concentration of intracellular microcystins in the surface water (R2 = 0.103, p < 0.05),

total microcystins in the surface water (R2 = 0.091, p < 0.05), cyanobacterial biomass in

the surface water (R2 = 0.129, p < 0.05) and cyanobacterial biomass in the overlying water

(R2 = 0.225, p < 0.0001). A strong and significant correlation was observed between

cyanobacterial biomass in surface water and overlying water (R2 = 0.90, p < 0.05)

Table 2.4: Pearson’s correlation values (R) between microcystin concentrations (MC) in the sediments (µg/g d.m.) and environmental variables.


51

CB sediments

Intracell. MC

Dissolved MC

Total MC

CB overlying

CB surface

OM

MC sediments

-0.06 0.29 * 0.11 0.30* 0.47 * 0.36 * 0.21

Note: *: a significant correlation (p < 0.05); - : a negative relationship; CB sediments: cyanobacterial biomass in the sediments (µg chl-a /g d.m.); Intracell. MC: intracellular microcystin concentration in the surface water (µg/L); Dissolved MC: dissolved microcystin concentration in the surface water (µg/L); CB overlying: cyanobacterial biomass in the overlying water (µg chl-a/L); CB surface: cyanobacterial biomass in the surface water (µg chl-a/L); OM: the percentage of organic matter (weight) in the sediments.

2.4.4 Prediction of microcystin concentrations in sediments from environmental variables

The total concentration of microcystins in the surface water (c(tMC water)),

cyanobacterial biomass in overlying water (c(CB overlying)) and surface water (c(CB

surface)), pH, temperature and salinity in the water, were used to fit linear regression

models in order to determine which set of variables best explained patterns in the

concentration of microcystins in the sediments (c(MC sediments)). The best regression

model was:

Log c(MC sediments) = 11.023 + 0.329Log c(tMC water) + 0.221Log c(CB overlying)

– 9.975 Log pH – 1.015Log temperature (R2 = 0.467, p < 0.05, standard error of the

estimate = 0.232).

Total microcystins in the surface water, cyanobacterial biomass in the overlying water,

pH and temperature explained 46.7% of the variability of microcystins in the sediments.

Partial and part correlation coefficients revealed that cyanobacterial biomass in overlying

water explained 20.8% of the total variation while total microcystins in water explained

12.3% of the total variance. Physicochemical parameters (pH and temperature) explained

13.6% of the total variability.


52

2.5 Discussion

Microcystins were detected in all sediment samples in this study, even at site 4 where

only little cyanobacterial biomass occurred in the water in most months (Figure 2.2),

indicating the wide presence of microcystins in the lake’s sediments. Microcystins in lake

sediments have their origin in three main processes: (1) lysis of cyanobacterial cells in

sediments; (2) transfer from a dissolved status to sediments by adsorption; (3) grazing by

aquatic animals and the subsequent sinking of faecal pellets containing microcystins

(Chen et al., 2008). In our studied lake, cyanobacterial blooms were dominated by toxic

Microcystis spp. and microcystins in the water occur most of the year (Kemp and John,

2006, Sinang et al., 2013, Reichwaldt et al., 2013) which is consistent with our study.

Microcystins can therefore accumulate in the sediments as a result of the sediment’s long-

term exposure to cyanobacterial biomass at the bottom of the lake and microcystins in the

water. The occurrence of microcystins in lake sediments associated with cyanobacterial

bloom history has been reported in earlier studies (Tsuji et al., 2001, Ihle et al., 2005,

Babica et al., 2006, Mohamed et al., 2007, Chen et al., 2008).

Microcystin concentration in the sediments had weak but significant correlation

withthe concentration of intracellular toxins in the surface water and cyanobacterial

biomass in the overlying water (Table 2.2). In contrast, Mohamed et al. (2007) reported

a significant and strong correlation between the concentration of microcystins in

sediments and intracellular microcystins in water. Additionally, no significant correlation

between microcystin concentration and cyanobacterial biomass in sediments was

observed in the present study. This might be because the current study focused on the

upper layer of sediments in the shallow lake, where benthic populations of Microcystis

are exposed to dynamic interactions with the planktonic population through the

recruitment and sediment processes. These interactions can induce rapid modifications in

the benthic populations by depleting them or by introducing new colonies, thus making


53

the population of Microcystis microcystin concentrations in sediment more dynamic.

There is also increasing evidence that the microcystin dynamics in freshwater lakes

largely resulted from successional replacement of a multitude of cyanobacterial

genotypes that differ in microcystin production (Sivonen and Jones, 1999; Kurmayer et

al., 2004, Briand et al., 2008).

The temporal variability of microcystins in the lake’s sediments was site specific and

the spatial variability in their concentration was different in each sampling month.

Although a significant change of microcystin concentration occurred over time at sites 1–

3 during the sampling months, the pattern was different between sites (Figure 2.2).

Noticeably, a significant increase in the concentration of cyanobacterial biomass in the

water column, from August to December did not result in a significant change in the

concentration of microcystins in the sediments (Figure 2.2). Furthermore, we did not find

a correlation between cyanobacterial biomass in the sediments and microcystins in the

sediments. Instead, cyanobacterial biomass in the overlying water and total microcystin

concentrations in the surface water accounted for most of the variability in the

concentration of microcystins in the sediments. This highlights clearly that the variability

of microcystins in the sediments can be predicted by multiple environmental factors and

emphasises the important link between the water and sediment, with processes that take

place in the water exerting a strong influence on the sediment.

A multiple linear regression analysis revealed that the microcystin concentration in

sediments decreased with increasing pH and temperature. This result is in agreement with

previous studies reporting the influence of pH and temperature on the capacity of

sediments to adsorb and degrade microcystins. In these studies, it was observed that

higher temperatures increased the sediments’ capability to biodegrade microcystins,

aiding the removal of these toxins from aquatic systems (Wang et al., 2007, Ho et al.,

2007). This might because higher temperature could increase the metabolic activity of the


54

bacteria capable of degrading microcystins. Previous studies have also shown that lower

pH increases the capacity of sediments to adsorb microcystins (Miller et al., 2001, Liu et

al., 2008, Wu et al., 2011). Wu et al. (2012) suggested that pH acts in this way by

influencing the surface charge heterogeneity of microcystins and sediments. Overall,

cyanobacterial biomass in overlying water and total microcystin concentrations in the

water (the sum of dissolved microcystins and intracellular microcystins) accounted for

the most variability in the concentration of microcystins in the sediments. This finding

emphasises the important connection between the water and sediment, with processes

taking place in the water exerting a strong influence on the sediment.

Approximately 53% of the variability of microcystins in the sediments could not be

explained by the multiple linear regression analysis developed for this study. The

unexplained variability could be caused by other environmental factors. For example,

wind mixing is potentially an important contributing factor needing further consideration.

The wind speed during the sampling period ranged from 0.6 to 15.6 m/s with varying

wind directions (Figure 2.1B). As a result of a wind mixing, increased exchange of

microcystins between sediments and water could occur, with microcystins spreading

across the lake through water movement. The recruitment process could also explain the

high heterogeneity of microcystin concentrations observed on a temporal and spatial scale

(Latour et al., 2004, 2007) because Microcystis colonies might have a resuspension from

shallow zones and redeposition in deeper zones and therefore cause uneven distribution

of Microcystis population. In addition, biodegradation may play a key role in mediating

the fate of microcystins. Biodegradation of microcystins by bacteria has been reported in

lake water (Lahti et al., 1997, Edwards et al., 2008, Ho et al., 2012b, Ho et al., 2012c). A

previous study from 2010 to 2011 by Song et al. (2014) showed that sediments from Lake

Yangebup could contribute to the removal of microcystin-LR from the water by both the

biodegradation and adsorption ability of the sediment while biodegradation was shown to


55

be the dominant removal process. Therefore, the effect of the recruitment process and

bacterial communities on the distribution of microcystins in the sediments is required in

future studies. Such work should improve the prediction of the variability of microcystins

in the sediments.

The physical and chemical properties of sediments – for example, organic matter

content and particle size fraction (sand, slit and clay) – have the potential to influence

their capacity to adsorb and degrade microcystins (Miller et al., 2001, Mohamed et al.,

2006, Chen et al., 2008, Munusamy et al., 2012, Song et al., 2014). In the current study,

no clay was present in the sediments and no significant correlation between the

concentration of microcystins and organic matter content in sediments was observed

(Table 2.2). In contrast, Mohamed et al. (2007) observed that in the Nile River, Egypt,

the capacity of sediments to adsorb microcystins was significantly correlated to their clay

and organic matter content. Wu et al. (2011) found that the adsorption process for

microcystins to sediments mainly depended on a sediment’s organic matter. The

significance of organic matter and clay in the binding and degradation of microcystins to

soils and sediments has been reported in many other studies (Donati et al., 1994, Rapala

et al., 1994, Lam et al., 1995b, Lambert et al., 1996, Morris et al., 2000, Miller et al., 2001,

Grut macher et al., 2010).

