No. 193
Mälardalen University Press Licentiate Theses
No. 193
COMMUNITIES OF MICROALGAE AND BACTERIA IN
PHOTOBIOREACTORS TREATING MUNICIPAL WASTEWATER
COMMUNITIES OF MICROALGAE AND BACTERIA IN
PHOTOBIOREACTORS TREATING MUNICIPAL WASTEWATER
Ivo Krustok
2015
Ivo
Krustok
2015
School of Business, Society and Engineering
School of Business, Society and Engineering
Copyright © Ivo Krustok, 2015
ISBN 978-91-7485-192-2
ISSN 1651-9256
Printed by Arkitektkopia, Västerås, Sweden
Summary
Everyone who uses water produces wastewater. This inevitability creates
several problems that increase with the growth of the population and industry.
What to do with the wastewater, how to purify it and how to design the
infrastructure are all important questions that each municipality has to deal
with, taking into account ever growing demands to reduce environmental
impact. In these conditions scientists and engineers have turned to biological
processes to help treat the water. Currently the most commonly used
wastewater treatment method known as the activated sludge process involves
bacteria that help break down the pollutants. While it has been used
successfully for around 100 years now, it has many limitations when faced
with modern demands. As an alternative, microalgae reactors, commonly
known as photobioreactors, have been suggested.
Microalgae are microscopic water organisms that can use photosynthesis
to form sugars from CO2 and water. To do this they require energy from light,
hence the photo part of the photobioreactor. In addition to taking up CO2 from
their environment, they take up nutrients such as nitrogen and phosphorous
compounds. This is a reason why microalgae have great potential for use in
wastewater treatment. When grown in wastewater together with the
microorganisms already present, they are able to reduce the amount of
pollutants by taking them up into their cells, effectively purifying the water.
Since wastewater has its own microbial community, the biological
processes taking place in a wastewater treating photobioreactor are more
complex compared to growing a single species of algae in a sterile medium.
With the work presented in this licentiate, we characterized the algae and
bacterial communities present in photobioreactors treating wastewater in
addition to finding the most optimal ways to grow algae originating from a
local lake in a wastewater medium. We looked at the species found, most
important metabolic pathways, growth dynamics for both algae and bacteria
and water purification dynamics.
Overall, we were successful in inoculating municipal wastewater from
Västerås wastewater treatment plant with algae from Lake Mälaren. The
dominant algae growing in our systems belonged to the genera Scenedesmus,
Desmodesmus and Chlorella. We also saw that the bacterial community was
involved in synthesis of vitamins essential for algae growth. The information
presented in this thesis is another step towards a better design of control and
monitoring systems in full-scale photobioreactor plants.
1
Sammanfattning
Alla som använder vatten (både hushåll och industri) producerar ett
avloppsvatten. Detta orsakar ofrånkomligen problem, vilka ökar i takt med att
befolkningen ökar. Vad som skall göras med avloppsvattnet, hur det ska renas
samt hur infrastrukturen skall planeras är viktiga frågor som varje kommun
måste handskas med. Både forskare och ingenjörer har länge intresserat sig
för biologiska processer för att rena vattnet. I dagsläget är den vanligaste
metoden aktiv slam-processen, där bakterier bryter ner föroreningarna i
avloppsvattnet. Aktiv slam-processen har använts i över 100 år, men det finns
vissa begränsningar när det gäller att motsvara dagen krav på effektiv och
energisnål vattenrening. Ett alternativ till aktiv slam-processen kan
mikroalger. Mikroalgerna får växa till i reaktorer som då kallas
fotobioreaktorer.
Mikroalger är mikroskopiskt små vattenlevande organismer som använder
fotosyntes till att producera socker av CO2 och vatten. För att åstadkomma
detta kräver de energi i form av ljus, därav namnet fotobioreaktorer. Förutom
att ta upp CO2 från omgivningen tar mikroalgerna även upp näringsämnen,
t.ex. kväve och fosfor. Detta är en av anledningarna till varför mikroalgerna
har stor potential att användas i vattenreningssystem. Om mikroalgerna växer
till i avloppsvatten kan de minska mängden föroreningar i vattnet genom
upptag av ämnen i cellen. Därigenom uppnås en effektiv rening av vattnet.
Eftersom avloppsvatten har en egen mikrobiell flora, så är de biologiska
processerna som sker i en fotobioreaktor mer komplexa om man jämför med
system där man odlar enstaka arter av alger i ett sterilt medium. Genom arbetet
som presenteras i denna avhandling, har vi karakteriserat algerna och
bakterierna som förekommer i en fotobioreaktor innehållande avloppsvatten.
Vi har också identifierat den bästa metoden att odla alger från Mälaren i ett
medium bestående av avloppsvatten. Vi studerade vilka arter som förekom,
upptag av olika ämnen, tillväxt av alger och bakterier samt reningsgraden av
vattnet.
Resultaten visar att vi lyckades ympa in alger från Mälaren i vatten från ett
kommunalt avloppsreningsverk. De mest förekommande algerna som växte i
våra system tillhörde grupperna Scenedesmus, Desmodesmus och Chlorella.
Vi såg också att bakterierna syntetiserade vitaminer som var viktiga för
algernas tillväxt.
Resultaten som presenteras i denna avhandling är ett steg i rätt riktning för
att åstadkomma fungerande storskaliga system med fotobioreaktorer.
