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THE USE OF AN ANIONICPOLYACRYLAMIDE BLEND TO CONTROLTHE CYANOBACTERIA MICROCYSTISAERUGINOSAKyla Jayne IwinskiNorthern Michigan University

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Recommended CitationIwinski, Kyla Jayne, "THE USE OF AN ANIONIC POLYACRYLAMIDE BLEND TO CONTROL THE CYANOBACTERIAMICROCYSTIS AERUGINOSA" (2013). All NMU Master's Theses. 415.https://commons.nmu.edu/theses/415

THE USE OF AN ANIONIC POLYACRYLAMIDE BLEND TO CONTROL THE

CYANOBACTERIA MICROCYSTIS AERUGINOSA

By

Kyla Jayne Iwinski

THESIS

Submitted

to

Northern Michigan

University

In partial fulfillment of the

requirements

For the degree

of

MASTER OF BIOLOGY

Office of Graduate Education and Research

2013

SIGNATURE APPROVAL FORM

Title of Thesis: The Use of Anionic Polyacrylamide Blend to Control the Cyanobacteria

Microcystis Aeruginosa

This thesis by Kyla J. Iwinski is recommended for approval by the student’s Thesis

Committee and Department Head in the Department of Biology and by the Assistant

Provost of Graduate Education and Research.

Committee Chair: Dr. Mac Strand Date

First Reader: Dr. Donna M. Becker Date

Second Reader : Dr. Erich Ottem Date

Department Head: Dr. John Rebers Date

Dr. Brian D. Cherry Date

Assistant Provost of Graduate Education and Research

i

ABSTRACT

THE USE OF AN ANIONIC POLYACRYLAMIDE BLEND TO CONTROL THE

CYANOBACTERIA MICROCYSTIS AERUGINOSA

By

Kyla Jayne Iwinski

Microcystis aeruginosa is a widespread, toxin producing cyanobacterium that

causes negative ecological, economical, and human health impacts. The use of

polyacrylamides (PAM) as an algal control is gaining notice and attention. Previous

work with PAM has shown anionic PAMs to be ineffective or less effective than cationic

PAMs at flocculating algal cell due to the opposing negative charges of the PAM and the

cell. In this study the 703d#3 Floc Log, an anionic PAM blend with a cationic bridging

agent, was used to treat Microcystis aeruginosa and the green algae Pseudokirchneriella

subcapitata. The treatment reduced cell number and chlorophyll a concentrations

significantly in M. aeruginosa and the non-target comparison species P. subcapitata. The

extent of the reductions varied depending on the initial cell density of the samples as well

as the day after sampling the measurements was taken. The treatment was more effective

at lower cell concentrations and the majority of reductions occurred immediately after

treatment. Anionic PAMs can be effective at flocculating cyanobacteria when a cationic

bridging material is present. It is a viable option for treatment and provides a safer

alternative to cationic PAMs which have a higher toxicity to fish.

ii

Copyright by

Kyla Jayne Iwinski

2013

iii

DEDICATION

My Thesis study is dedicated to my family. A special thanks to my parents, Joyce

and Steve Iwinski for their endless support and guidance. Also to my sisters Virginia and

Seva Iwinski, they are not only my sisters, but my colleagues and friends. I also want to

dedicate this to my Marquette family for keeping me grounded, happy and making the

many hours working at the kitchen table much more enjoyable.

iv

ACKNOWLEDGMENTS

First of all I would like to thank my adviser Dr. Mac Strand for his help,

guidance, and for support through the years. Without him I would not have had the

opportunity to begin this journey let alone finish it. I would also like to thank my

committee members Dr. Donna Becker and Dr. Erich Ottem for reviewing my thesis as

well as their expertise. Dr. Becker was integral in learning culturing techniques and

provided valuable knowledge on microorganisms. Without Dr. Ottem I would not have

been able to obtain the confocal images that added a new and exciting dimension to my

project.

I would like to thank Dr. John Rebers for teaching me direct cell counts and for

taking the time to review my thesis. I would also like to thank Dr. Jill Leonard for her

guidance and support throughout my entire graduate school experience. I want to

acknowledge the time that Jing F. Niu spent helping me with equipment and instruments,

working through laboratory procedures, and for always showing an interest in me and my

project. I would also like to thank Applied Polymer Systems, Inc. for supplying me with

materials as well as their time and information.

Finally I would like to thank my family. Without their love and emotional as well

as financial support this would not have been possible.

This thesis follows the format prescribed by the MLA Style Manual and The

Northern Michigan University Guide to the Preparation of Theses (2012).

v

TABLE OF CONTENTS

List of Tables (vii)

List of Figures (viii)

Introduction 1

Chapter One: Literature Review

Cyanobacteria 3

Microcystis aeruginosa 4

Cyanobacterial Control 5

Polyacrylamides (PAMs) 6

How They Work 7

Toxicity 9

Algae Treatment with Polyacrylamide 11

Experimental Approach

Target vs. Non-Target Species 12

Confocal Laser Scanning Microscopy 13

Chapter Two: Experimental and Analytical Methods

Experimental Methods

Algae Culture 15

Polyacrylamide Treatment 16

Analytical Methods

Direct Cell Counts and Chlorophyll a Analysis 17

Statistical Analysis 17

Pigment and Cell Imaging with Confocal Laser Scanning Microscopy 18

vi

Chapter Three: Results

Polyacrylamide Treatment 19

Density Effects 20

Day Effects 21

Among Trial Comparisons 23

Cell Counts and Chlorophyll a: Correlations 23

Cofocal Imaging of M. aeruginosa 24

Chapter 4: Discussion

Treatment Effects 25

Algal Cell Properties 25

Cell Surface Structure 26

Extracellular Material 28

Other Considerations

Between Species Variation 29

Confocal Imaging of M. aeruginosa 24

Conclusions and Future Studies 31

Works Cited 33

Appendices

A. Tables 38

B. Figures 48

C. Culture Media 65

vii

LIST OF TABLES

Table 1.1: Methods for Cyanobacterial Control 39

Table 1.2: 703d#3 Pond Log Acute and Chronic Toxicity Test 40

Table 3.1: Changes in Cell Counts and Chlorophyll a Levels, Day 0 to Day 13 41

Table 3.2: Comparison of Control and Treated Samples at Each Density 42

Table 3:3: Differences Among and Between Sampling Days 43

Table 3.4: Comparison of Experimental Replicates (trials) 43

Table 3.5: Percent Change in Cell Number by Day and Density Group 44

Table 3.6: Cell Counts and Chlorophyll a Values: M. aeruginosa 45

Table 3.7: Cell Counts and Chlorophyll a Values: P. subcapitata 46

Table 3.8: Correlations Matrix: M. aeruginosa vs. P. subcapitata 47

Table 3.9: Correlations Matrix: Chlorophyll a and Cell Counts 47

viii

LIST OF FIGURES

Figure 1.1: Lake Erie, NASA’s Aqua Satellite October 9, 2011 5

Figure 1.2: Flocculation vs. Coagulation 7

Figure 1.3: Anionic PAM Cation Bridging 8

Figure 2.1: Experimental Design 49

Figure 3.1: Relationship between Cell Counts and Chlorophyll a Readings 50

Figure 3.2: Relationships between Cell Counts and Chlorophyll a Readings 50

Figure 3.3: M. aeruginosa Control vs. Treatment Flasks (Photo) 51

Figure 3.4: Flocculated algal material in D1 and D3 (M. aeruginosa) 51

Figure 3.5: P. subcapitata Control vs. Treatment Flasks 52

Figure 3.6: Flocculated algal material in D1 and D3 (P. subcapitata) 52

Figure 3.7: M. aeruginosa Control vs. Treated, Cell Count Comparison 53

Figure 3.8: M. aeruginosa Control vs. Treated, Chlorophyll a Comparisons 54

Figure 3.9: P. subcapitata Control vs. Treated, Cell Count Comparison 55

Figure 3.10: P. subcapitata Control vs. Treated, Chlorophyll a Comparison 56

Figure 3.11: Average Percent Change by Day (All Densities) 57

Figure 3.12: M. aeruginosa Control, Percent Change by Day and Density (cell count) 58

Figure 3.13: M. aeruginosa Control, Percent Change by Day and Density (chl) 59

Figure 3.14: P. subcapitata Control vs. Treated, Percent Change (cell counts) 60

Figure 3.15: P. subcapitata Control vs. Treated, Percent Change (Chlorophyll a) 61

Figure 3.16: Recovery of Algae (4 weeks after end of Treatment Period) 62

Figure 3.17: Confocal Laser Scanning Image of Untreated M. aeruginosa (1) 63

ix

Figure 3.18: Confocal Laser Scanning Image of Untreated M. aeruginosa (2) 64

Figure 3.19: Confocal Laser Scanning Image of Untreated M. aeruginosa (2) 64

1

INTRODUCTION

Cyanobacteria, commonly referred to as blue green algae, are photosynthetic

bacteria found in nearly every ecosystem on the planet, from the arctic to deep sea

thermal vents. Cyanobacteria species are very diverse and may exist in a unicellular or

filamentous form and vary in size from less than a micrometer to over 60 micrometers.

As primary producers and nitrogen fixers, cyanobacteria are important contributors to

aquatic ecosystem energetics and biogeochemical cycling.

Cyanobacteria can negatively affect humans, phytoplankton, and other organisms

through toxin production and dense growth, or bloom formation. Cyanobacteria are

harmful to humans and other organisms in a number of ways. They tend to dominate

phytoplankton populations in eutrophic waters with the ability to out-compete other algal

species. Cyanobacterial blooms can shade out other primary producers causing a

reduction in habitat and nutrients and bloom die offs may lead to anoxic conditions and

fish kills as the algae die and decompose. Massive growths of these microorganisms

draw the attention of water authorities and utilities, environmental and health agencies,

and water user groups, because they present water treatment, supply, conservation, and

health problems (Codd 2000). Toxins produced by certain types of cyanobacteria can

sicken or kill competing aquatic organisms. Cyanotoxins can also be harmful to domestic

animals, livestock, and humans that ingest or come in contact with the toxins.

During the last few decades, cyanobacterial harmful algal blooms (CHABs) have

increased in frequency and distribution globally. A greater number of species are being

identified, new toxins are being uncovered, more fisheries resources have been affected,

and more money is being spent combating the problem through research, monitoring, and

2

treatment (Hoagland et al. 2002). Many cyanobacterial treatments are available and in

use today. Current treatments include mechanical methods, chemical methods, and

biological methods (Pan et al. 2011). Few safe, sustainable chemical solutions are

available and the use of polyacrylamides (PAMs) to control CHABs in the open

environment is gaining notice and consideration. This study will look at the use of a low

toxicity, anionic polyacrylamide blend and test its effectiveness at flocculating and

controlling the cyanobacteria Microcystis aeruginosa, a widespread freshwater species

that is capable of producing a potent hepatotoxin.

3

CHAPTER I

LITERATURE REVIEW

Cyanobacteria

Cyanobacteria are a widespread and diverse group of photosynthetic, gram

negative bacteria found in marine, brackish, and freshwater bodies globally.

Cyanobacteria and cyanobacterial blooms can cause many ecological, economic and

health issues. These include increased pH, the shading out of other algae and aquatic

organisms, the production of cyanotoxins, as well as oxygen depletion. Dying blooms

sink to the bottom, depleting oxygen levels through microbial respiration during

decomposition leading to anoxic or hypoxic conditions (Hudnell 2008). Oxygen

depletion also causes the uncoupling of phosphorus from iron oxides in sediment

resulting in resuspension of phosphorus in the water column and increased probability of

new CHABs (Hudnell 2008). These effects can result in the mortality of aquatic

organisms, decreased growth of submerged vegetation, decreased biodiversity, and

decreased ecosystem stability.