The present study has highlighted the important role played by the interaction of water

and sediments in the distribution of microcystins in an aquatic system. Generally speaking,

the concentration of microcystins in the sediments was significantly correlated to the

concentration of intracellular microcystins and cyanobacterial biomass in the surface

water. A significant part (46.7%) of the variability of microcystins in lake sediments could

be explained by a combination of total microcystins in the surface water, cyanobacterial

biomass in the overlying water and physicochemical parameters of the water (pH and

temperature). However, the spatial variability of microcystins concentrations in


56

sediments was different in each sampling month and their temporal variability was site

specific.

For the management of bloom-prone water bodies, it is important to holistically

understand the fate of microcystins in the aquatic systems. Current studies and policies

focusing on microcystins in aquatic systems almost exclusively concentrate on the water

column while sediments are mostly neglected. However, there is strong evidence from

this and earlier studies that microcystins are widely present in sediments. Considering the

potential ecological hazards with the occurrence of microcystins in lake sediments,

researchers and water management authorities should include sediments when assessing

the potential hazards of microcystins in aquatic systems.To gain a broader understanding

of the occurrence of microcystins in the sediments, it will be necessary to further

investigate the variability of microcystins in different aquatic systems. As shown in the

current study, there is an important connection between the water and the sediments that

influences the distribution of microcystins in aquatic systems. However, the present study

was limited to a shallow lake where there is a relatively high interaction between water

and sediments. The correlation between the variability of microcystins in sediments and

environmental variables may differ in a deep lake. Therefore, different aquatic systems

need to be investigated to broaden this understanding.

2.6 Acknowledgements

This work was supported by the Australian Research Council Linkage Scheme

(LP0776571) and the Water Corporation of Western Australia. Haihong Song was

financially supported by a China Scholarship sponsored by the China Scholarship Council

and The University of Western Australia (UWA). The authors would like to thank the

UWA Centre for Applied Statistics for statistical advice and Dr Kristin Argall, Shian Min

Liau and Randica Jayasinghe for comments on an earlier draft.

Chapter 3. Contribution of sediments in the removal of microcystin-LR from the water

SONG, H., REICHWALDT, E. S. & GHADOUANI, A. 2014. SONG, H.,

REICHWALDT, E. S. & GHADOUANI, A. 2014. Contribution of sediments in the

removal of microcystin-LR from the water. Toxicon. 10.1016/j.toxicon.2014.02.019.

58


CHAPTER 3: CONTRIBUTION OF SEDIMENTS IN THE REMOVAL OF MCLR

59

3.1 Abstract

Microcystins are produced by several species of cyanobacteria and can harm aquatic

organisms and human beings. Sediments have the potential to contribute to the removal

of dissolved microcystins from the water body through either adsorption to sediment

particles or biodegradation by the sediment’s bacterial community. However, the relative

contribution of these two removal processes remains unclear and little is known about the

significance of sediment’s overall contribution. To study this, changes in the

concentration of microcystin-LR (MCLR) in the presence of sediment, sediment with

microbial inhibitor, and non-sterile lake water were quantified in a laboratory experiment.

Our results show that, in the presence of sediment, MCLR concentration decreased

significantly in an exponential way without a lag phase, with an average degradation rate

of 9 µg/d in the first 24 h. This indicates that sediment can contribute to the removal of

MCLR from the water immediately and effectively. Whilst both, the biodegradation and

adsorption ability of the sediment contributed significantly to the removal of MCLR from

the water body, biodegradation was shown to be the dominant removal process. Also, the

sediment’s ability to degrade MCLR from the water was shown to be faster than the

biodegradation through the bacterial community in the water. The present study

emphasizes the importance of sediments for the removal of microcystins from a water

body. This will be especially relevant in shallow systems where the interaction between

the water and the sediment is naturally high. Our results are also useful for the application

of sediments to remove microcystins at water treatment facilities.

Keywords: Cyanobacterial toxins; Microcystins; Environmental fate; Sediment;

Biodegradation; Adsorption;


60

3.2 Introduction

The occurrence of cyanobacterial blooms in aquatic systems poses a threat to the

ecosystem’s organisms and public health and cause extreme aesthetic problems

(Ghadouani and Coggins, 2011, Chorus et al., 2000, Ghadouani et al., 2003, Ghadouani

et al., 2006). Several genera of cyanobacteria, such as Microcystis spp., Anabaena spp.,

Nostoc spp., and Oscillatoria spp. can produce microcystins (Carmichael, 1997).

Microcystins have caused livestock, pet, and wildlife death across the world and have

also implicated human illness and death through water or dialysis fluid (Codd et al., 1999,

Chorus et al., 2000, Falconer, 2001, El-Shehawy et al., 2012). There are over 90

microcystin variants reported so far and microcystin-LR (MCLR) is among the most

common (Hotto et al., 2008). Because of the potential for chronic toxicity from

microcystins, the World Health Organization (WHO) has established a provisional

guideline value of 1 µg/L and 20 µg/L for total MCLR concentrations in drinking and

recreational water, respectively (WHO, 1998, WHO, 2003).

To assess the potential risk of human and animal exposure to microcystins, it is crucial

to understand the fate of microcystins in aquatic systems. Microcystins are produced

within the cyanobacterial cells and released into the surrounding water when cells lyse

under unfavourable conditions (Watanabe et al., 1992b). The toxins thus end up in a

dissolved form in the water body. However, dissolved microcystins in natural waters

usually do not last for long. Many processes can influence the persistence of the dissolved

microcystins in aquatic systems. In the water, these processes include photolysis, the up-

take by organisms, biodegradation by bacteria (Welker et al., 2001), and adsorption to

settling particles (Wörmer et al., 2011). Sediments also have the ability to remove

microcystins from the water. The two major processes for this are biodegradation by the


61

bacterial community within the sediment and adsorption to sediment particles (Corbel et

al., 2014).

Adsorption of microcystins to sediments occurs when microcystins bind to the surface

of the sediments, usually at a specific site and previous studies have reported on the

presence of microcystins in natural sediment samples (Tsuji et al., 2001, Chen et al.,

2006a, Mohamed et al., 2007). The capability of sediments to adsorb microcystins was

further confirmed in batch experiments in the laboratory (Mohamed et al., 2007,

Munusamy et al., 2012). Also, Rivasseau et al. (1998) reported that after three days in

laboratory experiment only 10% of microcystins were adsorbed on suspended articles and

7% on the sandy sediments. However, although current studies clearly indicated that

adsorption on sediments contributes to the detoxification of the microcystins under

natural conditions, the significance of its contribution compared to the other

detoxification mechanisms is not yet clear.

Biodegradation is the second major process that influences the fate of microcystins in

aquatic systems (Dziga et al., 2013). Biodegradation of microcystins by bacteria was

reported in lake water (Jones and Orr, 1994, Lahti et al., 1997, Edwards et al., 2008, Ho

et al., 2012b, Ho et al., 2012c), lake sediments (Cousins et al., 1996, Holst et al., 2003),

estuarine water (Lemes et al., 2008), sand filters (Bourne et al., 2006), various biofilms

(Saito et al., 2003, Babica et al., 2005, Wu et al., 2010) and artificial media (Ji et al.,

2009). Meanwhile, bacteria capable of degrading cyanobacterial toxins have been isolated

from water, sand filters, coastal lagoon and even an operating anthracite bio-filter (Jones

and Orr, 1994, Bourne et al., 1996, Takenaka and Watanabe, 1997, Park et al., 2001, Holst

et al., 2003, Chen et al., 2007, Eleuterio and Batista, 2010, Ho et al., 2012b). Therefore,

Ho et al. (2012a) highlights biological treatment options to be implemented in treatment

plants for optimum cyanobacterial toxins removal.


62

Understanding the underlying mechanisms for the contribution of sediments to the

removal of microcystins in the water is important; this includes a quantification of the

relative contribution of biodegradation and adsorption by sediments. Firstly, it helps to

understand the fate of microcystins in aquatic systems which may lead to better

management. Secondly, better treatment methods for microcystin-contaminated water

may be established based on the understanding of the natural attenuation mechanisms of

microcystins in the aquatic system. Gru zmacher et al. (2010) investigated the parameters

affecting microcystin elimination during sediment passage and found that adsorption of

microcystins played a minor role, while biodegradation was identified as the dominant

process. Their study used autoclaved sediments. However, an earlier study indicated that

autoclaving sediments might destroy soil structure, decrease the surface areas of clays, or

alter the surface charge of sandstones, and hence influence the capacity of sediments to

adsorb microcystins (Trevors, 1996). Therefore, the relative contribution of these two

removal processes in natural lake sediments needs to be further clarified.

Overall, the objective of our study is to identify the role of natural sediments in the

removal of microcystins from the water. More specifically, the study aims to (1) quantify

the contribution of the sediments to the removal of microcystins from the water; (2)

compare the relative contribution of biodegradation and adsorption ability of a sediment

to the removal of microcystins from the water. We studied this in a laboratory experiment

by bringing sediments and lake water from the field into the lab and exposing it to

different conditions.

3.3 Material and methods

3.3.1 Experimental design

The decay of dissolved microcystin was quantified in the laboratory by incubating a

commercially available variant, microcystin-LR (MCLR; Sapphire Bioscience,


63

Australia), with different combinations of water and sediments from Lake Yangebup,

Western Australia. MCLR was added at an initial concentration of 20 µg/L and changes

in concentration were quantified under seven different treatment conditions (Table 3.1).