2
List of papers
I.
II.
III.
Krustok I., Nehrenheim E., Odlare M. 2012. Cultivation Of
Microalgae For Potential Heavy Metal Reduction In A Wastewater
Treatment Plant. Poster presentation at International Conference
on Applied Energy 2012, Suzhou, China
Krustok I., Odlare M., Shabiimam M.A., Truu J., Truu M., Ligi T.,
Nehrenheim E., 2015. Characterization of algal and microbial
community growth in a wastewater treating batch photo-bioreactor
inoculated with lake water. Algal Research, Available online 18
February 2015
Krustok I., Odlare M., Truu M., Truu J., Ligi T., Tiirik K.,
Nehrenheim E., 2015. Effect of lake water on algal biomass and
microbial community structure in municipal wastewater based labscale photobioreactors. Submitted to Applied Microbiology and
Biotechnology
3
List of Papers Not Included
I.
II.
III.
IV.
V.
VI.
4
Krustok I., Nieto J.G.D., Odlare M., Nehrenheim E., 2014. Algae
Biomass Cultivation in Ammonium Rich Reject Water – The
Potential for Simultaneous Wastewater Treatment and Energy
Recovery. Presented at the 5th International Symposium on Energy
from Biomass and Waste, Venice, Italy.
Krustok I., Nehrenheim E., Odlare M., Shabiimam M.A., Truu J.,
Ligi T., Truu M., 2014. Characterization of algal and microbial
community dynamics in a wastewater photo-bioreactor using
indigenous algae from Lake Mälaren. Presented at the 4th
international Conference on Algal Biomass, Biofuels and
Bioproducts, Santa Fe, USA.
Nehrenheim, E., Odlare, M. Krustok, I., Olsson J., Ribé V.,
Shabiimam M.A., Diaz J.G., Nordlander E., 2013. ACWA - algae
cultivation for simultaneous water treatment and biogas substrate
production. Poster at the 14th International Waste management and
Landfill Symposium.
Ribé V., Nehrenheim E., Shabiimam M.A., Krustok I., Thorin E.,
2013. AlTox: biomass production using potentially toxic landfill
leachates as substrates for algae cultivation. Presented at the 14th
International Waste management and Landfill Symposium.
Shabiimam M.A., Krustok I., Nehrenheim E., Odlare M., 2013.
Microalgae cultivation for potential nutrient and heavy metal
reduction in landfill leachate. Presented at the 14th International
Waste management and Landfill Symposium.
Krustok I., Truu J., Truu M., Preem J-K., Nehrenheim E., Odlare
M., Mander Ü. 2012. Bacterial Community Activity, Structure and
Succession in Hybrid Constructed Wetland Treating Domestic
Grey Water. Presentation at the 1st Congress of Baltic
Microbiologists, Riga, Latvia
Contents
List of Abbreviations .................................................................................. 7
Acknowledgements .................................................................................... 8
1 Introduction ............................................................................................. 9
1.1 Objectives of the thesis................................................................... 10
1.2 Contributions .................................................................................. 10
1.3 Thesis outline ................................................................................. 11
2 Background and related work................................................................ 12
2.1 Microalgae for wastewater treatment ............................................. 12
2.2 Microalgal biomass for biogas production ..................................... 14
2.3 Photobioreactor design ................................................................... 14
2.3.1 Nutrient supply ........................................................................ 14
2.3.2 Gas exchange........................................................................... 15
2.3.3 Light ........................................................................................ 15
2.3.5 Harvesting ............................................................................... 16
2.4 Community analysis ....................................................................... 17
3. Methodology ........................................................................................ 19
3.1 Wastewater and lake origin and properties .................................... 19
3.2 Experimental setup ......................................................................... 19
3.3 Algal growth dynamics .................................................................. 20
3.4 Molecular methods and community analysis ................................. 20
3.5 Pollutant removal ........................................................................... 21
3.6 Statistical analysis .......................................................................... 21
4 Results and Discussion .......................................................................... 22
4.1 Algae community and growth dynamics ........................................ 22
4.2 Bacterial dynamics and community analysis ................................. 27
4.3 Functional analysis of the metagenomes ........................................ 32
5
4.4 Nutrient dynamics and metal removal ............................................ 32
4.4.1 Carbon ..................................................................................... 32
4.4.2 Nitrogen and phosphorous....................................................... 32
4.4.3 Metals ...................................................................................... 34
5 Conclusions ........................................................................................... 35
6 Future work ........................................................................................... 36
References ................................................................................................ 37
Papers ....................................................................................................... 41
6
List of Abbreviations
ANOVA – Analysis of Variance
DOC – Dissolved Organic Carbon
LWR – Lake Water Reactor
M5NR - M5 Non-Redundant Protein Database
NH4-N – Ammonium Nitrogen
NO3-N – Nitrate Nitrogen
OD – Optical Density
PCR-DGGE - Polymerase Chain Reaction Denaturating Gradient Gel
Electrophoresis
PE – Purification efficiency
RT-PCR – Real-Time Polymerase Chain Reaction
SSU - SILVA Small Subunit
STAMP - Statistical Analysis of Metagenomic Profiles
SWR – Sterilized Wastewater Reactor
TOC – Total Organic Carbon
TP – Total Phosphorous
TWR – Tap Water Reactor
WWR – Wastewater Reactor
WWTP – Wastewater Treatment Plant
7
Acknowledgements
This research was conducted at the School of Business, Society and
Engineering, Mälardalen University, Västerås, Sweden with financial support
from the Knowledge Foundation (2011006), VINNOVA (2012-01243), SVU
(12-123), Purac and Mälarenergi and the Ministry of Education and Research
of the Republic of Estonia (grants IUT2-16 and 3.2.0801.11-0026). I would
like to thank my supervisors Monica, Emma and Jaak, co-authors Marika
Truu, Shabiimam M.A., Teele Ligi and Kertu Tiirik and co-workers for all
their help and support. A special thanks goes to Veronica Ribé and Javier
Campillo from Mälardalen University for helping me with my experiments,
Kristjan Oopkaup and Mae Uri from the Institute of Ecology and Earth
Sciences at the University of Tartu and Viktor Sjöberg from Örebro University
for their assistance and advice.