The four major groups of cyanotoxins are microcystins, cylindospermopsins,

anatoxins, and saxitoxins and are capable of causing hepatoxic, neurotoxic, or dermatoxic

effects (EPA 2013). Human health effects associated with exposure to high

concentrations of cyanobacterial toxins include stomach and intestinal illness, trouble

breathing, allergic responses, skin irritation, liver damage, and neurotoxic reactions, such

as tingling fingers and toes (CDC 2010). Effects of long-term exposure to low levels of

cyanobacterial toxins is currently being investigated and is believed to be associated with

illnesses such as liver cancer and digestive-system cancer (CDC 2010). Recent studies in

4

China have shown a positive correlation between dissolved microcystins in surface and

ground water and high incidence of cancer and tumor production (Tian et al. 2013).

Microcystis aeruginosa

This study focuses on the species Microcystis aeruginosa, a planktonic,

unicellular cyanobacterium that thrives in temperate climates and shallow, freshwater

lakes. Cells are spherical, 3-6 micrometers in diameter and irregularly arranged in

mucilaginous, spherical, or irregularly lobed colonies (Amos 2006). Although normally

colonial in nature, under laboratory culture conditions and they are often present as single

cells (Teixeira and Rosa 2006). M. aeruginosa has a relatively fast growth rate and

doubling time. A comparison of 25 species in a study done by the University of

Michigan in 2007 saw an average growth rate across all strains of 0.27 day-1 and an

average doubling time of 2.8 days (Wilson et al. 2006).

M. aeruginosa is particularly difficult to control as its already high resistance to

treatment is increased by the formation of dense colonies surrounded by a mucilaginous

layer (Drabkova 2007). It also is able to survive long term in sediments and control

vertical movement and buoyancy through gas vacuolate regulation, allowing it to

maintain a position of optimal light and nutrients in the water column (Drabkova 2007).

Many factors influence the formation of Microcystis blooms such as high nutrient

levels, warm temperatures, shallow water, and lake stratification. Microcystis blooms

have been increasing in number globally in small ponds and water bodies as well as

5

in massive lakes such as Lake Erie.

According to the US Environmental

Protection Agency, blooms of

Microcystis aeruginosa in Lake Erie

have been causing issues during the

summer months since the late 90’s with

increasing frequency in the 2000’s

(EPA 2013) (Figure 1.1).

Most strains of Microcystis are capable of producing the cyanotoxin microcystin

which is a cyclic hepatoxin that primarily affects the liver and is able to bioaccumulate

within common aquatic vertebrates and invertebrates such as fish, mussels, and

zooplankton (EPA 2013). Scums that dry on the shores of lakes may contain high

concentrations of microcystin for several months, allowing toxins to dissolve in the water

even when the cells are no longer alive (EPA 2013).

Cyanobacterial Control

Many methods of cyanobacterial control are in practice today. The use of

algaecides is one of the most commonly implemented control methods. Although they

can be effective, the majority of algaecides are non-selective and may cause cells to lyse

open releasing high concentrations of cyanotoxins into the water. This not only puts

humans at risk in recreational and drinking water but the high concentrations may also

increase the probability of cyanotoxin accumulation in the food web (Hudnell 2008).

Algaecides precipitate out of the water column quickly and may pose a threat to other

aquatic organism such as benthic invertebrates. Ruiqiang Liu and Barnett (2005) found

Figure 1.1: M. aeruginosa bloom in Lake Erie,

NASA’s Aqua satellite October 9, 2011

6

that over 90% of copper applied as an algaecide in catfish ponds bound to suspended

sediments that eventually settled and became associated with bottom sediments. Certain

copper based algaecides also select for copper resistant cyanobacteria, leading to the

development of copper resistant strains. Garcia-Villada et al. (2004) exposed M.

aeruginosa to lethal doses of copper sulfate and found that spontaneous mutation

occurred in some cells decreasing their sensitivity to copper.

Coagulation and flocculation of algal particles and excess nutrients are common

treatment methods carried out using a variety of chemicals and materials. Materials used

to induce coagulation and flocculation of algae and nutrients in fresh and salt water

include, but are not limited to, aluminum sulfate, calcium hydroxide (lime), cationic and

anionic polyacrylamides, clays, and modified clays. The pros and cons of these methods

as well as other commonly implemented algal controls are included in Table 1.1.

Polyacrylamides (PAMs)

Polyacrylamide (PAM) has been used in agricultural studies by the United States

Department of Agriculture for decades. Its soil stabilizing and flocculating properties

have substantially improved water quality by reducing sediments, nitrogen (N), ortho and

total phosphorous (P), chemical oxygen demand (COD), pesticides, weed seeds, and

microorganisms in runoff (Sojka 2001).

PAMs in their original state are hydrophilic while also being nonionic (Daughton

1988). PAMs nonionic character is often modified by chemical conversion to cationic

and anionic forms (Daughton 1988). Nonaqueous PAM dispersions are used as

flocculating agents and settling aids in water treatment facilities, e.g., potable and

industrial waters such as coal washery streams, and as mobility-control aids in secondary

7

Flocculation vs. Coagulation

Flocculation(bridging between particles)

Coagulation(no charge repulsion)

oil recovery. PAM used for erosion control and water treatment is a large molecule (12-

15 megagrams per mole), increasing the molecular weight of PAM increases the length of

the polymer chain and viscosity of the solution. Green et al. (2000) demonstrated that

high molecular weight PAMs were most effective for flocculation.

Mode of Action

Aggregation of colloidal particles can occur by reducing the stability of the

original suspension, by neutralizing electrical forces of repulsion, or by the addition of

chemicals (polymers) to link

particles by bridging action (Ives

1978). There are many types and

forms of polyacrylamides with the

key variations being molecular

weight, charge, charge density, and

water content. Depending on the

PAM, they work to precipitate

particulate either through coagulation, flocculation or both. Although similar, there are

key differences between the two (Figure 1.2). Coagulation is based on the reduction of

electrostatic charges of particulate, thereby reducing the repulsive force between them

(Moss and Dymond 1978). Flocculation on the other hand is caused by high molecular

weight materials forming a physical bridge between two or more particulate resulting in a

loose, random, three dimensional structure or “floc” (Moss and Dymond 1978). In many

cases, anionic and cationic charged PAMs will precipitate particulate through both

coagulation and flocculation.

Figure 1.2: Coagulation occurs when particles aggregate

through charge reduction, while flocculation occurs when a

physical or chemical bridge is formed between particles

8

Polyacrylamides have three different charge types that promote flocculation of

particles, cationic, anionic and nonionic (Mason et al. 2005). Polymers chemically bridge

reactive groups increasing floc size and the

degree of flocculation, floc size, and

settling time depends on the type of

polymer and the suspended solids in water.

Positively charged cationic polymers

directly attach to the negative surface of

clay and other particulate (Mason et al.

2005). Anionic polymers form a cationic

bridge between two or more particles (Figure 1.3). With a negatively charged polymer

and a negatively charged soil particle repulsion would take place, however divalent

cations, such as calcium, bridge the negative charge on the PAM and the negative charge

on the soil particle together producing a divalent cation bond (Green et al. 2000). By

using a large molecule anionic polyacrylamide and a cationic bridging agent, the

processes of coagulation and flocculation can both be employed, effectively reducing the

repulsive forces of the particulate which facilitates flocculation and settling (Moss and

Dymond 1978). Nonionic polymers attach to the clay surface producing flocculation

through van der Waals forces when the polymer and the target particles are brought close

enough together to overpower repulsive forces (Moss and Dymond 1978, Mason et al.

2005). In addition to chemical and ionic bridging, mechanical bridging of colloids and

other suspensions using polymers is also effective. Some long chain polymers are able to

Figure 1.3: Anionic PAM attaches to negative

particles through cationic bridging

9

bind multiple particles causing them to aggregate despite electrostatic and repulsive

forces due to the sheer size and tackiness of the molecule.

The main properties of flocculated substances include texture, organic matter

content, and types of ions on the particles or in the solution (Green et al. 2000). Charge

density is the percentage of electrostatic charge on the polymer chain. Commercially

available polymers generally have a charge density between 2 to 40% (Green et al. 2000).

Specific PAM formulations must be optimized for specific substances depending on the

PAMs charge and molecular weights, and the texture, organic matter, and charge of the

substance being targeted (Green et al. 2000). Therefore, different types of PAMs are

often tailored to different and specific water and soil chemistries to get the best treatment

results.

Toxicity

Polyacrylamides used in the United States in open environments and water

treatment facilities are regulated by the Food and Drug Administration. Concern is often

expressed regarding the acrylamide monomer of PAM because it is a potent neurotoxin

and carcinogen. Acrylamide is used in the synthesis of polyacrylamides and in approved

PAMs, the residual acrylamide monomer is present in very low concentrations and

legally must not exceed 0.05% (Moore et al 2008). In addition to approved PAMs

containing low amounts of acrylamide monomer, it has been demonstrated that the

acrylamide monomer is also easily metabolized by microorganisms and has a half life of

tens of hours (Moore et al. 2008). During degradation that proceeds at 10% per year, the

acrylamide monomer is not released and the acute toxicity of all polyelectrolytes is

10

reduced in the presence of solids, as the PAM binds to the suspended solids (Green et al.

2000).

Other toxicity variables of PAM include charge densities and chemistries. Many

types of polyacrylamides exist and toxicities have been found to range from less than 1

mg/l to as high as 4,000 mg/l depending on the PAM and test species. In clear water,

most cationic PAMs were found to be toxic to fish species at low doses as compared to

anionic and nonionic PAM which exhibited low to no toxicity at the same levels (Dow

Chemical Co. 1970, Biesinger et al. 1976, Biesinger and Stokes 1986). It is believed the

toxicity of cationic PAM to fish is the result of a decreased ability to obtain oxygen from

water (Dow Chemical Co. 1970). The cationic PAM is electrostatically attracted to the

negative charge on gill surface. The gill secretions are also flocculated by the PAM,

coating the oxygen absorbing membrane (Dow Chemical Co. 1970). This causes a

decrease in oxygen transfer resulting in death from suffocation (Moore et al. 2008).

Bioaccumulation does not occur in any of the PAM forms as the high molecular weight

of the polymer prevents it from passing through biological membranes (Moore et al.

2008).

The specific polyacrylamide blend being used in this study is the 703d#3 Floc

Log manufactured by Applied Polymer Systems, Inc. in Woodstock, Georgia. The

703d#3 log contains a blend of water treatment components and polyacrylamide co-

polymer for water clarification (APS 2013). It is classified as an ANSI/NSF Standard 60

Drinking Water Treatment Chemical Additive, in the same category as food grade and

drinking water treatment PAMs. Third party EPA certified 48 and 96 hour acute toxicity

11

tests and 7 day chronic toxicity tests have been conducted on the 703d#3 Floc Log (Table

1.2).

Algae Treatment with Polyacrylamide

It is widely acknowledged that algal removal is more difficult than inorganic

particles due to their low specific density, motility, and morphological characteristics

(Chen and Yeh 2005). However, algal flocculation and coagulation is proposed to have

the same mode of action as inorganic colloidal particles. Although larger than true

colloids (1 to 100nm), algae possess many similar surface properties (Tenney et al. 1969).

Particles and colloids, including algal cells, are negatively charged either as a result of

dissociation or ionization of surface functional iogenic groups, adsorption of ions

originating from organic matter, or lattice imperfections in inorganic particles

(Henderson et al. 2008). This results in electrostatic repulsion to occur between the

particles, causing a stable colloidal system. In water treatment systems, destabilization is

usually achieved by addition of cationic chemicals such as metal salts (Al3+

or Fe3+

) or

cationic polymers, which interact with the particle surface to induce neutralization effects

(Henderson et al. 2008). The actual mechanism is still disputed, but it has been

demonstrated that algal adsorption of metal cations appears to occur via ion-exchange

with the metal cations competing with protons for negatively charged binding sites on the

cell wall (Gardea-Torresdey et al. 1990).