Different treatments were designed to test for the overall contribution of sediments, the

adsorption ability of sediments, the biodegradation ability of the sediments, and

biodegradation ability of lake water to remove MCLR from water. For example, MCLR

was added into sterile lake water with sediments to test the overall contribution of

sediments to the fate of MCLR. In contrast, a microbial inhibitor (10% sodium azide;

Alkali Metals Ltd.), was added together with MCLR into sterile lake water with sediments

to test the adsorption ability of sediments. Additionally, there were two controls. The

positive control in which MCLR was added into sterile lake water was to verify that there

was no decrease in MCLR in the absence of bacteria and sediment. The negative control,

in which no MCLR was added into sterile lake water with sediments, was to test if

unwanted natural microcystins was introduced into the experiment by using the lake water

and lake sediments. Each treatment was replicated four times, and the experiment ran for

two weeks.


64

Table 3.1: Experimental design and incubation conditions.

Treatment

Non-sterile lake water

Sterile lake water

Raw sediments

Microbial inhibitor

MCLR

1 + * 2 + + - 3 + + * 4 + + *# 5 + + *** 6 + + + * 7 + *

+ Indicates incubation conditions for each treatment; * Indicates MCLR was added into the water once; *** Indicates MCLR was repeated added into the water for three times; - Indicates that MCLR was absent; # No aeration.

3.3.2 Sampling

Lake water and sediments for the laboratory experiment were collected from an urban

shallow lake, Lake Yangebup (32°6'40"S, 115°50'00"E), Perth, Western Australia.

Samples were collected in April during a toxic Microcystis bloom. The upper 8 cm of

sediment cores were collected with a transparent, polycarbonate sediment corer with a

stainless-steel cutter (50 mm in diameter). A 10 L water container was used to collect

surface water sample. Sediments and water collected were stored on ice and were

transported to the laboratory within two hours.

3.3.3 Experimental set-up

In the laboratory, sediment samples were thoroughly mixed and refrigerated overnight

at 4°C before the commencement of the experiment. All laboratory tools and glassware

were autoclaved before use, and preparation of the set-up was done in a laminar flow

cabinet to limit exposure to toxic vapours. All lake water was filtered with a GF/F filter

(0.7 µm, Whatman) prior to the experiment to remove suspended particles and

cyanobacterial cells but to keep the natural bacterial community. When sterile water was

used, the water was further filtered through a special capsule filter (0.8 µm/0.2 µm,


65

AcroPak™ 500 Capsules with Supor® Membrane, Pall Corporation). When sediment

was required in a treatment, 200 g of the mixed sediment sample was placed in a 1500 ml

Erlenmeyer flask. Then 500 ml of either non-sterile or sterile lake water, which contained

20 µg/L MCLR, was added into the Erlenmeyer flask slowly to minimize the stirring of

sediment. All flasks were sealed with rubber stoppers with glass tubes that reached into

the water connected to air pumps for aeration unless otherwise stated. To avoid photolysis

of MCLR, all flasks were wrapped with aluminium foil and kept in dark conditions at 24

± 1°C.

A water sample of 5 ml was taken with a polyethylene syringe connected with a tube

by opening the flaks every 12 h to quantify the concentration of MCLR. The first water

samples were taken 1 h after the experiment commenced. Water samples were kept at -

20°C before quantification of MCLR with a High Performance Liquid Chromatography

(HPLC)-PDA system.

3.3.4 Sediment analysis

The sediment was analysed for particle size and organic matter content. The analysis

of particle size was adapted from Srivastava et al. (2013). The adaptions included heating

the samples for a quicker digestion of the organic matter and removing the hydrogen

peroxide by sequential dilution instead of evaporation. Organic matter by loss-on-ignition

was analysed according to Srivastava et al. (2013) but with samples dried in an oven at

110°C overnight instead of freeze drying. The organic matter content of sediments was

19.98 ± 0.45%. The results of particle size distribution show that all the sediment samples

were sand with a content of clay, silt, fine sand and coarse sand as 0%, 0%, 2.37 ± 1.07%

and 97.03 ± 1.07% respectively.


66

3.3.5 MCLR analysis

Thawed water samples were centrifuged to remove any suspended particles before they

were applied to Solid Phase Extraction (SPE) cartridges (Waters Oasis HLB). SPE

cartridges were used for cleaning and concentrating microcystins. After application of the

samples, the SPE cartridges were rinsed with 10 ml of 10% (v/v) and 20% (v/v) methanol,

respectively. MCLR was eluted with 5 ml of 100% methanol + 0.1% trifluoroacetic acid

(TFA) (v/v) from the SPE cartridges into 6 ml glass vials. The glass vials were placed in

a water bath (40°C), and the liquid samples were evaporated to dryness under a mild

nitrogen stream. Dry samples were re-dissolved in 30% acetonitrile (v/v) for

quantification of MCLR in a Waters HPLC system (Alliance 2695) which was equipped

with PDA (1.2 nm resolution) and an Atlantis T3 3 µm column (4.6 x 150 mm i.d).

Mobile phases used for the HPLC were acetonitrile with 0.05% TFA (v/v) and Milli-Q

water with 0.05% TFA (v/v). The column temperature was maintained at 37.5 ± 2.5°C

and more details of the analysis method were described in Sinang et al. (2013). The limit

of detection per microcystin peak was 1.12 ng. MCLR was identified by PDA spectra at

238 nm and quantified by commercially available MCLR standard (Sapphire Bioscience,

Australia; purity ≥ 95%).

3.3.6 Data analysis

One Way Repeated Measures ANOVA or Friedman Repeated Measures ANOVA on

Ranks was performed to test for the difference in MCLR concentrations within each

treatment. T-test or Mann-Whitney Rank Sum Test was performed to compare the

difference in MCLR concentration of two treatments at the same sampling time. All

statistical analyses were conducted in SigmaPlot 11.0 (Systat Software Int.). Significance

levels were set as p < 0.05.


67

3.4 Results

3.4.1 Overall contribution of sediments to the removal of MCLR from the water

No MCLR was detected in treatment 2, which served as negative control and in which

no MCLR was added. This indicated that the quantified MCLR in all the other treatments

did not originate from the lake water or sediments but was due to the addition of MCLR.

No significant removal of MCLR was detected in treatment 1 (Friedman Repeated

Measures ANOVA, χ2 = 29.2, d.f. = 10, p > 0.05), which served as positive control and in

which MCLR was added in sterile lake water (Figure 3.1A). The results indicate that no

significant removal of MCLR occurred in sterile lake water which means that the removal

of MCLR in any of the other treatments was due to the treatment conditions.

When MLCR was added in sterile lake water with sediment (treatment 3), MCLR

concentration decreased significantly from the commencement of the experiment (Figure

3.1A). The most rapid decrease occurred within the first 24 h with an average degradation

rate of 9 µg/d. MCLR concentration decreased significantly, without an observed lag

phase each time MCLR was added into the sterile lake water and sediment system (Figure

3.1B). The percentage of remaining MCLR over time after each MCLR addition indicated

an exponential decay regression wtt CC exp0 , where Ct equals the concentration of

MCLR at time as precents relative to the initial MCLR concentration, C0 equals 100%

and is the initial MCLR concentration, w equals first-order rate constant (h-1), and t equals

the time (hours) since the commencement of the experiment. MCLR half-life for spike

one, spike two and spike three was 13 h, 22 h and 21 h, respectively.

Aeration had a significant effect on the removal rate of MCLR added to sterile lake

water with sediments (Figure 3.1C). MCLR concentration in the treatment with aeration

became significantly lower than that in the treatment without aeration (treatment 4) from

hour 36 (Two sample t-test, t = -5.9, p < 0.001, d.f. = 6) until the end of the experiment.

Without aeration, MCLR concentration decreased quickly within the first 24 h (from 100%


68

to 10%) but followed by a significant increase from 10% to 80%. MCLR concentration

then decreased consistently with an average degradation rate of 1.3 µg MCLR d-1. In

contrast, with aeration, MCLR concentration decreased from the beginning with a higher

degradation rate (3.8 µg/ d) than that without aeration.


69

Figure 3.1: Mean removal of MCLR from the water by (A) sterile lake water with (▲) and without sediments (●); (B) sterile lake water with sediments without a microbial inhibitor for three consecutive additions. Numbers denote the respective additions; curves of best fit are first order exponential decay: addition 1: c(MCLR) = c(MCLR)0 -0.057t (adj. R2 = 0.908, p < 0.05); addition 2: c(MCLR) = c(MCLR)0 -0.04t (adj. R2 = 0.997, p < 0.05); addition 3: c(MCLR) = c(MCLR)0 -0.05t (adj. R2 = 0.939, p < 0.05); (C) sterile lake water with sediments with (▲) and without aeration (●); (D) sterile lake water with sediments with (▲) and without a microbial inhibitor (●); curves of best fit are first order exponential decay for the treatment without addition of microbial inhibitor: c(MCLR) = c(MCLR)0 -0.057t

(adj. R2 = 0.908, p < 0.05) and linear regression for the treatment with addition of microbial inhibitor: c(MCLR) = 91.52 – 0.29t (adj. R2 = 0.838, p < 0.05); (E) non-sterile lake water (●) and sterile lake water with sediments (▲). Error bars represent standard errors in all panels (n = 4). Data of the following treatments are shown twice within this figure to simplify comparisons: Treatment 3 (▲) is shown in panels A, C and E; Treatment 5 (●) is shown in panels B and D.