In addition, I thank my wife, who has supported me during my research and
has understood if I cannot be home some evenings or weekends. I am also
very grateful to my parents my parents, who have always helped me follow
my passions and have not been angry that I can’t visit too often.
8
1 Introduction
Current municipal wastewater treatment plants pose several problems.
They have high energy costs and are struggling with modern pollutants such
as hormones and pharmaceutical residues. There is also a growing need for
nutrient recycling as today most of the nutrients and trace elements end up in
wastewater streams and are disposed of, not recycled. These waste streams are
difficult to reuse as the pollutants, especially heavy metals, pharmaceuticals
and hormones, make it hard to use these materials as fertilizers in food
production. All these problems need to be addressed in order to create a
sustainable cycle of resources and energy.
Microalgae pose an interesting answer to many of the problems currently
associated with wastewater treatment plants. Photobioreactors using
wastewater as a growth medium for algae can treat the wastewater in a
biological manner, use less energy and produce biomass in the process.
Wastewater is a highly accessible medium for algae growth as it is produced
in large quantities and its collection and treatment infrastructure is already in
place in many large cities. Because of the amount of microorganisms already
present in wastewater, the resulting consortium of bacteria and algae in the
photobioreactor would be robust and able to break down or take up many of
the pollutants in wastewater (Muñoz and Guieysse, 2006). The resulting
biomass will be nutrient rich and creates an effective way of recovering
nutrients (Riaño et al, 2012, Muñoz et al. 2009; Su et al. 2011; Termini et al.
2011). It could also be an important energy resource as it is can be used in
biodiesel (Sivakumar et al. 2012; Rawat et al. 2013) or biogas production
(Mussgnug et al., 2010; Olguín, 2012; Passos et al., 2013; Zhao et al., 2013).
The ability to add an energy production element to wastewater treatment is
very interesting. Ideally microalgae can double their biomass in one day
(Demirbas and Demirbas, 2011) and as Muñoz and Guieysse (2006) point out,
several studies have shown that mixed consortium photobioreactors (such as
the ones one would get when mixing algae with wastewater microorganisms)
have higher capacity of producing biomass than pure cultures. They also use
CO2 from the atmosphere and produce O2 while using energy from the sun
making them a reusable and carbon negative energy source.
Industrial scale production of algae has however lagged severely behind
the expectations from lab experiments. The main reasons cited for this delay
are high cost and energy input issues involved in growing algae (Amer et al.,
2011; Beal et al., 2012). In contrast to this there are authors (Norsker et al.,
9
2011; Sivakumar et al., 2012) who have found that the problem is our limited
understanding of complex biological systems and by optimizing the
photobioreactors, they can become cost and energy efficient.
At current point there is too little knowledge about the community
dynamics and interactions within the photobioreactors. Much of the science
currently available was done before the advent of molecular biology and due
to the limitations of classical microbiological methods, very little is known
about the true community (Ferrero et al., 2012). Authors like Lakaniemi et al.,
(2012) and Subashchandrabose et al., (2011) have concluded that the
understanding of interactions between micro-organisms in photobioreactors
can increase the amount of biomass produced.
1.1 Objectives of the thesis
The overall objective of this thesis was to study the potentials for
microalgae cultivation in municipal wastewater. The thesis provides results
from different mixtures, concentrations of wastewater, pollutant removal of
microalgae and algal/bacterial community dynamics and composition in labscale photobioreactor systems.
The specific objectives were to (1) study the growth of algae in different
ratios of lake water and waste water, the retention of heavy metals and
nutrients throughout the algae cultivation process (Paper I), (2) investigate the
dynamics of microbial and algal community in a wastewater photo-bioreactor
after introduction of indigenous algae from a nearby in-land lake and how the
nitrogen and phosphorous concentrations change throughout the algae
cultivation process (Paper II) and to (3) compare the microbial communities
in inoculated and non-inoculated photobioreactos to find connections between
the growth of biomass, the reduction of nutrients and pollutants and
community in the photobioreactors (Paper III).
1.2 Contributions
This licentiate thesis contributes to the scientific knowledge on wastewater
treating photobioreactor systems. Because the majority of studies in the
scientific literature are based on pure cultures or specific growth mediums,
this thesis provides important addition to the community interactions in a
complex growth medium composed of waste and mixed microbial consortia.
These contributions can be very valuable in designing future water treatment
photobioreactors that have an added value of producing biomass.