Studies conducted on the use of polymers to flocculate algae have revealed that

while cationic polymers will flocculate algal cells, nonionic and anionic polymers were

not as effective or were ineffective (Tenney et al. 1969, McGarry 1970, Divakaran and

Pillai 2002, Granados et al. 2012). This was hypothesized to be due to charge; cationic

12

polymer attached directly to the negatively charged algal cells but the anionic polymers

did not have this same strength of attraction (Tenney et al. 1969). A study on the effect

of chitosan, a cationic polymer obtained from crustacean shells, revealed significant

reduction of algae and effective removal (Divakaran and Pillai 2002). Granados et al.

(2012) compared biosalts, chitosan, and polyelectrolytes (polyacrylamides) as algal

recovery agents and found that polyelectrolytes were the most effective. They

recommended the use of cationic PAMs because although more toxic, they were more

reactive than nonionic and anionic PAMs (Granados et al. 2012).

In comparing the results of algal flocculation studies with agricultural and erosion

control studies, an important point needs to be taken into account. It is a well known fact

that calcium and other cations play an essential role in anionic PAM/ sediment reactions

and has been extensively documented. Positively charged calcium ions in the soil, from

gypsum additives, chemical weathering, or from other sources serve as a bridging agent

between clays/ soils and the anionic PAM molecules (Wallace and Wallace 1996, Sojka

2001). In this study, the 703d#3 Floc Log contains cations that act as bridging agent.

The addition of the bridging agent may improve reaction between anionic

polyacrylamides and algal cell, contributing to more effective results compared to past

studies.

Experimental Approach

Target vs. Non-Target Species

Microcystis aeruginosa was chosen as the target species, it is widespread and a

common nuisance cyanobacterium that causes problems globally due to its dense blooms

and toxin production. In addition to treating the target M. aeruginosa, a non-target

13

species of green algae Pseudokirchneriella subcapitata (previously known as

Selenastrum capricornutum) was treated. The additional treatment of P. subcapitata is

for a variety of reasons. Algae are important and effective indicators of environmental

health and toxicity issues. Environmental stress effects are most easily and rapidly

manifested in unicellular organisms and therefore the effects of environmental

contaminants are generally assessed using microalgae as model organisms (Yarlagadda et

al. 2007). The measurement of in vivo chlorophyll a fluorescence of green algae has

been found to be one of the most sensitive tools for the rapid detection of toxicity and

environmental stressors (Fai et al. 2007). Pseudokirchneriella subcapitata was selected

for use because it is the algal species most commonly used in laboratory bioassays

(Graham et al. 2009). It is also one of the most sensitive species because its growth tends

to integrate and reflect most sub-lethal effects, making it a valuable species for toxicity

testing (Fai et al. 2007). P. subcapitata was also chosen for treatment and comparison

with M. aeruginosa because of their commonality in the environment, the fact that they

are non-mat forming, are normally presented in a single cell form, and are similar in size

(< 20μm).

Confocal Laser Scanning Microscopy

Confocal laser scanning microscopy (CLSM) is a relatively new technology that

is used in many different biological fields to obtain three-dimensional (3-D) images from

living organisms. This is achieved by illuminating the specimen with a focused scanning

laser beam while rejecting the florescent signals outside of the lasers focus plane

(Murphy and Davidson 2013). Small sections are imaged individually and then pieced

14

together to create a clear 3-D image. CLSM offers multiple advantages over

conventional wide field fluorescence microscopy, especially when dealing with living

cells. CLSM allows for increased resolution of the spatial organization of fluorescent

structures (Hernandez et al. 2004). CLSM is also valuable for viewing complex three

dimensional objects, such as details in thick tissue sections and spherical cells (Murphy

and Davidson 2013).

Using CLSM as a rapid toxicity and viability indicator in living cells is a novel

idea and currently being implemented and researched. It has been established that

chlorophyll and other pigment autofluorescence is a convenient method for

differentiating living and dead algal cells (Pouneva 1997). Confocal microscopy 3-D

images can be used to identify the location and type of pigment within the cell that is

being affected (Rowen 1989). Yarlagadda et al. (2007) used CLSM and chlorophyll

autofluorescence in the diatom Cocconeis scutellum to determine the effects of chlorine

concentrations. The study revealed a significant, dose dependent decrease in the MFI

(mean fluorescence intensity) of chlorophyll in confocal image analysis in diatoms

treated with chlorine (Yarlagadda et al. 2007). Using CLSM and measuring fluorescence

provided a rapid and effective measure of an environmental pollutant. It may be possible

to employ similar techniques to visualize the effects of polyacrylamide treatment on M.

aeruginosa.

15

CHAPTER II

EXPERIMENTAL AND ANALYTICAL METHODS

The experimental section of this study consists of three parts. The first is algal

culture; the second is a series of experiments treating algae with a polyacrylamide (PAM)

blend. The last part used confocal scanning laser microscopy (CLSM) to take

autofluorescent images of Microcystis aeruginosa. Analytical methods included direct

cell counts and measuring chlorophyll a levels.

Experimental Methods

Algae Culture

Pure cultures of Microcystis aeruginosa and Pseudokirchneriella subcapitata

(previously Selenastrum capricornutum) were obtained from the Canadian Phycological

Culture Centre (CPCC) at the University of Waterloo in Waterloo, Ontario, Canada. The

acquired strain of M. aeruginosa is a non toxic strain. Psuedokirchneriella subcapitata

was cultured in Bold’s Basal Medium (BBM) using the methodology of CPCC

(Appendix C) and M. aeruginosa was cultured in BG-11 (CPCC) (Appendix C).

P. subcapitata was kept on a Lab-Line Instruments 120-volt electric shaker 24 hours a

day at 115 rpm on a 12:12 light to dark ratio at ~20-30 uE/m2/s and at a temperature

between 18-26°C. M. aeruginosa was kept in a Percival Scientific incubator at 22.5°C on

a 12:12 light to dark cycle under florescent lighting at ~15-20 uE/m2/s. Due to M.

aeruginosa’s ability to control its buoyancy through the use of gas vacuoles within the

cell, agitation of the cells to keep them in suspension and mix nutrients was not

necessary. Cultures were inoculated into fresh media every 2-3 weeks, each time

16

increasing the flask size, media volume, and amount of culture inoculated. Once the

cultures had grown enough to be kept in 1,000 mL Erlenmeyer flasks they were

maintained and inoculated into fresh media (100 mL of culture into 400 mL fresh media),

every four weeks for an eight month period before PAM experiments began.

Polyacrylamide Treatment

Algal treatment with PAM was set up using different starting culture cell

densities. The same experiment was performed on Microcystis aeruginosa and

Pseudokirchneriella subcapitata. An initial high density culture was put into four 500mL

flasks, the first was not diluted (300mL of culture), the second was at a 1:1 dilution

(150mL media: 150mL of culture), the third was a 10:1(270mL culture: 30mL media),

and the last was 100:1 dilution (297mL media: 3mL culture) (Figure 2.1). Each dilution

had one control and on treated flask equaling a total of eight flasks for each species.

Initial cell counts and chlorophyll a reading were taken and then 0.116 grams (386ppm)

of 703d#3 Floc Log was measured on a Mettler Toledo AB104 scale and added to each

treatment flask. The chosen dosage was lower than the NOEC (no observed effect

concentration) for Ceriodaphnia dubia acute toxicity tests (MACTEC 2004).

Microcystis aeruginosa and P. subcapitata were placed on Corning 6794-220

Laboratory Stirrer and Hot Plates and set to medium/high (8-9) for 24 hours. After the 24

hour treatment period, cell counts and chlorophyll a readings were again taken. From

this point, cell counts were taken every other day and chlorophyll analysis was done

every fourth day for a total of 8 cell counts and 5 chlorophyll a readings. Sample flasks

17

were maintained under initial culture conditions. The entire experiment was performed

three times for each species.

Analytical Methods

Direct Cell Counts and Chlorophyll a Analysis

Cell counts were performed using a Hausser Hy-Lite, Improved Nebauer

hemocytometer using the methodology of Barker (1996). Samples were pulled from each

sample flask using a Nichipet EX10~100 microliter pipette and analyzed under an

Olympus CX31 light microscope and counted using a Clay Adams Laboratory Counter.

P. subcapitata required a period of settling once placed on the slide so that all cells would

settle into the same plane of view for accurate counting (~3-5 minutes). Direct count data

was analyzed by calculating the cells/ mL. Calculations were done using the Chang

Bioscience Online Hemacytometer Calculator (Chang Bioscience 2002). The online

calculator was checked for accuracy by performing the calculations by hand, and then

was used to complete all cell count calculations as an accurate and time saving method.

Chlorophyll a analysis was based on Standard Methods (2005). Cells were

concentrated in an Eppendorf Centrifuge 5810R and chlorophyll a readings were taken

using a Spectronic 20 Genesys spectrophotometer.

Statistical Analysis

The differences between cell counts and chlorophyll a values from before

treatment until the last day of sampling were analyzed by calculating the percent change

between day 0 and day 13 as well running a paired t-test comparing values from day 0

and day 13 of the three trials. The overall difference between control and treated groups

18

throughout the 13 day sampling period was analyzed using the Wilcoxon signed-rank

test. Freidman’s non-parametric test was used to analyze whether there were differences

across the sampling days and if so between which days. Comparisons between trials

were performed to test the uniformity of the experimental replicates. Analyses were

performed using the Kruskall-Wallis non-parametric test to look for similarities and

differences among the three trials for each species and variable (cell counts and

chlorophyll a). Correlation analyses were performed using Spearman’s non-parametric

test between cell counts and chlorophyll a values for control and treated groups of M.

aeruginosa and P. subcapitata, as well as between species to see if control groups and

treated groups followed the same trend.

Pigment and Cell Imaging with Confocal Laser Scanning Microscopy

Confocal images of autofluorescent pigments within Microcystis aeruginosa were

taken using an Olympus Confocal Laser Scanning Microscope (CLSM). These images

were not taken or analyzed for use as quantification of the effects of the polyacrylamide

blend treatment on the cells but rather as a visual supplement to the other viability and

health measurements. Images were taken from experiment samples of matching treated

and untreated flasks. Due to difficulty gathering images from single, free floating cells,

cells that had clumped and adhered together were targeted. Images were taken under a

60x objective using Alexafluor lasers with excitation at 546 and 405 with a three hour

scan time. Images were visually analyzed for changes in pigment fluorescence intensity

as well as pigment distribution.

19

CHAPTER III

RESULTS

Polyacrylamide Treatment

Polyacrylamide treatment was effective at reducing algal cells and chlorophyll a

in both M. aeruginosa and P. subcapitata. The extent of the reductions was varied and

dependent on starting cell density as well as the sampling day after treatment. From day

0 before treatment until day 13 on the final day of sampling, PAM treated M. aeruginosa

cell numbers decreased by an average of 34% and chlorophyll a by 58% (Table 3.1).

Treatments reduced cell numbers of the non-target species P. subcapitata by an average

23% however chlorophyll a increased by 16% overall (Table 3.1). Controls of M.

aeruginosa had an average growth of 76% in both cell number and chlorophyll a levels

and P. subcapitata control cell counts increased 335% and chlorophyll a 326% (Table

3.1).

Across the entire sampling period from day 0 to day 13, significant differences

were observed between controls and treatments for both M. aeruginosa and P.

subcapitata at all densities (Table 3.2). Density 1 (D1) had the highest initial cell

density, density 2 (D2) was a 1:1 dilution media to culture, density three (D3) a 10:1

dilution, and density 4 (D4) 100:1. Microcystis aeruginosa control and treated cell

counts differed at density 1 (V=296, p=<0.0001), density 2 (V=292, p=<0.0001), density

3 (V=299, p=<0.0001), and density 4 (V=297, p=<0.0001). Chlorophyll a values also

differed at density 1 (V=113, p=0.003), density 2 (V=119, p=0.001), density 3 (V=102,

p=0.002), and density 4 (V=104, p=0.001) (Table 3.2). The control and treatment also

20

differed in the non-target species P. subcapitata. Cell counts differed at density 1

(V=297, p=<0.0001), density 2 (V=294, p=<0.0001), density 3 (V=296, p=<0.0001), and

density 4 (V=275, p=<0.0001). Chlorophyll a values followed the same trend at density

1 (V=120, p=<0.001), density 2 (V=109, p=0.006), density 3 (V=116, p=0.002), and

density 4 (V=119, p=0.001) (Table 3.2). Means and standard deviations were listed

separately for control and treated cell count and chlorophyll a values in Table 3.2.