70

3.4.2 Adsorption ability versus biodegradation ability of sediments

The addition of the microbial inhibitor had a significant effect on the removal of MCLR

added to sterile lake water with sediments (Figure 3.1D). In the presence of the microbial

inhibitor (treatment 6), MCLR concentration decreased following a linear curve (p < 0.05,

adjusted R2 = 0.838) with an average degradation rate of 1.75 µg MCLR/d. In contrast, in

the absence of the microbial inhibitor, MCLR concentration dropped significantly from

the commencement of incubation following a first order exponential decay curve (p <

0.05, adjusted R2 = 0.908) with an average degradation rate of 6.6 µg MCLR/d. Without

microbial inhibitor, MCLR concentration decreased to below detection level within the

first 36 h. In comparison, during this period of time, no significant reduction of MCLR

was observed with the addition of microbial inhibitor.

3.4.3 Contribution of water versus contribution of sediment to the removal of MCLR from water

Significant reduction of MCLR over time was observed in both non-sterile lake water

and sterile lake water with sediment (Figure 3.1E). Nevertheless, MCLR concentration

changed differently in these two treatments. A lag phase of 60 h was observed in non-

sterile lake water after which MCLR concentration decreased consistently with an

average degradation rate of 1.5 µg MCLR/d. In comparison, when MCLR was added in

sterile lake water with sediments, more than 90% removal of MCLR was observed within

the first 60 h of the experiment and the average degradation rate was 3.8 µg MCLR/d

(Figure 3.1E). Therefore, the removal of MCLR from the water by the sediment’s

adsorption and biodegradation ability was instant and much faster than the biodegradation

through bacteria from the water.


71

3.5 Discussion

This study aimed to quantify the significance of the sediment’s contribution to the

removal of microcystins from the water and to differentiate between the relative

contributions of biodegradation ability and adsorption ability of sediments.

Microcystin-LR concentration decreased exponentially in the presence of sediment

with no observed lag phase. This is in contrast to earlier studies which documented lag

periods ranging from a few days to several weeks before microcystins were degraded

(Jones and Orr, 1994, Lam et al., 1995a, Chen et al., 2010). The length of the lag phase

in an earlier study depended on the concentration of bacteria, bacterial activity, initial

concentration of the toxin, and incubation temperature (Mazur-Marzec et al., 2009). The

rapid removal of MCLR with no lag phase in our study indicated the occurrence of

MCLR-degrading bacteria in the sediments of Lake Yangebup. This might be due to the

fact that the sediments used in this experiment were sampled during a Microcystis bloom

and therefore large amounts of microcystin-degrading bacteria might already have been

present in the sediment (Giaramida et al., 2013). The ability to remove MCLR did not

decrease during repeated MCLR addition in our experiment indicating that sediments can

significantly contribute to the removal of microcystins from a water body.

The addition of a microbial inhibitor to the sediment, leading to the quantification of

the adsorption ability of the sediment only, had a significant effect on the removal rate of

MCLR from the water. With a microbial inhibitor, MCLR concentration did not

significantly decrease within the first 36 h, while MCLR concentration decreased to under

detection level within the first 36 h in the absence of the inhibitor. As the latter treatment

represents the sum of adsorption and biodegradation, the difference between the two

curves indicated the contribution of the biodegradation ability of the sediment. This

emphasizes that the exponential decay of MCLR in the presence of sediment was mainly

caused by its biodegradation ability. However, although adsorption of MCLR was


72

negligible compared to biodegradation at the start of the incubation, in the long term, a

significant loss of MCLR was observed due to adsorption (Figure 3.1D). This long-term

contribution of adsorption to the removal of MCLR from the water in our study is in

contrast to the results of Grut macher et al. (2010), where MCLR concentration did not

change significantly during contact with autoclaved sediment. The discrepancy is likely

due to differences in the texture, pH and organic matter content of the used sediment; also,

their sediment was autoclaved while a microbial inhibitor was used in our study.

Autoclaving can influence the adsorption ability of sediments by changing the sediment’s

structure (Trevors, 1996). In contrast, the use of sodium azide solution prevents microbial

activity within the soil without altering the adsorption properties of the soil (Miller et al.,

2001).

Our results indicated that biodegradation contributes more to the removal of MCLR

from water than adsorption, which has also been shown earlier for microcystin

elimination in sandy aquifer material (Grut macher et al., 2010) and in sand filters (Ho et

al., 2006). Biodegradation by microbial activity in sediments has also been reported as

the predominant process of the removal of other cyanobacterial toxins, e.g., nodularin

(Torunska et al., 2008). Therefore, this emphasizes the importance to consider the

biodegradation ability of sediments when using them in water treatment facilities to

remove microcystins.

Compared to the immediate and fast biodegradation of MCLR in the presence of

sediment, the biodegradation of MCLR by bacterial activity in the water in the absence

of sediment had a lower rate and a lag time of 60 h (Figure 3.1E). Similar results were

reported in earlier studies with microcystins incubated with water and sediments collected

from Taihu Lake in China (Chen et al., 2008); Furthermore, cylindrospermopsin

degradation only took place in the sediments but not in the water body (Klitzke et al.,

2010) and nodularin removal was more effective in the presence of the natural community


73

of benthic bacteria than in seawater (Torunska et al., 2008). The more effective

biodegradation of MCLR in the presence of sediments in this study might indicate that

more efficient or a higher concentration of bacteria capable of degrading MCLR occur in

the sediments of Lake Yangebup than in the water.

Our study indicated that the indigenous microcystin-degrading bacterial community

from Lake Yangebup sediment preferred oxygen rich over low oxygen conditions. As

many lake sediments are completely deprived of oxygen for a prolonged period of time,

future research should evaluate the oxygen-need of microcystin-degrading bacteria

communities as this would help to identify the optimum function conditions for these

bacteria communities.

Previous studies investigated the effect of nutrients on the biodegradation rate of

microcystins and the results were contradictory. Chen et al., (2010) found that the addition

of glucose or low levels of NH4-N had no effect on the anoxic biodegradation of

microcystin, whereas the addition of NO3-N significantly inhibited the biodegradation.

Park et al. (2001) reported that added organic nutrients inhibited the degradation of

microcystins while Holst et al. (2003) found that glucose addition could stimulate the

anoxic biodegradation of microcystins in sediment slurries. Holst et al. (2003) found that

the addition of NO3-N significantly stimulated microcystin degradation under anoxic

conditions, and suggested that this process was coupled to denitrification while Chen et

al., 2010 reported the opposite. The contradictory results may be because different

bacterial communities were active in degrading microcystins. Therefore, to further

identify the optimum function conditions for the bacteria communities in the current study,

investigation on the effect of nutrients on the degradation rate of microcystins is

recommended for future study.

We can conclude from this study that (1) sediments contribute significantly to the

removal of microcystins from the water column; (2) whilst both the biodegradation and


74

the adsorption ability of sediments can contribute significantly to the removal of

microcystins from the water column, biodegradation was shown to be the dominant

removal process. Our study indicates that biodegradation ability of sediments should be

more emphasized when sediments are considered to remove microcystins in water

treatment facilities; (3) sediments and water from Lake Yangebup, which experiences

frequent toxic Microcystis blooms, is rich in effectively microcystin-degrading bacteria.

These bacteria could be studied further to achieve optimized operational conditions to

remove microcystins effectively in water treatment facilities.




financially supported by China Scholarship sponsored by the China Scholarship Council

and The University of Western Australia (UWA). The authors would like to thank UWA

Centre for Applied Statistics for statistical advice and Dr. Krystyna Haq and an

anonymous reviewer for comments on an earlier draft.

Chapter 4. Extraction of microcystins from lake sediments using supercritical carbon dioxide

SONG, H., REICHWALDT, E. S. & GHADOUANI, A. 2014. Extraction of microcystins

from lake sediments using supercritical carbon dioxide. Toxins (submitted)

CHAPTER 4: EXTRACTION OF MICROCYSTINS FROM LAKE SEDIMENTS

77

4.1 Abstract

Microcystins are produced by certain species of cyanobacteria and they adversely

affect aquatic systems and human populations around the world. Understanding the fate

of microcystins in aquatic systems, both in water and in sediments, is essential for

managing the effects of these toxins. However, studying the fate of microcystins in lake

sediments has been greatly hindered by the lack of an effective analysis method. The

present study investigates whether the use of supercritical carbon dioxide has the potential

to be an effective method of extracting microcystins from lake sediment samples.