The algal/bacterial community dynamics and interactions are studied using
modern molecular methods. Metagenomic analysis of the microorganisms
within the reactors can be beneficial in understanding the community
interactions and designing molecular markers to study the community
10
dynamics with detail never achieved before. With this information new
possibilities to study and improve the microbial consortia in photobioreactors
are provided for future research.
1.3 Thesis outline
This licentiate thesis is comprised of three scientific papers (Paper I-III)
and the results found in them. The main parameters of interest are shown in
Figure 1.
The papers included in this thesis (Papers I-III) share a common goal of
describing parameters connected to the photobioreactors and their effects on
the emerging microbial population in the studied photobioreactors. Figure 1
shows the main parameters of interest in Papers I-III. Different parameters
concentrated on in the different papers are marked on the figure however each
new paper took the previous parameters also into consideration to improve the
quality of the data.
Figure 1. Main parameters of interest in the thesis. Blue areas mark
parameters concentrated on in Paper I, purple areas mark parameters
concentrated on in Paper II and green areas and lines mark parameters
concentrated on in Paper III.
The licentiate thesis is comprised of the following chapters:
Chapter 1 Introduction of the thesis by giving its objectives, author
contributions and outline.
Chapter 2 Background information on the field and related research.
Chapter 3 Methodologies used in the studies.
Chapter 4 Results of the studies and discussion.
Chapter 5 Main conclusions of the thesis.
Chapter 6 Possibility of future work.
11
2 Background and related work
2.1 Microalgae for wastewater treatment
Microalgae have been studied as an interesting treatment option for
wastewater for several decades and are often used for tertiary wastewater
treatment. Authors such as Muñoz and Guieysse (2006) however feel that they
could also be used in the secondary treatment of wastewater and have listed
BOD, nutrient, heavy metal, pathogens as well as heterotrophic pollutant
removal as the main factors in algal wastewater treatment. Since microalgae
are used as indicators of ecological changes, they could be used for toxicity
monitoring for the inflowing water.
Figure 2. Value-addition of the consortia of cyanobacteria/microalgaebacteria after bioremediation (Subashchandrabose et al., 2011).
12
Wastewater is produced in high quantities wherever there is human habitat
which makes it an accessible medium for algae growth. Using wastewater
would remove the cost of fresh water and chemicals used to make feed
solutions for the algae. The resulting consortium of bacteria and algae in the
photobioreactor is thought to be more robust and accustomed to the limiting
components in the wastewater. In addition to energy production, using algae
photobioreactors to treat wastewater can give more value to the wastewater
treating process by producing different metabolites that can be sold (Fig. 2)
(Subashchandrabose et al., 2011).
As Muñoz and Guieysse (2006) point out, several studies have shown that
a mixed consortium has a higher capacity of producing biomass than pure
cultures. The algae-bacterial consortium offers several advantages over a pure
culture system. In natural systems most microalgae and cyanobacteria live in
association with other microbes so they influence each other in many ways
(Subashchandrabose et al., 2011). In addition to the CO2/O2 exchange between
the algae and bacteria there are different inhibiting and enhancing mechanisms
in the interaction between the two (Fig. 3). While the microalgae raise the pH
and dissolved organic carbon (DOC) of the system and excreting inhibitory
metabolites having a negative effect on the bacteria, they can also aid the
bacterial growth by releasing extracellular compounds. In a similar way,
bacteria can release algae growth promoting factors or reduce the O2
concentration, giving benefit to the algae while some bacteria can produce
algicidal extracellular metabolites inhibiting the microalgae (Muñoz and
Guieysse, 2006).
Figure 3. Positive (dashed line) and negative (plain line) interactions
between microalgae and bacteria (Muñoz and Guieysse, 2006)
As was established in the introduction there is still very little knowledge
about the community interactions in the photobioreactors (Ferrero et al.,
2012). This is a big gap in the research field as authors like Lakaniemi et al.,
13
(2012) and Subashchandrabose et al., (2011) have concluded that the
understanding of interactions between micro-organisms in photobioreactors is
needed to increase the amount of biomass produced while keeping energy
costs down.
2.2 Microalgal biomass for biogas production
Because of photosynthesis microalgae can produce lipids, proteins and
carbohydrates in large amounts over short periods of time. These products can
be useful as they can be processed into different biofuels and useful chemicals
(Demirbas, 2011). Producing biogas from microalgal biomass is very
interesting as the biomass growth could reach five to 30 times that of crop
plants (Sheehan et al., 1998). Using algae to simultaneously treat wastewater
and produce biofuels also has a potential to offset the costs related to
wastewater treatment (Christenson and Sims, 2011).
Singh and Dhar (2011) reported in their review that regardless of the
operating conditions and species used in the different studies, the proportion
of methane in the biogas produced fell mostly in the range of 69–75%, which
is comparable to biogas production from food waste (Zhang et al. 2007) and
the quality of converting organic matter into methane was satisfactory.
There are several studies where different microalga strains are compared in
their ability to produce biogas (Mussgnug et al., 2010; Frigon et al., 2013) and
it seems that the production potential is highly related to the algae species.
Another important factor is pre-treatment, which can have a substantial effect
on the yield and quality of the produced biomass (Ometto et al., 2014)
2.3 Photobioreactor design
There are many parameters to consider when designing photobioreactors.
