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2013
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
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