Application of Hydrogen Peroxide for the Removal of ...uwa.edu.au/__data/assets/pdf_file/0010/1637497/Barrington_2007.pdf · Application of Hydrogen Peroxide for the Removal of Cyanobacteria

Application of Hydrogen Peroxide for the Removal of Cyanobacteria and Cyanotoxins from Wastewater Treatment

Ponds in Western Australia

Danielle Jennifer Barrington Bachelor of Engineering (Environmental)

Bachelor of Science (Chemistry/Environmental Chemistry) The University of Western Australia

(Barrington 2007)

Supervised by Dr Anas Ghadouani

The University of Western Australia

In collaboration with the Water Corporation

Acknowledgements

Sincerest thanks to my supervisor Anas, for all your help this last two years. Nothing you

do goes unappreciated. Though please don’t dance on podiums anymore.

Lots of hugs go out to Penny and Hannah, for braving the ‘poo water’ for the greater good of my honours thesis

Thanks to Di, for taking the time to teach me how to use… well, the entire lab!

Dean Puzey - your help in the form of resources and time was greatly appreciated, especially risking drowning to row us into the centre of pond one

Brett Kerenyi- thanks for driving; sorry about the speeding ticket!

Thankyou to the third and final year SESE students for helping me stay sane in the computer lab, in particular to Dan for helping me get through MATLAB, but also

providing distraction and comic relief in times of stress

The Scrubs scriptwriters- I couldn’t have gotten through my degree without them!

To my absolutely fantastic family, who have always supported me in my chosen career path, even if that career is to become a tree-hugging hippie who wants to save the world

“Not I, not anyone else, can travel that road for you,

You must travel it for yourself.

I answer that I cannot answer,

You must find out for yourself.”

-Walt Whitman

Abstract

Harmful cyanobacterial blooms have increased in frequency and intensity within Western

Australia in recent decades. Freshwater blooms are primarily of the species Microcystis

aeruginosa, which contains variants of the cyanobacterial hepatotoxin microcystin, which

is detrimental to human and animal health. Cyanobacterial blooms are common in

wastewater treatment plants, and treated wastewater is currently released to waterways.

Wastewater plant effluent is required to be free of toxins prior to release to the

environment, and to achieve this, application of hydrogen peroxide has been proposed by

the Water Corporation to induce cyanobacterial cell death and toxin degradation.

Hydrogen peroxide is a known and marketed algicide, and is environmentally benign as

its degradation products are hydrogen and oxygen gases.

For the purposes of this study, samples were collected from the Burekup wastewater

treatment plant in order to undergo analysis in the laboratory. A statistically robust

factorial design was devised to establish the response of cyanobacterial biomass and

cyanobacterial toxins to the addition of various doses of hydrogen peroxide over time.

Fluorescence was measured using a bbe-Moldaenke FluoroProbe to determine the

photosynthetic activity of the sample, and cyanobacterial toxins were measured using an

Abraxis PN 520011, Microcystins/Nodularins (ADDA) ELISA Kit, Microtiter Plate

(96T).

Results suggest that hydrogen peroxide is effective at reducing the biomass of algal

communities within wastewater treatment ponds, and effective doses displayed an

exponential chlorophyll a decay curve with ANOVA p values less than 0.01, and r2

values greater than approximately 0.8. It is likely that the optimal application dose for

hydrogen peroxide to wastewater treatment ponds is above 0.148 gL-1. The minimum

detection limit for microcystin and nodularin concentration was 0.1 μgL-1, and all

samples tested within this study were below this concentration according to the analysis.

Hence the behaviour of microcystin concentrations with hydrogen peroxide application

could not be examined.

Our interpretation of the results based upon algal ecophysiology suggests that the

reduction of algal photosynthetic activity following the addition of hydrogen peroxide is

due to hydroxyl radicals attacking the cell wall and photosynthetic apparatus of

cyanobacteria, and destroying pigments and photosynthetic proteins to induce cell death.

It is believed that microcystin toxicity is reduced by hydroxyl radicals inducing oxidative

cleavage of the Adda bond, effectively severing the toxin molecule.

It is expected that further analysis will indicate an effective dose rate for wastewater

treatment plants to reduce cyanobacterial cell and toxin concentrations to within

acceptable levels, within acceptable monetary and time limits. Where cyanobacteria

removal methods are already in place, hydrogen peroxide may be viewed as a more

environmentally sensitive method of water treatment.

Acronyms

CRM – Certified Reference Materials

ELISA – Enzyme Linked Immunosorbent Assay

GC – Gas Chromatography

HPLC – High Performance Liquid Chromatography

LC – Liquid Chromatography

MMPB – 3-methoxy-2-methyl-4-phenylbutyric acid

MRM – Multiple Reaction Monitoring

MS – Mass Spectrometry

PDA – Photodiode Array

PPIA – Protein Phosphatase Inhibition Assay

SIM – Selected Ion Monitoring

SRM – Selected Reaction Monitoring

TLC – Thin Layer Chromatography

UV – Ultraviolet

WHO – World Health Organization

WWTP – Wastewater Treatment Plant

Table of Contents

1 Introduction.......................................................................................... 15

2 Literature Review ................................................................................ 17

2.1 Cyanobacteria in Western Australia ................................................................. 17

2.2 Western Australian Wastewater........................................................................ 18

2.3 Cyanobacteria and cyanobacterial toxins.......................................................... 19

2.4 Detection of cyanobacteria by fluorometry ...................................................... 26

2.5 Detection of cyanobacterial toxins.................................................................... 29

2.6 Prevention of high cyanobacterial concentrations ............................................ 36

2.7 Degradation and removal of cyanobacteria and cyanotoxins ........................... 37

2.8 Mode of action of hydrogen peroxide............................................................... 42

3 Methodology ......................................................................................... 45

3.1 Sampling and incubation................................................................................... 45

3.2 Determination of chlorophyll a......................................................................... 51

3.3 Determination of extracellular microcystin and nodularin concentrations....... 53

3.4 Data Analyses ................................................................................................... 56

4 Results ................................................................................................... 57

4.1 Cyanobacterial biomass .................................................................................... 57

4.2 Cyanobacterial toxins........................................................................................ 91

5 Discussion.............................................................................................. 93

5.1 Chlorophyll a concentration.............................................................................. 93

5.2 Cyanobacterial toxins........................................................................................ 96

6 Recommendations ................................................................................ 99

6.1 Refine analysis method ..................................................................................... 99

6.2 Field trial ......................................................................................................... 101

6.3 Hydrogen peroxide dosage protocol ............................................................... 101

7 Conclusion........................................................................................... 102

8 References ........................................................................................... 103

Figures

Figure 1: Microcystis aeruginosa bloom at Ron Stone Park, Mount Lawley (Fon Sing

2006) ................................................................................................................................. 17

Figure 2: Water Corporation Cyanobacterial Monitoring, Brunswick Junction WWTP . 18

Figure 3: Cells of Microcystis aeruginosa aggregating (Ghadouani 2006)..................... 21

Figure 4: Microcystis aeruginosa bloom at Ron Stone Park, Mount Lawley (Fon Sing

2006) ................................................................................................................................. 22

Figure 5: Chemical structure of Microcystin-LR.............................................................. 24

Figure 6: bbe-Moldaenke FluoroProbe assembled for laboratory use.............................. 28

Figure 7: Adding the Stop Solution to samples on an ELISA plate (Barrington 2007) ... 34

Figure 8: 1) Hydroxyl radicals attack the 4-5 or 6-7 bonds of the microcystin’s Adda

moiety. 2) Bonds 4-5 or 6-7 of Adda become dihydroxylated. 3) Oxidative cleavage of

the microcystin molecule occurs at the 4-5 or 6-7 bond of the Adda moiety. .................. 44

Figure 9: Locations of water samples collected in mid-2007 (Google 2007)................... 46

Figure 10: Lake adjacent to Sir Charles Gairdner Hospital, Nedlands (Barrington 2007)47

Figure 11: Laboratory incubation attempt (Barrington 2007) .......................................... 48

Figure 12: Collecting concentrated samples by use of a plankton net (Barrington 2007) 49

Figure 13: Preparing the boat for sample collection (Barrington 2007)........................... 50

Figure 14: Rowing the boat to collect samples from Pond 1 of the Burekup WWTP...... 51

Figure 15: Dosed samples undergoing incubation under fluorescent light (Barrington

2007) ................................................................................................................................. 52

Figure 16: Analysing a sample on the bbe-Moldaenke Fluoroprobe (Barrington 2007).. 53

Figure 17: Adding 50 μL of antibody solution to samples ............................................... 55

Figure 18: In situ chlorophyll a monitoring, Burekup WWTP. Standard error bars are

shown but are small in comparison to chlorophyll a values ............................................. 57

Figure 19: Total algal chlorophyll a concentration over time after dosing with various

concentrations of hydrogen peroxide ranging from 0 to 296 gL-1. Error bars represent the

standard error, where ten fluorescence measurements were taken for each repetition..... 58

Figure 20: Cyanobacterial chlorophyll a concentration over time after dosing with various

concentrations of hydrogen peroxide ranging from 0 to 296 gL-1. Error bars represent the


Figure 21: Dosed samples at 24 hours incubation. Left sample is a deionised water

control, the right sample has been dosed with 1.48gL-1 hydrogen peroxide (Barrington

2007) ................................................................................................................................. 60

Figure 22: Dosed samples at 24 hours incubation time. From left: control, 0.0296 gL-1,

0.148 gL-1, 0.296 gL-1, 0.740 gL-1, 1.48 gL-1 (Barrington 2007) ...................................... 61

Figure 23: Total algal chlorophyll a concentration normalised to the control. Error bars

represent the standard error, where ten fluorescence measurements were taken for each

repetition. .......................................................................................................................... 62

Figure 24: Cyanobacterial chlorophyll a concentration normalised to the control. Error

bars represent the standard error, where ten fluorescence measurements were taken for

each repetition. .................................................................................................................. 63

Figure 25: Chlorophyta chlorophyll a concentration normalised to the control. Error bars


repetition. .......................................................................................................................... 64

Figure 26: Diatom chlorophyll a concentration normalised to the control. Error bars


repetition. .......................................................................................................................... 65

Figure 27: Cryptophyta chlorophyll a concentration normalised to the control. Error bars


repetition. .......................................................................................................................... 66

Figure 28: Cyanobacterial chlorophyll a decay for the control. Error bars represent the


Figure 29: Cyanobacterial chlorophyll a decay for addition of 0.00296 gL-1 hydrogen

peroxide. Error bars represent the standard error, where ten fluorescence measurements

were taken for each repetition........................................................................................... 69



















Figure 36: Cyanobacterial chlorophyll a decay for addition of 296 gL-1 hydrogen



Figure 37: Cyanobacterial chlorophyll a first order rate constants plotted against

hydrogen peroxide dose. The line of best fit is a 3 parameter sigmoid. Error bars represent

the standard error. ............................................................................................................. 78

Figure 38: Total algal chlorophyll a decay for the control. Error bars represent the


Figure 39: Total algal chlorophyll a decay for addition of 0.00296 gL-1 hydrogen



Figure 40: Total algal chlorophyll a decay for addition of 0.0296 gL-1 hydrogen peroxide.

Error bars represent the standard error, where ten fluorescence measurements were taken

for each repetition. ............................................................................................................ 82










Figure 44: Total algal chlorophyll a decay for addition 1.48 gL-1 hydrogen peroxide.






Figure 46: Total algal chlorophyll a decay for addition of 296 gL-1 hydrogen peroxide.



Figure 47: Total algal chlorophyll a first order rate constants plotted against hydrogen

peroxide dose. The line of best fit is a 3 parameter sigmoid. Error bars represent the

standard error. ................................................................................................................... 90

Figure 48: Combined microcystin and nodularin concentrations detected by ELISA

analysis. All measurements were below the minimum reliable detection limit of 0.1 μgL-1

........................................................................................................................................... 92

Tables

Table 1: Advisory levels for cyanobacteria in wastewater effluent (Water Corporation

2004) ................................................................................................................................. 19

Table 2: Statistical analysis of total algal chlorophyll a concentration normalised to the

control ............................................................................................................................... 62

Table 3: Statistical analysis of cyanobacterial chlorophyll a concentration normalised to

the control ......................................................................................................................... 63

Table 4: Statistical analysis of chlorophyta chlorophyll a concentration normalised to the

control ............................................................................................................................... 64

Table 5: Statistical analysis of diatom chlorophyll a concentration normalised to the

control ............................................................................................................................... 65

Table 6: Cyanobacterial first order rate constant for the control ...................................... 68

Table 7: Cyanobacterial first order rate constant for addition of 0.00296 gL-1 hydrogen

peroxide............................................................................................................................. 69


peroxide............................................................................................................................. 70


peroxide............................................................................................................................. 71


peroxide............................................................................................................................. 72


peroxide............................................................................................................................. 73


peroxide............................................................................................................................. 74


peroxide............................................................................................................................. 75

Table 14: Cyanobacterial first order rate constant for addition of 296 gL-1 hydrogen

peroxide............................................................................................................................. 76

Table 15: Average first order rate constants for cyanobacterial chlorophyll a................. 77

Table 16: Total algal first order rate constant for the control ........................................... 80

Table 17: Total algal first order rate constant for addition of 0.00296 gL-1 hydrogen

peroxide............................................................................................................................. 81


peroxide............................................................................................................................. 82


peroxide............................................................................................................................. 83


peroxide............................................................................................................................. 84


peroxide............................................................................................................................. 85

Table 22: Total algal first order rate constant for addition of 1.48 gL-1 hydrogen peroxide

........................................................................................................................................... 86


........................................................................................................................................... 87

Table 24: Total algal first order rate constant for addition of 296 gL-1 hydrogen peroxide

........................................................................................................................................... 88

Table 25: Average first order rate constants for total algal chlorophyll a ........................ 89

Table 26: Total microcystin and nodularin concentrations (μgL-1) detected by ELISA .. 92

Table 27: Fluoroprobe data for chlorophyll a concentration for the control, repetition 1

......................................................................................................................................... 116


......................................................................................................................................... 116


......................................................................................................................................... 117

Table 30: Fluoroprobe data for chlorophyll a concentration with 2.96 x 10-3 gL-1

hydrogen peroxide addition, repetition 1 ........................................................................ 117























Table 42: Fluoroprobe data for chlorophyll a concentration with 7.4 x 10-1 gL-1 hydrogen

peroxide addition, repetition 1 ........................................................................................ 123





Table 45: Fluoroprobe data for chlorophyll a concentration with 1.48 gL-1 hydrogen












Table 51: Fluoroprobe data for chlorophyll a concentration with 296 gL-1 hydrogen






1. Introduction 15

1 Introduction

Harmful cyanobacterial blooms have increased in frequency and intensity within Western

Australia in recent decades, mainly due to increased nutrient loadings to waterways

(Griffin et al. 2001; Pennifold and Davies 2001; Robson and Hamilton 2004). Freshwater

blooms are primarily of the species Microcystis aeruginosa, which contains variants of

the cyanobacterial hepatotoxin microcystin (Kemp and John 2006). Hepatotoxins are

detrimental to human and animal health, and microcystin variants have been reported to

have caused liver disease resulting in death, in particular the death of 56 haemodialysis

patients at a Brazil clinic in 1996 (Carmichael 2001; Jochimsen et al. 1998). Hepatotoxins

have also been implicated in causing liver cancer (Carmichael 1992; Carmichael 1997;

Nishiwaki-Matsushima et al. 1992; Tsuji et al. 1997; Ueno et al. 1996).

Cyanobacterial blooms occur in most waterway types, including wastewater treatment

ponds (Fleming et al. 2002). Treated wastewater is currently released to waterways, and it

is a legal requirement that such effluent is free of toxins prior to release to the

environment (Water Corporation 2007). Given the possibility for future wastewater

treatment and redistribution for scheme use, human interaction with cyanobacteria could

be greatly increased in coming years. Thus knowledge of the degradation rates of

cyanobacteria and their toxins is of utmost importance to the water industry.

Water industries worldwide currently use various algicides to remove cyanobacteria

before releasing water into the environmental flow. However, many of these methods

generate environmentally harmful by-products (Tsuji et al. 1997), and few studies have

been conducted on their effectiveness for the removal of cyanobacteria and

cyanobacterial toxins from water treatment ponds. Hydrogen peroxide is a known and

marketed algicide, and is environmentally benign as its degradation products are oxygen

gas and water (Barroin and Feuillade 1986). The Water Corporation has thus proposed

the application of hydrogen peroxide to induce cyanobacterial cell death and toxin

degradation.

16 1. Introduction

This project had several aims:

1. Determine a hydrogen peroxide dosage for the effective removal of

cyanobacteria and cyanotoxins from wastewater treatment ponds, that optimises

cost, response time and environmental impacts, and provide results to assist in the

creation of a protocol for treatment of cyanobacteria in wastewater treatment

plants Australia wide;

2. Determine the decay functions for both cyanobacterial cells and their toxins as

a function of dosage and response time;

3. Establish the response time and the relationship between the concentration of

hydrogen peroxide added and the decay functions of cyanobacterial biomass and

cyanotoxins.

The Australian water industry will benefit from the knowledge gained through this

project, in particular through the eventual development of a protocol for dosing

wastewater with hydrogen peroxide to remove cyanobacteria and their toxins. This will

be of great socioeconomic and environmental benefit within Australia, by reducing the

risk of cyanobacterial toxicosis to humans and animals. The acquired knowledge could

also be of use for dosing natural systems such as lakes and rivers, in particular areas of

the Swan and Canning Rivers which commonly suffer cyanobacterial blooms in the warm

summer months.

2. Literature Review 17

2 Literature Review

2.1 Cyanobacteria in Western Australia

Increased nutrient loadings into the Swan River and its tributaries has led to an increased

frequency of algal blooms in recent years (Robson and Hamilton 2004; Griffin et al.

2001; Pennifold and Davies 2001). Algal blooms have also occurred in Western

Australian freshwater bodies such as lakes (Figure 1) and water treatment ponds

(unpublished data, Water Corporation 2007). Blooms generally belong to the genera of

Microcystis, Anabaena, Aphanizomenon and Nodularia, and the predominant algal toxin

in fresh waters of the Swan Coastal Plain is microcystin-LR (Kemp and John 2006). This

cyanotoxin is detrimental to human and animal health (Bourne et al. 2006).

Figure 1: Microcystis aeruginosa bloom at Ron Stone Park, Mount Lawley (Fon Sing 2006)

18 2. Literature Review

2.2 Western Australian Wastewater

In Western Australia, wastewater from households, businesses and industry is transported

to Wastewater Treatment Plants managed by the Water Corporation, where it is treated to

a prescribed standard prior to release to the environment (Water Corporation 2006). In

the smaller WWTPs of the South-West Region, wastewater is treated using an outdoor

consecutive pond system, and raw water samples are collected from WWTPs monthly.

Analysis of samples from the Brunswick Junction WWTP polishing pond, the final water

body prior to release, show elevated levels of potentially toxic phytoplankton, in

particular Microcystis aeruginosa, throughout the year (Figure 2).

Date

17 Aug '06 13 Sept '06 26 Oct '06 30 Oct '06 9 Nov '06 14 Dec '06 10 Jan '07 15 Feb '07 14 Mar '07 30 May '07 13 Jun '07

Cya

noba

cter

ial c

ell d

ensi

ty (c

ells

/mL)

0.0

2.0e+4

4.0e+4

6.0e+4

8.0e+4

1.0e+5

1.2e+5

Figure 2: Water Corporation Cyanobacterial Monitoring, Brunswick Junction WWTP

It is a legal requirement that treated effluent has a cyanobacterial density below Water

Corporation guideline levels (Table 1) prior to release to the environment, and the Water

Corporation has previously used various algicides to achieve this aim. However, many

current removal methods generate environmentally harmful by-products such as

Maximum Detection Limit


trihalomethanes (Tsuji et al. 1997). Hence application of the environmentally benign

algicide hydrogen peroxide has been proposed by the Water Corporation to induce cell

lysis and toxin degradation.

Table 1: Advisory levels for cyanobacteria in wastewater effluent (Water Corporation 2004)

Indicative cyanobacteria level (cells mL-1) Indicative toxin level1 (μgL-1) Microcystis aeruginosa Anabaena

circinalis Microcystis aeruginosa

Anabaena circinalis

Occupational Health and Safety Spray aerosol exposure 5000 5000 1 3 Public Health Direct exposure to grass/pasture Low risk, no advisory level Low risk, no advisory level Spray aerosol exposure See above See above Primary recreational contact Level 1: Direct contact not restricted 20000 20000 2 4 Level 2: Direct contact restricted 100000 (or scums) 100000 (or scums) 20 20 Wildlife and Domestic Animals Livestock pasture grazing Low risk, no advisory level Low risk, no advisory level Livestock drinking water 50000 100000 10 20 High value animals2 10000 20000 2 4 Wildlife3 50000 100000 10 20

1Should be applied to the total cell-bound and extracellular microcystins/saxitoxins 2These might include stud animals, horses, family pets 3These advisory levels are based on those proposed by the CSIRO for general livestock and poultry exposure

2.3 Cyanobacteria and cyanobacterial toxins

2.3.1 Cyanobacteria

Cyanobacteria belong to the kingdom Monera, division eubacteria, and although they are

not technically plants, they are often mistakenly known as blue-green algae because of

their ability to photosynthesise (Ressom et al. 1994). They thrive over a large range of

salinity and temperature (Svrcek and Smith 2004), and prefer habitats with neutral or

slightly alkaline pH (Ressom et al. 1994).

In natural ecosystems, cyanobacteria are responsible for providing oxygen to aerobic

microorganisms and converting inorganic nitrogen into organic forms (Antoniou et al.

2005). They are the organisms responsible for providing the rich oxygen atmosphere of

Earth (Svrcek and Smith 2004) and need only light, carbon dioxide, water and some


minerals to survive (Ressom et al. 1994). Cyanobacteria are also primary producers with

nutritional value to organisms at higher trophic levels, and thus increase the fertility of

their natural habitats (Chorus and Bartram 1999).

Cyanobacteria are single celled organisms which have a tendency to aggregate into

colonies (Figure 3), resulting in cyanobacterial blooms (Figure 4) (Carmichael 2001),

which are defined as having a cell density in excess of 106 cells L-1 (Hitzfeld et al. 2000).

Such blooms have been reported in freshwater in over 45 countries, as well as in marine

environments (Codd et al. 2005). Favourable conditions for blooms are stagnant water,

low wind speeds, temperatures between 15 and 30 degrees Celsius, a pH ranging from 6-

9, thermal stratification and high nutrient loads (Carmichael 1994; Chorus and Bartram

1999). The typical controlling nutrient for producing blooms is phosphorus, with nitrogen

also being of great importance (Chorus and Bartram 1999). However, water bodies do not

require eutrophic conditions to produce cyanobacterial blooms, and reports of blooms

under oligotrophic conditions have been documented (Chorus and Bartram 1999; de

Figueiredo et al. 2004).