Extraction conditions and modifiers were first optimised using sediment samples which

were spiked with microcystin-LR. With extraction pressure set at 120 bar, extraction

temperature at 40°C, equilibration time at 30 min and water as the modifier, the highest

amount of total microcystins were extracted The supercritical extraction (SFE) method

with optimised extraction conditions was used for sediment samples spiked with

microcystin-LR and natural field sediment samples, and its extraction efficiency was

compared to the conventional extraction method. The conventional method recovered

more spiked microcystin-LR (recovery rate, which is the percentage of spiked

microcystin-LR recovered, was 69.6%) than the SFE method (recovery rate 4%), while

the SFE method was more effective in extracting microcystin variants that naturally

occurred in the sediment samples (0.72 µg/g d.m.) than the conventional method (0.28

µg/g d.m.). This study confirmed the capability of using supercritical carbon dioxide to

extract microcystins from sediments, as a complementary method to the conventional

method. As supercritical carbon dioxide and water are used but no chemical solvent or

clean-up procedure is needed, this method is environmentally friendly, time-saving and

cost-effective.


78

Keywords: Microcystins; Microcystins extraction; Sediments; Environmental fate;

Supercritical extraction; Supercritical carbon dioxide

4.2 Introduction

Cyanobacteria (blue-green algae) inhabit aquatic and terrestrial environments

throughout the world and many species are known to produce cyanobacterial toxins

(Codd et al., 1994, El-Shehawy et al., 2012). The rapid increase in volume or

accumulation of cyanobacteria in an aquatic system can form cyanobacterial blooms.

These blooms not only affect water’s taste, odour and appearance, but more significantly,

they can produce cyanobacterial toxins which may cause acute and possibly chronic

health problems in humans and animals (Vasconcelos, 2001, Azevedo et al., 2002, Liu et

al., 2002, Žegura et al., 2011, de la Cruz et al., 2011).

Microcystins are one of the most commonly reported groups of toxins produced by

certain species of cyanobacteria such as Microcystis spp., Anabaena spp., Oscillatoria

spp. and Nostoc spp. They have been connected with numerous deaths of aquatic animals,

domestic animals and even human beings (Tricarico et al., 2008). So far, over 90 different

microcystin variants have been identified and the number continues to increase due to

improved instruments and detection methods (Hotto et al., 2008). Microcystins are cyclic

heptapeptides with the general structure: cyclo-(D-Ala1-X2-DMeAsp3-Z4-Adda5-D-

Glu6-Mdha7). The main differences in microcystin variants are due to the presence of

different amino acids in positions X and Z (Carmichael, 1997).

To assess the potential risk of human and animal exposure to microcystins, it is crucial

to understand the fate of these toxins in aquatic systems. While their fate in water has

been widely studied (Kotak et al., 1996, Hyenstrand et al., 2003, Moreno et al., 2004,

Ibelings et al., 2005, Ihle et al., 2005, Song et al., 2007b, Chen et al., 2008) and good

methods exist for extracting microcystins from cyanobacterial biomass (Lawton et al.,


79

1994), only a limited number of studies have been carried out on the dynamics of

microcystins in lake sediments (Ihle et al., 2005, Chen et al., 2008). One reason for this

limited understanding is the lack of effective methods for quantifying microcystins in

sediment samples.

Current available methods for analysing microcystins from lake sediments include the

direct analysis and indirect analysis methods. Direct analysis usually involves the use of

various organic solvents, which are usually methanol with acetic acid or TFA, or EDTA-

sodium pyrophosphate solution (Tsuji et al., 2001, Ihle et al., 2005, Babica et al., 2006,

Chen et al., 2006a). These organic solvent-based extraction methods usually involve

clean-up and pre-concentration procedures, making them time consuming and expensive.

Tsuji et al. (2001) have described another method which involves the indirect detection

of microcystins. Using this indirect detection method, total microcystins were oxidised

into microcystin-specific fragment 2-methyl-3-methoxy-4-phenyl-butyric acid (MMPB)

which could be analysed by mass spectrometry. This method shortened the analysis time

and used less organic solvents. However this method only detects the total toxin

concentration and cannot be used to analyse microcystin variants. Another method,

combining solvent extraction and Lemieux oxidation, was developed by Wu et al. (2012).

In this optimised analytical procedure, a sequential solvent extraction using 50% (v/v)

methanol and EDTA-sodium pyrophosphate was selected as the optimal extraction

solvent for free microcystin analysis. After the extraction, the purified extracts and

sediment residuals were analysed by this optimised Lemieux oxidation method to

determine the total number of microcystins in lake sediments (Wu et al., 2012). As is the

case with the MMPB method, this method only measures total microcystins present.

Therefore, the development of an effective, time-saving and more selective detection

method is needed to reliably quantify the concentration of microcystin variants in

sediments.


80

Supercritical fluid extraction (SFE) has emerged as a superior alternative technique for

the extraction of a variety of compounds from natural environments. The method takes

advantage of the unique physicochemical features of supercritical fluids, which are in a

state between the liquid and gas phase (Sunarso and Ismadji, 2009). The most commonly

employed supercritical solvent is carbon dioxide because it is non-toxic, non-flammable,

relatively inexpensive, and has a low critical temperature (304.1 K) and a moderate

critical pressure (7.38 MPa) (Medina, 2012). Extraction of targeted compounds from a

large number of materials by supercritical carbon dioxide has been widely studied (Mattea

et al., 2009, Herrero et al., 2010, Sahena et al., 2010). Compared with the conventional

extraction methods, the advantages of SFE is its reduced extraction time, reduced

consumption of organic solvents, production of cleaner extracts, and environmental

benignity (Huang et al., 2012).

In a series of studies by Pyo et al. (Pyo, 1999, Pyo and Shin, 2001, Pyo and Lim, 2006),

microcystin variants were successfully extracted from cyanobacterial biomass using

supercritical carbon dioxide. The simultaneous extraction and purification of

microcystins LR, RR and YR was also achieved using supercritical carbon dioxide (Pyo

et al., 2004). In these studies, the best extraction efficiency was observed at 40 °C. Pyo

and Shin (2001) suggested that the most suitable medium for the supercritical fluid

extraction of microcystins from cyanobacterial cells was a ternary mixed fluid (90%

carbon dioxide, 8.7% methanol and 1.3% water), with water playing an important role.

Supercritical carbon dioxide has also been used in the extraction of various chemical

contaminants from soils and sediments, including PCBs, PAHs, DDT, dioxins and furans,

and pesticides (Anitescu and Tavlarides, 2006). Therefore, the SFE method shows

potential regarding its application in the extraction of microcystins in sediments.

The aim of this study is to evaluate the applicability and efficiency of SFE in extracting

microcystins from lake sediments. As part of this evaluation, extraction conditions were


81

optimised for an enhanced recovery rate of microcystins from lake sediments. A

comparison of SFE and conventional methods for both natural and spiked sediment

samples was conducted.

4.3 Materials and methods

Commercially available variant microcystin-LR (MCLR) (purity ≥ 95%, Sapphire

Bioscience, Australia) was added to natural sediment samples. Preliminary investigations

had detected the presence of microcystins variants in these samples. Spiked sediment

samples were used to optimise the extraction conditions (modifier, extraction pressure

and equilibration time) for the supercritical fluid extractor. The SFE method, conducted

under optimised extraction conditions, was used to extract microcystins from spiked

sediment samples and natural field sediment samples, and its extraction efficiency was

compared with that of the conventional extraction method. Differences in extraction

efficiency between different extraction conditions were analysed using a one-way

analysis of variance (ANOVA) in SigmaPlot 11.0 (Systat Software Int.). Significance

levels were set as p < 0.05.

4.3.1 Natural and spiked sediment samples

Sediments were collected from an urban shallow lake, Lake Yangebup (32°6'40"S,

115°50'00"E), in Perth, Western Australia. The upper 8 cm of the sediment layer was

collected with a sediment corer at random sites. In the laboratory, the collected sediments

were mixed, lyophilised to remove the water, sieved through a 212-µm mesh, and stored

at -20°C before use. This stored sediment sample was labelled ‘natural sediment sample’.

To prepare the spiked sediment sample, 4 g (dry weight) of natural sediment sample was

mixed with 10 ml of Milli-Q (Millipore, UK) water. The MCLR was then added to

achieve a concentration of 1.25 µg/g d.m. As 24 h has been shown to be a sufficient

equilibration time for the adsorption of microcystins to soils and sediments (Chen et al.,


82

2006a), the spiked sample was placed on a shaker for 24 h to reach equilibrium before its

lyophilisation in a freeze dryer. The freeze-dried sample was labelled ‘spiked sediment

sample’ and stored at -20°C before use.

Toxic cyanobacterial blooms occur in Lake Yangebup most of the year (Kemp and

John, 2006, Reichwaldt et al., 2013) and microcystins were found in the sediments in

preliminary studies. Therefore, microcystin variants were believed to be present in the

natural sediment samples. As spiked sediment samples were prepared from natural

sediment samples, it was expected that these microcystin variants would also be present

in spiked sediment samples. To distinguish them from the spiked MCLR, these

microcystin variants were referred as ‘other microcystin variants’. The procedure for

extracting microcystins from sediments using supercritical carbon dioxide is shown in

Figure 4.1.