Density Effects

The effectiveness of PAM treatment was dependent on the initial density of the

cell culture with lower initial cell density having the greatest reductions. Cell counts

increased in M. aeruginosa controls across all densities from day 0 to day 13, the smallest

at density 1 (mean ± 1SD = 31.6% ± 10.2, p=0.048) and increasing from density 2 (65.1

± 21.3, p=0.013), density 3 (99.4% ± 30.9, p=0.021), to the highest growth at density 4

(104.6% ± 156, p=0.034) (Table 3.1, 3.6, Figure 3.3, 3.7). In the treated groups there was

a slight increase in cell number at density 1 (6.5% ± 18.5, p=0.607) but cell reductions at

density 2 (22.2% ± 47.4, p=0.476), density 3 (49.1% ± 5.1, p=0.021), and density 4

(71.5% ± 37.1, p=0.174). Chlorophyll a levels follow a similar trend with increases

across all controls, density 1 (18% ± 8.8, p=0.073), density 2 (48% ± 6.1, p=0.003),

density 3 (167.9% ± 77.7, p=0.068), and density 4 (71.4% ± 87.8, p=0.242). Chlorophyll

values decreased in all treated density groups and the reductions were significant at

density 3 (83% ± 24.7, p=0.047) and density 4 (100% ± 0, p=0.01) (Table 3.1, 3.6, Figure

3.3, 3.8).

21

Non-target species P. subcapitata also showed variation in growth and response

to treatment depending on initial culture density with the greatest reductions occurring at

lower densities. Cell counts increased in all controls from density 1 (52.5% ± 28.3,

p=0.014) density 2 (95.3% ± 53.1, p=0.004), density 3 (309% ± 199.7, p=0.049), and

greatest increase at density 4 (884% ± 545, p=0.106) (Table 3.1, 3.7, Figure 3.5, 3.9). In

P. subcapitata treatments cell numbers increased overall at density 1 (22.3% ± 7.4,

p=0.008) and density 2 (20.6% ± 29.5, p=0.473), but decreased at density 3 (79.4% ±

45.5, p=0.167) and density 4 (55.3% ± 51.7, p=0.205). Chlorophyll a controls also

increased at density 1 (101.2% ± 17.7, p=0.003), density 2 (150% ± 15.7, p=0.015),

density 3 (289% ± 10.9, p=0.041), and density 4 (766% ± 15.8, p=0.004). Increases also

occurred in most of the treatments including density 1 (46.2% ± 51.7, p=0.355), density 2

(20.6% ± 39.3, p=0.497), and density 4 (87.2% ± 100, p=0.763), but decreased at density

3 (93.8% ± 9.1, p=0.034) (Table 3.1, 3.7, Figure 3.5, 3.10).

Day Effects

The greatest reductions in cell counts and chlorophyll a values occurred

immediately following treatment in both M. aeruginosa and P. subcapitata. PAM

treatment effects varied depending on the sampling day (Table 3.3, 3.5, Figure 3.11).

Cell counts increased (8.6% ± 6.4 on average per day in M. aeruginosa controls and

23.4% ± 4.8 in P. subcapitata controls (Table 3.5). Chlorophyll a increased 16.4% ± 16.6

per day for M. aeruginosa and 42.8% ± 29.3 in P. subcapitata control groups (Table 3.5).

In M. aeruginosa treated samples, the greatest reduction in cell number occurred on day 1

immediately after treatment (23.7% ± 16.9), accounting for 57.7% of all reductions

22

during the sampling period (Figure 3.5, 3.6). Cell counts decreased 3.3% ± 5.5 on day 3,

10% ± 14.1 on day 3, 0.1% ± 6.5 on day 9, and 3.8% ± 12.3 on day 13. On days 7 and 11

there were small increases in cell number (0.2% ± 11.9 and 2.2% ± 12.8). Reduction in

chlorophyll a occurred on days 1 (22.1% ± 11.7), 5 (22.2% ± 32.8), and 9 (16.2% ±

45.4). On average there was no effect from day 9 to day 13 (Table 3.5, 3.6, Figure 3.11).

In treated samples P. subcapitata cell number decreased 62.5% ± 16.9 on day 1,

accounting for 74.6% of all cell reductions that occurred during the sampling period.

Cell number also decreased on day 3 (21.3% ± 30.4) (Table 3.5, 3.7, Figure 3.11). Cell

growth occurred on the remaining sampling days 5 (4.5% ± 12.5), 7 (80.7% ± 109.4), 9

(27.2% ± 23.2), 11 (29.2% ± 36.9), and 13 (147.7% ± 240.1). Chlorophyll a levels

decreased on days 1 (39.6% ± 22.6) and 5 (38.6% ± 64.5). Chlorophyll a levels

increased on remaining days 9 (11.9% ± 30.7) and 13 (22.4% ± 4.9) (Figure 3.5, 3.7,

Figure 3.11).

Cell counts and chlorophyll a concentrations for both species revealed significant

differences overall in sampling days for both cell counts (Table 3.3). Microcystis

aeruginosa cell counts differed overall in control (Q=182.5, p=<0.0001) and treated

samples (Q=31, p=<0.0001). However, pair wise differences among days varied between

the treatments and controls (Table 3.3). Chlorophyll a controls followed a similar trend

with a significant overall difference in control (Q=81.2, p=<0.0001) and treated days

(Q=47.5, p=<0.0001) as well as pair wise differences between specific days (Figure 3.3).

Pseudokirchneriella subcapitata days also differed overall in control (Q=204.4,

p=<0.0001) and treated (Q=123.4, p=<0.0001) cell counts and control

(Q=109.8,p=<0.0001) and treated (Q=28.7,p=<0.0001) chlorophyll a values (Figure 3.3).

23

Pair wise differences among certain days were also significant and varied in control and

treated samples (Figure 3.3).

Among Trial Comparisons

The experiment was replicated in three trials and the trials were analyzed for

similarities and differences. The majority of the M. aeruginosa trials (cell number and

chlorophyll a) were similar with the exception of density 1 control trials (K=30.1,

p=<0.0001) and the density 2 treated trials (K=30.9, p=<0.0001) (Table 3.3). In contrast,

two or more trials in all P. subcapitata control and treated densities differed, except in

chlorophyll a density 3 controls and treatment, and density 4 treatments. Initial cell

densities were more varied in the non-target species than in M. aeruginosa trials (Table

3.3).

Cell Counts and Chlorophyll a: Correlations

Microcystis aeruginosa and P. subcapitata cell count and chlorophyll a

concentrations were strongly correlated within each species and between species (Figure

3.1-3.2). M. aeruginosa treatment density groups 2 through 4 strongly correlated,

however density 1 cell counts were negatively correlated with all chlorophyll densities

and density 1 chlorophyll groups were weakly correlated with all cell count density

groups (Table 3.9). Pseudokirchneriella subcapitata treated samples show strong

positive correlations between all cell count and chlorophyll groups, with the exception of

chlorophyll density group 3 which have weak correlations with all cell count density

groups (Table 3.9). Strong positive correlations were also observed between M.

24

aeruginosa and P. subcapitata control groups (cell counts and chlorophyll a); however

correlations between treated samples of the two species varied (Table 3.9).

Confocal Images

Images of M. aeruginosa revealed two distinct pigments (Figure 3.17). Red cell

fluorescence occurred with excitation at 546 nm and blue fluorescence at 405. These

pigments were simultaneously present within the cells. Images present the pigments

separately as well together, in which case the cells looked purple in color (Figures 3.17-

3.19). Red pigmentation appeared to be uniform throughout the cells while blue coloring

was more disperse and concentrated. In comparing images from treated and untreated

samples, it appeared that untreated cells contained more red pigment and blue dominated

in the treated cells (Figures 3.18, 3.19). However without replication or quantification of

the pigment analysis this cannot be confirmed.

25

CHAPTER IV

DISCUSSION

Treatment Effects

Treatment with the 703d#3 Floc Log effectively reduced cell numbers and

chlorophyll a concentrations in both M. aeruginosa and the non-target species P.

subcapitata. Effects were strongest on day 1 immediately following treatment and were

also dependent on initial starting density of the cultures and varied by species. Treatment

with the PAM blend reduced M. aeruginosa cells 4.3-43.4% and P. subcapitata 18.2-

98.5% within the first 24 hours after treatment (Table 3.1, Figure 3.11). The large

variation in reductions was dependent on the initial density of the culture, with treatment

being more effective in lower density cultures. There was also variation between M.

aeruginosa and the non-target species P. subcapitata in not only the effects of the PAM

treatment, but also in the recovery of the algae in the days following. Cultures of P.

subcapitata experienced greater reductions after treatment but also recovered rapidly

after initial treatment as compared to M. aeruginosa. The recoveries could be due to

resuspension of flocculated cells as well as growth of remaining cells in suspension. Due

to the observed recoveries, field applications of this material will most likely need to be

repeated periodically to knock down new or re-suspended cells.

Algal Cell Properties

Physical characteristics of algal cells such as size, surface area, and density play a

key role in flocculation potential. Algal cells are smaller than 30µm with a density

similar to water, and a sedimentation velocity lower than 10-6

m/s (Granados et al. 2012).

26

Soil particles on the other hand are much denser, yielding a higher settling rate. M.

aeruginosa also contains gas vacuoles that allow the cell to regulate its buoyancy to an

optimum light level by rising or sinking based on photosynthetic need. This fluctuation

in buoyancy of the cells may also effect settling of flocculated algae. The addition of

mass and other particulate to aid in settling may increase the effectiveness of PAM

treatment and cell settling. Natural waters contain organic and inorganic particles, from

sediments and inorganic nutrients to decaying plant matter and algae. Treating the algal

cells in field waters would likely see higher settling rates as the PAM would attach to the

algae along with the other particulate, adding mass. Surface characteristics that affect the

accessibility of the particle such as size, thickness, and shape will also influence the

amount of PAM sorption (Lu et al. 2002). Anionic PAMs, unlike cationic and nonionic

PAMs, do not enter the interlayer space of expanding layer silicates due to charge

repulsion of the PAM molecules with the clay particles (Theng 1982). The sorption of

anionic PAM is limited to surface attachment on the accessible outer layer and is

facilitated by cationic bridging (Theng 1982). The binding nature of anionic PAM

therefore may vary depending on the surface structure and characteristics of M.

aeruginosa and P. subcapitata.

Cell Surface Structure

Variation in algal response to treatment with 703d#3as well as differences in

species may be explained by a number of factors involving the physical and chemical

properties of the cells and the cell surface. Cell surface structure is responsible for the

number of receptor sites available as well as how the cell will react to different

27

substances. The number of receptor sites and chemical and physical structure differs

greatly among algal genera. Kaulbach et al. (2005) observed proton and cationic metal

(Cd) absorption in P. subcapitata and bacterial cells, and showed that although the algae

had fewer receptor sites on the cell wall, the absorption ability was much higher, and both

species were able to bind and buffer chemicals and cationic metals in the water column.

The cell surface area and number of receptor sites is likely responsible for the density and

time dependent variations in the results. Lu et al. (2002) found that 85% of particulate

(soil and clay) reacted within 5 hours of treating with an anionic PAM, and the remainder

within 22 hours. The greatest reductions of algae in this study were also on day1 after

PAM treatment (Table 3.5, Figure 3.11). The optimum polymer/solid ratio has been

found to be directly proportional to the surface area of the solid; therefore, a decrease in

particle size means an increase in flocculent demand (Moss and Dymond 1978). Once

the available PAM has reacted and attached to the cell surface of the algae, the reaction is

complete. Increasing the number of cells will also increase the surface area and if it is

too large for the amount of polymer in the system, there will be un-reacted cells.