Figure 3: Cells of Microcystis aeruginosa aggregating (Ghadouani 2006)


Figure 4: Microcystis aeruginosa bloom at Ron Stone Park, Mount Lawley (Fon Sing 2006)

Under favourable temperature, nutrient, meteorological and pH conditions,

cyanobacterial blooms can persist for several weeks (Perez and Aga 2005). Such blooms

are common in wastewater treatment ponds (Fleming et al. 2002), including those of

South-West Western Australia (unpublished data, Water Corporation 2007).

2.3.2 Cyanobacterial toxins

Many genera of cyanobacteria contain cyanotoxins, and approximately 50% of blooms

can be expected to contain toxic species (Carmichael 1992). Such toxins are thought to

act primarily as protective compounds against grazing by zooplankton (Carmichael

1992). High concentrations of cyanobacterial toxins can be released into surrounding

waters following the destruction, whether natural or anthropogenic, of algal blooms

(McElhiney and Lawton 2005).


Toxins are compounds which have a detrimental effect on other tissues, cells or

organisms (Svrcek and Smith 2004), and in cyanobacteria include hepatotoxins,

neurotoxins, cytotoxins, dermatoxins and lipopolysaccharides (Wiegand and Pflugmacher

2005). The most common cyanobacterial toxins are variants of the hepatotoxins

microcystin (Codd 2000).

2.3.3 Microcystins

Microcystins are cyclic heptapeptides with five invariant amino acids and two variants

(Antoniou et al. 2005; Carmichael 1992; Fawell et al. 1993), amounting to more than 75

different microcystin chemicals (Codd 2000). Microcystins are named according to their

variant amino acids using a two letter suffix: arginine (R), leucine (L), tyrosine (Y),

alanine (A), phenylalanine (F) and tryptophan (W). The microcystins generally detected

in freshwater bodies are microcystin-LR, -RR and YR (Perez and Aga 2005).

One invariant amino acid found in microcystins is 3-amino-9-methoxy-10-phenyl-2,6,8-

trimethyl-deca-4(E),6(E)-dienoic acid (Adda), which increases the hydrophobicity of the

whole microcystin molecule (Perez and Aga 2005), and is essential for the toxin’s

expression of biological activity (An and Carmichael 1994). The other invariant amino

acids are alanine, methylaspartic acid and glutamic acid (Al Momani et al. In press 2007).

Microcystins derive their toxicity because they are potent inhibitors of serine/threonine

protein phosphatase 1 and 2A enzymes (Carmichael and An 1999; MacKintosh et al.

1990).

Microcystins are stable with respect to biological degradation (Harada 1996), but

isomerise on exposure to UV light (Tsuji et al. 1994). The extent of isomerisation is

dependent on the pigment concentration within the water sample (Tsuji et al. 1994).

Thermal destruction of the toxin occurs above 120ºC, meaning the common removal

method of boiling is insufficient for degradation (Harada 1996).

The most common cyanobacterial toxin, which is also of the greatest risk to human

health, is microcystin-LR (Figure 5) (Carmichael 1992; Fawell et al. 1993). This toxin

contributes 23-94% of the total toxin concentration of most cyanobacterial blooms (Perez


and Aga 2005). This toxin is stable in unsterilised reservoir water for approximately one

week, and in deionised water for up to 27 days (Cousins et al. 1996).

Figure 5: Chemical structure of Microcystin-LR

2.3.4 Health risks and effects

Microcystin blooms cause bad odour and taste, and cyanobacterial toxins pose a

significant health risk to humans and animals on exposure (Antoniou et al. 2005; Codd

2000; Cousins et al. 1996; Drikas et al. 2001; Mohamed 2001). The most common routes

of human contact with cyanobacterial toxins are through the contamination of drinking

water (Carmichael 1994; Codd et al. 1999a; Gilroy et al. 2000; Ueno et al. 1996; Falconer

1999), the recreational use of lakes and rivers containing cyanobacteria (Pilotto et al.

1997) and via the ingestion of blue-green algal supplements (Gilroy et al. 2000). Water

containing blooms probably becomes unpalatable to humans well before concentrations

reach levels which pose a threat to health (Fitzgerald et al. 1999). Although cyanotoxins

Adda Moiety


are rarely ingested or absorbed in quantities large enough to induce acute effects, long

term exposure can result in chronic toxicosis (Bogialli et al. 2005).

Another possible exposure route is through the ingestion of plant and animal matter in

which the toxins have bioaccumulated (Codd et al. 1999b; McElhiney et al. 2001; Carbis

et al. 1997; Vasconcelos 1995; Eriksson et al. 1989). Although the large size of

microcystin molecules prevents their diffusion through cell walls, it is believed the

gastrointestinal uptake could be a viable mechanism for transporting hepatotoxins into the

body (Perez and Aga 2005).

Cyanobacterial poisoning of humans and animals has been reported since the 1800s

(Carmichael 1994). A bloom in Lake Bonney, South Australia, in 1959, claimed the lives

of approximately 300 sheep, five cattle and one horse (Hallegraeff 1992). The 1996 death

of 56 haemodialysis patients in a Brazil clinic was confirmed to be a result of microcystin

in treatment water (Carmichael 2001; Jochimsen et al. 1998).

Microcystin-LR is probably responsible for the most common cyanobacterial toxicosis,

acute hepatotoxicosis (Carmichael 1997). Symptoms of this condition include weakness,

anorexia, pallor, cold limbs, breathing difficulties, vomiting and diarrhea, sometimes

leading to death within days or hours (Carmichael 1992). The toxin causes separations of

liver cells leading to blood accumulations and hepatocyte necrosis, which may result in

death by hemorrhaging shock or liver failure (Carmichael 1997; Bhattacharya et al.

1997).

There has also been evidence to suggest that microcystins may cause liver cancer

(Carmichael 1992; Carmichael 1997; Nishiwaki-Matsushima et al. 1992; Tsuji et al.

1997; Ueno et al. 1996), and in Florida a correlation has been found between liver cancer

and residents’ proximity to wastewater treatment plants, where cyanobacteria may bloom

in treatment ponds (Fleming et al. 2002). A similar correlation was found with

cyanobacterially contaminated ponds, ditches and rivers in the Jian-Su and Guangxi

provinces of China (Ueno et al. 1996). Hence it is of importance to public health that the

formation and destruction of cyanobacterial blooms and cyanotoxins be investigated.


2.3.5 World Health Organization guidelines

Microcystin-LR is the microcystin variant specified under the WHO Guidelines for

Drinking Water Quality (1993). In drinking water, total microcystin-LR (sum of the

intracellular and extracellular concentrations) must be found below 1 μgL-1. This is a

provisional guideline, as the health effects of microcystin-LR are not known

quantitatively (World Health Organization 1993). The WHO daily tolerable intake for

microcystin-LR is 40 ngkg-1 body weight (Chorus and Bartram 1999). In recreational

waters, the World Health Organization recommends a guideline level of 100 000 cells

mL-1 for Microcystis aeruginosa, which equates to approximately 20 μgL-1 of

microcystin-LR (World Health Organization 2003).

Although microcystin-LR is the most toxic of the microcystin family, it is often not the

most prevalent. Utilising the 1 μgL-1 limit for other species of microcystins assumes

higher than actual toxicity, but, due to algal blooms often containing several microcystin

variants, this assumption is often necessary (Chorus and Bartram 1999).

2.4 Detection of cyanobacteria by fluorometry

2.4.1 Fluorescence of cyanobacteria

Most current techniques for measuring cyanobacterial biomass are constrained by a lack

of in situ and temporal resolution (Beutler et al. 2002), high associated maintenance costs

and low specificity and sensitivity (Leboulanger et al. 2002). A method which has proven

successful in identifying algal groups in prior studies is spectrofluorescence, particularly

when performed over multiple wavelengths (Beutler et al. 2002). This classification

process is based upon the fluorescence of the photosynthetic pigment chlorophyll a,

which is found in all phytoplankton species (Gregor and Marsalek 2004). Chlorophyll a

concentrations can be utilised to determine total biomass, depending on the average

chlorophyll a concentration of a cell. In cyanobacteria, a chlorophyll a concentration of

50μgL-1 is indicative of a cell density of approximately 106 cells mL-1 (World Health

Organization 2003).


During photosynthesis, energy not utilised by photosystem II for photochemistry is

emitted by chlorophyll a, from the antenna system, as red light (Gregor and Marsalek

2004). This fluorescence is a measure of the photosynthetic electron transfer rate of the

organism (Beutler et al. 2002), dependent on the chlorophyll a concentration. Each algae

group has a slightly different fluorescence spectrum dependent on its composition (van

den Hoek et al. 1995). Cyanobacteria fluoresce at approximately 650nm, though this can

vary slightly under differing light conditions, nutrient availability, cell age and cell

history (Gregor et al. 2007).

2.4.2 Traditional chlorophyll a detection method

The traditional chlorophyll a detection method is outlined by the ISO 260 (1992) method.

Phytoplankton samples are collected on a filter and extracted with hot ethanol, followed

by acidification and measurement of light absorbance (Gregor et al. 2007). The total

chlorophyll a content is calculated from absorbance measurement, however this method

often results in high within sample variance, dependent on the equation utilised to

determine concentrations (Gregor and Marsalek 2004), and is extremely time consuming.

2.4.3 Multispectral fluorescence analysis

Multispectral fluorescence probes utilise diodes which emit light at 450nm, 525nm,

570nm, 590nm and 370nm (Gregor and Marsalek 2004). Such probes determine the

chlorophyll a concentrations of several algal groups, including chlorophyta,

cyanobacteria, diatoms and cryptophyta, as well as yellow substances. Concentrations are

determined by comparison of fluorescence with normal curves for each species (Gregor

and Marsalek 2004; Beutler et al. 2002).

The bbe-Moldaenke Fluoroprobe (Figure 6) is a submersible probe which performs

multispectral fluorescence and can be used both in the laboratory and in situ (Beutler et

al. 2002). In situ spectrofluorescence allows for the detection of spatial and temporal

variations in concentrations, and is a rapid and continuous monitoring method. It also


requires less skill than the traditional methods of microscopic identification and counting

(Leboulanger et al. 2002; Gregor et al. 2007; Beutler et al. 2002).

Errors in chlorophyll a detection by the bbe-Moldaenke FluoroProbe may arise from the

shading, scattering or reabsorption of light emitted from the diodes, or the fluorescence

response of other chemicals within the pelagic column (Gregor and Marsalek 2004).

Studies have also suggested that non-linear concentration responses may occur for total

chlorophyll a concentrations above 50μL-1 (Gregor and Marsalek 2004). Leboulanger et

al. (2002) determined that fluorescence quenching errors could occur due to

photoinhibition, decreasing the measured concentrations of chlorophyll a.

Figure 6: bbe-Moldaenke FluoroProbe assembled for laboratory use


2.4.4 Comparison of fluorescence analysis methods

Results of phytoplankton chlorophyll a measurements by multispectral fluorescence

analysis utilising the bbe-Moldaenke FluoroProbe and the standard extraction method

have been compared. The methods are highly correlated with an r-squared value of 0.97

(Gregor and Marsalek 2004), and multispectral fluorescence analysis also correlated well

with microscopic cell counts (Leboulanger et al. 2002). However, the multispectral probe

measured consistently lower values than the standard extraction method, likely due to the

calibration of the instrument by High Performance Liquid Chromatography (HPLC),

which separates some microcystin isomers which fluoresce at 665nm (Gregor and

Marsalek 2004), and thus such compounds are excluded from chlorophyll a

measurements by the FluoroProbe.

2.5 Detection of cyanobacterial toxins

Many studies have been conducted into the detection of cyanobacterial toxins, and

current analytical procedures can only detect the compounds in the dissolved state

(Nicholson and Burch 2001). Thus the determination of extracellular cyanotoxins is

possible, but to determine intracellular toxins, or the total toxins in a sample, cell lysis

must be induced. This is normally performed by freeze drying the sample, followed by

freeze thawing or sonication in solution (Nicholson and Burch 2001). It has been

determined that sonication is more effective at cell lysis than freeze thawing, indicating

that past studies utilising the thawing method may have underestimated toxin

concentrations (Rapala et al. 2002). The cyanotoxins are then extracted using water and

organic solvents, with ratios specific to the toxin to be extracted (Nicholson and Burch

2001).

A range of techniques are available for qualifying and quantifying the toxins present in

water samples. These include both qualitative and quantitative physicochemical and

biological methods (Svrcek and Smith 2004). The method to be utilised can be

determined by rapid screening methods such as microscopic examination (Harada et al.

1999). The most common physicochemical analysis method for microcystins is HPLC


coupled with UV, PDA or MS analysis. Biological methods include bioassays, PPIA and

ELISA. The methods differ in their accuracy, specificity, mode of detection, ease of use

and time requirements (Ward et al. 1997). For microcystins to be detected, the toxin must

be able to be unequivocally defined, an analytical standard present, and a LD50 dosage

known for the specific compound, so that toxins can be converted to toxicity values

relative to microcystin-LR (Nicholson and Burch 2001).

2.5.1 High Performance Liquid Chromatography

HPLC is a sensitive detection method which is often coupled with single wavelength UV

analysis, PDA or MS. The long history of HPLC in laboratories means it is a well

documented and refined method of chemical identification (Nicholson and Burch 2001).

HPLC-UV analysis observes the UV response of samples at a single wavelength, 238nm.

However, microcystins with aromatic acid constituents absorb at approximately 222nm

(Lawton et al. 1994). This results in single wavelength UV detection being a poor method

compared to MS and PDA, due to its low sensitivity and selectivity (Perez and Aga

2005).

The most common method for detecting microcystins is HPLC coupled with PDA

(Hyenstrand et al. 2001). HPLC-PDA is a sensitive detection method which satisfactorily

separates most microcystins on a chromatography column prior to identification by photo

diodes (Nicholson and Burch 2001). HPLC-PDA records not only the UV-response of the

compound, but also the spectrum of the analyte over various wavelengths (Nicholson and

Burch 2001).

HPLC-MS is another sensitive identification technique, but as such incurs very large

costs, and is not widely used for microcystin detection. As several microcystins are of

equivalent molecular mass, identification of intact compounds can be difficult

(McElhiney and Lawton 2005), and several microcystins have equivalent responses to

PDA (Nicholson and Burch 2001). However, microcystins produce unique patterns of

ions in the mass spectra, meaning HPLC-MS is a highly accurate identification technique

(Namikoshi et al. 1992; Kondo et al. 1992; Rinehart et al. 1994; Lawton et al. 1995). The


sensitivity of HPLC-MS can be further improved by the use of SIM, SRM, or MRM

(Perez and Aga 2005).

A major drawback to the use of HPLC is the lack of commercially available microcystin

standards, making identification difficult (Rapala et al. 2002; Mountfort et al. 2005),

often leading to an overestimation of toxicity by assuming the toxin is microcystin-LR

(Nicholson and Burch 2001). Thus there has been interest from the scientific community

into the production of Certified Reference Materials to assist in toxin detection via this

method. HPLC is time consuming and expensive, and difficult to calibrate to be

quantitatively accurate (Rapala et al. 2002).

2.5.2 Bioassay

The traditional method for determining toxicity is through the use of animal based assays.

In particular, mouse bioassays have been widely utilised in the past, though for the

detection of microcystins they have very limited sensitivity, are non-specific to various

toxins, and have poor reproducibility (Chorus and Bartram 1999; Chu et al. 1990; Perez

and Aga 2005). Other bioassays have been investigated for quantifying and qualifying

microcystins, including an African locust (Hiripi et al. 1998), larvae of the crustacean

Thamnocephalus platyurus (Torokne et al. 2000) and species of the genera Daphnia

(Kyselkova and Marsalek 2000). However, these organisms are also unable to distinguish

between various cyanobacterial toxins. Considering the growing public opposition to

toxicity testing on live animals (Falconer 1993), it may be considered morally

unacceptable to utilise animals to obtain quantitative data on toxins, especially when such

accuracy requires the infection of many specimens (Nicholson and Burch 2001).

2.5.3 Protein Phosphatase Inhibition Assays

PPIAs utilise microcystins’ mode of toxicity, by binding to a subunit of protein

phosphatases 1 and 2A in an irreversible fashion (Carmichael and An 1999; Ward et al.

1997). After the addition of a water sample to a protein phosphatase 1 or 2A plate, p-


nitrophenyl phosphate is added. Where protein phosphatase has already bound

microcystins, it will not react with the additive. Unbound protein phosphatase

dephosphorylates the compound, forming the yellow coloured compound p-nitrophenyl

(Carmichael and An 1999). Thus an increase in colour indicates a low concentration of

microcystins in the water sample, whilst a colourless or pale solution indicates high

microcystin concentrations (Carmichael and An 1999). Quantitative concentrations can

be determined by the use of spectrophotometric plate readers.

Although PPIA results correlate well with HPLC at high concentrations of toxins

(Carmichael and An 1999; Rapala et al. 2002; Heresztyn and Nicholson 2001), results

lack agreeability with other methods at low toxin concentrations (Perez and Aga 2005;

Heresztyn and Nicholson 2001), and PPIA cannot be used where the third amino acid of

the microcystin is aspartic acid (Rapala et al. 2002). PPIA also binds to other protein

phosphatase inhibitors such as okadaic acid, cayculin A and tautomycin (Metcalf et al.

2001), and has varying specificity for different toxins (Carmichael and An 1999;

Mountfort et al. 2005; Chorus and Bartram 1999). The detection method is based on

microcystins’ activity, not structure (Carmichael and An 1999), and as protein

phosphatase 2A inhibits microcystins approximately 50 times more than protein

phosphatase 1, results can differ significantly between the two tests (Heresztyn and

Nicholson 2001).

The main detection issue for PPIA is the occurrence of false positives due to its ability to

react with other protein phosphatases within samples. However, methanol extraction of

the samples, or the application of molecular sieves prior to analysis, have been shown to

significantly reduce such reactivity (Carmichael and An 1999). A study by Heresztyn and

Nicholson (2001) displayed that detection of microcystins by PPIA was not affected by

up to 50% methanol in applied abstracts.

PPIA is a cheap detection method which gives reasonable results when applied to water

samples, without the necessity for sample cleanup or pre-concentration (Rapala et al.

2002). However, because there is no known direct relationship between enzyme

inhibition and mammal toxicity, data suggests that these assays may overestimate sample

toxicity (Nicholson and Burch 2001).


2.5.4 Enzyme Linked Immunosorbent Assays

ELISA methods have high sensitivity and are easy to use; their sensitivity depends upon

the cross-reactivity of microcystins, determined by their similarity in chemical structure

to other antibodies. ELISA tests have sensitivity in the range of parts per billion

(Carmichael and An 1999), and are best suited to water bodies with consistent toxin

profiles (Nicholson and Burch 2001).

ELISA test kits can be purchased commercially, and are based upon polyclonal

antibodies that bind microcystins (Carmichael and An 1999; Engstrom-Ost et al. 2002).

The first ELISA plate was designed and tested by Chu et al. (1990), and consisted of a

plate coated with anti-microcystin-variant leucine-arginine antibody, where microcystin-

LR peroxidase was used as an enzyme marker. The peroxidase competed with the

microcystin-LR in the water sample for binding sites of the antibody attached to the plate

(Carmichael and An 1999). Since this, two forms of ELISA have been developed, direct

and indirect.

Direct ELISA follows the principles of Chu et al. (1990), where an anti-microcystin

antibody is attached to a microtiter plate, and microcystin-LR peroxidase competes with

microcystin in the sample for binding sites (Carmichael and An 1999). Indirect ELISA

utilises two antibodies, microcystin-LR-bovine serum albumin, which is allowed to react

with the water sample first, a horseradish peroxidase conjugated goat IgG antibody,

which develops the colour of the plate, and a stop solution containing dilute sulfuric acid

(Figure 7), allowing for spectrophotometric measurement (Carmichael and An 1999).

ELISA is convenient to use and allows for rapid determination of microcystin

concentration (Mathys and Surholt 2004; Rapala et al. 2002). In several studies, results

have been comparable to those collected via HPLC-PDA or LC-MS (Fastner et al. 2002;

Rapala et al. 2002; Mathys and Surholt 2004). However, in a study where microcystin

concentration was determined in animal livers, ELISA determined concentrations to be

greater than one thousand times higher than results measured by LC-MS and GC-MS. It

has been inferred that this was due to cross-reactivity of the plates with a liver

component, rather than microcystin-LR (Orr et al. 2003).


ELISA detection provides approximately equivalent responses for microcystin-LR, YR

and RR (Mountfort et al. 2005), and concentrations determined from ELISA kits are

expressed in terms of microcystin-LR equivalents (McElhiney and Lawton 2005).

However, other microcystin variants show different responses, and thus accurate analysis

is unlikely in samples where several microcystins are present (Perez and Aga 2005).

Figure 7: Adding the Stop Solution to samples on an ELISA plate (Barrington 2007)

2.5.5 Other methods

Several other methods of detecting cyanobacterial toxins have been investigated, but

most require further testing before they can be implemented as viable indicators of toxin

concentrations.

Electrochemical detection has shown promise, although it is unable to detect microcystins

that do not contain arginine, tryptophan or tyrosine (Meriluoto et al. 1998). Capillary

electrophoresis lacks sensitivity and has not yet been sufficiently developed to become a


monitoring tool (Boland et al. 1993; Onyewuenyi and Hawkins 1996; Bouaicha et al.

1996; Bateman et al. 1995; John et al. 1997; Siren et al. 1999). TLC has been investigated

by separating toxin components followed by examination of the UV spectra of the

components on the plates, although it is not yet a robust detection method (Poon et al.

1987; Al-Layl et al. 1988; Ojanpera et al. 1995; Pelander et al. 1996; Pelander et al.

1998). The oxidation of microcystins to produce MMPB, which can then be detected

using GC, GC combined with MS (Sano et al. 1992; Kaya and Sano 1999; Tanaka et al.

1993) or HPLC combined with fluorescence studies (Sano et al. 1992) has shown good

results in trials, though it is unable to differentiate between toxins.

2.5.6 Comparison of detection methods

Although the common cyanobacterial toxin detection methods show promise in

determination of concentrations in water samples, none alone provide precise

measurement of total concentration, toxicity, and the various microcystins present

(Harada et al. 1999). Immunoassays have detection limits 10-2-10-5 lower than

physicochemical methods (Codd et al. 2001), allowing for detection where algal blooms

may not yet be evident.

A study by Fastner et al. (2002) provided 31 international laboratories with a standard

sample of microcystin-LR, and an environmental sample. Various laboratories analysed

the samples using the methods HPLC-PDA, HPLC-UV, HPLC-MS, ELISA and PPIA.

ELISA kits showed significantly higher reproducibility than other detection methods for

the standard sample, though all results correlated well. Thus it may be inferred that the

precision of each detection method is comparable, and should be chosen based on the

availability of instrumentation, time and monetary constraints, and whether toxicity,

microcystin profile or overall concentration are of most importance.


2.5.7 Potential problems with analysis

Several problems have arisen during laboratory analysis of microcystins. At current, even

practices which show good results have some associated errors arising from their method,

and attempts should be made to minimise such errors during analysis.

Several studies have determined that plastics should be avoided, wherever possible, in

microcystin analysis. It has been shown that final microcystin concentrations were lower

using plastic rather than glass equipment (Metcalf et al. 2000b; Rapala et al. 2002;

Hyenstrand et al. 2001). This is possibly due to hydrophobic interactions between

microcystins and plastic surfaces (Hyenstrand et al. 2001). Another problem is that

common additives present in the lab ware absorb at 238nm, and could thus affect

spectrophotometric analysis (Ikawa et al. 1999).