The availability of commercial photobioreactors is limited and many
configurations of photobioreactors have been devised and built (Behrens,
2011). Differences in the lights used (Tang et al., 2011; Park et al., 2012)
medium, temperature, biomass production and reactor type (Muñoz et al.,
2009, Christenson and Sims, 2011) vary a lot. In this chapter the main
parameters of interest are discussed.
2.3.1 Nutrient supply
The primary nutrients algae need to grow are carbon, nitrogen, and
phosphorus. In addition, there are micronutrients that are needed in trace
amounts including silica, calcium, magnesium, potassium, iron, manganese,
sulphur, zinc, copper, and cobalt (Knud-Hansen et al., 1998). Christenson and
Sims, (2011) concluded that domestic wastewater treatment plants and many
14
other waste water treatment facilities are good candidates for algae-based
treatment due to the wastewater compositions where the nitrogen to
phosphorus ratio is similar to the Redfield ratio of 16:1, which is assumed to
be best for algae growth. They also contain a lot of trace elements that algae
can use for growth.
2.3.2 Gas exchange
Gas exchange within a photobioreactor is an important engineering
problem. There is a need to direct CO2 to the system while removing O2 in the
process. Both activities help to enhance algae growth as excess oxygen
concentrations (above air saturation) inhibit photosynthesis (Christenson and
Sims, 2011). CO2 is the most important carbon source for algae and adding it
into the photobioreactor can improve growth significantly. Authors (Chiu et
al., 2008; Pegallapati and Nirmalakhandan, 2013) have reported faster growth
when 2-5% of CO2 was mixed in with the air that was pumped in.
Bubbling carbon dioxide into the culture is a simple solution from the
engineering perspective but it is often not effective enough. Bubble residence
time may be too short, much of the CO2 pumped in ends up in the atmosphere
(Mata et al., 2010). This greatly reduces the carbon neutrality of the algae
photobioreactor in addition to wasting energy and cost effectiveness.
When a re-usable carbon source, such as industrial flue gas is near, it can
also be bubbled into the reactors. This helps reduce the cost of running the
photobioreactor and carbon emissions from the flue gas producer. In a labscale study conducted by Chen et al., (2012) blue-green algae were able to
adsorb the CO2 in flue gas and capable of fixing 2,234 kg of CO2 per annum.
When used in larger scale, this kind of systems could help fix a considerable
amount of CO2 in biomass that would otherwise end up in atmosphere.
2.3.3 Light
Light availability is one of the major limiting factors in photobioreactor
design. It is the main medium through which energy is added to the algae in
the system so parameters such as light wavelength, intensity, penetration,
regime etc. must be considered.
As with other photosynthesising organisms, such as plants, wavelengths
between 400 and 700 nm (Photosynthetically Active Radiation) need to be
most prevalent.
For the most part fluorescent light tubes have been used as the light source
as research has shown their effectiveness (Tang et al., 2011) and due to their
availability and relative cheapness. A lot of research is currently being done
on LED lights due to their low energy use and ability to have specific
wavelengths (Park et al., 2012, Zhao et al., 2013) but as they are usually more
expensive than the equivalent fluorescent tubes, they are not yet mainstream.
15
Generally algal growth activity increases with light intensity until around
200-400 μmol/m2s (Muñoz and Guieysse, 2006). Similar results have been
presented by Tang et al., (2011) who found that 350 μmol/m2s was the optimal
light intensity for algal growth. They detected no difference between 350 and
400 μmol/m2s. With LED lights there have been reports of even higher light
intensities giving very good results. Zhao et al., (2013) found optimal growth
light intensity to be between 1200 and 1600 μmol/m2s.
One major challenge with lighting a photobioreactor is light penetration.
This is particularly a problem when the photobioreactor uses raw wastewater
which is dark in coloration and can have a high amount of particulate matter.
The main ways to deal with this problem are increasing the surface area to
volume ratio of the reactor and/or adding vigorous mixing so the cells can
have enough time in the illuminated sections of the reactor (Christenson and
Sims, 2011).
In nature light is not consistent and a 24 hour light regime is in place. This
has been replicated in lab experiments with varying degrees of success. Tang
et al., (2011) found that photoperiods of 12 h light/12 h dark and 15 h light/9
h dark achieved a higher cell density than constant light. However, the total
biomass productivity was higher with continuous light. Having a photoperiod
similar to summer conditions is appealing as turning the light off for several
hours every day helps save energy needed to run the photobioreactor.
2.3.5 Harvesting
Harvesting of the algae grown in mixed culture photobioreactors is an
engineering challenge which is not yet completely solved. This is largely due
to the size of the objects needing to be harvested - unicellular eukaryotic algae
3–30 μm and cyanobacteria 0.2–2 μm – and relatively dilute cultures
(Christenson and Sims, 2011). Due to this, harvesting is an expensive and time
consuming process and can account for 25-60% of the total cost of microalgae
production (Grima et al., 2003).
Flocculation has been used in wastewater treatment plants for a long time
for dewatering sludge and the infrastructure in place makes it a good candidate
for algae harvesting. Udom et al., (2013) investigated the costs and life cycle
impacts of several coagulants and found that in jar tests many of them
including ferric chloride, alum and cationic polymers, could achieve > 91%
algae recovery without pH adjustment.