Divakaran and Pillai (2002) found in study with chitosan that reductions of algae required

higher dosages to remove greater initial turbidities. An increase in the anionic PAM

dosage may be necessary to treat higher densities of algal cells.

In addition to receptor sites, active regulation of processes at the cell surface can

affect reaction of the cell and PAM. Certain genera of cyanobacteria produce a 2-

dinemsional lattice type surface cell layer made up of proteins or glycoproteins known as

an S-layer, or “surface layer” (Smarda et al. 2002). The genus Microcystis contains the

greatest number of S-layer producing strains, including M. aeruginosa. Although not

28

fully understood, S-layers have a variety of functions including protective coatings,

molecular sieves and molecule and ion traps (Smarda et al. 2002). Some species of

cyanobacteria are able to mineralize, precipitate, and shed fine grain gypsum

(CaSO4*2H2O) and calcite (CaCo3) with the S-layer, which is hypothesized to reduce

local calcium levels (Smarda et al. 2002). If ion regulation is occurring at the cell

surface, it could decrease effectiveness during the cationic bridging process that aids in

flocculation of anionic molecules with anionic PAM. It is unclear whether this

mechanism is responsible for the variation between M. aeruginosa and P. subcapitata,

but it provides a possible explanation for the treatment response of M. aeruginosa.

Extracellular Material

Substances found outside of the cell such as secreted materials, products of lysed

cells, or inorganic and organic materials can contribute to PAM effectiveness. For

example, it has been well documented that certain cations (e.g., Ca, Mg, K) improve the

flocculation potential of anionic PAMs with negatively charged particulate (Lu et al.

2002). Lu et al. (2002) recorded that organic matter also has the ability to negatively

affect PAM sorption to soil materials. Algogenic organic matter produced by M.

aeruginosa has been documented to cause coagulation inhibition in semi-closed water

areas such as water treatment facilities and reservoirs (Takaara et al. 2010). These

substances, mostly comprised of lipopolysaccharides and RNA, are present as

extracellular organic matter and surface organic matter and are able to form complexes

with cations from the coagulant which seriously deteriorates its capabilities and requires

higher dosages for effective treatment (Takaara et al. 2002). As treatment of M.

29

aeruginosa in this study relies partly on cationic bridging between negatively charged

molecules, it is possible that types of organic matter produced by the live or lysed cells

within the closed container may impede PAM potential.

Other Considerations

Between Species Variation

The sensitivity and response of algae to pollutants or other stress factors are

species specific (Aruoja 2011). Microcystis aeruginosa and P. subcapitata controls

followed a similar trend by the end of the sampling period. Slower growth occurred at

the higher densities as the cells entered into a stationary and grew rapidly at lower

densities. The recovery of treated M. aeruginosa and P. subcapitata was more variable.

By the end of the 13 day sampling period P. subcapitata was in a growth phase while M.

aeruginosa fluctuated between cell growth and cell reduction. Samples were left to grow

under the same experimental and culture conditions after data collection had ceased.

Microcystis aeruginosa populations with the exception of density 1 (D1) flasks contained

few living cells or pigment while P. subcapitata populations recovered at all densities

(Figure 3.16). These same recovery trends were observed in all 3 trials in the weeks after

the experiment ended. Divakaran and Pillai (2002) used chitosan, a cationic PAM, to

flocculate algal particles. He found that when flocculated algae were placed in fresh

media, the algae recovered in the new flask even though the originating algae were bound

up by the polymer. Depending on the algae type, it may be necessary to treat more than

once as all algal cells are not removed and flocculated cells are still viable and

30

resuspension or growth of remaining cells is possible. Further study of the response after

treatment of other potentially harmful cyanobacteria compared to other non-target algal

species would provide valuable insight as to how cyanobacteria in general recover after

PAM treatment or if the lack of recovery of M. aeruginosa was species specific.

Confocal Imaging of Cyanobacteria

Confocal imaging of M. aeruginosa revealed two distinct pigments within the

cells, red cell fluorescence with excitation at 546 nm and blue at 405 (Figure 3.17).

Chlorophyll a has an excitation peak at 440nm while phycoerythrin has a peak at 580nm;

the red pigment within the images is likely phycoerythrin while the blue pigment

chlorophyll a (Xupeng et al. 2010). The pigments appeared to differ between treated and

untreated cells, with red dominating in untreated cells and blue in the treated cells (Figure

3.18-3.19). The major photosynthetic light harvesting components in cyanobacteria are

chlorophyll a and the phycobiliproteins (Hernandez et al. 2004). Chlorophyll a is found

within the thylakoid membrane while the phycobiliproteins are attached to the thylakoid

membranes outer surface (Lee 2008). The three main phycobilins (phycobiliproteins)

include phycocyanin, allophycocyanin, and phycoerythrin (Graham et al. 2009). In

Microcystis sp. and most red algae, phycoerythrin serves as the major light harvesting

pigment for photosynthesis which then passes the energy to chlorophyll a (Kursar et al.

1981, Graham et al. 2009). The observed changes in pigmentation between treated and

untreated cells could mean the PAM treatment causes interference in either light

absorption transfer between pigments. Without further investigation it is impossible to be

certain at this time, but the use of CLSM and pigment analysis to determine toxin or

treatment effects on cyanobacteria is a viable option and merits further study.

31

Conclusions and Future Studies

Microcystis aeruginosa and the non-target P. subcapitata were directly and

effectively treated by the 703d#3 Floc Log based on the reductions in cell number and

chlorophyll a concentrations in treated samples. Effectiveness and response to treatment

was varied based on multiple factors including species, cell density, and time. Although

the treatments on average were not as effective as documented effects on inorganic

particulate, there was still reduced growth and/or significant reductions in treated algae

versus controls. Treatments were most effective at lower cell densities. The PAM most

likely either became saturated in the higher density samples due to the high number and

surface area of the cells was unable to flocculate the remaining material, or there was not

enough mass or density to the cells themselves to be effectively settled. The majority of

cell reductions happened on the first day immediately following treatment. Reaction of

polymer in a small enclosed environment happens rapidly and available PAM binds to

suspended particulate within 24 hours. After the initial treatment, M. aeruginosa did not

recover and maintained a steady fluctuation of small increases and decreases while P.

subcapitata recovered rapidly. A variety of factors could contribute to the difference in

treatment response and recovery including cell surface properties, organic and inorganic

extracellular material, as well life cycle and stress response variations between the

species.

The 703d#3 Floc Log and other anionic polyacrylamide blends provide a viable

method for reducing and controlling harmful algal populations. Based on this study, it

seems that application of the material at lower cell densities prior to dense bloom

32

formation would be most effective. However further study using increased dosage of

PAM on higher density cultures or in natural waters in field situation would contribute

valuable information. The PAM material will also likely need to be applied in more than

one application to control recovering populations and possibly resuspending cells.

Further research is necessary to determine the specific mechanisms that contribute to the

variation among species. Examination of other harmful algae and non-target algae and

which species are affected by the anionic PAM would provide valuable information for

application in the field. In general, anionic PAM is an environmentally safe, low toxicity

application that can be used to reduce and control M. aeruginosa populations.

33

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38

APPENDIX A

TABLES

39

Table 1.1: Methods for Cyanobacteria Control

METHOD DESCRIPTION ADVANTAGES DISADVANTAGES

Algacides1,2

(Metal

compounds and

photosensitizers)

Affects certain cell functions

causing cell death

Inorganic: copper sulfate, silver

nitrate, potassium permanganate,

peroxides, potassium, etc.

Organic: Reglone A, simazin,

Diuron, paraquat, etc.

Also: photosensitizers

-Most commonly used control

method

-Very effective at killing cells and/

or inhibiting new growth

-Works quickly, easily available,

and relatively inexpensive

-Some selectivity depending on type

-Non-selective effects and toxicity to other

organisms.

-Death of mature cells can lead to cyanotoxin

release into water column.

-Some cyanobacteria gain a resistance to

certain algaecides

-Effects are temporary

Coagulants and

flocculents2

Used to precipitate and flocculate

cyanobacterial cells, phosphorus,

and other nutrients (e.g. aluminum,

iron, calcium, copper, clay

materials, PAM,)

-Materials cause precipitation of

nutrients and/or cyanobacterial cells

through charge disruption, physical

attraction, or mechanical adsorption

-Removing nutrients provides

longer lasting control

-Results are not permanent

-May be less effective in shallow lakes if

bottom sediments are continually disturbed

-Mixing devices are required for reaction of

some flocculants

-Certain flocculants and coagulants are be

toxic, using the wrong material may result in

ineffective results and/ or death of fish and

other aquatics

Sediment

removal,

capping, drying1

Removal and immobilization of

phosphorous and cyanobacterial

cells within sediments

-Sediment capping prevents

sediment and dormant cells from re-

suspending in the water column

-Drying is able to kill/ desiccate

some dormant cyanobacterial cells

-Sediments removal eliminates

nutrient laden bottom sediments and

dormant cells

-Dredging is expensive and managing bottom

muck and sediments is difficult

-Usually limited to shallow bottomed lakes

-May cause issues for benthic organisms

Sonication/

Ultrasound 1

Disrupts gas vacuolate in

cyanobacteria, causing cells to

settle

-Causes cells to settle out of water

column without disrupting cell wall,

preventing release of toxins

-Some studies also show disruption

of photosynthetic processes

-Gas vacuolate regeneration has been

observed in as little as 24 hours

-May have negative effects on other non-

target aquatic organisms

Mechanical

biomass removal1

Removal of algal biomass directly

through the use of booms and

filtration

-Removes cyanobacteria as well as

nutrients contained within the cells

-Useful under massive bloom/

scum/ mat conditions

-Only removes portion of population

- Only cost effective/ beneficial for very dense

blooms, mats, or scums

-Disposal of odorous and potentially toxic

materials

Hypolimnetic

aeration/

oxygenation 1

The use of pumps, aerators, and or

other devises to increase oxygen at

the bottom of a pond or lake

-Increased oxygen reduces

phosphorus release from sediment

-Increases aerobic breakdown of

organic sediments by bacteria

-Aeration of stratified lakes may bring

nutrient rich waters to the surface causing

intense algal blooms

-Ineffective if water body is too shallow

Hypolimnetic

withdrawal 1

Selective discharge of low oxygen,

high in phosphorus waters. In

stratified lakes phosphorus

concentrations are greatest in the

hypolimnion, it is released from

sediments under anoxic conditions.

-Low cost

-Removes phosphorus

-Relatively simple method

-Only applicable in stratified lakes

-Also limited to smaller lakes as it is not as

effective in larger lakes

-Discharge of cooler, more nutrient rich

waters may affect downstream organisms

Biomanipulation1 Based on manipulations of the

trophic cascade. Top down control

of algae by the introduction of

larger and selective fish species.

-Introducing fish that feed on fish

that prey on zooplankton increases

the zooplankton population, which

in turn increases grazing on

cyanobacteria

-Usually ineffective in highly eutrophic lakes

and large lakes as it is too difficult to

manipulate the fish population

-Requires the service of a skilled limnologist

-Grazing on certain cyanobacteria by

zooplankton is limited, such as Microcystis,

due to large colonies and them being poorly

edible

Biological

control 1

Addition of other organisms to

control cyanobacteria (other algae,

growth of macrophytes and

periphyton, viruses, bacteria, fungi,

herbivorous fish, etc.)