Methanol has posed some issues in microcystin analysis (Hyenstrand et al. 2001).

Methanol concentrations up to 30% have been shown to produce false positive results

when using an ELISA method, and may also remove ELISA components from microtiter

plate wells (Metcalf et al. 2000b). However, the addition of methanol does appear to

decrease microcystin interactions with plastic surfaces (Hyenstrand et al. 2001). Thus,

although methanol is a useful solvent throughout the extraction process, it should be

removed prior to ELISA analysis, and samples dissolved in pure distilled water before

addition to wells.

Storage of cyanobacterial samples has been shown to affect concentrations of

microcystins. In the presence of pigments, degradation of microcystins can occur rapidly,

leading to underestimates of toxin concentrations (Nicholson and Burch 2001; Tsuji et al.

1994). Alternatively, cyanobacteria may grow significantly during long distance

transport, resulting in the overestimation of microcystin concentrations (Jia et al. 2003).

2.6 Prevention of high cyanobacterial concentrations

Several methods of preventing algal blooms have been suggested, including reducing

nutrient inputs to prevent eutrophication (Svrcek and Smith 2004), altering the

hydrophysical conditions to favour those of other phytoplankton (Hrudey et al. 1999),


destratifying the water body by aeration or mechanical mixers (Svrcek and Smith 2004)

or destroying cyanobacterial gas vacuoles by ultrasonic radiation (Nakano et al. 2001).

Chemically treating water to remove cyanobacteria and their toxins should not be

implemented unless physical measures such as barriers, offtake by bank filtration and a

variation of intake depth have been trialed for both prevention and degradation (Hitzfeld

et al. 2000). If such techniques are unsuccessful, chemical treatment may be the only

viable treatment option.

2.7 Degradation and removal of cyanobacteria and cyanotoxins

Many artificial methods for both destroying cyanobacterial blooms and degrading

cyanobacterial toxins have been investigated in the past three decades. Some common

degradation methods include dilution, adsorption, consumption, flocculation, bacterial

degradation, chemical degradation and photolysis (Jones and Orr 1994; Tsuji et al. 1994;

Lahti et al. 1997; Welker et al. 2001). Several such methods also destroy other algal

species, though often the decay of other species is not as pronounced (Yousef et al.

2003).

Cyanobacterial toxins differ in their retention time in water bodies (Sivonen and Jones

1999; Chorus 2001; Welker et al. 2001), though a study by Welker and Steinberg (2000)

estimated the half-lives of various microcystins to be approximately 90-120 days within

both natural and artificial water bodies. Natural removal of cyanotoxins from water

bodies is likely due to native bacteria (Bourne et al. 1996; Cousins et al. 1996; Park et al.

2001; Christoffersen et al. 2002; Rapala et al. 2005). Microcystins are relatively stable if

left untreated, though photolytic degradation can occur very quickly in waters where

pigment concentrations are high (Tsuji et al. 1994).

2.7.1 Copper reagents

Using a chelated organic copper algicide on Microcystis aeruginosa has been shown to

induce rapid cell lysis, followed by the release of cyanotoxins to the water column.


Following application to a lake, toxins around the banks remained constant for

approximately nine days, followed by a 90-95% reduction within the next three (Jones

and Orr 1994). Murray-Gulde et al. (2002) found that although copper reagents were

effective at causing cell lysis, they had very small margins of safety between which

application was effective and aquatic biota were harmed. Copper sulphate is the algicide

currently used by the Water Corporation on South-West WWTPs (Dean Puzey pers.

comm. 2007).

2.7.2 Chlorine reagents

Chlorine compounds remove cyanobacterial toxins at varying pH values, with the

optimum rate occurring at pH below 8 (Acero et al. 2005; Nicholson et al. 1994).

However, there is a high possibility that chlorinated by-products are of risk to human and

animal health (Antoniou et al. 2005). In the presence of organic matter, such as humic

and fulvic acids, chlorine may be consumed, producing non toxic by-products (Kull et al.

2006), though in water treatment plants concentrations of organic matter may be too low

to promote this. Preoxidation of the cyanobacteria cells themselves can lead to

trihalomethanes being introduced into the treatment plant (Tsuji et al. 1997).

2.7.3 Permanganate

An oxidation agent used to degrade cyanotoxins is permanganate, where pH does not

appear to significantly influence degradation rates (Rodrıguez et al. 2007). A 1998 study

found that permanganate can reduce microcystin-LR concentrations by 90% within ten

minutes of application (Rositano et al. 1998), and a full-scale trial conducted in

Wisconsin water treatment plants found that permanganate removed approximately 61%

of microcystins (Karner et al. 2001). However, little is known about the possibility of

toxic by-products from permanganate oxidation (Svrcek and Smith 2004).


2.7.4 Lime and aluminium sulfate

Lime and aluminium sulfate have been investigated as flocculants of cyanobacterial

blooms, such that cells can be removed without inducing cell lysis (Chow et al. 1999;

Lam et al. 1995; Mohamed 2001). Flocculation is the process whereby gentle mixing

encourages particles to aggregate into large flocs, followed by sedimentation, for easier

removal (Svrcek and Smith 2004). Most studies have shown that both lime and

aluminium sulfate are valid flocculation agents for cyanobacteria, and one study

determined that after flocculation microcystins took approximately 26 days to degrade

within the cells (Lam et al. 1995).

2.7.5 Activated Carbon

Activated carbon has been found to assist in cyanotoxin degradation by adsorption, and

has often been coupled with other removal methods. Studies have shown that the

performance of the carbon depends on the particular microcystins and their solubilities

and concentrations, and the dose and origin of the carbon (Lawton and Robertson 1999;

Mohamed et al. 1999). The best performance carbon has been identified as that

originating from wood, followed by coal, where the poorest came from coconuts and

peat-moss (Donati et al. 1994). Lambert et al. (1996) found that by incorporating

coagulation-sedimentation, dual media filtration and chlorination, then activated carbon,

greater than 80% of microcystins could be removed from water.

2.7.6 Filtration

Sand filters are another option for cyanotoxin degradation, and their method of action

appears to work by adsorption and biodegradation (Grutzmacher et al. 2002; Ho et al.

2006). In column tests conducted by Ho et al. (2006) there appeared to be a three day

period before degradation began, with no microcystin detected after four days.

Grutzmacher et al. (2002) conducted degradation through sand filters with both intra and

extracellular cyanotoxins. This study indicated that dissolved cyanotoxins could be


removed by up to 95%, with half lives of approximately one hour. Biodegradation was

the main removal mechanism and adsorption was slow. Where toxins were primarily

within live cells an 85% reduction was recorded.

Other filtration work has also had positive results for the removal of cyanobacterial

toxins. Even domestic water filters can remove between 10-60% of cyanobacterial cells,

depending on their morphology, although none can do so without several filtration cycles

(Lawton et al. 1998). Reverse osmosis can remove 95-99% of microcystins LR and RR

from salt and tap water (Neumann and Weckesser 1998), and nanofiltration is highly

effective against microcystins due to steric hindrance (Teixeira and Rosa 2006b).

Teixeira and Rosa also found that coupling dissolved gas flotation with nanofiltration

resulted in complete removal of microcystins with an 84% recovery of water (2006a).

2.7.7 Bacteria

Bacterial communities from frequently eutrophic ponds have shown promise as possible

treatment options for degrading microcystins (Christoffersen et al. 2002; Park et al.

2001). Evidence has been gathered to suggest that during the healthy stages of

microcystin blooms, organisms capable of degrading the toxins are established

(Heresztyn and Nicholson 1997).

In one study, microcystin was added to a frequently eutrophic lake containing indigenous

bacteria, and the resulting decrease in both microcystin and dissolved organic carbon

concentrations suggested that microbial activity was degrading both classes of

compounds at comparable rates (Christoffersen et al. 2002). Another study isolated a

bacterium from a hypertrophic lake and determined its ability to degrade microcystins in

a dark laboratory environment (Park et al. 2001). Although promising results have been

gathered, the utilisation of indigenous or introduced bacteria as toxin degraders may not

be feasible for wastewater treatment ponds, especially as repeatability has not yet been

investigated thoroughly.


2.7.8 Advanced oxidation processes

Oxidation processes which occur at near ambient temperature and pressure are known as

‘advanced oxidation processes’, and generate hydroxyl radicals for use as oxidants (Glaze

et al. 1987).

Ozonation has been found to successfully degrade microcystins, with the greatest rates

achieved at acidic pH values. Rositano et al. (1998) found that ozone removed 99% of

microcystins within the first 15 seconds of application, and Himberg et al. (1989)

determined that the application of ozone and activated carbon completely removed the

toxins. Analysis of the degradation products show they are most likely benign (Brooke et

al. 2006). Apart from expense, the major complication of ozone oxidation is that ozone

prefers to react with dissolved organic carbon than cyanobacterial toxins, which can

significantly impact degradation rates (Rositano et al. 1998).

Many advanced oxidation processes have been found to effectively treat cyanotoxins

through the use of hydrogen peroxide coupled with other chemical or physical methods

(Svrcek and Smith 2004). Hydrogen peroxide is easily degraded by photolysis, and is

thus considered an environmentally benign reactant (Antoniou et al. 2005).

When applied alone, hydrogen peroxide appears relatively ineffective at degrading

cyanobacterial toxins, with only 17% removal recorded 60 minutes after addition

(Rositano et al. 1998). This is likely because, although kinetically the degradation is

favoured, hydrogen peroxide requires a catalyst to produce hydroxyl radicals in

significant amounts (Svrcek and Smith 2004).

Rositano et al. (1998) found that application of ozone and hydrogen peroxide to a

solution of microcystin-LR reduced concentrations by 54% so quickly that researchers

could not determine rates. The same study determined that by combining the application

of hydrogen peroxide with UV irradiation, approximately 50% of microcystin-LR could

be removed within the first 30 minutes. Although this result is numerically not as

significant as some other treatment methods, it is easily the most cost effective in water

treatment plants (Svrcek and Smith 2004), and is environmentally safe as it degrades to

form oxygen gas and water (Barroin and Feuillade 1986). The UV radiation causes

isomerisation of the microcystin and production of hydroxyl radicals from the hydrogen


peroxide. A study by Bandala et al. (2004) suggests that the rate of degradation by

hydrogen peroxide and UV light can be further increased by the addition of Fenton

reagents, where the addition of iron ions can result in complete degradation of

microcystins in 35-40 minutes.

2.8 Mode of action of hydrogen peroxide

Hydroxyl radicals are the most reactive oxygen species, and are generated from hydrogen

peroxide (Drabkova et al. 2007). Although some radicals are generated in the absence of

UV light, the concentration of hydroxyl radicals, and hence the toxicity of the solution, is

greatly increased by UV irradiance (Drabkova et al. 2007).

2.8.1 Induction of cyanobacterial cell death

A study by Barroin and Feuillade (1986) indicated that for a given set of light conditions,

the concentration of hydrogen peroxide required to substantially decrease photosynthetic

activity in cyanobacteria is likely a threshold value. It is probabilistic that cyanobacteria

are degraded by hydrogen peroxide at much lower concentrations than other algae species

as they are prokaryotic, their photosynthetic apparatus are not contained within

organelles, and thus have a direct connection with the plasma membrane (Barroin and

Feuillade 1986). Hydrogen peroxide inhibits photosynthetic electron transfer in

Photosystem II by inhibiting the ascorbate peroxidase system of hydrogen peroxide

detoxication (Samuilov et al. 2001), and by causing the division of D1 peptide bonds

(Lupinkova and Komenda 2004). D1 is a crucial protein in Photosystem II, and has been

observed by polyacrylamide gel electrophoresis and immunoassays to rapidly disappear

when dosed with sufficient hydrogen peroxide concentrations (Lupinkova and Komenda

2004). Hydrogen peroxide also destroys pigments including billiproteins, carotenoids and

chlorophyll a at low concentrations (Barroin and Feuillade 1986), and inhibits the fixation

of carbon dioxide by attacking enzymes involved in the Calvin cycle (Samuilov et al.

2001).


2.8.2 Induction of microcystin degradation

Hydroxyl radicals attack the conjugated diene structure of microcystin variants to form

dihydroxylated products. Oxidative cleavage then occurs at the 4-5 and/or 6-7 bonds of

the Adda moiety (Figure 8), effectively destroying the toxicity of the microcystin (Perez

and Aga 2005).


Figure 8: 1) Hydroxyl radicals attack the 4-5 or 6-7 bonds of the microcystin’s Adda moiety. 2) Bonds 4-5 or 6-7 of Adda become dihydroxylated. 3) Oxidative cleavage of the microcystin molecule occurs at the 4-5 or 6-7 bond of the Adda moiety.

-OH

-OH

1

2

3

3. Methodology 45

3 Methodology

3.1 Sampling and incubation

When sampling to determine water quality, samples should be taken from various points

within the water body to account for horizontal and vertical stratification (Perez and Aga

2005). However, in this study the sample could be taken from any point or depth of the

wastewater treatment pond, as spatial variation was not being considered. An opaque

container was used to transport the samples, as algae can grow during long distance

transport (Jia et al. 2003) and isomerise under UV light (Perez and Aga 2005).

The objective of the laboratory analysis was to determine reaction rates, not toxin

concentrations within the water body. Samples were analysed as soon as possible after

collection, to prevent large changes in toxin concentration due to cell lysis or degradation

(Perez and Aga 2005; Heresztyn and Nicholson 1997). Due to their low tendency to

hydrolyse, the pH of stored samples was not important (Rositano and Nicholson 1994).

3.1.1 Incubation attempt

During the winter months there was a lack of cyanobacterial blooms in South-West

wastewater treatment ponds. Thus water samples were collected from the metropolitan

area and incubated, to determine whether a Microcystis aeruginosa bloom could be

cultured using locally sourced water samples.

Samples suspected to contain Microcystis aeruginosa were collected from several water

bodies (Figure 9) including Ron Stone Park in Mount Lawley, the lake adjacent to Sir

Charles Gairdner Hospital in Nedlands (Figure 10), John Oldham Park in Perth, David

Carr Park in Perth, and at various locations along the Canning River. Several

unconcentrated samples were taken at each site. At each location a concentrated sample

was also collected by filtering large amounts of water using a plankton net. Both

concentrated and unconcentrated samples were incubated at 25 degrees Celsius for one

week under fluorescent light on a 12 hour day/night cycle (Figure 11). However, at one

46 3. Methodology

week’s incubation time, no samples showed a noticeable increase in cyanobacterial

concentration.

Figure 9: Locations of water samples collected in mid-2007 (Google 2007)

PPeerrtthh CCBBDD

3. Methodology 47

Figure 10: Lake adjacent to Sir Charles Gairdner Hospital, Nedlands (Barrington 2007)

48 3. Methodology

Figure 11: Laboratory incubation attempt (Barrington 2007)

3.1.2 Study site

Preliminary samples were collected from the Burekup and Brunswick Junction WWTPs

in the South-West of Western Australia in April 2007 (Figure 12). These samples were

examined under a light microscope, and the presence of Microcystis aeruginosa was

confirmed. There appeared to be a decrease in cyanobacterial concentration as the

wastewater passed through the treatment stages. Water from both plants was deemed

suitable for analysis, though the Brunswick Junction samples did display lower

cyanobacterial levels due to the application of copper sulfate as an algicide.

3. Methodology 49

Figure 12: Collecting concentrated samples by use of a plankton net (Barrington 2007)

In situ spectrofluorescence monitoring during early spring indicated cyanobacterial levels

greater than 50 μgL-1 at the Burekup WWTP. This was considered an appropriate level

for testing the reactions of blooms to hydrogen peroxide dosing using both

spectrofluorescence and ELISA. On several occasions samples were collected from pond

one at the Burekup WWTP. 20 L samples were collected from the centre of the pond by

the use of a metal dinghy powered by oars, so that pelagic water samples could be

collected without disturbing benthic sediments (Figure 13 and Figure 14).

50 3. Methodology

Figure 13: Preparing the boat for sample collection (Barrington 2007)

3. Methodology 51

Figure 14: Rowing the boat to collect samples from Pond 1 of the Burekup WWTP

Carboys of raw water were transported to UWA covered in black plastic, so as to avoid

stimulating photodecomposition or growth. Samples were stored without light at 25

degrees Celsius.

3.2 Determination of chlorophyll a

Raw water samples were analysed within one week of collection, as advised by Harada et

al. (1999). The hydrogen peroxide doses chosen for investigation ranged from 0.00296

gL-1, approximately the highest dosage found to have no effect on microcystin

concentrations (Lawton and Robertson 1999), to 296 gL-1. The upper dosage limit was

equal to the addition of 50% hydrogen peroxide, as purchased by the Water Corporation

for use as an algicide, to raw wastewater at a 1:1 ratio. Although this is likely an

impractical dosage in the field, it gave a fair indication of the effect of excess hydrogen

52 3. Methodology

peroxide on Microcystis aeruginosa and microcystins. The moderate doses considered

were 2.96 gL-1 and 0.0296 gL-1. From this initial investigation, a second hydrogen

peroxide dosing experiment was conducted using hydrogen peroxide concentrations of

0.0296 gL-1, 0.148 gL-1, 0.296 gL-1, 0.740 gL-1 and 1.48 gL-1.

Conical flasks were placed at 25 degrees Celsius under fluorescent lighting, and filled

with 250 mL of raw water. 25 mL of each sample was analysed by spectrofluorescence,

where ten measurements were taken, to determine the original chlorophyll a

concentration, and returned to the flask. 250 mL of each hydrogen peroxide stock

solution, and a control of deionised water, were added to raw water samples in triplicate.

Incubation at 25 degrees Celsius was continued for 48 hours (Figure 15).

Figure 15: Dosed samples undergoing incubation under fluorescent light (Barrington 2007)

The initial sampling period was set at 1 hour, as a study by Rositano et al. (1998) found

that with the application of hydrogen peroxide alone, 17% of toxins were removed within

the hour following application. Measurement of cyanobacterial and total algal biomass

3. Methodology 53

were determined by multiwavelength spectrofluorescence using the bbe-Moldaenke

FluoroProbe, where ten repetitions were performed for each fluorescence measurement.

Chlorophyll a concentrations of chlorophyta, diatoms, cryptophyta and yellow substances

were also measured. 25mL of sample were taken from each flask hourly up to six hours,

and multiwavelength spectrofluorescence performed. Samples were also taken at

approximately 18, 24 and 48 hours after hydrogen peroxide addition, to determine the

longer term reaction characteristics.

Figure 16: Analysing a sample on the bbe-Moldaenke Fluoroprobe (Barrington 2007)

3.3 Determination of extracellular microcystin and nodularin

concentrations

A study by Fastner et al. (2002) determined that microcystin concentration measurements

of adequate precision could be performed by the methods of HPLC-PDA, HPLC-UV,

54 3. Methodology

HPLC-MS, PPIA and ELISA. Chromatographic measurements were deemed too

expensive for this study, and required calibration which was difficult without well

defined microcystin standards (Rapala et al. 2002; Mountfort et al. 2005). Protein

Phosphatase Inhibition Assays were considered cheaper, but have a tendency for the

occurrence of false positives due to the inhibition of other protein phosphatases within

water samples (Carmichael and An 1999), and lack agreeability with other methods at

low concentrations (Perez and Aga 2005; Heresztyn and Nicholson 2001). ELISA

appears to have high reproducibility and sensitivity, and is cheap compared to

chromatographic methods, though a possible error in measurement may arise from cross-

reactivity of coated ELISA plates (Orr et al. 2003). ELISA was chosen as the most

suitable method of analysing microcystin concentrations and degradation rates in water

sampled from wastewater treatment ponds.

Although samples can be pre-concentrated and contaminants, such as dissolved organic

matter, removed using solid phase extraction cartridges (Nicholson and Burch 2001), the

collected water samples had reasonably high cyanobacterial concentrations and were

undergoing ELISA analysis, so did not require cleanup (Harada et al. 1999; Mountfort et

al. 2005; Carmichael and An 1999).

Conical flasks were placed at 25 degrees Celsius under fluorescent lighting, and filled

with 250 mL of raw water. 50 μL of each sample was analysed by ELISA, to determine

the original extracellular microcystin and nodularin concentrations. 250mL of each

hydrogen peroxide stock solution, and a control of deionised water, were added to raw

water samples in duplicate. Incubation at 25 degrees Celsius was continued for 48 hours.

Duplicate samples were taken from each flask hourly up to six hours, and ELISA

performed. Samples were also taken at approximately 18 and 24 hours after hydrogen

peroxide addition and analysed by ELISA to determine the longer term reaction

characteristics.

To determine microcystin concentrations, the Abraxis PN 520011,

Microcystins/Nodularins (ADDA) ELISA Kit, Microtiter Plate (96T) was utilised. This

test kit allows for detection of nodularins, DM-MCRR and MCLR and MC-LR, YR, LF,

RR and LW, with a minimum detection limit of 0.1 ppb. At each time step, duplicate

3. Methodology 55

50 μL aliquots of each sample were pipetted onto the microtiter plate coated with an

analog of microcystins conjugated to a protein. 50 μL of an antibody solution was then

pipetted into each well (Figure 17), and the samples incubated at room temperature for 90

minutes. Wells were emptied and washed with 250 μL of wash solution and dried on

paper towels. 100 μL of enzyme conjugate was added to each well and incubated for 30

minutes. Wells were again emptied and washed, and 100 μL of horseradish peroxidase

added to each well. Samples were incubated for 20 minutes, in the absence of light,

before the addition of 50 μL of stop solution. The microtiter plate was then placed in a

Mertech Inc. AccuReader 965 to determine the absorbance of each sample, and hence its

microcystin concentration.

Figure 17: Adding 50 μL of antibody solution to samples

56 3. Methodology

3.4 Data Analyses

3.4.1 Chlorophyll a concentration

At each time step, ten fluorescence measurements were taken using the bbe-Moldaenke

FluoroProbe. These values were averaged and their standard deviation calculated.

Chlorophyll a concentrations contributed by cyanobacteria, chlorophyta, diatoms,

cryptophyta and their sum were expressed as percentages of their original concentration,

and normalised to the control chlorophyll a values over the 48 hour incubation period.

Exponential decay curves were fit to the normalized concentration curves for each

hydrogen peroxide dose over time. r2 and ANOVA probability values were calculated to

determine whether the exponential fits were statistically significant. These fits also

provided equations for the curves and their associated error values.

To determine the cyanobacterial and total algal decay rates without normalising to the

control, exponential decay curves were fit to each repetition of each hydrogen peroxide

dose and normalised. r2 and ANOVA probability values were calculated to determine

whether the exponential fits were statistically significant. These fits again provided

equations for the curves and their associated error values.

3.4.2 Extracellular microcystin and nodularin concentrations

Extracellular microcystin and nodularin concentrations determined by ELISA were

plotted over time. As all values were below the minimum detection limit of 0.1 μgL-1,

further analysis of this data was not possible.