Though it can be a cheap option, chemical flocculation results in
contamination of the biomass. There are natural polymers that may minimize
this problem however more studies looking into biological and physical
flocculation may provide an answer to this problem (Vandamme et al., 2013).
16
There are processes using microfiltration and centrifugation for algae
harvesting but their complexity and cost may be limiting and more research is
needed (Bilad et al., 2013)
2.4 Community analysis
Although physical and chemical parameters are important to the
development of algae in photobioreactors, community analysis of the growing
algae can give valuable information to improve both the quality and the
amount of biomass produced.
Historically photobioreactor research has focused on fine tuning the
parameters to enhance growth and choosing the best strains for a given task.
With mixed cultures, such as those that are produced in wastewater treating
photobioreactors this kind of data will give limited resources. Much of the
research on mixed culture photobioreactors has been done before widespread
use of molecular biology methodologies so there is very little known about the
true community composition (Ferrero et al., 2012).
In recent years genomic studies of photobioreactors have started to appear
in the literature. Since novel molecular biology methods such as metagenome
sequencing are getting cheaper and easier, there is hope of getting a deep
understanding of the interactions between micro-organisms in mixed culture
photobioreactors. With such information, biomass production and water
treatment efficiency could be increased and fine-tuned to specific use cases
(Subashchandrabose et al., 2011; Lakaniemi et al., 2012). In addition,
development of molecular probes can simplify molecular analysis further and
can allow for rapid monitoring of the communities present (Carney et al.,
2014).
Using Polymerase Chain Reaction Denaturating Gradient Gel
Electrophoresis (PCR-DGGE) and Real-Time Polymerase Chain Reaction
(RT-PCR) Erkelens et al., (2014) analysed the impact of the microalgae
harvesting on the bacterial load in a pilot scale microalgae raceway pond.
Krohn-Molt et al., (2013) took a more detailed look at a mixed-species
bacterial biofilm in a photobioreactor associated with Chlorella vulgaris and
Scenedesmus obliquus, analysing the community composition and metabolic
diversity, Combining metagenome sequencing with GS FLX Titanium from
Roche and HiSeq 2000 from Illumina, they were able to get 350 Mbp of
sequenced DNA, 165 Mbp of which was assembled in contigs with a size up
to 0.2 Mbp.
Carney et al., (2014) took a different approach using an amplicon based
method sequencing the hypervariable region V4 of the eukaryotic small
subunit (SSU) rRNA to distinguish algae and the hypervariable region V6 of
the bacterial SSU rRNA to distinguish bacteria. This methodology gives a
more limited sample of the DNA found in the photobioreactor than the one
17
used by Krohn-Molt et al., (2013). However, since Carney et al., (2014) used
a mixed algae community instead of specific species, their results give a better
overview of the eukaryotic community that can develop in a wastewater
treating photobioreactor.
18
3. Methodology
3.1 Wastewater and lake origin and properties
Wastewater was sampled from the inflow of the municipal Wastewater
Treatment Plant (WWTP) in the city of Västerås, Sweden. The plant treats
sewage from 118 000 population equivalents, using a conventional activated
sludge process. The inflowing raw wastewater is screened, pre-precipitated
with iron sulphate and biologically treated with activated sludge process.
Glycol is added to support the pre-denitrification process. Samples were taken
from the top layer in the centre of the mixed basin before the chemical
precipitation step.
Lake water, used as an inoculant to add algae to the photobioreactor system,
was sampled from a yacht harbour near the WWTP from the upper layer (0.5
m) of Lake Mälaren, the third largest lake in Sweden (Kvarnäs, 2001).
Sampling was done following the SS/ISO 5667-3:2004 standard with
sterilised equipment and were immediately transported to a refrigerator at 4°C.
3.2 Experimental setup
The experiments described in Papers I-III follow a similar setup but ascend
in complexity and volume. The first experiments were designed as proof of
concept with 250 ml flasks (Paper I). Lake- and wastewater ratios of 30/70,
50/50 and 70/30 were tested and compared to pure lake- and wastewater
samples. Due to the small volume, stirring was not added and the flasks were
manually shaken every 24 hours. Since 70/30 wastewater/lake water mixture
performed most optimally, this was the ratio used in Paper II and Paper III.
In Paper II reactors with a volume of 1 L were used. 4 separate reactors
were used in all 3 experiments:
1. a tap water reactor (TWR) containing 30% tap water and 70%
waste water,
2. a lake water reactor (LWR) containing 30% lake water and 70%
wastewater and
3. a wastewater reactor (WWR) containing 100% wastewater.
As a control a sterilized wastewater reactor (SWR) was set up to test for
contamination.
Three separate experiments with similar conditions were conducted with
this set up – one in August, one in November and one in December – to test if
19
lake water sampling season had an effect on algal growth in the
photobioreactor.
In Paper III 20 L photobioreactors were designed and used to get more
stable results and more accurate data (Fig. 4). Two experiments were
conducted, one using three TWRs and another using three LWRs as described
earlier.
The specific physical parameters involved in each set up are described in
the respective papers but they follow data published by Tang et al., (2011).
With each experiment specific limitations had to be accounted for due to the
volume or type of reactor used.
Figure 4. The purpose built photobioreactors (usable volume 20 L) used
for experiments in Paper III.