-Organisms may directly feed on

cyanobacteria or they may have

allelopathic or other indirect

negative effect

- May use of excess nutrients in

their own growth, limiting the

availability to cyanobacteria

-In terms of other algae, viruses, bacteria,

fungi, and protozoa, few successes have been

documented – in lab success has been difficult

to translate in the field

-Growth of other aquatic plants may be a

nuisance for recreational activities such as

boating and swimming

1: Drabkova (2007), 2: Jancula and Marsalek (2011)

40

Table 1.2: Acute and chronic toxicity tests for APS 703d#3 Floc Log

TEST SPECIES EXPOSURE TIME 703d#3 (ppm)

Acute toxicity (EPA-821-R-02-012)

LC 50 (Survival) Ceriodaphnia dubia 48h 673

NOAEC (Survival) Ceriodaphnia dubia 48h 420

LC 50 Oncorhynchus mykiss 96h 2928

Chronic toxicity (EPA-821-R-02-013)

IC 25 (Survival) P. promelas 7d 77.8

NOEC (Survival) P. promelas 7d 52.5

IC 25 (Growth) P. promelas 7d 50.1

NOEC (Growth) P. promelas 7d 52.5

IC 25 (Survival) C. dubia 7d 78.7

NOEC (Survival) C. dubia 7d 52.7

IC 25 (Reproduction) C. dubia 7d 66.8

NOEC (Reproduction) C. dubia 7d 52.5 The LC50 is a calculated percentage of effluent at which 50 percent of the organisms die in the test period.

The IC25 is also a calculated percentage of effluent. It is the level at which the organisms exhibit 25

percent reduction in a biological measurement such as reproduction or growth. NOEC stands for no

observable effects concentration and represents the highest concentration the test organisms are exposed to in which no effects are observed.

41

Table 3.1 Changes in Cell Counts and Chlorophyll a Levels between Day 0 and Day 13

D1C D1T D2C D2T D3C D3T D4C D4T

M. aeruginosa Control Mean % Change: 76% (cells), 76% (chl) Treated Mean % Change: -34% (cells), -58% (chl)

Change in

Cells/mL

513,333± 202,312

105,556± 302,312

486,778± 96,055

-180,556 ±359,259

190,111± 49,058

-87,889± 22,574

35,111± 11,432

-19,778 ±16,533

% Change

31.6%±

10.2%

6.5%±

18.5%

65.1% ±

21.3%

-22.2%±

47.4%

99.4%±

30.9%

-49.1%±

5.1%

104.6%±

156%

-71.5%±

37.2%

p-value 0.048* 0.607 0.013* 0.476 0.021* 0.021* 0.034* 0.174

Change in Chl

a (mg/L)

0.333±

0.065

-0.045±

0.232

0.442±

0.044

-0.416±

0.482

0.264±

0.126

-0.139±

0.054

0.030±

0.031

-0.030±

0.005

% Change

17.8%±

8.8%

-2.4% ±

12.2%

47.7%±

6.1%

-47.1% ±

54.2

167.9% ±

77.7%

-82.5% ±

24.7%

71.4% ±

87.8%

-100.0%±

0%

p-value 0.073* 0.771 0.003* 0.273 0.068 0.047* 0.242 0.010*

P. subcapitata Control Mean % Change: 335% (cells), 326% (chl) Treated Mean % Change: -23% (cells), 16% (chl)

Change in

Cells/mL

1,802,667±

375,108

773,333±

120,000

1,635,556±

181,516

367,778±

726,639

1,266,000±

505,065

-330,333

±269,408

389,000±

238,910

-21,556

±20,167

% Change

52.5%± 28.3%

22.3%± 7.4%

95.3% ± 53.1%

20.6%± 29.5%

309%± 199.7%

-79.4%± 45.4%

884%± 544%

-55.3%± 51.7%

p-value 0.014* 0.008* 0.004* 0.473 0.049 0.167 0.106 0.205

Change in Chl

a (mg/L)

0.197 ±

0.019

0.085±

0.124

0.206±

0.045

0.038±

0.079

0.123±

0.045

-0.045±

0.015

0.091±

0.010

0.004±

0.021

% Change 101.2% 46.2% 150% 23.3% 289% -93.8% 766% 87.2%

p-value 0.003* 0.355 0.015* 0.497 0.041* 0.034* 0.004* 0.763

* Values in bold represent statistically significant change between day 0 and day 13(p=<0.05)

42

Table 3.2 Comparison of Control and Treated Samples at Each Density (Wilcoxon sign rank for matched pairs)

D1

Control Treated

D2

Control Treated

D3

Control Treated

D4

Control Treated

M. aeruginosa

Cell Counts

Mean ±SD

(Cells/mL)

1.8E+06 ±

1.8E+05

1.6E+06 ±

1.2E+05

1.0E+06 ±

1.8E+05

7.0E+05 ±

5.9E+04

2.7E+05 ±

7.7E+04

1.2E+05 ±

2.7E+04

4.4E+04 ±

1.8E+04

1.2E+04 ±

4.9E+03

p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001

V 296 292 299 297

M. aeruginosa

Chlorophyll a

Mean ±SD

(mg/L)

1.98 ±

0.16

1.89 ±

0.09

1.08 ± 0.23 0.63 ±

0.16

0.28 ± 0.11 0.07 ±

0.06

0.06 ±

0.02

0.01 ±

0.01

p-value 0.003 0.001 0.002 0.001

V 113 119 102 104

P. subcapitata

Cell Counts

Mean ±SD

(Cells/mL)

4.2E+06 ±

6.7E+05

3.2E+06 ±

5.9E+05

2.4E+06 ±

5.9E+05

1.4E+06 ±

3.9E+05

8.4E+05 ±

4.5E+05

6.9E+04 ±

1.4E+05

1.8E+05 ±

1.3E+04

1.5E+04 ±

1.9E+04

p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001

V 297 294 296 275

P. subcapitata

Chlorophyll a

Mean ±SD

(mg/L)

0.28 ±

0.08

0.20 ±

0.06

0.22 ±

0.08

0.14 ±

0.04

0.10 ±

0.05

0.02 ±

0.02

0.05 ±

0.03

0.004 ±

0.005

p-value 0.001 0.006 0.002 0.001

V 120 109 116 119

*Values in bold represent statistically significant differences (p=<0.05)

43

Table 3.3 Differences among and between sampling days, p<0.05 represents significant difference of overall group (control/treated),

specific days that differ are listed in pair wise column (≠) (Friedman’s test)

Table 3.4 Comparison of experimental replicates (trials), p<0.05 represents significant differences between trials, specific trials that

differ are listed below the p-value (≠) (Kruskall-Wallis)

Microcystis Control Treated

Overall Pair wise Overall Pair wise

Cell Counts

Q= 182.5

p=<0.0001

0,1,3≠7,9,11,13 9≠0,1,3,

≠9,11,13 11,13≠0,1,3, ,7

7≠11,13

Q=31.0

p=<0.0001

0≠1,3, ,7,9,11,13

Chlorophyll

Q=81.2

p=<0.0001

0,1, ≠9,13

9,13≠0,1,

Q=47.5

p=<0.0001

0≠1, ,9,13

Pseudokirchneriella Control Treated

Overall Pair wise Overall Pair wise

Cell Counts

Q=204.4

p=<0.0001

0≠ ,7,9,11,13 9≠0,1,3,13

1,3≠7,9,11,13 11≠0,1,3, ,7

≠0,11,13 13≠0,1,3, ,7,9 7≠0,1,3,11,13

Q=123.4

p=<0.0001

0≠1,3, ,7,9 9≠0,3, ,13

1≠0,11,13 11≠1,3,

3, ≠0,9,11,13 13≠1, ,7,9 7≠0,13

Chlorophyll

Q=109.8

p=<0.0001

0,1≠ ,9,13

,9≠0,1,13

13≠0,1, ,9

Q=28.7

p=<0.0001

0≠

≠0,13

13≠

Microcystis D1 Cont. D1 Treat. D2 Cont. D2 Treat. D3 Cont. D3 Treat. D4 Cont. D4 Treat.

Cell Counts

K=30.074

p=<0.0001 1≠2, 2≠3

K= 2.596

p=0.273

K=5.250

p=0.072

K=30.916

p=<0.0001 1≠2, 2≠3

K=0.286

p=0.867

K=2.805

p=0.246

K=5.395

p=0.067

K=0.887

p=0.642

Chlorophyll

K=10.785

p=0.005

1≠2

K=2.677

p=0.262

K=4.374

p=0.112

K=20.080

p=<0.0001

1≠2, 1≠3

K=0.771

p=0.680

K=1.355

p=0.508

K=2.461

p=0.292

K=4.307

p=0.116

Pseudokirchneriella D1 Cont. D1 Treat. D2 Cont. D2 Treat. D3 Cont. D3 Treat. D4 Cont. D4 Treat.

Cell Counts

K=48.188

p=<0.0001

1≠2, 2≠3

K=53.791

p=<0.0001

1≠2,1≠3,2≠3

K=40.077

p=<0.0001

1≠2, 2≠3

K=51.977

p=<0.0001

1≠2, 2≠3

K=19.648

p=<0.0001

2≠3

K=22.135

p=<0.0001

1≠2, 1≠3

K=24.248

p=<0.0001

1≠2, 2≠3

K=19.520

p=<0.0001

1≠2, 1≠3

Chlorophyll

K=27.518

p=<0.0001

1≠2, 2≠3

K=29.774

p=<0.0001

1≠2, 2≠3

K=17.076

p=0.000

1≠2, 2≠3

K=29.096

p=<0.0001

1≠2, 2≠3

K=3.522

p=0.172

K=1.043

p=0.594

K=7.869

p=0.020

1≠2

K=1.628

p=0.443

44

Table 3.5 Percent Change in Cell Number by Day and Density Group *D1 = Density Group 1 (High Density Culture) D2 = Density Group 2 (1:1 Dilution, Media: High Density Culture)

D3 = Density Group 3 (10:1 Dilution, Media: High Density Culture) D4 = Density Group 4 (100:1 Dilution, Media: High Density Culture)

MICROCYSTIS (Cell Counts)

Control

Treated Day D1 D2 D3 D4 Average

1 5.2 2.9 -1.7 -18.5 -3.0

1 -4.3 -16.3 -30.6 -43.4 -23.7

3 -2.9 25.5 8.6 5.7 9.2

3 -5.2 4.7 -5.0 -7.8 -3.3

5 5.6 -2.3 16.9 25.0 11.3

5 -4.8 1.3 -6.3 -30.9 -10.2

7 3.9 15.2 17.0 19.3 13.8

7 17.2 -10.3 -2.9 -3.3 0.2 9 6.2 0.4 16.0 36.9 14.9

9 7.5 1.6 -1.3 -8.0 -0.1

11 3.2 17.0 15.1 8.4 10.9

11 -7.6 11.4 -9.9 15.0 2.2

13 7.2 -3.4 1.2 6.7 2.9

13 5.9 -13.8 -4.4 -2.8 -3.8

PSUDEOKIRCHNERIELLA

Control

Treated

1 7.0 10.4 -13.8 53.0 14.2

1 -18.4 -44.4 -98.5 -88.9 -62.5

3 4.3 -2.9 42.9 17.3 15.4

3 -2.8 10.1 -56.4 -35.9 -21.3

5 4.4 21.2 26.0 30.4 20.5

5 -11.2 0.3 16.7 12.0 4.5

7 1.0 22.0 21.4 67.2 27.9

7 18.5 11.3 242.9 50.0 80.7

9 9.8 -0.6 43.4 23.7 19.1

9 14.5 16.6 15.6 61.9 27.2 11 16.2 5.1 13.3 62.9 24.4

11 15.9 2.7 14.4 83.8 29.2

13 0.6 17.1 34.6 24.7 19.3

13 10.6 47.4 507.1 25.6 147.7

MICROCYSTIS (Chlorophyll a)