4. Results 57

4 Results

4.1 Cyanobacterial biomass

4.1.1 In situ monitoring

In situ monitoring of chlorophyll a levels in the three pond system at Burekup WWTP

indicated significant levels of algae throughout the process, gradually decreasing as the

wastewater passes through the treatment stages (Figure 18)

Algal species

Chlorophyta Cyanobacteria Diatoms Cryptophta Yellow Substances Total Algae

Chl

orop

hyll

a (u

g/L)

0

50

100

150

200

250

300

350

Pond 1Pond 2Pond 3

Figure 18: In situ chlorophyll a monitoring, Burekup WWTP. Standard error bars are shown but are small in comparison to chlorophyll a values

4.1.2 Fluoroprobe investigation 1

An initial investigation was performed by dosing raw water samples with hydrogen

peroxide concentrations of 0.00296 gL-1, 0.0296 gL-1, 2.96 gL-1 and 296 gL-1. Results of

the total algal and cyanobacterial chlorophyll a concentration over a 45 hour period

58 4. Results

(Figure 19 and Figure 20) allowed for the determination of a smaller concentration range

to be investigated on a second set of raw water samples.

Time since hyrogen peroxide addition (hours)

0 10 20 30 40 50

Perc

ent o

f orig

inal

con

cent

ratio

n

0

50

100

150

200

250

Control0.00296 g/L0.0296 g/L2.96 gL296 g/LExponential Fit BlankExponential Fit 0.00296 g/LExponential Fit 0.0296 g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 19: Total algal chlorophyll a concentration over time after dosing with various concentrations

of hydrogen peroxide ranging from 0 to 296 gL-1. Error bars represent the standard error, where ten

fluorescence measurements were taken for each repetition.

4. Results 59

Time since hydrogen peroxide addition (hours)

0 10 20 30 40 50

Per

cent

of o

rigin

al c

once

ntra

tion

0

20

40

60

80

100

120

140

Control0.00296 g/L0.0296 g/L2.96 g/L296 g/LExponential Fit ControlExponential Fit 0.00296 g/LExponential Fit 0.0296 g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 20: Cyanobacterial chlorophyll a concentration over time after dosing with various

concentrations of hydrogen peroxide ranging from 0 to 296 gL-1. Error bars represent the standard

error, where ten fluorescence measurements were taken for each repetition.

4.1.3 Fluoroprobe investigation 2

The initial Fluoroprobe investigation indicated that a practical hydrogen peroxide dose

for reducing cyanobacterial and total algal biomass in wastewater treatment ponds was

between 0.00296 gL-1 and 2.96 gL-1. Hence raw water samples were dosed with hydrogen

peroxide concentrations of 0.0296 gL-1, 0.148 gL-1, 0.296 gL-1, 0.740 gL-1 and 1.48 gL-1.

A highly visible difference was observed between samples of various doses at 24 hours

incubation time (Figure 21 and Figure 22). This was further confirmed by chlorophyll a

data for the algal species cyanobacteria, chlorophyta, diatoms and their sum (data

provided in Appendix A).

60 4. Results

Figure 21: Dosed samples at 24 hours incubation. Left sample is a deionised water control, the right sample has been dosed with 1.48gL-1 hydrogen peroxide (Barrington 2007)

4. Results 61

Figure 22: Dosed samples at 24 hours incubation time. From left: control, 0.0296 gL-1, 0.148 gL-1, 0.296 gL-1, 0.740 gL-1, 1.48 gL-1 (Barrington 2007)

Data for chlorophyll a concentration was converted to a percentage of the original

concentration for that replicate, and normalised relative to a raw water sample dosed with

deionised water (the control). An exponential decay curve was plotted for each dosage

and each algal species, where

[Chl a] = [Chl a]0e-bt

[Chl a] = The chlorophyll a concentration at a given time t (relative to control)

[Chl a]0 = The initial chlorophyll a concentration (t=0) (relative to control)

b = First order rate constant (hour-1)

t = Time since hydrogen peroxide addition (hours)

62 4. Results

4.1.3.1 Total Algal Chlorophyll a

The total algal chlorophyll a concentration, normalised to the deionised water blank,

showed statistically significant exponential decay for concentrations of 0.148 gL-1 and

above (Figure 23 and Table 2). Hydrogen peroxide doses of 0.00296 gL-1 and 0.0296 gL-1

did not induce significant chlorophyll a decay over time.

Time since hydrogen peroxide addition (hours)0 10 20 30 40 50

Tota

l alg

al C

hl a

con

cent

ratio

n no

rmal

ised

to c

ontro

l

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.00296 g/L0.0296g/L0.148 g/L 0.296 g/L0.740 g/L1.48g/L2.96g/L296g/LExponential Fit 0.00296g/LExponential Fit 0.0296g/LExponential Fit 0.148g/L Exponential Fit 0.296g/LExponential Fit 0.740 g/LExponential Fit 1.48 g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 23: Total algal chlorophyll a concentration normalised to the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 2: Statistical analysis of total algal chlorophyll a concentration normalised to the control

H2O2 dose (gL-1) a b r2 ANOVA Regression probability

Statistically significant (P<0.01)?

0.00296 ± 0.00004 0.9788 ± 0.1221 0.0021 ± 0.0080 0.0117 0.7816 No 0.0296 ± 0.0003 0.4770 ± 0.1213 0.0000 ± 0.0141 0.0000 1.0000 No

0.148 ± 0.002 0.8943 ± 0.0877 0.2381 ± 0.0471 0.8981 <0.0001 Yes 0.296 ± 0.002 1.0935 ± 0.0899 0.1376 ± 0.0291 0.9221 <0.0001 Yes 0.740 ± 0.002 1.3323 ± 0.1857 0.2150 ± 0.0630 0.8276 0.0003 Yes 1.48 ± 0.02 1.3492 ± 0.1600 0.1569 ± 0.0447 0.8611 0.0001 Yes 2.96 ± 0.02 1.0866 ± 0.0619 0.1606 ± 0.0218 0.9644 <0.0001 Yes

296 ± 1 1.0270 ± 0.0746 0.6045 ± 0.0827 0.9472 <0.0001 Yes

4. Results 63

4.1.3.2 Cyanobacteria

The cyanobacterial chlorophyll a concentration, normalised to the control, showed

statistically significant exponential decay for all concentrations of 0.148 gL-1 and above,

though interestingly not for the highest concentration of 296 gL-1 (Figure 24 and Table 3).

Hydrogen peroxide doses of 0.00296 gL-1 and 0.0296 gL-1 did not induce significant

chlorophyll a decay over time.

Time since hydrogen peroxide addition (hours)0 10 20 30 40 50C

yano

bact

eria

Chl

a c

once

ntra

tion

norm

alis

ed to

con

trol

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.00296 g/L0.0296 g/L0.148 g/L0.296g/L0.740 g/L1.48 g/L2.96 g/L296g/LExponential Fit 0.00296 g/LExponential Fit 0.0296g/LExponential Fit 0.148 g/LExponential Fit 0.296 g/LExponential Fit 0.740 g/LExponential Fit 1.48g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 24: Cyanobacterial chlorophyll a concentration normalised to the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 3: Statistical analysis of cyanobacterial chlorophyll a concentration normalised to the control



0.00296 ± 0.00004 0.9087 ± 0.0956 0.0089 ± 0.0083 0.1787 0.2570 No 0.0296 ± 0.0003 0.5848 ± 0.0978 0.0000 ± 0.0092 0.0000 1.0000 No

0.148 ± 0.002 0.9713 ± 0.0759 0.1892 ± 0.0328 0.9210 <0.0001 Yes 0.296 ± 0.002 1.1105 ± 0.0944 0.2520 ± 0.0422 0.9251 <0.0001 Yes 0.740 ± 0.002 1.1024 ± 0.0881 0.4256 ± 0.0614 0.9400 <0.0001 Yes 1.48 ± 0.02 1.0814 ± 0.1011 0.2817 ± 0.0497 0.8929 <0.0001 Yes 2.96 ± 0.02 0.9740 ± 0.0385 0.4552 ± 0.0325 0.9837 <0.0001 Yes

296 ± 1 0.8676 ± 0.1532 0.1774 ± 0.0712 0.4323 0.0388 No

64 4. Results

4.1.3.3 Chlorophyta

The chlorophyta chlorophyll a concentration, normalised to the control, showed

statistically significant exponential decay for all concentrations of 0.148 gL-1 and above

(Figure 25 and Table 4). Hydrogen peroxide doses of 0.00296 gL-1 and 0.0296 gL-1 did

not induce significant chlorophyll a decay over time.


hlor

ophy

ta c

hl a

con

cent

ratio

n no

rmal

ised

to c

ontro

l

0.0

0.5

1.0

1.5

2.0

0.00296 g/L0.0296 g/L0.148 g/L0.296 g/L0.740 g/L1.48 g/L2.96 g/L296 g/LExponential Fit 0.00296 g/LExponential Fit 0.0296 g/LExponential Fit 0.148 g/LExponential Fit 0.296 g/LExponential Fit 0.740 g/LExponential Fit 1.48 g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 25: Chlorophyta chlorophyll a concentration normalised to the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 4: Statistical analysis of chlorophyta chlorophyll a concentration normalised to the control



0.00296 ± 0.00004 0.9941 ± 0.1280 0.0026 ± 0.0084 0.0156 0.7491 No 0.0296 ± 0.0003 0.4489 ± 0.1316 0.0000 ± 0.0162 0.0000 1.0000 No

0.148 ± 0.002 0.8648 ± 0.1040 0.2683 ± 0.0625 0.8571 0.0001 Yes 0.296 ± 0.002 1.1670 ± 0.1028 0.1216 ± 0.0291 0.9097 <0.0001 Yes 0.740 ± 0.002 1.4488 ± 0.2271 0.1837 ± 0.0648 0.7897 0.0006 Yes 1.48 ± 0.02 1.4618 ± 0.1945 0.1408 ± 0.0473 0.8311 0.0002 Yes 2.96 ± 0.02 1.2388 ± 0.1107 0.1181 ± 0.0290 0.9093 <0.0001 Yes

296 ± 1 1.0430 ± 0.0742 0.8116 ± 0.1188 0.9584 <0.0001 Yes

4. Results 65

4.1.3.4 Diatoms

The diatom chlorophyll a concentration, normalised to the control, showed statistically

significant exponential decay for all concentrations of 0.148 gL-1 and above (Figure 26

and Table 5). Hydrogen peroxide doses of 0.00296 gL-1 and 0.0296 gL-1 did not induce

significant chlorophyll a decay over time.

Time since hydrogen peroxide addition (hours)0 10 20 30 40 50 60

Dia

tom

Chl

a c

once

ntra

tion

norm

alis

ed to

con

trol

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.00296 g/L0.0296 g/L0.148 g/L0.296 g/L0.740 g/L1.48 g/L2.96 g/L296 g/LExponential Fit 0.00296 g/LExponential Fit 0.0296 g/LExponential Fit 0.148 g/LExponential Fit 0.296 g/LExponential Fit 0.74 g/LExponential Fit 1.48 g/LExponential Fit 2.96 g/LExponential Fit 296 g/L

Figure 26: Diatom chlorophyll a concentration normalised to the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 5: Statistical analysis of diatom chlorophyll a concentration normalised to the control



0.00296 ± 0.00004 0.7483 ± 0.1350 0.0000 ± 0.0108 0.0000 1.0000 No 0.0296 ± 0.0003 0.4383 ± 0.1220 0.0000 ± 0.0154 0.0000 1.0000 No

0.148 ± 0.002 0.8966 ± 0.0874 0.2469 ± 0.0479 0.8987 <0.0001 Yes 0.296 ± 0.002 0.9819 ± 0.0720 0.1215 ± 0.0242 0.9308 <0.0001 Yes 0.740 ± 0.002 1.1875 ± 0.1231 0.2609 ± 0.0529 0.8962 <0.0001 Yes 1.48 ± 0.02 1.2214 ± 0.1130 0.1853 ± 0.0380 0.9129 <0.0001 Yes 2.96 ± 0.02 1.0170 ± 0.0420 0.1943 ± 0.0175 0.9817 <0.0001 Yes

296 ± 1 1.0675 ± 0.0963 0.7154 ± 0.1277 0.9346 <0.0001 Yes

66 4. Results

4.1.3.5 Cryptophyta

The cryptophyta chlorophyll a concentration, normalised to the control, did not display an

obvious growth or decay pattern with the addition of various doses of hydrogen peroxide

(Figure 27).


rypt

ophy

ta C

hl a

con

cent

ratio

n no

rmal

ised

to c

ontro

l

-200

-100

0

100

200

0.00296 g/L0.0296 g/L0.148 g/L0.296 g/L0.740 g/L1.48 g/L2.96 g/L296 g/L

Figure 27: Cryptophyta chlorophyll a concentration normalised to the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

4. Results 67

4.1.3.6 First order rate constants

Exponential decay curves were fitted to each repetition at each hydrogen peroxide

dosage. Regression curves were statistically significant for most hydrogen peroxide

doses, and thus the total and cyanobacterial chlorophyll a degradation first order rate

constants were determined assuming a first order reaction. Such a reaction assumes that

the rate of decay of chlorophyll a is dependent upon its concentration at a given time, and

not upon the hydrogen peroxide concentration. This can be assumed as the hydrogen

peroxide is clearly at a concentration in excess of the chlorophyll a.

First order reaction concentrations can be expressed as:

[Chl a] = [Chl a]0e-bt

Where:

[Chl a] = The chlorophyll a concentration at a given time t

[Chl a]0 = The initial chlorophyll a concentration (t=0)

b = First order rate constant (hour-1)

t = Time since hydrogen peroxide addition (hours)

And, as the rate of decay is dependent upon the concentration at any time t:

d[Chl a]/dt = -b[Chl a]

The rate constant of a reaction is not the rate at which the reaction will occur, but allows

for such a rate to be calculated for given reactant concentrations. Greater rate constants

infer faster decay rates (Atkins 1998). Rate constant values were averaged for each

hydrogen peroxide concentration and the half life determined (Table 15 and Table 25).

4.1.3.6.1 Cyanobacterial chlorophyll a decay

It is apparent from Figure 24 that cyanobacterial chlorophyll a exhibits an exponential

decay function when dosed with hydrogen peroxide above a certain concentration. The

68 4. Results

first order rate constant for each hydrogen peroxide addition was determined (Figure 28

and Table 6, Figure 29 and Table 7, Figure 30 and Table 8, Figure 31 and Table 9, Figure

32 and Table 10, Figure 33 and Table 11, Figure 34 and Table 12, Figure 35 and Table

13, Figure 36 and Table 14).


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25

30

Repetition 1Repetition 2Repetition 3Exponential Fit Repetition 1Exponential Fit Repetition 2Exponential Fit Repetition 3

Figure 28: Cyanobacterial chlorophyll a decay for the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 6: Cyanobacterial first order rate constant for the control

a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 19.6502 ± 0.9846 0.0029 ± 0.0030 0.1084 0.3529 No 2 16.1393 ± 1.4204 0.0000 ± 0.0049 0.0000 1.0000 No 3 19.2890 ± 1.6710 0.0000 ± 0.0048 0.0000 1.0000 No Average rate constant N/A

Cyanobacterial chlorophyll a concentration does not exhibit the characteristics of

exponential decay for the control solution.

4. Results 69

Time since hydorgen peroxide addition (hours)0 10 20 30 40 50

Cya

noba

cter

ial C

hl a

con

cent

ratio

n (u

g/L)

0

10

20

30

40


Figure 29: Cyanobacterial chlorophyll a decay for addition of 0.00296 gL-1 hydrogen peroxide. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 7: Cyanobacterial first order rate constant for addition of 0.00296 gL-1 hydrogen peroxide



exponential decay for the addition of 0.00296gL-1 hydrogen peroxide.

70 4. Results


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

2

4

6

8






exponential decay for the addition of 0.0296gL-1 hydrogen peroxide.

4. Results 71


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

1

2

3

4

5

6

7




a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 5.2147 ± 0.3605 0.1808 ± 0.0284 0.9402 <0.0001 Yes 2 5.1204 ± 0.4075 0.1603 ± 0.0308 0.9111 <0.0001 Yes 3 5.3019 ± 0.2613 0.1558 ± 0.0189 0.9677 <0.0001 Yes Average rate constant 0.1656 ± 0.0260 hour-1

Cyanobacterial chlorophyll a concentration exhibits statistically significant exponential

decay for the addition of 0.148 gL-1 hydrogen peroxide.

72 4. Results


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25







4. Results 73


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25







74 4. Results

Time since hydrogen peroxide addition (hours) 0 10 20 30 40 50

Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

2

4

6

8




a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 6.2759 ± 0.7241 0.2443 ± 0.0550 0.8136 0.0004 Yes 2 5.6769 ± 0.2626 0.2930 ± 0.0252 0.9758 <0.0001 Yes 3 7.0024 ± 0.7523 0.2531 ± 0.0533 0.8713 <0.0001 Yes Average rate constant 0.2635 ± 0.0458 hour-1



4. Results 75


Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25

30







76 4. Results


0 10 20 30 40 50

Cya

noba

cter

ial C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25

30


Figure 36: Cyanobacterial chlorophyll a decay for addition of 296 gL-1 hydrogen peroxide. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 14: Cyanobacterial first order rate constant for addition of 296 gL-1 hydrogen peroxide



exponential decay for the addition of 296gL-1 hydrogen peroxide.

The average first order rate constant and half life of cyanobacterial chlorophyll a were

determined for each hydrogen peroxide dose which displayed a statistically significant

exponential decay (Table 15).

4. Results 77

Table 15: Average first order rate constants for cyanobacterial chlorophyll a

H2O2 dose (gL-1) Rate constant (hour-1) Half life (hours) 0.148 ± 0.002 0.1656 ± 0.0260 4.1867 ± 0.6573 0.296 ± 0.002 0.2448 ± 0.0330 2.8315 ± 0.3817 0.740 ± 0.002 0.4207 ± 0.0524 1.6476 ± 0.2052 1.48 ± 0.02 0.2635 ± 0.0458 2.6305 ± 0.4572 2.96 ± 0.02 0.4438 ± 0.1035 1.5618 ± 0.3642

The rate constants were plotted against the hydrogen peroxide doses, and a sigmoidal

regression attempted (Figure 37). A sigmoidal fit was chosen as it is likely that

chlorophyll a decay exhibits a threshold effect when dosed with hydrogen peroxide, such

that there is no visible response at doses below a threshold algicidal concentration.

Sigmoidal fits also assume there is some algicidal concentration at which rate constants

do not increase further, assuming a threshold rate constant has been reached, as would be

expected from this system.

78 4. Results

Hydrogen Peroxide Concentration (g/L)

0 1 2 3

Firs

t ord

er ra

te c

onst

ant (

/hou

r)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Figure 37: Cyanobacterial chlorophyll a first order rate constants plotted against hydrogen peroxide dose. The line of best fit is a 3 parameter sigmoid. Error bars represent the standard error.

4. Results 79

Where a sigmoidal regression was attempted

First order rate constant = a/{1+exp[-(x-x0)/b]} (hour-1)

x0 = 0.3733 ± 0.0248 gL-1

a = 0.0868 ± 0.0285 hour-1

b = 0.2134 ± 0.0355 gL-1

x = hydrogen peroxide dose (gL-1)

r2 = 0.8321

ANOVA p <0.0001

Which is statistically significant, and hence indicates that the first order rate constant of

cyanobacterial chlorophyll a decay increases sigmoidally with increasing hydrogen

peroxide concentration.

4.1.3.6.2 Total algal chlorophyll a decay

It is apparent from Figure 23 that total algal chlorophyll a exhibits an exponential decay

function when dosed with hydrogen peroxide above a certain concentration. The first

order rate constant for each hydrogen peroxide addition was determined (Figure 38 and

Table 16, Figure 39 and Table 17, Figure 40 and Table 18, Figure 41 and Table 19,

Figure 42 and Table 20, Figure 43 and Table 21, Figure 44 and Table 22, Figure 45 and

Table 23, Figure 46 and Table 24).

80 4. Results


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

20

40

60

80

100

120

140

160

180

200


Figure 38: Total algal chlorophyll a decay for the control. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 16: Total algal first order rate constant for the control

a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 98.0316 ± 9.4992 0.0000 ± 0.0053 0.0000 1.0000 No 2 98.0316 ± 9.4992 0.0000 ± 0.0053 0.0000 1.0000 No 3 117.1566 ± 11.3511 0.0000 ± 0.0053 0.0000 1.0000 No Average decay rate N/A

Total algal chlorophyll a concentration does not exhibit the characteristics of exponential

decay for the control solution.

4. Results 81

Time since hydorgen peroxide addition (hours)0 10 20 30 40 50

Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

20

40

60

80

100

120

140

160


Figure 39: Total algal chlorophyll a decay for addition of 0.00296 gL-1 hydrogen peroxide. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.




decay for addition of 0.00296 gL-1 hydrogen peroxide.

82 4. Results


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

10

20

30

40

50

60






decay for addition of 0.0296 gL-1 hydrogen peroxide.

4. Results 83


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

5

10

15

20

25

30

35




a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 24.7896 ± 3.1185 0.1915 ± 0.0532 0.8421 0.0002 Yes 2 25.7223 ± 3.4108 0.1964 ± 0.0571 0.8180 0.0003 Yes 3 26.6582 ± 2.1042 0.1810 ± 0.0328 0.9317 <0.0001 Yes Average decay rate 0.1896 ± 0.0474 hour-1

Total algal chlorophyll a concentration exhibits statistically significant exponential decay

for the addition of 0.148 gL-1 hydrogen peroxide.

84 4. Results


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

20

40

60

80

100

120

140




a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 116.9648 ± 6.5145 0.0972 ± 0.0160 0.9557 <0.0001 Yes 2 111.2270 ± 10.3738 0.1136 ± 0.0296 0.8948 <0.0001 Yes 3 98.4906 ± 6.3926 0.1478 ± 0.0240 0.9519 <0.0001 Yes Average decay rate 0.1195 ± 0.0234 hour-1



4. Results 85


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

50

100

150

200




a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 147.1269 ± 20.9134 0.1892 ± 0.0601 0.8181 0.0003 Yes 2 117.8792 ± 10.2468 0.2046 ± 0.0386 0.9205 <0.0001 Yes 3 144.8794 ± 21.1722 0.1847 ± 0.0614 0.8082 0.0004 Yes Average decay rate 0.1928 ± 0.0539 hour-1



86 4. Results

Time since hydrogen peroxide addition (hours) 0 10 20 30 40 50

Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

10

20

30

40

50

60

70


Figure 44: Total algal chlorophyll a decay for addition 1.48 gL-1 hydrogen peroxide. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.


a b r2 ANOVA Regression probability Statistically significant (P<0.01)? 1 41.4229 ± 4.6484 0.1513 ± 0.0410 0.8715 <0.0001 Yes 2 36.3585 ± 3.0817 0.1473 ± 0.0307 0.9247 <0.0001 Yes 3 50.5170 ± 7.6134 0.1194 ± 0.0491 0.7747 0.0008 Yes Average decay rate 0.1393 ± 0.0414 hour-1



4. Results 87


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

20

40

60

80

100

120

Repetition 1Repetition 2Repetition 3Exponential Fit Repetition 1Col 45 vs Col 46 Exponential Fit Repetition 2Exponential Fit Repetition 3






88 4. Results


Tota

l alg

al C

hl a

con

cent

ratio

n (μ

g/L)

0

20

40

60

80

100


Figure 46: Total algal chlorophyll a decay for addition of 296 gL-1 hydrogen peroxide. Error bars represent the standard error, where ten fluorescence measurements were taken for each repetition.