3.3 Algal growth dynamics
One of the main parameters of interest in all papers was algal growth. In
Paper I optical density (OD) at 630 nm was used as a reference of algal growth.
Due to the complex nature of the wastewater samples this proved to be a poor
indicator of true algae growth. For papers II-III chlorophyll α concentration
was measured as described in Bellinger and Sigee, (2010) to get a better
indication of algal growth.
The algae community (Paper II) was studied using Alphaphot-2 YS2
microscope (Nikon Instruments Inc., Tokyo) using a 60x lens.
3.4 Molecular methods and community analysis
Bacterial analysis presented in this thesis (Papers II-III) was conducted
using the DNA extracted from the water samples. DNA was extracted using
MoBio PowerWater DNA extraction kit (Mobio Laboratories Inc., Carlsbad,
CA, USA) in all experiments.
The development of the bacterial community (Paper II) was estimated
using 16S rRNA gene copy numbers. To achieve this RT-PCR was used with
20
primers for the amplification of bacterial 16S rRNA gene 111bp fragment
from V6 hypervariable region. The data was analysed as described by Nõlvak
et al., (2012).
To describe the bacterial, archaeal and algal communities (Paper III), the
metagenomes of the samples were sequenced and analysed.
DNA concentrations were measured with the Quant-iT™ PicoGreen®
dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). The samples were diluted
in the EB buffer (Qiagen, Venlo, Netherlands) and prepared using the Nextera
DNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The
manufactures protocol was modified by using 100 ng input DNA instead of
50ng and the second purification step was replaced with the NucleoSpin kit
(Macherey-Nagel, Düren, Germany).
The sample concentrations were measured with the Qubit Fluorometer and
samples were normalized. Sequencing was done using the MiSeq Benchtop
Sequencer system (Illumina, San Diego, CA, USA).
The sequenced samples were analysed using MG-RAST software (version
3.3.7.3) with SILVA Small Subunit and M5 non-redundant protein (M5NR)
databases
3.5 Pollutant removal
Nutrient concentrations were measured in the beginning and the end of the
experiments in Papers I and II and nutrient dynamics with samples being taken
every 4 days were analysed in Paper III. The nutrients under investigation
were ammonium (NH4), nitrate (NO3) and total phosphorous (TP) as they are
important for the growth of microorganisms in the system and their removal
by the system is an important goal. Nitrogen and phosphorous concentrations
need to be reduced in all wastewater treatments systems as they cause
eutrophication when their concentrations rise too high in natural waters.
Metal concentrations in the beginning and the end of the experiments were
also analysed in Papers I-III. In the first paper Fe, Al, As, Ba, Cd, Co, Cu, Mn,
Ni and Zn concentrations were measured to get an overview of the problematic
metals within the wastewater used (Paper I). In Papers II and III we focused
on Cr, Co, Ni, Cu, Zn, As and Cd as they were the main metals of interest.
3.6 Statistical analysis
In Papers I and II only descriptive statistics were used to analyse the data
due to low replication.
In Paper III principal component analysis (PCA) was performed on arcsine
square root transformed data. STAMP (Statistical Analysis of Metagenomic
Profiles) v 2.0.2 software was used to analyse the differences in community
and functional systems in different metagenomic profiles extracted from the
samples (Parks and Beiko, 2010).
21
Two-way Analysis of Variance (ANOVA) (Excel 2010) was used to
determine the significance while comparing the treatments.
4 Results and Discussion
4.1 Algae community and growth dynamics
As only OD was used as an indicator of algae growth in Paper I, the results
left many questions unanswered. However, we did see that mixtures
containing more wastewater and hence more nutrients supported the growth
of algae better. To have a clearer understanding of the algae growth
chlorophyll α concentration was used as an indicator in Papers II and III.
Paper II showed algal growth in all reactor set-ups: LWR, TWR and WWR.
There were however differences in the maximum growth and growth rate
depending on the reactor type and the season during which sampling occurred.
Not surprisingly, the algal growth rate was fastest and overall maximum
growth highest in the LWR with lake water sampled during the summer
season. This is most likely because of a higher concentration of active algae
present in the lake water. While both the LWR and TWP reached a comparable
maximum chlorophyll a concentration, the growth rate in the reactor with lake
water was 2-3 days faster.
In November, algae growth was considerably slower compared to the
summer season, in all reactors and the maximum chlorophyll a concentration
was around half of what it was during the summer. LWR had the highest
maximum chlorophyll a concentration and growth rate, showing that the
addition of lake water had a positive effect on algal growth.
In experiments conducted in December, after the lake had frozen, algal
growth was lower still and there was no noticeable effect in adding lake water
to the reactors.
As the benefits of adding the lake water had contradicting results in Paper
II, Paper III was designed to have triplicates of both LWRs and TWRs. There
was a considerable difference in the chlorophyll α concentration on the 16th
day between the two experiments. The LWR did significantly (p<0.001) better
than the water mixtures having 38% higher concentration than the TWR. The
LWRs also had a higher growth rate reaching its peak chlorophyll a value at
12 days, after which it started to stabilize (Fig. 5). However the TWRs had
less variation between the triplicates resulting in a lower standard deviation.