Control

Day D1 D2 D3 D4 Average

Treated 1 <0.01 -4.8 18.9 28.6 14.2

1 -8.3 -23.1 -36.8 -20.0 -22.1

5 3.9 1.3 25.4 -11.1 4.9

5 0.5 -15.7 -11.1 -62.5 -22.2 9 10.3 48.5 59.5 43.8 40.5

9 -3.2 -5.2 43.8 -100.0 -16.2

13 3.8 3.1 12.7 4.4 6.0

13 9.5 -13.8 4.3 0.0 0.0

PSUEDOKIRCHNERIELLA

1 10.4 -1.7 66.7 120.1 48.9

1 -33.1 -30.8 -55.0 <0.01 -39.6

5 52.3 64.5 57.8 131.7 76.6

5 32.2 -0.5 -86.1 -100.0 -38.6

9 -12.9 14.1 9.8 11.4 5.6

9 52.9 14.7 -20.0 0.0 11.9

13 37.5 35.5 34.6 52.3 40.0

13 8.3 56.3 25.0 0.0 22.4

45

Table 3.6 Cell Counts and Chlorophyll a Values: M. aeruginosa

M. aeruginosa Cell Numbers by Day and Density

Day D1

Control STD D1

Treated STD D2

Control STD D2

Treated STD D3

Control STD D3

Treated STD D4

Control STD D4

Treated STD

0 1,625,556 114,762 1,615,556 171,960 747,222 97,501 813,778 110,205 191,222 11,315 179,111 30,977 33,556 18,090 27,667 12,662

1 1,711,111 337,068 1,545,556 295,453 769,222 126,476 681,333 92,118 187,889 14,362 124,222 16,188 27,333 16,921 15,667 8,007

3 1,661,111 279,291 1,465,556 116,492 965,222 97,500 713,111 22,843 204,000 19,757 118,000 10,333 28,889 8,701 14,444 1,836

5 1,752,222 265,002 1,395,556 72,444 943,000 59,580 722,444 38,176 238,444 14,175 110,556 12,025 36,333 11,667 10,000 1,732

7 1,820,000 273,882 1,636,667 88,192 1,086,111 41,910 648,333 210,178 281,000 34,226 107,333 13,968 43,333 8,000 9,667 4,842

9 1,933,333 92,075 1,760,000 206,640 1,091,222 74,893 659,111 212,612 325,111 54,668 105,889 14,245 59,333 8,686 8,889 3,791

11 1,994,444 226,086 1,625,556 61,944 1,276,778 138,544 734,333 151,495 374,333 67,013 95,444 19,892 64,333 15,836 10,222 6,736

13 2,138,889 313,516 1,721,111 141,474 1,234,000 18,774 633,222 359,677 381,333 39,048 91,222 11,673 68,667 14,518 7,889 4,114

M. aeruginosa Chlorophyll a by Day and Density

Day D1

Control STD D1

Treated STD D2

Control STD D2

Treated STD D3

Control STD D3

Treated STD D4

Control STD D4

Treated STD

0 1.868 0.060 1.896 0.165 0.926 0.046 0.884 0.034 0.157 0.022 0.169 0.018 0.042 0.010 0.030 0.005

1 1.851 0.134 1.738 0.096 0.881 0.055 0.679 0.134 0.187 0.009 0.107 0.024 0.053 0.015 0.024 0.027

5 1.922 0.098 1.747 0.044 0.893 0.235 0.573 0.301 0.234 0.005 0.039 0.019 0.047 0.019 0.009 0.015

9 2.121 0.167 1.691 0.359 1.326 0.157 0.543 0.381 0.374 0.108 0.021 0.005 0.068 0.019 0.000 0.000

13 2.201 0.188 1.851 0.067 1.368 0.049 0.468 0.496 0.421 0.130 0.030 0.037 0.071 0.024 0.000 0.000

46

Table 3.7 Cell Counts and Chlorophyll a values: P. subcapitata

P. subcapitata Cell Number by Day and Density

Day D1

Control STD D1

Treated STD D2

Control STD D2

Treated STD D3

Control STD D3

Treated STD D4

Control STD D4

Treated STD

0 3,434,444 1,529,852 3,464,444 1,631,037 1,715,556 893,590 1,786,667 750,607 409,556 170,703 416,000 165,188 44,000 0 39,000 0

1 3,675,556 1,387,569 2,826,667 1,927,151 1,893,333 832,052 992,667 1,463,346 353,111 333,433 6,111 6,678 67,333 45,994 4,333 4,000

3 3,833,778 1,370,486 2,745,556 1,742,011 1,844,444 675,642 1,093,222 1,602,749 504,556 192,178 2,667 2,728 79,000 56,312 2,778 3,717

5 4,003,556 1,405,163 2,439,111 1,861,806 2,233,333 883,503 1,096,000 1,600,268 635,667 260,856 3,111 2,411 103,000 96,141 3,111 1,836

7 4,076,444 1,107,808 2,890,000 1,761,082 2,743,333 800,146 1,220,556 1,726,090 771,667 280,629 10,667 16,462 172,222 155,249 4,667 3,930

9 4,479,111 912,064 3,308,667 1,303,294 2,724,444 1,034,807 1,423,667 1,951,679 1,098,778 519,426 12,333 18,009 213,111 194,075 7,556 8,113

11 5,203,333 1,321,401 3,833,111 2,054,593 2,862,222 1,112,207 1,461,667 1,749,220 1,244,778 533,656 14,111 21,895 347,222 250,727 13,889 13,268

13 5,237,111 1,294,195 4,237,778 1,733,979 3,351,111 776,268 2,154,444 1,455,413 1,675,556 530,538 85,667 143,472 433,000 238,910 17,444 20,167

P. subcapitata Chlorophyll a by Day and Density

Day D1

Control STD D1

Treated STD D2

Control STD D2

Treated STD D3

Control STD D3

Treated STD D4

Control STD D4

Treated STD

0 0.195 0.131 0.185 0.110 0.138 0.086 0.161 0.093 0.043 0.014 0.047 0.016 0.012 0.021 0.005 0.008

1 0.215 0.113 0.123 0.091 0.135 0.051 0.112 0.132 0.071 0.021 0.021 0.000 0.026 0.004 0.005 0.004

5 0.327 0.197 0.163 0.129 0.223 0.096 0.111 0.151 0.112 0.040 0.003 0.005 0.061 0.032 0.000 0.000

9 0.285 0.100 0.249 0.197 0.254 0.107 0.127 0.161 0.123 0.030 0.002 0.004 0.067 0.050 0.000 0.000

13 0.392 0.116 0.270 0.168 0.344 0.086 0.199 0.151 0.166 0.044 0.003 0.005 0.103 0.025 0.009 0.015

47

Table 3.8 Correlations Matrix: M. aeruginosa vs. P. subcapitata

(Spearman’s Test, numbers represent r values)

A. Control Group, Cell Counts

B. Control Group, Chlorophyll a

Variables P-D1 CC

P-D2 CC

P-D3 CC

P-D4 CC

Variables

P-D1 CHL

P-D2 CHL

P-D3 CHL

P-D4 CHL

M-D1 CC 0.976 0.976 0.929 0.976

M-D1 CHL 0.800 1.000 0.900 0.900

M-D2 CC 0.952 0.881 0.929 0.952

M-D2 CHL 0.500 0.900 0.700 0.700

M-D3 CC 0.976 0.905 1.000 0.976

M-D3 CHL 0.900 0.900 1.000 1.000

M-D4 CC 0.929 0.881 0.976 0.929

M-D4 CHL 0.700 0.700 0.900 0.900

C. Treatment Groups, Cell Counts

D. Treatment Groups, Chlorophyll a

Variables P-D1 CC

P-D2 CC

P-D3 CC

P-D4 CC

Variables

P-D1 CHL

P-D2 CHL

P-D3 CHL

P-D4 CHL

M-D1 CC 0.738 0.619 0.619 0.619

M-D1 CHL 0.200 0.500 0.667 0.718

M-D2 CC -0.143 -0.024 0.071 0.071

M-D2 CHL -0.700 -0.300 0.821 -0.051

M-D3 CC -0.548 -0.500 -0.262 -0.262

M-D3 CHL -0.600 -0.100 0.975 0.308

M-D4 CC -0.286 -0.357 -0.095 -0.095

M-D4 CHL -0.667 -0.205 0.921 0.132

Table 3.9 Correlations Matrix: Chlorophyll a and Cell Counts

M. aeruginosa Cell vs. Chl a, Control P. subcapitata Cell vs. Chl a, Control

Variables D1 CC D2 CC D3 CC D4 CC

Variables D1 CC D2 CC D3 CC D4 CC

D1 CHL 0.900 0.900 1.000 1.000

D1 CHL 0.900 0.900 0.800 0.900

D2 CHL 0.700 0.700 0.900 0.900

D2 CHL 0.900 0.900 1.000 0.900

D3 CHL 1.000 1.000 0.900 0.900

D3 CHL 1.000 1.000 0.900 1.000

D4 CHL 0.900 0.900 0.700 0.700

D4 CHL 1.000 1.000 0.900 1.000

M. aeruginosa Cell vs. Chl a,Treated P. subcapitata Cell vs. Chl a, Treated

D1 CC D2 CC D3 CC D4 CC

Variables D1 CC D2 CC D3 CC D4 CC

D1 CHL -0.200 0.400 0.300 0.300 D1 CHL 0.800 0.900 0.600 0.600

D2 CHL -0.500 0.900 1.000 1.000 D2 CHL 1.000 0.900 0.900 0.900

D3 CHL -0.600 0.800 0.900 0.900 D3 CHL 0.103 -0.051 0.359 0.359

D4 CHL -0.564 0.872 0.975 0.975 D4 CHL 0.821 0.667 0.718 0.718

48

APPENDIX B

FIGURES

49

Figure 2.1: Experimental Design and Set Up

Microcystis aeruginosa

Density 1 (D1) High Density

Pseudokirchneriella subcapitata

Control Control

Control Control

Control

Control Control

Control Treated Treated Treated Treated

Treated Treated Treated Treated

Density 1 (D1) High Density

Density 2 (D2) 1:1 Dilution Media: D1

Density 2 (D2) 1:1 Dilution Media: D1

Density 2 (D2) 1:1 Dilution Media: D1

Density 3 (D3) 10:1 Dilution Media: D1

Density 4 (D4) 100:1 Dilution Media: D1

Density 4 (D4) 100:1 Dilution Media: D1

Density 3 (D3) 10:1 Dilution Media: D1

50

Figure 3.1 Relationship between Cell Counts and Chlorophyll a Readings

Figure 3.2 Relationships between Cell Counts and Chlorophyll a Readings

R² = 0.9811

-0.5

0

0.5

1

1.5

2

2.5

3

0.00E+00 5.00E+05 1.00E+06 1.50E+06 2.00E+06 2.50E+06

Ch

loro

ph

yll a

(m

g/L)

Cells/mL

M. aeruginosa Cell Count and Chlorophyll a

R² = 0.8662

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.E+00 1.E+06 2.E+06 3.E+06 4.E+06 5.E+06 6.E+06

Ch

loro

ph

yll a

(m

g/L)

Cells/mL

P. subcapitata Cell Count and Chlorophyll a

51

D1 D2

D3 D4

Figure 3.3 M. aeruginosa Control vs. Treatment Flaks, Density 1 (D1) through Density 4 (D4) *Photos taken on Day3

D1 D3

Figure 3.4 Flocculated algal material in D1 and D3 (M. aeruginosa) above

52

D1 D2

D3 D4

Figure 3.5 P. subcapitata Control vs. Treatment Flaks, Density 1 (D1) through Density 4 (D4) *Photos taken on Day3 after treatment

D1 D3

Figure 3.6 Flocculated algal material in D1 and D3 (P. subcapitata) above

53

Figure 3.7 M. aeruginosa Control vs. Treated, Cell Count Comparison, growth trends by day ad

density

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1 2 3 4 5 6 7 8

Cel

ls/

mL

Sample # (13d period)

M. aeruginosa Control vs. Treated (Cell Counts)

D1 Control

D1 Treated

D2 Control

D2 Treated

D3 Control

D3 Treated

0

20,000

40,000

60,000

80,000

1 2 3 4 5 6 7 8

Cel

ls/m

L

Sample # (13d period)