Table 24: Total algal first order rate constant for addition of 296 gL-1 hydrogen peroxide



for the addition of 296 gL-1 hydrogen peroxide.

The average first order rate constant and half life of total algal chlorophyll a were

determined for each hydrogen peroxide dose which displayed a statistically significant

exponential decay (Table 25).

4. Results 89

Table 25: Average first order rate constants for total algal chlorophyll a

H2O2 dose (gL-1) Rate constant (hour-1) Half life (hours) 1.48x10-1 ± 2.0x10-3 0.1896 ± 0.0474 3.6558 ± 0.9140 2.96x10-1 ± 2.0x10-3 0.1195 ± 0.0234 5.8004 ± 1.1358 7.40x10-1 ± 2.0x10-3 0.1928 ± 0.0539 3.5952 ± 1.0051

1.48 ± 2.0x10-2 0.1393 ± 0.0414 4.9759 ± 1.4788 2.96 ± 2.0x10-2 0.1398 ± 0.0310 4.9581 ± 1.0994

296 ± 1 0.5972 ± 0.0894 1.1607 ± 0.2017

The first order rate constants were plotted against the hydrogen peroxide doses and a

sigmoidal regression attempted (Figure 47). The first order rate constant for 296 gL-1

hydrogen peroxide addition was not included as it was not statistically significant for the

cyanobacterial rate constant, which is a component of the total algal rate constant. A

sigmoidal fit was chosen as it is likely that chlorophyll a decay exhibits a threshold effect

when dosed with hydrogen peroxide, such that there is no visible response at doses below

a threshold algicidal concentration. Sigmoidal fits also assume there is some algicidal

concentration at which the rate constants do not increase further, assuming a threshold

rate constant has been reached, as would be expected from this system.

90 4. Results

Hydrogen Peroxide Concentration (g/L)

0 1 2 3

Firs

t ord

er ra

te c

onst

ant (

/hou

r)

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

Figure 47: Total algal chlorophyll a first order rate constants plotted against hydrogen peroxide dose. The line of best fit is a 3 parameter sigmoid. Error bars represent the standard error.

4. Results 91

Where a sigmoidal regression was attempted

First order rate constant = a/{1+exp[-(x-x0)/b]} (hour-1)

x0 = -5.2204 ± 725.1127 gL-1

a =0.5299 ± 27.6140 hour-1

b = -7.6705 ± 190.2397 gL-1

x = hydrogen peroxide dose (gL-1)

r2 = 0.0183

ANOVA p = 0.8470

Which is not statistically significant, and hence indicates that the first order rate constant

of total algal chlorophyll a decay does not display sigmoidal regression with increasing

hydrogen peroxide concentration.

4.2 Cyanobacterial toxins

Duplicate raw water samples were dosed with hydrogen peroxide at 0.0296 gL-1, 0.148

gL-1, 0.296 gL-1, 0.740 gL-1 and 1.48 gL-1. Samples were taken at six time steps and

analysed using the Abraxis PN 520011, Microcystins/Nodularins (ADDA) ELISA Kit,

Microtiter Plate (96T) and the Metertech Inc AccuReader 965 (Table 26 and Figure 48).

92 4. Results

Table 26: Total microcystin and nodularin concentrations (μgL-1) detected by ELISA

Time since H2O2 addition (hours) Dose (gL-1) 0 1 2 3 4 23

0 g/L (control) 0.0200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0296 ± 0.0003 0.0000 0.0043 0.0000 0.0000 0.0000 0.0000 0.148 ± 0.002 0.0231 0.0000 0.0000 0.0000 0.0000 0.0000 0.296 ± 0.002 0.0000 0.0132 0.0644 0.0000 0.0000 0.0000 0.740 ± 0.002 0.0277 0.0000 0.0000 0.0000 0.0000 0.0000

1.48 ± 0.02 0.0000 0.0161 0.0000 0.0000 0.0000 0.0000


0 5 10 15 20 25

Mic

rocy

stin

/nod

ular

in c

once

ntra

tion

(ug/

L)

0.00

0.02

0.04

0.06

0.08

0.10

Blank0.0296 g/L0.148 g/L0.296 g/L0.74 g/L1.48 g/L

Figure 48: Combined microcystin and nodularin concentrations detected by ELISA analysis. All measurements were below the minimum reliable detection limit of 0.1 μgL-1

The minimum detection limit of the Abraxis PN 520011, Microcystins/Nodularins

(ADDA) ELISA Kit, Microtiter Plate (96T) is 0.1 μgL-1, and all samples tested within

this study were below this concentration according to the analysis (Figure 48). Hence the

behaviour of microcystin concentrations with hydrogen peroxide application could not be

examined.

Minimum Detection Limit

5. Discussion 93

5 Discussion

5.1 Chlorophyll a concentration

It is evident from this study that hydrogen peroxide is effective at inducing cyanobacterial

cell death, as has been suggested by the literature (Barroin and Feuillade 1986; Perez and

Aga 2005; Rositano et al. 1998; Svrcek and Smith 2004). The results of the chlorophyll a

analysis indicate it is highly likely that the application of hydrogen peroxide at a

concentration of 0.148 gL-1 and above induces cell lysis of cyanobacteria, chlorophyta

and diatoms in wastewater. There is no apparent correlation between cryptophyta

chlorophyll a and hydrogen peroxide dose. It also appears that the first order rate constant

of cyanobacterial chlorophyll a decay reaches an upper value of approximately 0.4438

hour-1, corresponding to a half life of 1.5618 hours.

Our interpretation of the results based upon algal ecophysiology suggests that the

reduction of algal photosynthetic activity following the addition of hydrogen peroxide is

due to hydroxyl radicals attacking the cell wall and photosynthetic apparatus of

cyanobacteria to induce cell death, as well as cleaving D1 peptide bonds and destroying

pigments such as billiproteins, carotenoids and chlorophyll a (Barroin and Feuillade

1986; Lupinkova and Komenda 2004; Samuilov et al. 2001).

5.1.1 Response of control and lower hydrogen peroxide doses

The control and lower hydrogen peroxide doses of 0.00296 ± 0.00003 gL-1 and 0.0296 ±

0.0004 gL-1 did not appear to induce cell death. In some instances algal biomass increased

during incubation, which was likely due to fluorescent lighting allowing for

photosynthesis. Where chlorophyll a concentration was seen to decrease within the

incubation period it is possible that natural cell death occurred, or there was some grazing

by zooplankton (Griffin et al. 2001). Zooplankton of the genera Daphnia were observed

to survive the 48 hour incubation period of the control and two lower hydrogen peroxide

concentrations.

94 5. Discussion

5.1.2 Response of higher hydrogen peroxide doses

Although hydrogen peroxide doses of 0.148 ± 0.002 gL-1 and above induced exponential

decay of cyanobacteria, chlorophyta and diatoms with high statistical significance, the

response did not increase orderly with dosage. Rates were disordered but similar at higher

hydrogen peroxide doses. It has been theorised that hydrogen peroxide induced

cyanobacterial death exhibits a threshold effect, whereby decay rates do not increase

above a certain hydrogen peroxide concentration (Barroin and Feuillade 1986). This is a

possible explanation for the disordered behaviour at higher hydrogen peroxide

concentrations.

Total algal chlorophyll a exhibited exponential decay at a hydrogen peroxide dose of

0.148 gL-1 and above, with high r2 values ranging between 0.8276 and 0.9644, and

ANOVA p values of 0.0003 or less. This indicated that total algal chlorophyll a

concentration modelled by exponential decay was statistically significant at these doses.

Cyanobacterial chlorophyll a exhibited exponential decay at hydrogen peroxide doses of

0.148 gL-1 to 2.96 gL-1, with high r2 values ranging between 0.8929 and 0.9837, and

ANOVA p values of less than 0.0001. This indicated that cyanobacterial chlorophyll a

concentration modelled by exponential decay was statistically significant at these doses.

However, at the highest dose, 296 gL-1 hydrogen peroxide, exponential decay was not

statistically significant. It is possible this was due to an unexplained phenomenon when

there is a large excess of hydrogen peroxide to chlorophyll a, the threshold effect as

described above, or could have been caused by an experimental error. It was noticed that

at the higher hydrogen peroxide doses of 1.48 gL-1, 2.96 gL-1 and 296 gL-1, oxygen

bubbles were visible within samples when undergoing spectrofluorometric analysis. It is

possible that this caused some interference with measurement, and thus affected the

concentration readings for these hydrogen peroxide doses.

5.1.3 Cyanobacterial first order rate constants

The first order rate constants determined for cyanobacteria at hydrogen peroxide doses of

0.148 ± 0.002 gL-1 and above were statistically significant, and exhibited half lives

5. Discussion 95

between 1.5618 and 4.1867 hours. A sigmoidal regression described the response of

cyanobacterial decay rate with increasing hydrogen peroxide dose with a high r2 value of

0.8321 and ANOVA p value of less than 0.0001. This regression is likely a statistically

significant fit as it assumes that cyanobacterial chlorophyll a concentration does not

decay until it reaches some threshold value of hydrogen peroxide dose, and that there is

some value of hydrogen peroxide dose above which the rate of decay cyanobacterial

chlorophyll a does not increase further.

5.1.4 Total algal first order rate constants

The total algal first order rate constant of chlorophyll a decay is not a scientifically robust

measurement, as it is probably highly dependent on the algal composition of the raw

water prior to hydrogen peroxide application. However, knowledge of the approximate

rate constant for algal decay from hydrogen peroxide addition is a useful tool in

wastewater management to determine appropriate algicidal doses for the induction of

algal cell death. Although exponential decay rates were statistically significant for

hydrogen peroxide doses of 0.148 ± 0.002 gL-1 and above, rates did not increase with

hydrogen peroxide concentration in an ordered fashion. This was evidenced by the

sigmoidal fit for the rate constant with various hydrogen peroxide doses not being

statistically significant. The lowest half life determined was approximately one hour, and

the longest six hours, but these did not correspond to the highest and lowest doses

respectively. Within the range of hydrogen peroxide doses from 2.5 x 10-3% to 25%, it

can be inferred that the longest half life of total algal species within a laboratory

environment would likely not be significantly greater than six hours.

5.1.5 Possible effect of UV radiation

In the laboratory incubation, it is not likely that factors other than hydrogen peroxide

addition, and possibly zooplankton grazing, significantly enhanced algal cell death.

However, when applied to outdoor wastewater treatment plants, it is highly probable that

wastewater will be subject to UV light for several hours per day. UV light has been

96 5. Discussion

proven to increase the decay rate of cyanobacterial biomass in hydrogen peroxide dosed

water by significantly increasing the concentration of hydroxyl radicals in solution

(Cornish et al. 2000; Lawton and Robertson 1999). Hence algal chlorophyll a decay

should be faster in the field than the laboratory. The ratio of light to dark hours in

WWTPs must also be considered.

5.1.6 Reliability of instrumentation

The reliability of the fluorescence instrument itself should be considered. Studies of the

bbe-Moldaenke Fluoroprobe have indicated that measurements of cyanobacterial

chlorophyll a concentration can encounter some variability depending on the age of algal

cells (Gregor et al. 2007), and that the probe may exhibit a nonlinear response in algal

chlorophyll a concentrations for samples containing algal biomass in excess of 50 μgL-1

(Gregor and Marsalek 2004). It has also been suggested that results from the probe could

be affected by the shading, scattering or reabsorption of light throughout the water

column (Gregor and Marsalek 2004), or fluorescence quenching errors when algal cells

have been exposed to high levels of sunlight, causing photoinhibition (Leboulanger et al.

2002). It is unlikely that fluorescence quenching occurred within this study, as samples

were only subjected to moderate levels of artificial light.

5.2 Cyanobacterial toxins

It was expected that hydrogen peroxide would reduce microcystin toxicity by the

induction of oxidative cleavage of the Adda bond by hydroxyl radicals (Perez and Aga

2005). However, the minimum detection limit of the Abraxis PN 520011,

Microcystins/Nodularins (ADDA) ELISA Kit, Microtiter Plate (96T) is 0.1 μgL-1, and all

samples tested within this study were below this concentration according to the analysis.

Hence the behaviour of microcystin concentrations with hydrogen peroxide application

could not be examined.

5. Discussion 97

5.2.1 Cyanobacterial toxin detection

Microcystins may have been undetected due to very low concentrations of cyanobacterial

toxins in the raw water sampled, or this may have been caused by an error within the

analysis. Studies have shown that microcystins can be degraded rapidly in the presence of

pigments (Nicholson and Burch 2001; Tsuji et al. 1994), which may have been collected

along with the raw water samples. Native bacteria within the samples could also have

considerably reduced the toxin concentrations of the water samples (Christoffersen et al.

2002). Although ELISA analysis was conducted within one week of sample collection,

this time period may have allowed for significant degradation by these two processes.

It is unlikely that other microcystin toxins were present but undetected by the ELISA

analysis, as the kit utilised has shown good cross reactivity with nodularins and

microcystins YR, LF, LR, RR and LW (Abraxis 2007). Preconcentration could have been

performed on samples to improve detection (Nicholson and Burch 2001), but this was not

initially performed as it was not deemed necessary for raw water with significant algal

concentrations (Harada et al. 1999; Mountfort et al. 2005; Carmichael and An 1999).

5.2.2 Microcystin interaction with plastic

Several studies have shown that hydrophobic interactions between microcystins and

plastic surfaces can reduce the concentration of toxins in samples (Hyenstrand et al.

2001). Hence it has been suggested that plastic lab ware use be restricted (Perez and Aga

2005), and in this study only the initial pipetting of samples into wells used plastic tips. It

is unlikely that original microcystin concentrations in solutions were significantly higher

than those measured; they were likely still below the detection limit.

5.2.3 Laboratory hydrogen peroxide dose

The preliminary results suggest that a hydrogen peroxide dose of approximately 0.148

gL-1 will reduce cyanobacterial biomass by 50% within 4.2 hours. This is approximately a

raw water : 50% hydrogen peroxide ratio of 2000:1. At this dosage, virtually all

98 5. Discussion

cyanobacteria, chlorophyta and diatom biomass will be removed within 48 hours of

application. However, the toxins released from such cell death pose a significant health

risk (Carmichael 1992; Fawell et al. 1993), which could not be investigated within this

study due to low levels of toxins within raw samples. Thus the behaviour of

cyanobacterial toxins with hydrogen peroxide addition should be investigated further.

6. Recommendations 99

6 Recommendations

6.1 Refine analysis method

6.1.1 Further analysis of extracellular toxins

The behaviour of cyanobacterial toxins, in particular microcystin variants and nodularin,

could not be investigated within this study due to undetectable levels within raw water

samples. However, such toxins do pose a substantial risk to human and animal health, and

thus the relationship between such toxins and hydrogen peroxide addition should be

investigated in samples with high microcystin concentrations, or where samples can be

preconcentrated prior to analysis. It is likely if samples were collected from wastewater

ponds during a bloom breakdown in summer that high levels of microcystins would be

observed. Hence it is recommended that further analysis be conducted during warmer

months to determine the response of cyanobacterial toxins to hydrogen peroxide addition.

6.1.2 Extraction of intracellular toxins

To further investigate the effects of hydrogen peroxide addition on both cell death and

toxin destruction, an analysis of intracellular toxins should be performed. This will allow

for the determination of the rates of cell death, toxin degradation, and toxin release from

cells. The results of ELISA analysis on extracellular microcystins indicated that toxin

levels within the Burekup samples were too low to warrant extraction of intracellular

toxins. However, an analytical method for this step has been devised from a review of the

literature, and is proposed here.

The technique deemed most appropriate for extracting intracellular toxins from

cyanobacteria is freeze-drying the solid portion of a water sample followed by thawing

and extraction in an appropriate solvent (Nicholson and Burch 2001; An and Carmichael

1994; Harada et al. 1999; Fastner et al. 1998; Mathys and Surholt 2004; Carmichael and

An 1999; Lawton et al. 1994).

100 6. Recommendations

The method of intracellular toxin extraction ruptures the cell walls through the freezing

and thawing techniques, followed by dissolution of the toxins in a chosen solvent

(Nicholson and Burch 2001). Appropriate precautions should be taken when handling the

freeze-dried samples as they pose a health hazard (Harada et al. 1999). Within this project

it is proposed that dosed samples be filtered at each time step and the filtrate snap frozen

followed by freeze drying for two days.

Samples should then be extracted. Triple extraction with 70-80% methanol has shown

promising results (An and Carmichael 1994; Lawton et al. 1994; Mathys and Surholt

2004; Fastner et al. 1998; Carmichael and An 1999; Rapala et al. 2002; Nicholson and

Burch 2001), and some studies also combined methanol extraction with centrifuging (An

and Carmichael 1994), stirring or sonication (Mathys and Surholt 2004; Rapala et al.

2002; Carmichael and An 1999), and rotary evaporation (Lawton et al. 1994). However,

satisfactory results have been gathered by air drying supernatants overnight, without the

need for further treatment (An and Carmichael 1994; Carmichael and An 1999; Mathys

and Surholt 2004). Although methanol contents up to 75% do not appear to affect toxin

detection during HPLC (Metcalf et al. 2000a), methanol may interfere with ELISA at

concentrations above 20%, and thus it has been suggested that dissolution in methanol

should be avoided (Metcalf et al. 2000b). From consideration of past studies, it is

recommended that freeze dried samples be dissolved in 75% methanol and the

supernatants dried overnight at 25-30 degrees Celsius under air or nitrogen.

Dried toxins should be completely free of methanol, and redissolved in water prior to

analysis. Some difficulty has been encountered in the past regarding the solubility of the

dried compounds in pure water. The addition of 0.9% sodium chloride solution appears to

assist in complete dissolution of toxins without interfering with ELISA detection (Mathys

and Surholt 2004), and is recommended by this project. Microcystin concentrations can

then be measured by the ELISA method, as outlined in section 3.3.

6. Recommendations 101

6.2 Field trial

Following a thorough laboratory analysis of the behaviour of both intracellular and

extracellular cyanobacterial toxins with hydrogen peroxide addition, a field trial should

be performed. It should be attempted to simulate water quality and common

meteorological conditions of South-West WWTPs. During hours of daylight, it would be

expected that cyanobacterial, chlorophyta and diatom decay rates would increase from

laboratory measurements, due to the significant increase in hydroxyl radical

concentration caused by UV light (Cornish et al. 2000; Lawton and Robertson 1999).

Another consideration is the shading of deeper cells by those above, as all cells receive

approximately equivalent fluorescent light doses within the laboratory environment.

Analysis of results from this trial will likely allow for a pilot study to be conducted by the

Water Corporation in South-West WWTPs. This should be conducted to ensure that the

observed decay of cyanobacterial biomass, total algal biomass and cyanobacterial toxins

within the laboratory and field can be scaled up to the WWTP scale.

6.3 Hydrogen peroxide dosage protocol

The pilot study will establish the behaviour of cyanobacterial and total algal biomass

within the WWTP environment. A protocol for the implementation of hydrogen peroxide

in WWTP ponds should then be designed. This method will outline appropriate doses of

hydrogen peroxide and the frequency of addition. Doses and frequency will likely depend

upon the cyanobacterial cell biomass and the likely weather patterns. It is suggested that

monthly monitoring of WWTP ponds be continued, and hydrogen peroxide added when

cyanobacterial biomass reaches some critical value outlined within the protocol, as

determined from the pilot study. Treatment of infected water prior to the formation of

bloom conditions will reduce the chance of high cyanobacterial biomass resulting before

the following measurement period. Early treatment will likely be economically

beneficial, as preventing large scale blooms will allow for less algicide use than

remediating the water once they occur.

102 7. Conclusion

7 Conclusion

The results of this study suggest it is highly likely the application of hydrogen peroxide at

a concentration of 0.148 gL-1 and above induces cell lysis in cyanobacteria, chlorophyta

and diatoms in wastewater. First order rate constants for cyanobacterial decay exhibit a

sigmoidal regression, indicating that it is likely that there is some hydrogen peroxide dose

below which cyanobacterial chlorophyll a decay is negligible, and a dose above which

the decay rate does not increase further. The greatest statistically significant

cyanobacterial chlorophyll a decay first order rate constant determined was 0.4438 hour-1,

corresponding to a half life of 1.5618 hours. Data collected on the degradation of

cyanobacterial toxins by hydrogen peroxide was insufficient to provide additional

information as to their behaviour after algicidal addition.

Depending on the results from a field trial, it may be possible to apply hydrogen peroxide

on the large scale to a WWTP. It is expected that further analysis of the results will

indicate an effective dose rate for WWTPs to reduce cyanobacterial cell and toxin

concentrations to within acceptable levels, within acceptable monetary and time limits.

This may allow for the addition of hydrogen peroxide to outdoor WWTPs throughout the

South-West region.

This treatment also has the potential to be used by water authorities throughout Australia

and other countries. Where cyanobacteria removal methods are already in place,

hydrogen peroxide may be seen as a more environmentally sensitive method of removal.

Hydrogen peroxide also has a possible application in under-developed areas where

drinking and bathing water sources suffer cyanobacterial blooms and are untreated prior

to human use.

8. References 103

8 References

Acero, J. L., Rodrigueza, E. and Meriluoto, J. 2005, 'Kinetics of reactions between chlorine and the cyanobacterial toxins microcystins', Water Research, vol. 39, no. 8, pp. 1628-1638.

Al-Layl, K. J., Poon, G. K. and Codd, G. A. 1988, 'Isolation and purification of peptide and alkaloid toxins from Anabaena flos-aquaeI using high-performance thin-layer chromatography', Journal of Microbiological Methods, vol. 7, pp. 251-258.

Al Momani, F., Smith, D. W. and Gamal El-Din, M. In press 2007, 'Degradation of cyanobacteria toxin by advanced oxidation processes', Journal of Hazardous Materials.

An, J. and Carmichael, W. M. 1994, 'Use of a colorimetric protein phosphatase inhibition assay and enzyme linked immunosorbent assay for the study of microcystins and nodularins', Toxicon, vol. 32, no. 12, pp. 1495-1507.

Antoniou, M. G., Armah, A. and Dionysiou, D. D. 2005, 'Cyanotoxins: New generation of water contaminants', Journal of Environmental Engineering, vol. 131, no. 9, p. 1239.

Atkins, P. W. 1998, Physical Chemistry, Sixth edn, Oxford University Press, Oxford.

Bandala, E. R., Martinez, D., Martinez, E. and Dionysiou, D. D. 2004, 'Degradation of microcystin-LR toxin by Fenton and Photo-Fenton processes', Toxicon, vol. 43, no. 7, pp. 829-832.

Barroin, G. and Feuillade, M. 1986, 'Hydrogen peroxide as a potential algicide for Oscillatoria rubescens D.C.' Water Research, vol. 20, no. 5, pp. 619-623.