The particle size and organic matter of the sediment used to prepare the natural

sediment samples were analysed. The method used to analyse particle size was adapted

from Bowman and Hutaka (2002). The adaptions included heating samples to facilitate a

quicker digestion of the organic matter and removing the hydrogen peroxide by sequential

dilution instead of evaporation. The particle size distribution analyser used was

the Malvern Mastersizer 2000 particle size analyser with a Malvern Hydro 2000G

automated wet dispersion accessory. Organic matter content, using loss-on-ignition, was

analysed according to Heiri et al. (2001) but with samples dried in an oven at 110°C

overnight instead of freeze drying. The organic matter content of sediment was 1.33 ±

0.05%. The results for particle size distribution showed that all the sediment samples were

sand with a content of clay, silt, fine sand and coarse sand as 0%, 0%, 11.99 ± 3.52% and

88.01 ± 3.52% respectively.


83

Figure 4.1: Extraction procedure for microcystins from sediments using supercritical carbon dioxide.

4.3.2 Supercritical fluid extraction

4.3.2.1 Supercritical fluid extractor

The extraction experiment was performed using a Hewlett-Packard 7680T SFE

Module. Food-grade carbon dioxide, 99.9% purity (BOC Gas Company, Australia) with

a flow rate of 1 ml/min, was used as solvent. A dry sediment sample (2.0–2.2 g) was

loaded into a stainless steel thimble, which was then pressured with supercritical carbon

dioxide after the addition of modifier. The supercritical carbon dioxide remained in the

thimber for a given equilibration time, followed by transfer of the solvent for an extraction

time of 15 min and then depressurized onto a trap (octadecyl silica) containing a

stationary phase. The trap was then washed with 5 ml of organic solvent, methanol +

0.01% TFA, and the dissolved extracts captured in glass vials.

Sediments mixed, lyophilized, sieved through a 212 µm opening size and stored at -20°C before use.

Sediments (2-2.2 g d.m.) and 10% water (w/w) loaded into SFE thimble.

Extraction with extraction conditions: extraction pressure = 120 bar, extraction temperature = 40 °C, equilibration time = 30 min, flow rate = 1 ml/min, extraction time = 15 min.

The trap was then washed with an organic solvent, methanol + 0.01% trifluoroacetic (TFA) (5 ml), to remove the dissolved compounds into glass vials.

The collected solvents in the glass vials were placed in a water bath (40°C), and the liquid samples were evaporated to dryness under a mild nitrogen stream.

Dry samples were re-dissolved in 1 ml 30% acetonitrile (v/v) for quantification of microcystin variants in HPLC-PDA.


84

4.3.2.2 Optimisation of extraction conditions

In the study by Pyo and Shin (2001), an extraction temperature of 40°C achieved the

highest solubility of microcystins in supercritical carbon dioxide, so the extraction

temperature for this study was also set at 40°C. It was suggested that the modifier acts by

competing with target analytes for the active site of the sample matrix rather than by

increasing analyte solubility (Bowadt and Hawthorne, 1995). Therefore, the identity of

the modifier may have a larger impact on the extraction efficiency than its concentration.

In the studies by Pyo et al., a mixture of 90% carbon dioxide and 10% modifiers was

effective in extracting microcystins from cyanobacterial biomass (Pyo, 1999, Pyo and

Shin, 2001, Pyo and Lim, 2006). Therefore, this study also used 90% (v/v) carbon dioxide

as the solvent.

To compare modifiers, spiked sediment samples were extracted with supercritical

carbon dioxide with different modifiers (10%, v/v) with extraction pressure set at 205 bar,

extraction temperature at 40°C and equilibration time at 20 min. The modifiers evaluated

were 10% methanol, methanol (8.7%) mixed with water (1.3%), and 10% water.

Experiments were performed in triplicate.

The solvating strength of a supercritical fluid is related to its density, a parameter

primarily dependent upon pressure. For a given temperature, the fluid density is

proportional to its pressure; a correct choice of pressure may lead to selective extractions

(Camel, 1998). With the temperature set at 40°C, equilibration time at 20 min, and the

selected modifier and other extraction conditions the same, the extraction efficiency for

total microcystins in the spiked lake sediment were compared at extraction pressures

ranging from from 80 to 160 bar at 20-bar intervals. Each experiment was carried out in

triplicate. This pressure range was chosen based on the mechanical condition of the

supercritical fluid extractor used in the experiment. With the temperature set at 40°C and

the modifier and pressure selected, the extraction efficiency for total microcystins in the


85

spiked lake sediment was compared for equilibration times of 10, 30 and 60 min. Each

experiment was carried out in triplicate.

4.3.3 Conventional extraction method

Sediment samples were extracted twice with 5% acetic acid in methanol containing

0.2% v/v TFA according to Babica et al. (2006), where conventional methods were

optimised. The extraction step was followed by centrifugation at 3500 g for 10 min. Forty

millilitres of pooled supernatants were diluted with 160 ml of deionised water to reach a

methanol concentration of less than 20% (v/v) before they were applied to Solid Phase

Extraction (SPE) cartridges (Oasis HLB 6cc/500mg, Waters, Australia). After application

of the samples, the cartridges were rinsed with 10 ml of 10% (v/v) methanol then 10 ml

of 20% (v/v) methanol. Microcystins were eluted with 5 ml of 100% methanol + 0.1%

TFA (v/v) from the SPE cartridges into 6-ml glass vials.

4.3.4 Microcystin analysis

The collected extracts in the glass vials from the supercritical fluid extractor were

placed in a water bath (40°C), and the liquid samples were evaporated to dryness under a

mild nitrogen stream. Dry samples were re-dissolved in 30% acetonitrile (v/v) for

quantification of MCLR in a Waters high performance liquid chromatography (HPLC)

system (Alliance 2695) which was equipped with a photo diode array (PDA) detector (1.2

nm resolution) and an Atlantis T3 3-µm column (4.6 x 150 mm i.d.). The mobile phases

used for the HPLC were acetonitrile with 0.05% TFA (v/v) and Milli-Q water with 0.05%

TFA (v/v). Column temperature was maintained at 37.5 ± 2.5°C. The limit of detection

per microcystin peak was 1.12 ng. The limit of quantification was 3x the limit of

detection. Microcystins were identified by their retention time and characteristic UV

absorption spectra (200–300 nm) and quantified by a commercially available MCLR

standard (Sapphire Bioscience, Australia; purity ≥ 95%).


86

4.4 Results

4.4.1 Optimisation of modifiers

With 10% methanol as a modifier, no added MCLR was recovered from the spiked

sediment samples. However, 22.93 ng/g d.m. of other microcystin variants were

recovered (Figure 4.2). When 8.7% methanol and 1.3% water were used as a modifier,

46.9 ng/g d.m. of added MCLR (recovery rate: 3.75%) and 34.11 ng/g d.m. of other

microcystin variants were recovered from the sediment, suggesting that extraction ability

was enhanced with the addition of water. Extraction efficiency was further improved

when 10% water was used as a modifier (90% carbon dioxide and 10% water), with 61.4

ng/g d.m. of added MCLR (recovery rate: 3.75%) and 346.96 ng/g d.m. of other

microcystin variants recovered (Figure 4.2). Therefore, water was chosen as the modifier

for the optimisation of extraction conditions. When 10% water was used as modifier, the

HPLC gave a cleaner chromatogram when compared with 8.7% methanol and 1.3% water

(Figure 4.3).


87

Figure 4.2: Microcystins extracted from spiked sediment samples using supercritical carbon dioxide with different modifiers: methanol (10%), methanol (8.7%) + water (1.3%), and water (10%). “*” represents no detectable level of MCLR. Error bar represent the standard errors of replicates (n = 3).


88

’

Figure 4.3: The first 20 min of the high performance liquid chromatogram of extracted microcystin variants using supercritical carbon dioxide from spiked sediment samples with different modifiers: (A) 10% water; (B) 8.7% methanol + 1.3% water. “*” represent microcystin variants other than MCLR.

12.6

76

13.9

68

17.6

99

19.2

11

AU

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Minutes0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00

* MCLR

MCLR

*

A

B

*

*


89

4.4.2 Optimisation of extraction pressure

To optimise the extraction pressure for the present study, microcystin variants were

extracted from sediment samples under different extraction pressures (Figure 4.4). The

extraction efficiency increased with increasing pressure of the extraction fluid until the

pressure reached 120 bar. At pressures over 120 bar, the extraction efficiency decreased.

However, while the the extraction efficiency at 120 bar was significantly higher than the

extraction efficiency at 80 bar and 160 bar (ANOVA, p < 0.05), it was not significantly

higher than the extraction efficiency at 100 bar and 140 bar. Therefore, the optimal

pressure could be in the range of 100 to 140 bar. However, to test the potential of this

SFE method, the extraction pressure for this study was set at 120 bar.

Figure 4.4: Total microcystins extracted from spiked sediment samples using supercritical carbon dioxide with different extraction pressures when all other extraction conditions are the same. Error bar represents the standard error of replicates (n = 3).

Pressure (bar)60 80 100 120 140 160 180

Ext

ract

ed to

tal M

Cs

(ng/

g d.

m.)

200

300

400

500

600

700


90

4.4.3 Optimisation of equilibration time

To optimise the equilibration time, microcystin variants were extracted from sediment

samples under different equilibration times. The total concentration of microcystins

recovered from spiked sediment increased significantly from 10 min to 30 min (ANOVA,

p < 0.05) but decreased significantly from 30 min to 60 min (ANOVA, p < 0.05) (Figure

4.5). With extraction pressure set at 120 bar, extraction temperature at 40°C and water as

the modifier, the highest amount of total microcystins were extracted. Therefore, 30 min

was chosen as the equilibration time for this study.