D4 Control

D4 Treated

54

Figure 3.8 M. aeruginosa Control vs. Treated, Chlorophyll a Comparisons, growth trends by

day and density

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5

Ch

loro

ph

yll a

(m

g/L)

Sample # (13d period)

M. aeruginosa Control vs. Treated (Chl a)

D1 Control

D1 Treated

D2 Control

D2 Control

D3 Control

D3 Treated

0.00

0.02

0.04

0.06

0.08

1 2 3 4 5

Ch

loro

ph

yll a

(m

g/L)

Sample # (13d period)

D4 Control

D4 Treated

55

Figure 3.9 P. subcapitata Control vs. Treated, Cell Count Comparison, growth trends by day

and density

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

1 2 3 4 5 6 7 8

Cel

ls/

mL

Sample # (13d period)

P. subcapitata Control vs. Treated (Cell Counts)

D1 Control

D1 Treated

D2 Control

D2 Treated

D3 Control

D3 Treated

0

100,000

200,000

300,000

400,000

500,000

1 2 3 4 5 6 7 8

Cel

ls/

mL

Sample # (13d period)

D4 Control

D4 Treated

56

Figure 3.10 P. subcapitata Control vs. Treated, Chlorophyll a Comparison, growth trends by

day and density

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 2 3 4 5

Ch

loro

ph

yll a

(m

g/L)

Sample # (13d period)

P. subcapitata Control vs. Treated (Chl a)

D1 Control

D1 Treated

D2 Control

D2 Treated

D3 Control

D3 Treated

0

0.02

0.04

0.06

0.08

0.1

0.12

1 2 3 4 5

Ch

loro

ph

yll a

(m

g/L)

Sample # (13d period)

D4 Control

D4 Treated

57

Figure 3.11 Average Percent Change by Day (All Densities): Cell Number and Chlorophyll a

-30

-20

-10

0

10

20

1 2 3 4 5 6 7 8

Pe

rce

nt

Ch

ange

(%)

Sample Number

M. aeruginosa Cell Number

Control

Treated

-100

-50

0

50

100

150

200

1 2 3 4 5 6 7 8 Pe

rce

nt

Ch

ange

(%)

Sample Number

P. subcapitata Cell Number

Control

Treated

-30

-20

-10

0

10

20

30

40

50

1 2 3 4 5 Per

cen

t C

han

ge (%

)

Sample Number

M. aeruginosa Chlorophyll a

Control

Treated

-60

-40

-20

0

20

40

60

80

100

1 2 3 4 5 Per

cen

t C

han

ge (%

)

Sample Number

P. subcapitata Chlorophyll a

Control

Treated

58

a. D1 M. aeruginosa (cell counts) b. D2 M. aeruginosa (cell counts)

c. D3 M. aeruginosa (cell counts) d. D4 M. aeruginosa (cell counts)

Figure 3.12 M. aeruginosa Control Groups, Percent Change by Day and Density (Cell Counts)

-10%

-5%

0%

5%

10%

15%

20%

1 2 3 4 5 6 7 8 % c

han

ge

Sample # (13d period) D1 Control

D2 Treated

-20%

-10%

0%

10%

20%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D2 Control

D2 Treated

-40%

-30%

-20%

-10%

0%

10%

20%

30%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D3 Control

D3 Treated

-60%

-40%

-20%

0%

20%

40%

60%

1 2 3 4 5 6 7 8 % c

han

ge

Sample # (13d period) D4 Control

D4 Treated

59

a. D1 M. aeruginosa (chlorophyll) b. D2 M. aeruginosa (cell counts)

c. D3 M. aeruginosa (chlorophyll) d. D4 M. aeruginosa (chlorophyll)

Figure 3.13 M. aeruginosa Control Groups, Percent Change by Day and Density (Chlorophyll a)

-10%

-5%

0%

5%

10%

15%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D1 Control

D1 Treated

-75%

-50%

-25%

0%

25%

50%

75%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D3 Control

D3 Treated

-40%

-20%

0%

20%

40%

60%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D2 Control

D2 Treated

-60%

-40%

-20%

0%

20%

40%

60%

80%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D4 Control

D4 Treated

60

a. D1 P. subcapitata (cell counts) b. D2 P. subcapitata (cell counts)

c. D3 P. subcapitata (cell counts) d. D4 P. subcapitata (cell counts)

Figure 3.14 P. subcapitata Control vs. Treated, Percent Change by Day and Density (cell counts)

-30%

-20%

-10%

0%

10%

20%

30%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D1 Control

D1 Treated

-60%

-40%

-20%

0%

20%

40%

60%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D2 Control

D2 Treated

-150%

0%

150%

300%

450%

600%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D3 Control

D3 Treated

-100%

-75%

-50%

-25%

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8

% c

han

ge

Sample # (13d period) D4 Control

D4 Treated

61

a. D1 P. subcapitata (cell counts) b. D2 P. subcapitata (cell counts)

c. D3 P. subcapitata (cell counts) d. D4 P. subcapitata (cell counts) Figure 3.15 P. subcapitata Control vs. Treated, Percent Change in by Day and Density (Chlorophyll a)

-40%

-20%

0%

20%

40%

60%

1 2 3 4 5 % c

han

ge

Sample # (13d period) D1 Control

D1 Treated

-100%

-75%

-50%

-25%

0%

25%

50%

75%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D2 Control

D2 Treated

-40%

-20%

0%

20%

40%

60%

80%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D3 Control

D3 Treated

-150%

-100%

-50%

0%

50%

100%

150%

1 2 3 4 5

% c

han

ge

Sample # (13d period) D4 Control

D4 Treated

62

M. aeruginosa

P. subcapitata

Figure 3.16 Recovery of Algae (4 weeks after end of Treatment Period) *Photos from Trial 2

63

Figure 3.17: Confocal laser scanning microscopy images revealed two distinct pigments

in M. aeruginosa (red at 546nm, blue at 405nm) (image of untreated cells) (Image by Dr.

Erich Ottem, Northern Michigan Univeristy, 2013)

64

Figure 3.18: Untreated M. aeruginosa cells, (blue at 405nm, red at 546nm, and final image is overlapped)

Figure 3.18: Untreated M. aeruginosa cells, (blue at 405nm, red at 546nm, and final image is overlapped) (Images by Dr. Erich

Ottem, Northern Michigan Univeristy, 2013)

65

APPENDIX C

CULTURE MEDIA

BOLD’S BASAL MEDIUM (MODIFIED)

This medium is highly enriched and is used for many of the green algae.

Reference: Stein, J. (ED.) Handbook of Phycological methods. Culture methods and

growth measurements. Cambridge University Press. 448 pp.

STOCK STOCK SOLUTION ml/Litre

1. KH2PO4 8.75 g/500 ml 10 ml

2. CaCl2.2H2O 12.5 g/500 ml 1 ml

3. MgSO4.7H2O 37.5 g/500 ml 1 ml

4. NaNO3 125 g/500 ml 1 ml

5. K2HPO4 37.5 g/500 ml 1 ml

6. NaCl 12.5 g/500 ml 1 ml

7. Na2EDTA. 2H2O 10 g/L 1 ml

KOH 6.2 g/L

8. FeSO4.7H2O 4.98 g/L 1 ml

H2SO4 (concentrated) 1 ml/L

9. Trace Metal Solution See below 1 ml

10. H3BO3 5.75 g/500 ml 0.7 ml

11. F/2 Vitamin Solution (optional) See below 1 mL

Stir and adjust the pH to 6.8. Filter-sterilize into sterile glassware using a 0.22 um filter

or autoclave the medium.

OPTIONS: The addition of F/2 vitamins or 5 ml of soil extract is also beneficial to some

algae. The CPCC currently adds F/2 vitamins to the BBM medium for all strains. To

prepare a less rich medium, prepare a 10% BBM solution.

66

Trace Metal Solution:

Substance g/Litre

1. H3BO3 2.86 g

2. MnCl2.4H2O 1.81 g

3. ZnSO4.7H2O 0.222 g

4. Na2MoO4.2H2O 0.390 g

5. CuSO4.5H2O 0.079 g

6. Co(NO3)2.6H2O 0.0494 g

Dissolve each of the above substances separately prior to adding the next on the list.

Substance Primary Stock (g/L) Concentrated Stock

(g/L)

Final

Concentration

Medium (mg/L)

1. Vitamin B12

(Cyanocobalamin)

0.001 0.0001 0.001

2. Biotin 0.001 0.0001 0.001

3. Thiamine 0.200 0.0200 0.200

BG-11 MEDIUM (Modified by J.Acreman)

Reference: Rippka, R., J. Deruelles, J. Waterbury, M. Herdman and R. Stanier. 1979.

Generic assignments, strain histories and properties of pure cultures of cyanobacteria. J.

Gen. Microbiol. 111: 1-61.

This medium is used for successfully for most cyanobacteria. Vitamin B12 may be added

for those species that require it. Use f/2 vitamin solution.

STOCK STOCK SOLUTION ml/Litre

1. NaNO3 (omitted for heterocystous species) 150 g/L 10 ml

2. K2HPO4.3H2O or *K2HPO4 40 g/L or *30 g/L 1 ml

3. MgSO4.7H2O 75 g/L 1 ml

4. CaCl2.2H2O 36 g/L 1 ml

5. Citric Acid combined with 6 g/L 1 ml

Ferric Citrate 6 g/L

6. Na2EDTA.2H2O 1 g/L 1 ml

7. Na2CO3 20 g/L 1 ml

8. Trace Metal solution See below 1 ml

9. F/2 vitamins See next page 1 ml

Adjusting the pH of the medium to approximately 7.5 will avoid heavy precipitation.

(Initial pH is approximately 8.5.) When making solid media, you can add agar directly to

medium. Omit NaNO3 for media used to culture heterocystous cyanobacteria e.g.

Nostoc,

Anabaena in order to maintain their ability to continue to produce the heterocysts.

67

OPTION: 0.5 g/L of HEPES buffer can be added to the final medium as a buffer. FeCl3

and EDTA added in a 1:1 ratio may be substituted.

Trace Metal Stock Solution:

Substance g/Litre

1. H3BO3 2.86 g

2. MnCl2.4H2O 1.81 g

3. ZnSO4.7H2O 0.222 g

4. Na2MoO4.2H2O 0.390 g

5. CuSO4.5H2O 0.079 g

6. Co(NO3)2.6H2O 0.0494 g

Add each substance in the order that they appear here and ensure that each is visually

dissolved prior to adding the next on the list. After all substances have been added the

stock solution should be topped up with distilled or MilliQ water to 1000 ml.

F/2 VITAMIN SOLUTION

(Guillard and Ryther 1962, Guillard 1975)

Reference: Guillard, R.R.L. and J.H. Ryther. 1962. Studies of marine planktonic

diatoms. I. Cyclotella nana Hustedt and Detonula confervacea Cleve. Can. J. Microbiol.

8: 229-239.

Reference: Guillard, R.R.L. 1975. Culture of phytoplankton for feeding marine

invertebrates in “Culture of Marine Invertebrate Animals.” (eds: Smith W.L. and

Chanley M.H.) Plenum Press, New York, USA. pp 26-60.

STOCK STOCK SOLUTION

1. Vitamin B12 (Cyanocobalamin) 5mg/5ml distilled H2O

2. Biotin 1 mg/10ml distilled H2O

To make the working solution add the following amounts of the stock solutions to 100 ml

of distilled water:

1. Vitamin B12 0.1 ml

2. Biotin 1.0 ml

3. Thiamine HCl 20 mg

Dispense working solution according to amounts required for media preparation. One ml

aliquots are conveniently stored in cryovials for periods of 1-2 months. Store the

remainder of the working solution in a polyethylene bottle of 100 ml. Wrap with Parafilm

to avoid moisture loss and store all solutions frozen