Bateman, K. P., Thibault, P., Douglas, D. J. and White, R. L. 1995, 'Mass spectral analyses of microcystins from toxic cyanobacteria using on-line chromatographic and electrophoretic separations', Journal of Chromatography A, vol. 712, no. 1, pp. 253-268.

Beutler, M., Wiltshire, K. H., Meyer, B., Moldaenke, C., Luring, C., Meyerhofer, M., Hansen, U. P. and Dau, H. 2002, 'A fluorometric method for the differentiation of algal populations in vivo and in situ', Photosynthesis Research, vol. 72, no. 1, pp. 39-53.

Bhattacharya, R., Sugendram, K., Dangi, R. S. and Rao, P. V. 1997, 'Toxicity evaluation of freshwater cyanobacterium Microcystis aeruginosa PCC7806:II. Nephrotoxicity in rats.' Biomedical Environmental Science, vol. 10, pp. 93-101.

Bogialli, S., Bruno, M., Curini, R., Di Corcia, A., Lagana, A. and Mari, B. 2005, 'Simple assay for analyzing five microcystins and nodularin in fish muscle tissue: hot water extraction followed by liquid chromatography-tandem mass spectrometry', Journal of Agricultural and Food Chemistry, vol. 53, no. 17, pp. 6586-6592.

104 8. References

Boland, M. P., Smillie, Chen, D. Z. X. and Holmes, C. F. B. 1993, 'A unified bioscreen for the detection of diarrhetic shellfish toxins and microcystins in marine and freshwater environments', Toxicon, vol. 31, no. 11, pp. 1393-1405.

Bouaicha, N., Rivasseau, C., Hennion, M. C. and Sandra, P. 1996, 'Detection of cyanobacterial toxins (microcystins) in cell extracts by micellar electrokinetic chromatography', Journal of Chromatography B, vol. 685, no. 1, pp. 53-57.

Bourne, D. G., Blakeley, R. L., Riddles, P. and Jones, G. J. 2006, 'Biodegradation of the cyanobacterial toxin microcystin LR in natural water and biologically active slow sand filters', Water Research, vol. 40, no. 6, pp. 1294-1302.

Bourne, D. G., Jones, G. J., Blakeley, R. L., Jones, A., Negri, A. P. and Riddles, P. 1996, 'Enzymatic pathways for the bacterial degradation of the cyanobacterial cyclic peptide toxin Microcystin LR', Applied and Environmental Microbiology, vol. 62, no. 11, pp. 4086-4094.

Brooke, S., Newcombe, G., Nicholson, B. and Klass, G. 2006, 'Decrease in toxicity of microcystins LA and LR in drinking water by ozonation', Toxicon, vol. 48, no. 8, pp. 1054-1059.

Carbis, C. R., Rawlin, G. T., Grant, P., Mitchell, G. F., Anderson, J. W. and McCauley, I. 1997, 'A study of feral carp Cyprinus carpio L., exposed to Microcystis aeruginosa at Lake Mokoan, Australia, and possible implication on fish health', Journal of Fish Diseases, vol. 20, no. 2, pp. 81-91.

Carmichael, W. W. 1992, 'Cyanobacterial secondary metabolites- the cyanotoxins', Journal of Applied Bacteriology, vol. 72, pp. 445-459.

Carmichael, W. W. 1994, 'Toxins of cyanobacteria', Scientific American, vol. 270, no. 1, pp. 78-86.

Carmichael, W. W. 1997, 'The cyanotoxins', Advanced Botanical Research, vol. 27, pp. 211-256.

Carmichael, W. W. 2001, 'Health effects of toxin-producing cyanobacteria: the cyanoHAMs', Human Ecological Risk Assessment, vol. 7, no. 5, pp. 1393-1407.

Carmichael, W. W. and An, J. 1999, 'Using an Enzyme Linked Immunosorbent Assay (ELISA) and a Protein Phosphatase Inhibition Assay (PPIA) for the detection of Microcystins and Nodularins', Natural Toxins, vol. 7, no. 6, pp. 377-385.

Chorus, I. 2001, 'Cyanotoxin occurrence in freshwaters- a summary of survey results from different countries', in Cyanotoxins: Occurrence, Causes, Consequences, ed. I. Chorus, Springer.

Chorus, I. and Bartram, J. 1999, Toxic Cyanobacteria in Water. A Guide to Public Health Consequences, Monitoring and Management, World Health Organization, London.

Chow, C. W. K., Drikas, M., House, J., Burch, M. D. and Velzeboer, R. M. A. 1999, 'The impact of conventional water treatment processes on cells of the cyanobacterium Microcystis aeruginosa', Water Research, vol. 33, no. 15, pp. 3253-3262.

8. References 105

Christoffersen, K., Lyck, S. and Winding, A. 2002, 'Microbial activity and bacterial community structure during degradation of microcystins', Aquatic Microbial Ecology, vol. 27, no. 2, pp. 125-136.

Chu, F. S., Huang, X. and Wei, R. D. 1990, 'Enzyme-Linked Immunosorbent Assay for Microcystins in Blue-Green Algal Blooms', Journal of the Association of Analytical Chemists, vol. 73, no. 3, pp. 451-456.

Codd, G. A. 2000, 'Cyanobacterial toxins, the perception of water quality, and the prioritisation of eutrophication control', Ecological Engineering, vol. 16, no. 1, pp. 51-60.

Codd, G. A., Bell, S. G., Kaya, K., Ward, C. J., Beattie, K. A. and Metcalf, J. S. 1999a, 'Cyanobacterial toxins, exposure routes and human health', European Journal of Phycology, vol. 34, no. 4, pp. 405-415.

Codd, G. A., Metcalf, J. S. and Beattie, K. A. 1999b, 'Retention of Microcystis aeruginosa and microcystin by salad lettuce (Lactuca sativa) after spray irrigation with water containing cyanobacteria', Toxicon, vol. 37, no. 8, pp. 1181-1185.

Codd, G. A., Metcalf, J. S., Ward, C. J., Beattie, K. A., Bell, S. G., Kaya, K. and Poon, G. K. 2001, 'Analysis of cyanobacterial toxins by physicochemical and biochemical methods', Journal of AOAC International, vol. 84, no. 5, pp. 1626-1635.

Codd, G. A., Morrison, L. F. and Metcalf, J. S. 2005, 'Cyanobacterial toxins: risk management for health protection', Toxicology and Applied Pharmacology, vol. 203, no. 3, pp. 264-272.

Cornish, B. J. P. A., Lawton, L. A. and Robertson, P. K. J. 2000, 'Hydrogen peroxide enhanced photocatalytic oxidation of microcystin-LR using titanium dioxide', Applied Catalysis B: Environmental, vol. 25, no. 1, pp. 59-67.

Cousins, I. T., Bealing, D. J., James, H. A. and Sutton, A. 1996, 'Biodegradation of microcystin-LR by indigenous mixed bacterial populations', Water Research, vol. 30, no. 2, pp. 481-485.

de Figueiredo, D. R., Azeiteiro, U. M., Esteves, S. M., Goncalves, F. J. M. and Pereira, M. J. 2004, 'Microcystin producing blooms- a serious global public health issue', Ecotoxicology and Environmental Safety, vol. 59, no. 2, pp. 151-163.

Donati, C., Drikas, M., Hayes, R. and Newcombe, G. 1994, 'Microcystin-LR adsorption by powdered activated carbon', Water Research, vol. 28, no. 8, pp. 1735-1742.

Drabkova, M., Admiraal, W. and Marsalek, B. 2007, 'Combined exposure to hydrogen peroxide and light - Selective effects on cyanobacteria, green algae, and diatoms', Environmental Science and Technology, vol. 41, no. 1, pp. 309-314.

Drikas, M., Chow, C. W. K., House, J. and Burch, M. D. 2001, 'Toxic Cyanobacteria', American Water Works Association Journal, vol. 93, no. 2, pp. 100-111.

Engstrom-Ost, J., Lehtiniemi, M., Green, S., Kozlowsky-Suzuki, B. and Viitasalo, M. 2002, 'Does cyanobacterial toxin accumulate in mysid shrimps and fish via copepods?' Journal of Experimental Marine Biology and Ecology, vol. 276, no. 1-2, pp. 95-107.

106 8. References

Eriksson, J. E., Meriluoto, J. and Lindholm, T. 1989, 'Accumulation of a peptide toxin from the cyanobacterium Oscillatoria agardhii in the freshwater mussel Anadonta cygnea', Hydrobiologia, vol. 183, no. 3, pp. 211-216.

Falconer, I. R. 1993, 'Measurement of toxins from blue-green algae in water and foodstuffs', in Algal Toxins in Seafood and Drinking Water, ed. I. R. Falconer, Academic Press, London, pp. 165-175.

Falconer, I. R. 1999, 'An overview of problems caused by toxic blue-green algae (cyanobacteria) in drinking and recreational water', Environmental Toxicology, vol. 14, no. 1, pp. 5-12.

Fastner, J., Codd, G. A., Metcalf, J. S., Woitke, P., Wiedner, C. and Utkilen, H. 2002, 'An international intercomparison exercise for the determination of purified microcystin-LR and microcystins in cyanobacterial field material', Analytical and Bioanalytical Chemistry, vol. 374, no. 3, pp. 437-444.

Fastner, J., Flieger, I. and Neumann, U. 1998, 'Optimised extraction of microcystins from field samples- a comparison of different solvents and procedures', Water Research, vol. 32, no. 10, pp. 3177-3181.

Fawell, J., Hart, J., James, H. A. and Parr, W. 1993, 'Blue-green algae and their toxins: analysis, toxicity, treatment, and environmental control', Water Supply, vol. 11, no. 3-4, pp. 109-121.

Fitzgerald, D. J., Cunliffe, D. A. and Burch, M. D. 1999, 'Development of health alerts for cyanobacteria and related toxins in drinking water in South Australia', Environmental Toxicology, vol. 14, no. 1, pp. 203-209.

Fleming, L. E., Rivero, C., Burns, J., Williams, C., Bean, J. A., Shea, K. A. and Stinn, J. 2002, 'Blue green algal (cyanobacterial) toxins, surface drinking water, and liver cancer in Florida', Harmful Algae, vol. 1, pp. 157-168.

Gilroy, D. J., Kauffman, K. W., Hall, R. A., Huang, X. and Chu, F. S. 2000, 'Assessing Potential Health Risks from Microcystin Toxins in Blue–Green Algae Dietary Supplements', Environmental Health Perspectives, vol. 108, no. 5, p. 435.

Glaze, W., Kang, J. and Chapin, D. 1987, 'The chemistry of water treatment processes involving ozone, hydrogen peroxide and ultraviolet radiation', Ozone Science and Engineering, vol. 9, no. 4, pp. 335-352.

Gregor, J. and Marsalek, B. 2004, 'Freshwater phytoplankton quantification by chlorophyll alpha: a comparative study of in vitro, in vivo and in situ methods', Water Research, vol. 38, no. 3, pp. 517-522.

Gregor, J., Marsalek, B. and Sipkova, H. 2007, 'Detection and estimation of potentially toxic cyanobacteria in raw water at the drinking water treatment plant by in vivo fluorescence method', Water Research, vol. 41, no. 1, pp. 228-234.

Griffin, S. L., Herzfeld, M. and Hamilton, D. P. 2001, 'Modelling the impact of zooplankton grazing on phytoplankton biomass during a dinoflagellate bloom in the Swan River Estuary, Western Australia', Ecological Engineering, vol. 16, no. 3, pp. 373-394.

8. References 107

Grutzmacher, G., Bottcher, G., Chorus, I. and Bartel, H. 2002, 'Removal of microcystins by slow sand filtration', Environmental Toxicology, vol. 17, no. 4, pp. 386-394.

Hallegraeff, G. M. 1992, 'Harmful algal blooms in the Australian region', Marine Pollution Bulletin, vol. 25, no. 5, pp. 186-190.

Harada, K.-I. 1996, 'Chemistry and detection of microcystins', in Toxic Microcystis, eds M. F. Watanabe, K.-I. Harada, W. W. Carmichael and H. Fujiki, CRC, Boca Raton, pp. 103-148.

Harada, K.-I., Kondo, F. and Lawton, L. A. 1999, 'Laboratory analysis of cyanotoxins', in Toxic Cyanobacteria in Water, eds I. Chorus and J. Bartram, World Health Organization, London, pp. 369-405.

Heresztyn, T. and Nicholson, B. C. 1997, 'Nodularin concentrations in Lakes Alexandrina and Albert, South Australia, during a bloom of the cyanobacterium (blue-green alga) Nodularia spumigena and degradation of the toxin', Environmental Toxicology and Water Quality, vol. 12, no. 4, pp. 273-282.

Heresztyn, T. and Nicholson, B. C. 2001, 'Determination of cyanobacterial hepatotoxins directly in water using a protein phosphatase inhibition assay', Water Research, vol. 35, no. 13, pp. 3049-3056.

Hiripi, L., Nagy, L., Kalmar, T., Kovacs, A. and Voros, L. 1998, 'Insect (Locusta migratoria migratorioides) test monitoring the toxicity of cyanobacteria', Neurotoxicology, vol. 19, no. 4-5, pp. 605-608.

Hitzfeld, B. C., Hoger, S. J. and Dietrich, D. R. 2000, 'Cyanobacterial toxins: Removal during drinking water treatment, and human risk assessment', Environmental Health Perspectives, vol. 108, pp. 113-122.

Ho, L., Meyn, T., Keegan, A., Hoefel, D., Brookes, J., Sainta, C. P. and Newcombe, G. 2006, 'Bacterial degradation of microcystin toxins within a biologically active sand filter', Water Research, vol. 40, no. 4, pp. 768-774.

Hrudey, S. E., Burch, M. D., Drikas, M. and Gregory, R. 1999, 'Remedial measures', in Toxic cyanobacteria in water: a guide to their public health consequences, monitoring, and management, eds I. Chorus and J. Bartram, World Health Organization, New York, pp. 275-312.

Hyenstrand, P., Metcalf, J. S., Beattie, K. A. and Codd, G. A. 2001, 'Losses of the cyanobacterial toxin microcystin-LR from aqueous solution by adsorpotion during laboratory manipulations', Toxicon, vol. 39, no. 4, pp. 589-594.

Ikawa, M., Phillips, N., Haney, J. F. and Sasner, J. J. 1999, 'Interference by plastic additives in the HPLC determination of microcystin-LR and -YR', Toxicon, vol. 37, pp. 923-929.

Jia, R. B., Zhang, X. H., Zhang, W. H., Zhang, G. M. and Wang, Z. S. 2003, 'Fluctuation of Microcystins in water plant', Journal of Environmental Science and Health, Part A, vol. 38, no. 12, pp. 2867 - 2875.

Jochimsen, E. M., Carmichael, W. W., An, J., Cardo, D. M., Cookson, S. T., Holmes, C. E. M., Antunes, M. B. d. C., Filho, D. A. d. M., Lyra, T. M., Barreto, V. S. T.,

108 8. References

Azevedo, S. M. F. O. and Jarvis, W. R. 1998, 'Liver failure and death after exposure to microcystins at a haemodialysis center in Brazil', New England Journal of Medicine, vol. 338, no. 13, pp. 873-878.

John, W., Raynor, M. W. and Rae, B. 1997, 'Micellar electrokinetic capillary chromatography of algal toxins', Journal of High Resolution Chromatography, vol. 20, no. 1, pp. 34-38.

Jones, G. J. and Orr, P. T. 1994, 'Release and degradation of microcystin following algicide treatment of a Microcystis aeruginosa bloom in a recreational lake, as determined by HPLC and protein phosphatase inhibition assay', Water Research, vol. 28, no. 4, pp. 871-876.

Karner, D. A., Standridge, J. H., Harrington, G. W. and Barnum, R. P. 2001, 'Microcystin algal toxins in source and finished drinking water', American Water Works Association Journal, vol. 93, no. 8, pp. 72-79.

Kaya, K. and Sano, T. 1999, 'Total microcystin determination using erythro-2-methyl-3-(methoxy-d(3))-4-phenylbutyric acid (MMPB-d(3)) as the internal standard', Analytica Chimica Acta, vol. 386, no. 1-2, pp. 107-112.

Kemp, A. and John, J. 2006, 'Microcystins associated with Microcystis dominated blooms in the Southwest Wetlands, Western Australia', Environmental Toxicology, vol. 21, no. 2, pp. 125-130.

Kondo, F., Ikai, Y., Oka, H., Ishikawa, N., Watanabe, M. F., Watanabe, M., Harada, K.-I. and Suzuki, M. 1992, 'Separation and identification of microcystins in cyanobacteria by frit-fast atom bombardment liquid chromatography/mass spectrometry', Toxicon, vol. 30, no. 3, pp. 227-237.

Kull, T. J., Sjovall, O., T , Tammenkoski, M. K., Backlund, P. H. and Meroluoto, J. A. O. 2006, 'Oxidation of the cyanobacterial hepatotoxin Microcystin-LR by chlorine dioxide: Influence of natural organic matter', Environmental Science and Technology, vol. 40, pp. 1504-1510.

Kyselkova, I. and Marsalek, B. 2000, 'Using of Daphnia pulex, Artemia salina and Tubifex tubifex for cyanobacterial microcystins toxicity detection', Biologia, vol. 55, no. 6, pp. 637-643.

Lahti, K., Rapala, J., Fardig, M., Niemela, M. and Sivonen, K. 1997, 'Persistence of cyanobacterial hepatotoxin, microcystin-LR in particulate material and dissolved in lake water ', Water Research, vol. 31, no. 5, pp. 1005-1012.

Lam, A. K. Y., Prepas, E. E., Spink, D. and Hrudey, S. E. 1995, 'Chemical control of hepatotoxic phytoplankton blooms: implications for human health', Water Research, vol. 29, no. 8, pp. 1845-1854.

Lambert, T. W., Holmes, C. F. B. and Hrudey, S. E. 1996, 'Adsorption of microcystin-LR by activated carbon and removal in full scale water treatment', Water Research, vol. 30, no. 6, pp. 1411-1422.

8. References 109

Lawton, L. A., Cornish, B. J. P. A. and MacDonald, A. W. R. 1998, 'Removal of cyanobacterial toxins (microcystins) and cyanobacterial cells from drinking water using domestic water filters', Water Research, vol. 32, no. 3, pp. 633-638.

Lawton, L. A., Edwards, C., Beattie, K. A., Pleasance, S., Dear, G. J. and Codd, G. A. 1995, 'Isolation and characterization of microcystins from laboratory cultures and environmental samples of Microcystis aeruginosa and from an associated animal toxicosis', Natural Toxins, vol. 3, pp. 50-57.

Lawton, L. A., Edwards, C. and Codd, G. A. 1994, 'Extraction and high-performance liquid chromatographic method for the determination of microcystins in raw and treated waters', Analyst, vol. 119, no. 7, pp. 1525-1530.

Lawton, L. A. and Robertson, P. K. J. 1999, 'Physico-chemical treatment methods for the removal of microcystins (cyanobacterial hepatotoxins) from potable waters', Chemical Society Review, vol. 28, no. 4, pp. 217-224.

Leboulanger, C., Dorigo, U., Jacquet, S., Le Berre, B., Paolini, G. and Humbert, J. F. 2002, 'Application of a submersible spectrofluorometer for rapid monitoring of freshwater cyanobacterial blooms: a case study', Aquatic Microbial Ecology, vol. 30, no. 1, pp. 83-89.

Lupinkova, L. and Komenda, J. 2004, 'Oxidative modification of the Photosystem II D1 protein by reactive oxygen species: From isolated protein to cyanobacterial cells', Photochemistry and Photobiology, vol. 79, no. 2, pp. 152-162.

MacKintosh, C., Beattie, K. A., Klumpp, S., Cohen, P. and Codd, G. A. 1990, 'Cyanobacterial microcystin-LR is a potent and specific inhibitor of protein phosphatases 1 and 2A from both mammals and higher plants', FEBS Letters, vol. 264, pp. 187-192.

Mathys, W. and Surholt, B. 2004, 'Analysis of microcystins in freshwater samples using high performance liquid chromatography and an enzyme-linked immunosorbent assay', International Journal of Hygiene and Environmental Health, vol. 207, no. 6, pp. 601-605.

McElhiney, J. and Lawton, L. A. 2005, 'Detection of the cyanobacterial hepatotoxins microcystins', Toxicology and Applied Pharmacology, vol. 203, no. 3, pp. 219-230.

McElhiney, J., Lawton, L. A. and Leifert, C. 2001, 'Investigations into the inhibitory effects of microcystins on plant growth, and toxicity of plant tissues following exposure', Toxicon, vol. 39, no. 9, pp. 1411-1420.

Meriluoto, J., Kincaid, B., Smyth, M. R. and Wasberg, M. 1998, 'Electrochemical detection of microcystins, cyanobacterial peptide hepatotoxins, following high performance liquid chromatography', Journal of Chromatography A, vol. 810, no. 1-2, pp. 226-230.

Metcalf, J. S., Bell, S. G. and Codd, G. A. 2000a, 'Production of novel polyclonal antibodies against the cyanobacterial toxin microcystin-LR and their application for the detection and quantification of microcystins and nodularin', Water Research, vol. 34, no. 10, pp. 2761-2769.

110 8. References

Metcalf, J. S., Bell, S. G. and Codd, G. A. 2001, 'Colorimetric Immuno-Protein Phosphatase Inhibition Assay for specific detection of Microcystins and Nodularins of cyanobacteria', Applied and Environmental Microbiology, vol. 67, no. 2, pp. 904-909.

Metcalf, J. S., Hyenstrand, P., Beattie, K. A. and Codd, G. A. 2000b, 'Effects of physicochemical variables and cyanobacterial extracts on the immunoassay of microcystin-LR by two ELISA kits', Journal of Applied Microbiology, vol. 89, no. 3, pp. 532-538.

Mohamed, Z. A. 2001, 'Alum and lime-alum removal of toxic and nontoxic phytoplankton from the Nile River water: laboratory study', Water Resources Management, vol. 15, no. 4, pp. 213-221.

Mohamed, Z. A., Carmichael, W. W., An, J. and El-Sharouny, H. M. 1999, 'Activated carbon removal efficiency of microcystins in an aqueous cell extract of Microcystis aeruginosa and Oscillatoria tenuis strains isolated from Egyptian freshwaters', Environmental Toxicology, vol. 14, no. 1, pp. 197-201.

Mountfort, D. O., Holland, P. and Sprosen, J. 2005, 'Method for detecting classes of microcystins by combination of protein phosphatase inhibition assay and ELISA: comparison with LC-MS', Toxicon, vol. 45, no. 2, pp. 199-206.

Murray-Gulde, C. L., Heatley, J. E., Schwartzman, A. L. and Rodgers, J. H. 2002, 'Algicidal Effectiveness of Clearigate, Cutrine-Plus, and Copper Sulfate and Margins of Safety Associated with Their Use', Archives of Environmental Contamination and Toxicology, vol. 43, no. 1, pp. 19-27.

Nakano, K., Lee, T. J. and Matsumura, M. 2001, 'In situ algal bloom control by the integration of ultrasonic radiation and jet circulation to flushing', Environmental Science and Technology, vol. 35, no. 24, pp. 4941-4946.