Figure 4.5: Total microcystins extracted from spiked sediment samples using supercritical carbon dioxide with different equilibration times when all other extraction conditions are the same. Error bar represents the standard error of replicates (n = 3).

4.4.4 Comparison between the supercritical fluid extraction and conventional extraction method

The extraction efficiency for both spiked sediment samples and natural sediment

samples were compared using the SFE and conventional extraction methods. For spiked

Equilibration Time (min)0 10 20 30 40 50 60 70

Ext

ract

ed to

tal M

Cs

(ng/

g d.

m.)

0

200

400

600

800

1000


91

sediment samples, the extraction efficiency for spiked MCLR and other microcystin

variants were observed and compared. For natural sediment samples, the total

concentrations of recovered microcystin variants were observed and compared.

When spiked sediment samples were extracted with the conventional method, 0.87

µg/g d.m. of MCLR (recovery rate: 69.6%) and 0.28 µg/g d.m. of other microcystin

variants were recovered (Figure 4.6). In contrast the SFE method recovered 49.95 ng/g

d.m. of the added MCLR (recovery rate: 4%) and a total concentration of 0.72 µg/g d.m.

of other microcystin variants. The conventional method recovered a higher amount of

added MCLR but a lower amount of other microcystin variants present in natural

sediment when compared with the SFE method. For natural sediment samples, a total

concentration of 631 ng/g d.m. of microcystins were extracted with the SFE method while

conventional method extracted 246 ng/g d.m. In natural sediment samples, MCLR were

detected by both methods. Using the conventional method, an average of 14.25 ng/g d.m.

of MCLR was recovered. In comparison, the SFE method recovered an average of 57.37

ng/g d.m. of MCLR. Therefore, the SFE method extracted microcystin variants that

naturally occur in the sediments more effectively than the conventional method.


92

Figure 4.6: Microcystins extracted from spiked sediment samples using the conventional and supercritical fluid (SFE) methods. Error bar represents the standard error of replicates (n = 3).

4.5 Discussion

A polar modifier is usually recommended for enhancing the solvating power of

supercritical carbon dioxide for polar organic compounds. Among a variety of different

modifiers for supercritical carbon dioxide, methanol is the most popular choice. However,

Pyo and Shin (2001) have suggested that the most suitable medium for SFE of

microcystins from cyanobacterial cells is a ternary mixed fluid (90% carbon dioxide,

8.7% methanol and 1.3% water) and that water plays an important role in this extraction

process (Pyo and Shin, 2001). The present study demonstrated that water as a modifier is

more effective than either 10% methanol or 8.7% methanol mixed with 1.3% water. This

might be because water has a higher dielectric constant than methanol (water: 84.4,

methanol: 35.74), and when water is used as modifier, the two functional groups of

opposite charge in the microcystins (NHCNH-N+H2 and COO-) can be separated more


93

easily (Mohsen-Nia et al., 2010). The extent of separation of these two functional groups

with opposite charge in the microcystins can affect the solubility of the microcystins in

supercritical fluid carbon dioxide with water as a modifier (Pyo and Lim, 2006, Arnáiz et

al., 2012).

Pressure and temperature are the two most important physical parameters in SFE and

they have both theoretical and practical implications for the extraction process. Together

they define the density of the supercritical fluid. In this study, the optimised extraction

pressure should be between 100 bar and 140 bar , which is far lower than the pressure set

by(Pyo and Shin, 2001) (250 atm) to extract microcystin from cyanobacterial cells using

supercritical carbon dioxide with a mixture of water and methanol as modifier. In this

study, the highest pressure that could be maintained by the supercritical extractor was 160

bar when the extraction pressure was optimised, so extraction efficiencies higher than 160

bar were not tested. This is a limitation of this study, and further tests could be conducted

to find out whether the extraction efficiency can be improved at extraction pressures

higher than 160 bar.

In the present study, for spiked sediment samples, the conventional method recovered

more spiked MCLR but less other microcystin variants; for natural sediment samples,

supercritical carbon dioxide with water as a modifier extracted more microcystin variants,

including MCLR. This result indicates that chemical solvents used in the conventional

method are more effective than supercritical carbon dioxide in extracting microcystins on

the surface of sediment samples but less effective in extracting microcystins strongly

bound to sediments. Spiked analytes are situated on the surface of sample matrices and

have little time to migrate to strong binding sites. In contrast, native pollutants in

sediments are often in contact with the matrix for a longer time and are therefore more

likely to be associated with stronger binding sites than recently spiked pollutants (Bowadt

and Hawthorne, 1995). For native microcystins binding to sediments, supercritical carbon


94

dioxide might be more powerful than chemical solvents when used in conventional

methods to break the bond between microcystins and sorptive sites in sediments.

Therefore, SFE method has an advantage in the extraction of microcystin variants in

natural sediments and can be used as a complementary method to the conventional

extraction method.

This is the first study to propose the use of supercritical carbon dioxide in the extraction

of microcystins from lake sediments. It confirmed the hypothesis that supercritical carbon

dioxide has the potential to extract microcystins from lake sediments. This study found

that supercritical carbon dioxide modified by water was effective in extracting

microcystins from natural sediment samples, which are typically difficult to extract using

the conventional method. Compared to the conventional method, SFE is a time-saving,

cheap and environmentally friendly method for the detection of microcystins in

sediments. Application of the SFE method will improve our understanding of the fate of

microcystins in sediments and aquatic systems.




financially supported by a China Scholarship sponsored by the China Scholarship Council

and UWA. The authors would like to thank Professor George Koutsantonis for the use of

the Supercritical Fluid Extractor and Shian Min Liau and Randica Jayasinghe for

comments on an earlier draft.

Chapter 5. Conclusions and recommendations

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

97

5.1 Significance of contribution

The study aimed to explore the role of sediments in the fate of microcystins in shallow

aquatic systems and to develop an effective method for quantifying microcystins in the

sediments. Microcystins occur in various aquatic systems across the world and they can

cause great harm to animals and humans. It is therefore important to understand their fate

in aquatic systems. As an important component of aquatic systems, sediments can play

multiple roles in the distribution and natural attenuation of microcystins. Hence, to better

manage the hazards of microcystins in aquatic systems, it is essential to understand their

variability in lake sediments and the role played by sediments in the natural attenuation

of these toxins in aquatic systems. In addition, developing an effective analysis method

for microcystins in sediment samples will facilitate better research in this area. To address

these issues, the study sought to answer three questions:

1. What are the main environmental variables contributing to the variability of

microcystins in sediments of a shallow lake (Aim 1)?

2. What is the relative contribution of the biodegradation ability and adsorption

ability of sediments to the removal of microcystins from the water (Aim 2)?

3. Can supercritical carbon dioxide extraction be used as a reliable method to

quantify microcystins in lake sediments (Aim 3)?

These three research questions are addressed in Chapters 2–4 of this thesis. The main

findings, which are chapter specific, have been summarised within the respective chapter

(Figure 5.1).

Figure 5.1: Conceptual framework of the study aims and main findings of the study. MCs refers to microcystins; CB refers to cyanobacterial biomass

CB in sediments

MCs in sediments

Sediments

Chapter 2: To identify the temporal and spatial variability of microcystins in the sediments of a shallow lake and determine the correlation of the variability with environmental variables (Aim 1).

CB in water

MCs in water

Water

Chapter 4: To evaluate the applicability and efficiency of supercritical carbon dioxide in the quantification of microcystins from lake sediments (Aim 3).

Chapter 3: To quantify the significance of the sediment’s contribution to the removal of microcystins from the water column and compare the relative contribution of the biodegradation and adsorption abilities of sediments to the removal of microcystins from the water column (Aim 2).

Main findings: MCs in sediments had weak but significant correlation with total MCs and cyanobacterial biomass in water column; variability of MCs in sediments could be significantly explained by a combination of total MCs in water, cyanobacterial biomass in overlying water, pH and temperature (Aim 1).

Main findings: Supercritical carbon dioxide has the capacity to extract microcystins from sediments; supercritical carbon dioxide with water as modifier was more effective in quantifying MCs from natural sediment samples than conventional method (Aim 3).

Main findings: Sediments contribute to the removal of MCLR from the water column immediately, effectively and continuously, while both biodegradation ability and adsorption ability of sediments contribute significantly to the removal of MCLR from the water column, biodegradation was the dominant process (Aim 2).

CH

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5.2 Variability of microcystins in the sediments

The variability of microcystin concentrations in lake sediments and its correlation with

environmental variables were analysed in Chapter 2. Microcystins occurred widely in the

sediments collected at an eutrophic shallow lake during a cyanobacterial bloom

dominated by Microcystis spp (Figure 2.2). The spatial variability of microcystin

concentrations in sediments was different in each sampling month and the temporal

variability of these toxins in the sediments was site specific (Figure 2.3). Microcystin

concentrations in lake sediments had weak but significant correlation with intracellular

microcystins in the water, total microcystins in the water and cyanobacterial biomass in

the water (Table 2.4). Analysis using a multiple linear regression model developed for

this study showed that a significant part of the variability of microcystins in lake

sediments (46.7%) could be explained by a combination of total microcystins in the water,

cyanobacterial biomass in the water and physicochemical parameters of the water (pH

and temperature).