Namikoshi, M., Rinehart, K. L., Sakai, R., Stotts, R. R., Dahlem, A. M., Beasley, V. R., Carmichael, W. W. and Evans, W. R. 1992, 'Identification of 12 hepatotoxins from a Homer Lake bloom of the cyanobacteria Microcystis aeruginosa, Microcystis viridis, and Microcystis wesenbergii. Nine new microcystins', Journal of Organic Chemistry, vol. 57, no. 3, pp. 866-872.

Neumann, U. and Weckesser, J. 1998, 'Elimination of microcystin peptide toxins from water by reverse osmosis', Environmental Toxicology and Water Quality, vol. 13, no. 2, pp. 143-148.

Nicholson, B. C. and Burch, M. D. 2001, Evaluation of analytical methods for detection and quantification of cyanotoxins in relation to Australian drinking water guidelines, National Health and Medical Research Council of Australia.

Nicholson, B. C., Rositano, J. and Burch, M. D. 1994, 'Destruction of cyanobacterial peptide hepatotoxins by chlorine and chloramine', Water Research, vol. 28, no. 6, pp. 1297-1303.

Nishiwaki-Matsushima, R., Ohta, T., Nishiwaki, S., Suganuma, M., Kiyomi Kohyama, K., Ishikawa, T., Carmichael, W. M. and Fujiki, H. 1992, 'Liver tumor promotion

8. References 111

by the cyanobacterial cyclic peptide toxin microcystin-LR', Journal of Cancer Research and Clinical Oncology, vol. 118, no. 6, pp. 420-424.

Ojanpera, I., Pelander, A., Vuori, E., Himberg, K., Waris, M. and Niinivaara, K. 1995, 'Detection of cyanobacterial hepatotoxins by TLC', Journal of Planar Chromatography, vol. 8, no. 1, pp. 69-72.

Onyewuenyi, N. and Hawkins, P. 1996, 'Separation of toxic peptides (microcystins) in capillary electrophoresis, with the aid of organic mobile phase modifiers', Journal of Chromatography A, vol. 749, no. 1-2, pp. 271-278.

Orr, P. T., Jones, G. J., Hunter, R. A. and Berger, K. 2003, 'Exposure of beef cattle to sub-clinical doses of Microcystis aeruginosa: toxin bioaccumulation, physiological effects and human health risk assessment', Toxicon, vol. 41, no. 5, p. 613.

Park, H.-D., Sasaki, Y., Maruyama, T., Yanagisawa, E., Hiraishi, A. and Kato, K. 2001, 'Degradation of the cyanobacterial hepatotoxin Microcystin by a new bacterium isolated from a hypertrophic lake', Environmental Toxicology, vol. 16, no. 4, pp. 337-343.

Pelander, A., Ojanpera, I., Sivonen, K., Himberg, K., Waris, M., Niinivaara, K. and Vuori, E. 1996, 'Screening for cyanobacterial toxins in bloom and strain samples by thin layer chromatography', Water Research, vol. 30, no. 6, pp. 1464-1470.

Pelander, A., Ojanpera, I. and Vuori, E. 1998, 'Analysis of cyanobacterial hepatotoxins by overpressured layer chromatography', Journal of Planar Chromatography, vol. 11, no. 5, pp. 365-369.

Pennifold, M. and Davies, J. 2001, 'Macrofauna and nutrient cycling in the Swan River Estuary, Western Australia: experimental results', Hydrological Processes, vol. 15, no. 13, pp. 2537-2553.

Perez, S. and Aga, D. S. 2005, 'Recent advances in the sample preparation, liquid chromatography tandem mass spectrometric analysis and environmental fate of microcystins in water', Trends in Analytical Chemistry, vol. 24, no. 7, pp. 658-670.

Pilotto, L. S., Douglas, R. M., Burch, M. D., Cameron, S., Beers, M., Rouch, G. J., Robinson, P., Kirk, M., Cowie, C. T., Hardiman, S., Moore, C. and Attewell, R. G. 1997, 'Health effects of exposure to cyanobacteria (blue-green algae) during recreational water-related activities', Australian and New Zealand Journal of Public Health, vol. 21, no. 6, pp. 562-566.

Poon, G. K., Priestley, I. M., Hunt, S. M., Fawell, J. K. and Codd, G. A. 1987, 'Purification procedure for peptide toxins from the cyanobacterium Microcystis aeruginosa involving high-performance liquid chromatography', Journal of Chromatography, vol. 387, pp. 551-555.

Rapala, J., Berg, K. A., Lyra, C., Niemi, R. M., Manz, W., Suomalainen, S., Paulin, L. and Lahti, K. 2005, 'Paucibacter toxinivorans gen. nov., sp. nov., a bacterium that degrades cyclic cyanobacterial hepatotoxins microcystins and nodularin',

112 8. References

International Journal of Systematic and Evolutionary Microbiology, vol. 55, no. 4, pp. 1563-1568.

Rapala, J., Erkomaa, K., Kukkonen, J., Sivonen, K. and Lahti, K. 2002, 'Detection of microcystins with protein phosphatase inhibition assay, high-performance liquid chromatography–UV detection and enzyme-linked immunosorbent assay: Comparison of methods', Analytica Chimica Acta, vol. 466, no. 2, pp. 213-231.

Ressom, R., Soong, F. S., Fitzgerald, J., Turczynowicz, L., El Saadi, O., Roder, D., Maynard, T. and Falconer, I. R. 1994, Health effects of toxic cyanobacteria (blue-green algae), National Health and Medical Research Council of Australia, Commonwealth Department of Human Services and Health, Australian Government Publishing Service, Canberra.

Rinehart, K. L., Namikoshi, M. and Choi, B. W. 1994, 'Structure and biosynthesis of toxins from blue-green algae (cyanobacteria)', Journal of Applied Phycology, vol. 6, no. 2, pp. 159-176.

Robson, B. J. and Hamilton, D. P. 2004, 'Three dimensional modelling of a Microcystis bloom event in the Swan River Estuary, Western Australia', Ecological Modelling, vol. 174, no. 1-2, pp. 203-222.

Rodrıguez, E., Majadoa, M. E., Meriluoto, J. and Aceroa, J. L. 2007, 'Oxidation of microcystins by permanganate: Reaction kinetics and implications for water treatment', Water Research, vol. 41, no. 1, pp. 102-110.

Rositano, J. and Nicholson, B. C. 1994, 'A method for the detection of cyanobacterial peptide toxins by HPLC', in Detection Methods for Cyanobacterial Toxins, eds G. A. Codd, T. M. Jeffries, C. W. Keevil and E. Potter, Royal Society of Chemistry, Cabridge, pp. 155-157.

Rositano, J., Nicholson, B. C. and Picronne, P. 1998, 'Destruction of cyanobacterial toxins by ozone', Ozone Science and Engineering, vol. 20, no. 3, pp. 223-238.

Samuilov, V. D., Bezryadnov, D. B., Gusev, M. V., Kitashov, A. V. and Fedorenko, T. A. 2001, 'Hydrogen peroxide inhibits photosynthetic electron transport in cells of cyanobacteria', Biochemistry-Moscow, vol. 66, no. 6, pp. 640-645.

Sano, T., Nohara, K., Shiraishi, F. and Kaya, K. 1992, 'A method for micro-determination of total microcystin content in water blooms of cyanobacteria (blue-green algae)', International Journal of Environmental Analytical Chemistry, vol. 49, pp. 163-170.

Siren, H., Jussila, M., Liu, H. W., Peltoniemi, S., Sivonen, K. and Riekkola, M. L. 1999, 'Separation, purity testing and identification of cyanobacterial hepatotoxins with capillary electrophoresis and electrospray mass spectrometry', Journal of Chromatography A, vol. 839, no. 1-2, pp. 203-215.

Sivonen, K. and Jones, G. J. 1999, 'Cyanobacterial Toxins', in Toxic Cyanobacteria in Water: A guide to their public health consequences, monitoring and management, eds I. Chorus and J. Bartram, World Health Organization, London, p. 400.

8. References 113

Svrcek, C. and Smith, D. W. 2004, 'Cyanobacteria toxins and the current state of knowledge on water treatment options: a review', Journal of Environmental Engineering and Science, vol. 3, no. 3, pp. 155-185.

Tanaka, Y., Takenaka, S., Matsuo, H., Kitamori, S. and Tokiwa, H. 1993, 'Levels of microcystins in Japanese lakes', Toxicology and Environmental Chemistry, vol. 39, no. 1-2, pp. 21-27.

Teixeira, M. R. and Rosa, M. J. 2006a, 'Integration of dissolved gas flotation and nanofiltration for M. aeruginosa and associated microcystins removal', Water Research, vol. 40, no. 19, pp. 3612-3620.

Teixeira, M. R. and Rosa, M. J. 2006b, 'Neurotoxic and hepatotoxic cyanotoxins removal by nanofiltration', Water Research, vol. 40, no. 15, pp. 2837-2846.

Torokne, A. K., Laszlo, E., Chorus, I., Sivonen, K. and Barbosa, F. A. R. 2000, 'Cyanobacterial toxins detected by Thamnotoxkit (A double blind experiment)', Environmental Toxicology, vol. 15, no. 5, pp. 549-553.

Tsuji, K., Naito, S., Kondo, F., Ishikawa, N., Watanabe, M. F., Suzuki, M. and Harada, K.-i. 1994, 'Stability of Microcystins from cyanobacteria: Effect of light on decomposition and isomerisation', Environmental Science and Technology, vol. 28, no. 1, pp. 173-177.

Tsuji, K., Watanuki, T., Kondo, F., Watanabe, M. F., Nakazawa, H., Suzuki, M., Uchida, H. and Harada, K.-i. 1997, 'Stability of microcystins from cyanobacteria- IV. Effect of chlorination on decomposition', Toxicon, vol. 35, no. 7, pp. 1033-1041.

Ueno, Y., Nagata, S., Tsutsumi, T., Hasegawa, A., Watanabe, M. F., Park, H.-D., Chen, G.-C., Chen, G. and Yu, S.-Z. 1996, 'Detection of microcystins, a blue-green algal hepatotoxin, in drinking water sampled in Haimen and Fusui, endemic areas of primary liver cancer in China, by highly sensitive immunoassay', Carcinogenesis, vol. 17, no. 6, pp. 1317-1321.

van den Hoek, C., Mann, D. G. and Jahns, H. M. 1995, Algae: An Introduction to Phycology, Cambridge University Press, Cambridge.

Vasconcelos, V. M. 1995, 'Uptake and depuration of the heptapeptide toxin microcystin-LR in Mytilus galloprovincialis', Aquatic Toxicology, vol. 32, no. 2-3, pp. 227-237.

Ward, C. J., Beattie, K. A., Lee, E. Y. C. and Codd, G. A. 1997, 'Colorimetric protein phosphatase inhibition assay of laboratory strains and natural blooms of cyanobacteria: comparisons with high-performance liquid chromatographic analysis for microcystins', FEMS Microbiology Letters, vol. 153, pp. 465-473.

Water Corporation 2006, Perth's Wastewater System: Protecting our Health and our Environment, Water Corporation, Perth.

Welker, M. and Steinberg, C. 2000, 'Rates of humic substance desensitized degradation of microcystin-LR in natural waters', Environmental Science and Technology, vol. 34, pp. 3415-3419.

114 8. References

Welker, M., Steinberg, C. and Jones, G. J. 2001, 'Release and persistance of Microcystins in natural waters', in Cyanotoxins: Occurrence, Causes, Consequences, ed. I. Chorus, Springer.

Wiegand, C. and Pflugmacher, S. 2005, 'Ecotoxicological effects of selected cyanobacterial secondary metabolites a short review', Toxicology and Applied Pharmacology, vol. 203, no. 3, pp. 201-218.

World Health Organization 1993, 'Chemical Aspects', in Guidelines for Drinking-Water Quality, Second edn, World Health Organization, Geneva.

World Health Organization 2003, 'Algae and Cyanobacteria in Fresh Water', in Guidelines for Safe Recreational Water Environments: Volume 1: Coastal and freshwaters, World Health Organization, Geneva.

Yousef, N., Pistorius, E. K. and Michel, K. P. 2003, 'Comparative analysis of idiA and isiA transcription under iron starvation and oxidative stress in Synechococcus elongatus PCC 7942 wild-type and selected mutants', Archives of Microbiology, vol. 180, no. 6, pp. 471-483.

Appendix A

bbe-Moldaenke Fluoroprobe Data


Time since addition (h)

Chlorophyta (μgL-1)

Standard Error

Cyanobacteria (μgL-1)

Standard Error

Diatoms (μgL-1)

Standard Error

Cryptophyta (μgL-1)

Standard Error

Yellow Substances (μgL-1)

Standard Error

Total algal (μgL-1)

Standard Error

0.000 57.582 0.092 17.890 0.110 16.671 0.141 6.922 0.215 5.372 0.028 99.065 0.091 1.033 63.871 0.586 18.890 0.131 20.192 0.119 0.000 0.000 6.298 0.027 102.013 0.824 2.050 52.993 0.849 23.161 0.880 20.477 1.534 4.396 2.365 6.975 0.197 101.026 1.037 3.000 73.977 0.848 21.589 0.199 23.027 0.258 0.369 0.169 5.789 0.024 118.962 1.195 4.033 74.166 0.788 21.050 0.243 22.962 0.367 2.773 0.261 5.048 0.018 120.950 1.164 5.033 73.292 0.819 20.829 0.132 22.578 0.255 3.754 0.550 4.300 0.032 120.452 1.334 5.983 75.358 0.571 22.101 0.242 24.813 0.361 3.222 0.251 4.353 0.015 125.495 0.930

17.950 58.059 0.230 16.589 0.094 12.175 0.195 8.025 0.293 5.672 0.024 94.847 0.252 23.983 63.252 0.173 15.891 0.047 12.873 0.187 1.400 0.164 5.334 0.020 93.420 0.113 48.000 89.532 0.258 20.033 0.169 14.401 0.337 7.561 0.513 6.014 0.056 131.528 0.244




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 57.382 0.332 18.541 0.087 20.604 0.069 2.785 0.072 4.973 0.017 99.310 0.405 1.000 62.039 0.705 17.402 0.134 19.668 0.178 0.000 0.000 5.915 0.024 99.108 1.011 2.017 59.144 1.139 15.112 0.176 18.694 0.275 0.000 0.000 4.796 0.020 92.949 1.586 2.983 68.611 0.948 17.933 0.214 19.311 0.439 2.375 0.311 5.109 0.037 108.233 1.358 4.000 47.471 0.710 12.668 0.118 13.660 0.265 0.202 0.110 3.715 0.026 74.002 1.014 5.000 65.391 0.792 18.172 0.117 20.931 0.217 0.000 0.000 3.823 0.017 104.496 1.114 5.950 65.071 0.440 18.857 0.106 19.507 0.180 0.270 0.110 3.685 0.018 103.704 0.629 17.933 38.891 0.097 9.950 0.045 6.208 0.078 0.887 0.109 4.309 0.009 55.937 0.093 23.967 74.298 0.289 19.843 0.060 17.313 0.200 0.149 0.100 6.517 0.014 111.599 0.238 47.967 97.559 0.367 20.585 0.042 14.326 0.127 7.882 0.146 6.392 0.036 140.348 0.268




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 56.797 0.343 18.223 0.085 21.660 0.093 2.810 0.089 4.521 0.031 99.490 0.421 0.983 54.385 0.650 15.930 0.105 16.789 0.157 1.815 0.270 5.663 0.026 88.916 0.939 2.000 78.157 0.967 22.510 0.138 23.530 0.298 2.555 0.568 6.113 0.047 126.752 1.626 2.967 77.332 1.429 19.690 0.304 23.899 0.524 1.902 0.251 5.256 0.022 122.825 2.046 4.000 62.988 1.101 16.683 0.211 20.150 0.419 0.613 0.235 3.950 0.035 100.437 1.552 4.967 85.991 1.108 24.591 0.171 28.788 0.333 0.025 0.025 4.119 0.029 139.394 1.593 5.917 84.112 0.985 24.898 0.212 26.927 0.322 2.559 0.289 4.271 0.051 138.497 1.464 17.933 64.598 0.105 17.192 0.091 13.253 0.177 2.415 0.246 5.596 0.028 97.459 0.111 23.967 67.481 0.458 15.438 0.023 11.414 0.175 0.000 0.000 5.920 0.015 94.333 0.371 47.950 123.790 0.881 25.405 0.082 18.920 0.190 4.721 0.206 6.638 0.039 172.833 0.993

Table 30: Fluoroprobe data for chlorophyll a concentration with 2.96 x 10-3 gL-1 hydrogen peroxide addition, repetition 1



Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 53.424 2.147 26.317 0.780 17.447 0.412 1.381 1.104 8.598 0.230 98.570 2.231 0.950 54.478 2.353 23.149 0.668 15.794 0.612 0.723 0.534 8.900 0.108 94.142 3.164 1.917 83.775 3.177 34.525 0.925 18.810 0.420 0.000 0.000 11.908 0.217 137.108 4.005 2.933 42.252 0.468 16.723 0.251 8.272 0.428 8.542 0.830 7.988 0.075 75.786 1.454 3.967 39.563 0.687 13.684 0.570 6.992 0.828 10.555 0.999 7.282 0.255 70.795 1.285 4.900 42.549 1.021 13.155 0.570 4.439 0.950 14.977 0.987 7.948 0.186 75.122 1.763 5.900 47.392 0.512 20.422 0.515 12.946 0.576 1.754 0.572 7.368 0.072 82.517 0.620

17.867 71.542 0.879 20.670 1.285 10.994 1.802 15.292 2.562 8.520 0.209 118.501 1.471 43.850 57.557 0.778 14.060 0.596 7.949 0.875 6.458 1.270 8.938 0.115 86.025 1.221




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 61.131 1.846 29.800 0.704 21.761 0.582 0.336 0.336 9.195 0.426 113.027 2.225 0.950 67.031 3.056 26.302 1.076 11.942 0.341 0.238 0.238 10.990 0.508 105.514 3.743 1.917 54.145 1.096 22.608 0.471 13.061 0.378 6.502 1.792 9.145 0.128 96.316 0.718 2.950 46.306 0.462 20.339 0.371 12.413 0.299 1.502 1.116 7.870 0.133 80.560 0.583 4.250 36.306 0.333 14.024 0.222 9.193 0.208 5.582 0.513 6.633 0.163 65.107 0.450 4.933 33.525 0.523 13.839 0.236 7.487 0.345 1.565 0.591 6.701 0.095 56.415 0.666 5.900 45.688 0.404 17.591 0.435 10.998 0.537 7.776 1.290 6.909 0.103 82.055 0.875

17.867 62.647 0.949 18.488 1.184 13.995 1.301 11.659 2.048 7.605 0.162 106.794 1.613 43.983 47.492 0.556 10.454 0.812 3.258 1.039 9.738 1.414 7.897 0.084 70.941 1.198




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 34.577 0.761 16.545 0.408 12.896 0.490 1.476 0.991 6.987 0.133 65.493 0.808 0.967 64.128 1.890 24.997 0.519 14.632 0.568 0.446 0.446 10.256 0.111 104.202 2.702 1.900 59.443 1.153 23.908 0.448 11.710 1.202 6.987 1.889 9.738 0.293 102.046 1.210 2.933 49.444 0.456 17.366 0.969 8.458 1.055 11.811 1.330 7.969 0.100 87.080 1.087 4.233 46.398 0.777 19.376 0.496 13.061 0.935 4.341 1.229 7.686 0.172 83.177 0.800 4.917 41.176 0.363 17.861 0.269 11.404 0.455 1.812 0.787 7.393 0.243 72.254 0.207 5.883 44.078 0.659 18.389 0.262 10.075 0.427 2.286 0.598 7.557 0.155 74.826 0.538

17.850 63.259 0.285 20.184 0.976 13.249 1.128 6.892 1.772 6.861 0.128 103.583 0.615 43.967 75.713 0.976 17.841 2.238 10.703 3.316 13.985 4.115 7.776 0.216 118.244 2.450




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 20.749 0.186 6.719 0.033 8.052 0.064 0.000 0.000 3.666 0.006 35.520 0.280 0.950 10.956 0.082 5.185 0.034 4.358 0.038 0.064 0.038 4.070 0.013 20.561 0.136 1.867 5.340 0.050 3.513 0.011 2.688 0.011 0.000 0.000 3.574 0.005 11.540 0.052 2.900 5.732 0.016 3.702 0.005 2.362 0.015 0.000 0.000 3.480 0.005 11.798 0.015 3.933 3.473 0.013 2.885 0.003 1.670 0.010 0.000 0.000 3.251 0.004 8.028 0.014 4.800 3.831 0.024 3.070 0.005 1.609 0.007 0.000 0.000 3.208 0.004 8.510 0.024 5.850 3.071 0.022 2.922 0.003 1.383 0.008 0.000 0.000 3.082 0.002 7.375 0.017 17.817 9.887 0.053 3.554 0.007 2.231 0.044 0.014 0.012 3.375 0.006 15.685 0.030 23.833 14.600 0.027 3.683 0.019 2.339 0.015 0.000 0.000 3.290 0.008 20.621 0.038 47.833 36.610 0.209 7.643 0.068 4.668 0.046 0.000 0.000 4.618 0.019 48.920 0.287




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error

Total algal (μgL-1) Error

0.000 19.124 0.151 6.383 0.024 7.741 0.044 0.000 0.000 3.628 0.006 33.247 0.213 0.933 10.677 0.082 4.874 0.012 4.036 0.021 0.000 0.000 4.087 0.008 19.588 0.107 1.867 5.514 0.036 3.669 0.007 2.420 0.023 0.000 0.000 3.592 0.007 11.603 0.049 2.883 6.215 0.039 3.594 0.009 2.242 0.014 0.000 0.000 3.559 0.005 12.051 0.042 3.900 2.888 0.013 2.841 0.004 1.407 0.007 0.000 0.000 3.240 0.004 7.137 0.010 4.783 5.457 0.039 3.479 0.008 1.844 0.011 0.000 0.000 3.314 0.006 10.776 0.052 5.833 3.540 0.011 2.900 0.003 1.442 0.011 0.000 0.000 3.133 0.004 7.882 0.011 17.800 5.662 0.027 2.608 0.006 1.343 0.020 0.000 0.000 3.100 0.005 9.613 0.013 23.817 17.892 0.019 3.740 0.020 1.940 0.009 0.000 0.000 3.590 0.011 23.570 0.021 47.817 24.321 0.113 5.134 0.038 3.287 0.007 0.000 0.000 3.894 0.010 32.743 0.152




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 14.307 0.099 5.185 0.015 5.774 0.032 0.000 0.000 3.433 0.004 25.265 0.143 0.933 8.730 0.080 4.295 0.013 3.488 0.027 0.000 0.000 3.836 0.007 16.512 0.115 1.883 4.471 0.026 3.303 0.007 2.271 0.011 0.000 0.000 3.352 0.007 10.045 0.038 2.883 3.370 0.017 2.744 0.007 1.703 0.014 0.000 0.000 3.241 0.005 7.818 0.020 3.900 2.373 0.020 2.557 0.004 1.241 0.009 0.000 0.000 2.994 0.003 6.171 0.014 4.817 2.554 0.016 2.507 0.004 1.280 0.008 0.000 0.000 2.975 0.004 6.339 0.016 5.833 2.168 0.015 2.394 0.005 1.120 0.004 0.000 0.000 2.875 0.003 5.683 0.018 17.800 11.901 0.042 5.251 0.024 4.947 0.032 0.028 0.020 3.098 0.009 22.128 0.061 23.800 9.446 0.023 2.959 0.012 2.270 0.010 0.000 0.000 2.873 0.004 14.673 0.037 47.817 20.696 0.074 4.733 0.034 2.449 0.020 0.000 0.000 3.413 0.010 27.878 0.119




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 16.831 0.139 6.304 0.032 6.905 0.049 0.396 0.092 3.651 0.011 30.435 0.187 1.017 9.787 0.045 5.285 0.007 4.082 0.022 0.000 0.000 4.041 0.011 19.153 0.058 1.933 7.060 0.045 4.114 0.018 3.173 0.023 0.000 0.000 3.691 0.009 14.347 0.071 2.950 4.158 0.044 3.027 0.011 1.898 0.008 0.000 0.000 3.244 0.007 9.087 0.060 3.983 6.406 0.055 3.056 0.020 2.622 0.028 0.000 0.000 3.296 0.005 12.086 0.102 4.867 8.020 0.068 3.384 0.020 2.994 0.017 0.000 0.000 3.037 0.005 14.396 0.100 5.900 8.258 0.028 2.974 0.010 3.233 0.011 0.000 0.000 2.877 0.005 14.464 0.043 17.883 0.000 0.000 1.422 0.004 0.000 0.000 0.000 0.000 2.154 0.002 1.422 0.004 23.900 0.000 0.000 1.101 0.006 0.000 0.000 0.147 0.009 1.958 0.003 1.246 0.008 47.883 0.000 0.000 0.986 0.004 0.000 0.000 0.111 0.004 1.252 0.004 1.097 0.004




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 18.720 0.080 6.500 0.009 7.413 0.019 0.000 0.000 3.758 0.005 32.632 0.099 1.000 9.288 0.040 5.130 0.015 4.378 0.046 0.106 0.045 3.986 0.013 18.907 0.057 1.917 5.942 0.051 3.813 0.017 3.001 0.018 0.000 0.000 3.826 0.008 12.755 0.058 2.933 5.222 0.053 3.400 0.015 2.346 0.024 0.000 0.000 3.572 0.006 10.968 0.090 3.967 7.023 0.078 3.698 0.027 2.913 0.018 0.000 0.000 3.467 0.010 13.635 0.117 4.850 6.989 0.036 3.105 0.011 2.774 0.020 0.000 0.000 3.297 0.007 12.869 0.057 5.883 8.738 0.026 3.042 0.009 3.227 0.012 0.000 0.000 3.288 0.008 15.007 0.031 17.867 0.806 0.013 1.780 0.003 0.987 0.008 0.000 0.000 2.586 0.003 3.574 0.009 23.883 0.000 0.000 1.318 0.005 0.000 0.000 0.004 0.004 2.287 0.002 1.322 0.004 47.867 0.000 0.000 1.169 0.006 0.000 0.000 0.054 0.011 1.461 0.002 1.224 0.007




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 17.175 0.070 5.997 0.009 6.794 0.026 0.000 0.000 3.561 0.007 29.966 0.095 0.967 12.667 0.060 5.933 0.012 5.399 0.022 0.000 0.000 4.157 0.011 23.998 0.060 1.883 8.028 0.061 4.338 0.019 3.671 0.019 0.000 0.000 3.698 0.006 16.035 0.062 2.900 6.758 0.074 3.762 0.021 2.780 0.019 0.000 0.000 3.351 0.005 13.300 0.108 3.950 6.729 0.064 3.642 0.008 2.623 0.024 0.000 0.000 3.144 0.011 12.992 0.079 4.817 7.689 0.043 3.319 0.014 3.043 0.018 0.000 0.000 3.098 0.006 14.054 0.069 5.867 8.193 0.025 2.963 0.006 3.355 0.010 0.000 0.000 2.903 0.006 14.510 0.035 17.817 0.000 0.000 1.378 0.005 0.000 0.000 0.041 0.007 2.241 0.002 1.418 0.004 23.850 0.000 0.000 1.104 0.005 0.000 0.000 0.118 0.008 2.008 0.002 1.220 0.005 47.833 0.000 0.000 1.145 0.006 0.000 0.000 0.108 0.007 1.230 0.002 1.252 0.006




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 63.148 0.316 18.334 0.111 17.094 0.177 8.861 0.258 5.162 0.012 107.436 0.488 1.000 76.419 0.290 20.142 0.032 16.614 0.132 0.118 0.082 7.414 0.044 113.292 0.349 2.017 59.263 0.054 14.580 0.052 13.960 0.038 0.061 0.048 5.041 0.021 87.862 0.083 2.967 64.301 0.287 13.025 0.090 14.950 0.134 2.666 0.215 4.646 0.024 94.943 0.281 4.000 65.052 0.123 11.319 0.033 16.369 0.066 2.700 0.093 3.680 0.028 95.436 0.117 4.967 47.003 0.060 6.393 0.052 11.413 0.108 1.393 0.159 2.470 0.031 66.203 0.048 5.933 53.031 0.144 6.320 0.065 12.981 0.100 1.478 0.137 1.562 0.023 73.809 0.101 17.950 8.117 0.061 1.488 0.017 1.901 0.057 1.401 0.047 2.660 0.007 12.907 0.021 23.967 0.266 0.022 1.621 0.016 0.695 0.044 0.602 0.064 2.497 0.007 3.184 0.022 47.967 0.000 0.000 1.161 0.013 0.063 0.023 0.211 0.040 1.296 0.006 1.431 0.009




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 60.742 0.469 18.551 0.186 18.954 0.235 4.018 0.155 4.779 0.020 102.263 0.768 0.983 85.771 0.283 22.384 0.046 18.596 0.079 0.086 0.044 8.052 0.039 126.839 0.315 2.000 48.026 0.054 11.490 0.038 10.371 0.022 0.000 0.000 4.717 0.013 69.887 0.092 3.000 45.397 0.054 9.007 0.029 9.997 0.055 0.787 0.099 3.867 0.021 65.191 0.049 3.983 54.717 0.100 9.668 0.053 13.638 0.104 1.046 0.145 3.561 0.019 79.074 0.078 4.950 54.865 0.064 7.476 0.040 13.156 0.095 2.207 0.126 2.726 0.018 77.706 0.071 5.933 43.741 0.125 5.313 0.050 10.998 0.165 0.426 0.137 1.873 0.022 60.478 0.071 17.950 4.333 0.034 1.645 0.017 1.591 0.041 0.488 0.047 2.467 0.003 8.056 0.015 23.950 0.001 0.001 1.938 0.012 0.871 0.013 0.181 0.027 2.574 0.004 2.995 0.007 47.967 0.000 0.000 1.204 0.007 0.001 0.001 0.231 0.009 1.346 0.003 1.433 0.007




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 46.941 0.726 20.813 0.555 16.255 0.951 5.731 1.700 5.728 0.177 89.738 0.722 0.950 68.110 0.209 17.218 0.019 14.149 0.085 0.000 0.000 6.834 0.017 99.479 0.277 1.983 50.382 0.108 11.861 0.051 11.211 0.046 0.015 0.015 4.620 0.021 73.467 0.125 2.967 40.966 0.050 8.225 0.030 8.879 0.033 0.117 0.047 3.679 0.023 58.185 0.041 3.950 47.384 0.078 7.307 0.040 11.076 0.059 2.177 0.103 3.187 0.009 67.945 0.081 4.917 36.701 0.063 5.067 0.041 8.409 0.069 0.548 0.104 2.611 0.011 50.722 0.064 5.867 23.335 0.062 3.081 0.007 5.345 0.047 0.000 0.000 2.112 0.006 31.762 0.025 17.917 2.990 0.015 1.148 0.011 1.356 0.019 0.722 0.031 2.301 0.008 6.219 0.017 23.933 0.000 0.000 1.670 0.015 0.424 0.024 0.504 0.035 2.473 0.005 2.597 0.008 47.933 0.000 0.000 1.127 0.010 0.023 0.011 0.096 0.022 1.161 0.004 1.246 0.003




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 62.535 0.255 19.869 0.124 19.349 0.166 5.513 0.295 5.032 0.012 107.265 0.484 0.933 128.184 0.153 18.894 0.062 26.145 0.113 3.121 0.199 6.001 0.039 176.346 0.110 1.967 93.644 0.106 12.982 0.118 19.323 0.216 1.006 0.334 3.648 0.020 126.957 0.158 2.967 52.700 0.046 5.328 0.037 9.054 0.064 0.417 0.110 2.148 0.014 67.497 0.043 3.933 45.233 0.028 3.702 0.034 7.415 0.044 0.694 0.065 1.422 0.009 57.045 0.020 4.900 43.619 0.086 2.920 0.026 7.744 0.070 0.729 0.064 0.759 0.012 55.012 0.046 5.850 35.592 0.100 2.235 0.011 6.578 0.070 0.090 0.033 0.729 0.007 44.494 0.047

17.917 0.409 0.024 2.222 0.019 1.355 0.034 0.531 0.055 3.139 0.009 4.515 0.020 23.933 0.000 0.000 1.591 0.005 0.000 0.000 0.168 0.010 1.993 0.004 1.760 0.007 47.917 0.000 0.000 0.842 0.012 0.066 0.016 0.064 0.026 0.863 0.005 0.973 0.007




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 59.586 0.323 18.732 0.146 19.038 0.304 2.957 0.229 4.526 0.029 100.314 0.564 0.917 92.029 0.064 14.298 0.016 18.542 0.047 0.056 0.037 4.630 0.030 124.923 0.092 1.950 64.459 0.095 8.544 0.034 11.931 0.045 0.514 0.086 3.373 0.015 85.447 0.135 2.950 46.318 0.090 4.344 0.041 7.548 0.056 1.196 0.094 1.844 0.014 59.406 0.092 3.917 39.582 0.039 3.306 0.029 6.727 0.046 0.406 0.074 1.060 0.010 50.022 0.038 4.867 32.618 0.087 2.741 0.064 5.731 0.049 0.000 0.000 0.808 0.010 41.093 0.029 5.833 28.039 0.083 1.744 0.021 4.922 0.036 0.001 0.001 0.811 0.004 34.706 0.029

17.900 0.000 0.000 1.501 0.008 0.000 0.000 0.097 0.007 1.603 0.003 1.599 0.007 23.917 0.000 0.000 1.263 0.005 0.000 0.000 0.042 0.008 1.474 0.004 1.305 0.007




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 61.672 0.328 19.582 0.135 20.173 0.208 3.409 0.128 4.635 0.015 104.836 0.605 0.917 133.592 0.207 19.407 0.060 27.063 0.127 3.881 0.210 5.575 0.045 183.943 0.175 1.967 78.447 0.081 10.877 0.029 15.812 0.068 0.144 0.102 3.387 0.012 105.282 0.107 2.950 59.600 0.089 6.027 0.047 10.780 0.077 1.435 0.119 1.889 0.013 77.844 0.118 3.917 51.365 0.097 4.300 0.020 9.127 0.058 1.377 0.054 1.011 0.008 66.168 0.059 4.867 34.642 0.109 2.594 0.030 5.965 0.059 0.579 0.065 1.095 0.003 43.780 0.048 5.817 40.656 0.106 2.335 0.048 8.007 0.106 0.614 0.113 0.284 0.006 51.616 0.064

17.900 1.212 0.024 2.425 0.041 1.237 0.028 0.693 0.043 2.883 0.010 5.566 0.025 23.933 0.000 0.000 1.552 0.021 0.000 0.000 0.047 0.011 1.573 0.005 1.598 0.026 47.883 0.000 0.000 0.816 0.009 0.010 0.010 0.159 0.018 0.803 0.004 0.984 0.003

Table 45: Fluoroprobe data for chlorophyll a concentration with 1.48 gL-1 hydrogen peroxide addition, repetition 1



Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 18.870 0.112 6.768 0.014 7.641 0.021 0.000 0.000 4.083 0.006 33.278 0.127 1.117 29.161 0.033 5.857 0.011 8.496 0.025 0.000 0.000 4.743 0.011 43.515 0.043 2.033 28.380 0.030 5.264 0.027 7.967 0.055 0.174 0.074 4.150 0.012 41.785 0.032 3.050 17.448 0.040 3.387 0.013 4.747 0.030 0.000 0.000 3.691 0.008 25.580 0.023 4.083 12.206 0.055 2.731 0.011 3.594 0.041 0.000 0.000 3.361 0.009 18.533 0.017 5.000 14.163 0.027 2.330 0.005 3.647 0.014 0.000 0.000 3.003 0.004 20.140 0.033 6.000 9.450 0.030 2.149 0.008 2.704 0.016 0.000 0.000 3.017 0.005 14.304 0.034 18.000 0.000 0.000 2.695 0.027 0.590 0.007 0.000 0.000 2.286 0.005 3.286 0.032 24.000 0.000 0.000 1.487 0.021 0.000 0.000 0.000 0.000 1.866 0.002 1.487 0.021 48.000 0.000 0.000 1.710 0.063 0.145 0.012 0.000 0.000 0.886 0.008 1.856 0.074




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 17.850 0.152 6.452 0.031 7.415 0.048 0.000 0.000 3.789 0.003 31.718 0.229 1.083 22.472 0.069 4.669 0.011 6.585 0.027 0.000 0.000 4.189 0.012 33.726 0.060 2.017 21.744 0.025 4.257 0.011 6.173 0.020 0.000 0.000 3.570 0.006 32.171 0.037 3.020 21.889 0.067 3.327 0.007 5.830 0.068 0.000 0.000 2.798 0.009 31.045 0.023 4.067 14.575 0.043 2.173 0.011 3.662 0.011 0.000 0.000 2.657 0.009 20.408 0.030 4.967 11.039 0.032 1.951 0.009 2.697 0.012 0.000 0.000 2.459 0.008 15.688 0.028 5.967 8.074 0.016 1.787 0.030 2.154 0.008 0.000 0.000 2.402 0.006 12.015 0.015 17.967 0.000 0.000 1.212 0.011 0.000 0.000 0.003 0.002 1.612 0.001 1.216 0.009 23.967 0.000 0.000 1.192 0.006 0.003 0.003 0.074 0.010 1.270 0.003 1.266 0.005 47.967 0.000 0.000 0.950 0.014 0.082 0.013 0.031 0.021 0.672 0.003 1.062 0.007




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 17.829 0.089 6.727 0.030 7.277 0.049 0.247 0.053 3.909 0.008 32.079 0.122 1.033 46.091 0.100 7.764 0.042 12.756 0.062 0.416 0.092 4.514 0.014 67.026 0.105 1.967 32.222 0.108 5.471 0.025 8.747 0.035 0.027 0.027 3.725 0.011 46.466 0.072 2.983 25.693 0.093 3.845 0.017 6.794 0.037 0.006 0.006 2.907 0.009 36.337 0.053 4.000 23.239 0.070 2.850 0.018 5.459 0.016 0.000 0.000 2.531 0.008 31.543 0.066 4.917 18.841 0.048 2.253 0.003 4.463 0.015 0.000 0.000 2.355 0.007 25.558 0.041 5.950 17.350 0.037 2.057 0.014 4.090 0.010 0.000 0.000 2.038 0.006 23.495 0.022 17.917 0.761 0.022 1.972 0.037 0.770 0.017 0.000 0.000 2.193 0.005 3.502 0.039 23.933 0.000 0.000 1.501 0.043 0.019 0.006 0.023 0.012 1.627 0.005 1.543 0.039 47.917 0.000 0.000 1.009 0.033 0.027 0.014 0.129 0.023 0.685 0.005 1.165 0.027




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 45.159 1.034 25.670 0.755 18.626 1.105 6.375 1.662 11.087 0.179 95.828 1.524 0.950 43.805 0.311 12.540 0.331 12.539 0.513 3.596 0.749 9.169 0.066 72.483 0.437 2.083 59.596 0.570 12.572 0.303 16.549 0.285 4.292 0.603 6.095 0.103 93.009 0.752 3.000 58.342 0.655 8.291 0.282 14.146 0.412 4.963 0.534 3.925 0.085 85.744 0.842 3.933 32.794 0.354 4.618 0.188 7.969 0.215 1.381 0.406 3.449 0.053 46.761 0.419 4.950 34.044 0.435 3.466 0.084 8.019 0.101 1.689 0.187 2.592 0.058 47.217 0.460 5.867 33.642 0.313 2.767 0.173 8.020 0.153 1.069 0.344 1.903 0.052 45.498 0.348 17.950 0.000 0.000 1.710 0.042 0.000 0.000 0.370 0.043 1.432 0.012 2.080 0.066 23.917 0.005 0.005 1.243 0.043 0.035 0.020 0.302 0.039 0.715 0.009 1.582 0.061 44.917 0.006 0.004 1.212 0.032 0.202 0.013 0.000 0.000 0.331 0.006 1.419 0.042




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 46.088 1.427 27.309 0.647 22.281 1.060 11.197 1.669 11.113 0.264 106.873 2.710 0.950 41.635 0.297 12.629 0.267 13.711 0.463 1.351 0.651 9.287 0.078 69.325 0.375 2.083 49.654 0.468 10.348 0.472 13.273 0.699 5.755 1.107 6.807 0.082 79.029 0.492 3.000 41.557 0.515 6.472 0.230 9.116 0.267 4.994 0.430 4.864 0.026 62.139 0.695 3.933 37.276 0.640 4.558 0.249 8.919 0.382 2.980 0.574 3.433 0.036 53.730 0.803 4.950 34.501 0.520 3.773 0.128 9.025 0.193 0.905 0.321 2.763 0.070 48.206 0.561 5.850 30.596 0.259 3.045 0.056 7.461 0.101 0.041 0.028 2.176 0.058 41.143 0.301 17.950 0.000 0.000 2.296 0.062 0.000 0.000 0.187 0.015 1.381 0.006 2.480 0.076 23.917 0.000 0.000 1.175 0.020 0.000 0.000 0.239 0.011 0.867 0.003 1.415 0.022




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 33.761 1.653 21.054 0.300 16.028 0.676 0.993 0.355 10.382 0.120 71.835 1.480 0.933 55.864 0.667 14.690 0.457 17.341 0.623 5.137 1.140 9.868 0.094 93.031 0.843 2.050 61.327 0.663 12.387 0.367 17.584 0.517 7.142 0.919 6.910 0.138 98.439 0.759 2.983 47.582 0.519 6.892 0.268 10.524 0.441 6.309 0.650 5.214 0.101 71.303 0.717 3.983 38.942 0.446 5.036 0.111 9.948 0.159 2.528 0.316 3.720 0.057 56.456 0.536 4.933 26.038 0.255 3.662 0.073 6.262 0.151 0.760 0.237 3.700 0.039 36.722 0.275 5.833 31.963 0.408 3.066 0.148 7.908 0.118 0.607 0.281 2.653 0.067 43.546 0.443 17.933 0.000 0.000 2.226 0.056 0.000 0.000 0.055 0.014 1.288 0.007 2.283 0.066 23.900 0.009 0.004 2.669 0.083 0.044 0.006 0.000 0.000 0.828 0.007 2.722 0.086

Table 51: Fluoroprobe data for chlorophyll a concentration with 296 gL-1 hydrogen peroxide addition, repetition 1



Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 31.305 0.774 21.063 0.169 16.542 0.459 0.236 0.144 10.360 0.047 69.145 0.710 0.950 29.622 0.418 17.819 0.107 13.531 0.204 0.000 0.000 9.359 0.061 60.975 0.649 2.083 2.489 0.097 9.355 0.087 2.189 0.076 0.000 0.000 10.247 0.017 14.032 0.128 3.000 0.000 0.000 9.728 0.072 0.000 0.000 0.000 0.000 11.569 0.093 9.728 0.072 3.933 0.000 0.000 10.497 0.124 0.000 0.000 0.000 0.000 9.490 0.090 10.497 0.124 4.950 0.000 0.000 9.073 0.086 0.000 0.000 0.000 0.000 12.136 0.155 9.037 0.086 5.867 0.000 0.000 6.211 0.069 0.000 0.000 0.000 0.000 5.848 0.041 6.211 0.069 17.950 0.000 0.000 5.405 0.139 0.000 0.000 1.024 0.016 0.123 0.007 6.428 0.131 23.917 0.136 0.030 6.415 0.082 0.284 0.026 0.000 0.000 0.000 0.000 6.833 0.082




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 43.212 0.825 24.704 0.404 17.859 0.749 5.406 1.086 10.310 0.148 91.180 1.289 0.950 22.099 0.109 13.489 0.142 8.219 0.150 0.699 0.283 9.772 0.030 44.508 0.119 2.083 9.182 0.273 12.254 0.045 4.167 0.047 0.000 0.000 12.016 0.032 25.603 0.254 3.000 0.000 0.000 12.382 0.075 0.000 0.000 0.000 0.000 15.619 0.169 12.382 0.075 3.933 0.000 0.000 13.751 0.079 0.000 0.000 0.000 0.000 19.850 0.223 13.751 0.079 4.950 0.000 0.000 10.966 0.053 0.000 0.000 0.000 0.000 13.134 0.158 10.966 0.053 5.850 0.000 0.000 8.388 0.126 0.000 0.000 0.000 0.000 5.987 0.039 8.388 0.126 17.950 0.018 0.009 6.496 0.057 0.191 0.064 0.308 0.103 0.260 0.010 7.014 0.096 23.917 0.212 0.036 7.015 0.056 0.196 0.028 0.000 0.000 0.000 0.000 7.422 0.050




Standard Error


Standard Error

Diatoms (μgL-1)

Standard Error


Standard Error


Standard Error


Standard Error

0.000 40.246 1.109 20.396 0.345 11.178 0.745 3.607 1.037 10.620 0.115 75.426 1.355 0.933 21.084 0.342 15.248 0.110 10.276 0.197 0.000 0.000 9.040 0.083 46.609 0.560 2.050 2.138 0.153 9.833 0.076 1.884 0.045 0.000 0.000 10.512 0.027 13.858 0.137 2.983 0.000 0.000 9.698 0.066 0.000 0.000 0.000 0.000 10.869 0.064 9.698 0.066 3.983 0.000 0.000 10.848 0.158 0.000 0.000 0.000 0.000 12.966 0.131 10.848 0.158 4.933 0.000 0.000 7.348 0.052 0.000 0.000 0.000 0.000 9.438 0.109 7.348 0.052 5.833 0.000 0.000 9.153 0.040 0.000 0.000 0.000 0.000 6.541 0.064 9.153 0.040 17.933 0.000 0.000 6.575 0.063 0.301 0.013 0.000 0.000 0.384 0.009 6.877 0.064 23.900 0.344 0.012 6.434 0.082 0.034 0.011 0.000 0.000 0.000 0.000 6.813 0.079

Documents

Application of Hydrogen Peroxide for the Removal of ...uwa.edu.au/__data/assets/pdf_file/0010/1637497/Barrington_2007.pdf · Application of Hydrogen Peroxide for the Removal of Cyanobacteria