While most research in this area has focused on microcystins in the water, the results

of this study showed that these toxins also widely occur in the sediments of a shallow

lake. Such results highlight the importance of studying microcystins present in sediments,

Microcystin concentrations in the sediments were significantly correlated to the

concentration of intracellular toxins in water and cyanobacterial biomass in water directly

above the sediments. This finding is in agreement with the study by Mohamed et al.

(2007). However, contrary to earlier studies (Mohamed et al., 2007 and Ihle et al., 2005),

this present study found no significant correlation between microcystins in sediments and

cyanobacterial biomass in sediments. Furthermore, a systematic study of the correlation

between the variability of microcystins and environmental variables showed that the

variability of microcystins in the sediments was influenced by multiple environmental


102

factors. These factors need to be considered when predicting the distribution of

microcystins in the sediments. For example, the multiple linear regression model

developed in this study indicated that processes in the water have a strong influence on

the distribution of microcystins in sediments.

To gain a broader understanding of the occurrence of microcystins in the sediments, it

will be necessary to further investigate the variability of microcystins in different aquatic

systems. As shown in the current study, there is an important connection between the

water and the sediments that influences the distribution of microcystins in aquatic systems.

However, the present study was limited to a shallow lake where there is a relatively high

interaction between water and sediments. The correlation between the variability of

microcystins in sediments and environmental variables may differ in a deep lake.

Therefore, different aquatic systems need to be investigated to broaden this understanding.

Additional environmental variables need to be considered to fully explain the

variability of microcystins in sediments. For example, 53% of the variability of

microcystins in the sediments could not be explained by the regression model developed

in this study. This variability could be caused by a number of environmental factors that

include wind mixing and the physical and chemical properties of sediments. What is clear

from this study is that the contribution of other environmental variables to the variability

of microcystins in sediments warrants further investigation.

5.3 Contribution of sediments to the removal of microcystins from the water

The significance of the sediment’s contribution to the removal of microcystins from

the water and the relative contribution of the biodegradation and adsorption ability of

sediments to the removal of microcystins from the water were studied in Chapter 3. In the

laboratory, changes in the concentration of MCLR in the presence of sediments (to test

the sum of adsorption ability and biodegradation ability), sediments with microbial


103

inhibitor (to test the adsorption ability of sediments) and non-sterile lake water (control)

were quantified (Table 3.1). When added to sediments, the concentration of MCLR in

sterile lake water decreased significantly in an exponential way, with no observed lag

phase (Figure 3.1). This result indicates that sediments contribute to the removal of

MCLR from water immediately, effectively and continuously. Comparison between the

treatments with bacterial inhibitor and without bacterial inhibitor indicated that the

exponential decay of MCLR in the presence of sediments was mainly caused by the

biodegradation ability of the bacterial community within the sediments (Figure 3.1). At

the start of incubation, the adsorption of MCLR was negligible when compared to its

biodegradation, but over time a significant loss of MCLR due to adsorption was observed.

Therefore, it can be concluded that while the biodegradation and adsorption ability of

sediments may contribute significantly to the removal of MCLR from the water,

biodegradation appears to be the dominant process.

Earlier studies have shown that sediments contribute to the removal of dissolved

microcystins from the water through adsorption to sediment particles or biodegradation

by the sediment’s bacterial community. This is the first study to compare these two

processes in the laboratory and identify that biodegradation is the dominant process. The

same result has been reported in studies on microcystin elimination in sandy aquifer

material (Grut macher et al., 2010), microcystin elimination in sand filters (Ho et al.,

2006) and the removal of other cyanobacterial toxins (Torunska et al., 2008). While these

studies observed that the biodegradation of microcystins was usually characterised by a

prolonged lag phase, this current study found no such lag phase. The difference in

findings might be due to the fact that the sediments used in this experiment were sampled

during a Microcystis bloom and MCLR-degrading bacteria may have been present in the

sediments. Nevertheless, this present study’s finding that sediments play a role in the

removal of microcystins from water helps us to better understand the environmental fate


104

of microcystins in aquatic systems and raises the possibility that these sediments could

be used to remove microcystins in water treatment facilities.

An important area of focus for future studies is the development of efficient technology

that uses sediments to effectively remove microcystins from water. Sediments with

different characteristics and from different aquatic environments could be considered in

these studies. This current study also demonstrated that bacteria present in sediments were

able to effectively facilitate the removal of microcystins through their biodegradation

ability. Hence, another focus for further investigation could be those bacterial

communities capable of biodegrading microcystins in sediments. Also, it would lead to a

better understanding of the bacterial communities in the sediments to look at the influence

of nutrients (e.g. C, N, P) on the growth of microcystin-degrading bacteria in the

sediments. Identification of these bacteria followed by research into optimising their

growth conditions may lead to an effective treatment technology for microcystin-

contaminated water bodies.

5.4 Quantification of microcystins in the sediments using supercritical carbon dioxide

The potential for the application of supercritical carbon dioxide in quantifying

microcystins from lake sediment samples was investigated in Chapter 4. Extraction

conditions and modifiers were first optimised using sediment samples spiked with

MCLR. The extraction efficiency of the SFE method under optimised extraction

conditions was then compared with the conventional extraction method using sediment

samples spiked with MCLR and natural field sediment samples. The conventional method

recovered more spiked MCLR (recovery rate 69.6%) than the SFE method (recovery rate

4%), while the SFE method was more effective in extracting microcystin variants that

naturally occurred in the sediment samples (0.72 µg/g d.m.) than the conventional method

(0.28 µg/g d.m.) (Figure 4.6).


105

As far as we know, this is the first study to raise the possibility of using supercritical

carbon dioxide to extract microcystins from lake sediments. It confirmed that supercritical

carbon dioxide is effective in extracting microcystins from lake sediments, and when

compared with the conventional method, SFE is a time-saving, cheap and

environmentally friendly method. This method shows great potential for furthering our

understanding of the fate of microcystins in sediments and aquatic systems.

In terms of using SFE technology to develop an effective method of quantifying

microcystins in the sediments, further research is needed. For example, the extraction

conditions for the SFE method developed in this study could be further optimised. In the

present study, extraction conditions were optimised using sediment samples with

consistent characteristics. These specific conditions may limit extraction efficiency when

the SFE method is applied to other types of sediment samples. Therefore, the optimisation

of extraction conditions for a range of sediments should be investigated. The optimisation

of extraction conditions for more microcystin variants is also suggested as a topic for

future study.

5.5 Policy and management implications

For the management of bloom-prone water bodies, it is important to holistically

understand the fate of microcystins in the aquatic systems. Current studies and policies

focusing on microcystins in aquatic systems almost exclusively concentrate on the water

column while sediments are mostly neglected. However, there is strong evidence from

this and earlier studies that microcystins are widely present in sediments. Considering the

potential ecological hazards with the occurrence of microcystins in lake sediments,

researchers and water management authorities should include sediments when assessing

the potential hazards of microcystins in aquatic systems.


106

The present study emphasizes the importance that sediments play for the removal of

microcystins from the water column. This will be especially important in shallow systems

where the interaction between the water column and the sediment is naturally high. The

results presented here add important information to our understanding of the fate of

microcystins in aquatic systems, and specifically on the two mechanisms responsible for

microcystin degradation at the water-sediment interface. This will inform the application

of sediments to remove microcystins at water treatment facilities. Hence, this study is of

high importance not only to the scientific community, but specifically also to

environmental engineers and water resource managers.

This work is of considerable importance from both process-level research and

management perspectives because nuisance blooms of microcystin-producing

cyanobacterial (mainly Microcystis spp.) are on the increase worldwide, and there is a

widespread need for both preventing the proliferation of blooms and mitigating the

negative impacts (i.e. toxicity) of existing blooms. The work is of dual importance from

an ecosystem-level perspective. It shows that the sources and fates of Microcystis are

closely coupled and mediated by closely linked (in time and space) physical-chemical-

biotic processes. It also shows that the amounts and distributions of toxins can be

predicted well from water column cyanobacterial (Microcystis spp.) biomass estimates

(when Microcystis spp. is the dominant cyanobacterial taxon), which was also recently

found by Otten et al (2012). This means that a water quality manager could get a fairly

good estimate of the potential toxicity (and its fate) of a water body simply by estimating

Microcystis biomass using conventional methods (i.e. cell counts, Chorophyll a

measurements).

Direct measurement of microcystins in the aquatic systems, particularly in the

sediments, is hard to achieve for water quality managers who often lack the expertise to

conduct biochemical and molecular analyses. It is therefore recommended that


107

cyanobacterial biomass and physical parameters (temperature, DO, pH) that impact the

distribution of microcystins in the aquatic systems be routinely measured to estimate the

microcystins in the water column and the sediments. Based on the level of microcystins

concentration in the aquatic systems, the water quality managers can determine the

actions that should be taken to remove microcystins from the aquatic systems.

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