22
2.0
a)
12
TWR
LWR1
LWR2
LWR3
1.8
1.4
11
10
1.2
9
1.0
pH
Chlorophyll a (mg/L)
1.6
b)
0.8
8
0.6
7
0.4
6
0.2
5
0.0
4
0
2
4
6
8
10 12
14 16 18
0
2
4
Days
6
8
10 12 14 16 18
Days
Figure 5. Dynamics of chlorophyll a (a) and pH (b) during the
experimental period. Shown are arithmetic means of the treatment triplicates
in the tap water reactors (TWR), lake water reactors (LWR) and separate
values for each lake water reactor (LWR 1-3) where needed due to the large
differences between LWR1-2 and LWR3. Error bars indicate confidence
intervals.
In addition to algal growth, the developed community was studied in Paper
II to assess the speciation resulting in algae growing in wastewater. Data from
microscoping showed that after 16 days of growth the two reactors with the
most diverse communities were the WWRs and the LWRs. Representatives
from many algae genera were found in the reactors. The most common ones
were: Scenedasmus, Desmodesmus, Chlorella, Oocystis, Selenastrum,
Monoraphidium, Diatoms and Sphaerocystis (Fig. 6). The differences
between the algae communities in different reactors were difficult to assess
with a microscope however dominance of certain algae was apparent. In all
experiments the dominant algae were Scenedesmus, Desmodesmus and
Chlorella.
1
3
2
4
23
Figure 6. Representative microscope images of the wastewater (WWR)
(a), sterilized wastewater (SWR) (b), lake water (LWR) (c) and tap water
(TWR) (d) reactors in the experiment performed in August presented in Paper
II. Arrows show examples of Chlorella (1), Scenedesmus (2), Oocystis (3) and
Monoraphidium (4). Images for each reactor were selected based on the most
variety of species present.
For a more detailed and exact analysis of the algae community, reactor
metagenomes were sampled and analysed in Paper III. Most eukaryotes in the
reactors were part of the phyla Chlorophyta which contains green algae (Fig.
7). This was especially true in the LWRs where the general algae abundance
was considerably higher than in the TWRs. There was also a difference in the
composition of the algae community (Table 1). In the TWRs the most
abundant algae species were Chlorella vulgaris, Nitzschia frustulum,
Phaeodactylum tricornutum, Gomphonema affine and Micractinium pusillum.
In the LWRs, the most abundant algae were Scenedesmus obliquus,
Desmodesmus costato-granulatus, Scenedesmus acutus, Chlorella vulgaris
and Pseudopediastrum kawraiskyi with Scenedesmus obliquus being the most
abundant in all replicates. In addition to the differences in the dominant
species, the relative abundance of the algae differed substantially between
TWR and LWR (Table 1).
24
Figure 7. Heat-maps based on the rRNA reads annotated using SILVA
SSU database of eukaryote phyla in the lake water (LW), wastewater (WW1,
WW2), tap water reactor (TWR1-3) and lake water reactor (LWR1-3)
samples. Colour intensity (white to red) shows relative abundance of the
specific phyla in the sample groups.
25
Table 1. The ten most abundant algae species (by average number of hits
in the metagenome) in the tap water reactors (TWR1-3) and lake water
reactors (LWR1-3) based on the rRNA reads annotated using the SILVA SSU
database of bacteria.
No
Species
1 Chlorella vulgaris
2 Nitzschia frustulum
3 Phaeodactylum
tricornutum
4 Gomphonema affine
5 Micractinium pusillum
6 Nitzschia palea
7 Pinnularia subcapitata
8 Grammonema striatula
9 Scenedesmus obliquus
10 Navicula sp. C20
No
Species
1 Scenedesmus obliquus
2 Desmodesmus costatogranulatus
3 Scenedesmus acutus
4 Chlorella vulgaris
5 Pseudopediastrum
kawraiskyi
6 Hydrodictyon
reticulatum
7 Chlorella pyrenoidosa
8 Ulva fasciata
9 Micractinium pusillum
10 Chlorella sorokiniana
26
TWR
1
5
17
16
TWR
2
40
22
8
TWR
3
113
33
18
14
1
1
7
8
5
5
LWR
1
916
200
10
5
11
1
0
3
1
LWR
2
1984
100
15
11
5
7
6
6
5
LWR
3
632
17
86
51
52
160
148
153
63
101
76
103
100
94
59
91
32
61
72
40
13
6
43
41
37
34
5
19
24
19
40
33
25
20
Average
53
24
14
13
6
6
5
5
5
4
Average
1177
106
4.2 Bacterial dynamics and community analysis
Bacterial community abundance changes were examined by quantifying
the 16S rRNA gene in each of the samples in Paper II and Paper III. In Paper
II the results were varied depending on the season of lake water sampling. In
the reactors studied in November and December, the 16S rDNA copy numbers
decreased until the 8th day in both experiments after which they stabilized.
This is in contrast to the chlorophyll a concentration seen in Paper II which
was stable until day 4-8 after which it started to grow. There were higher 16S
rDNA copy numbers in the beginning of the November experiment but in both
experiments they stabilized around the same value. In the experiment
conducted in August, the initial 16S rDNA gene copy number was 3-4 times
lower than in the experiments conducted in November and December. The
bacterial abundance was also stable, not showing a similar decrease as seen in
the other 2 experiments.
The bacterial community dynamics were also studied in Paper III with an
updated and more sensitive methodology (Fig. 8). The difference between the
LWRs and the TWRs bacterial 16S sRNA gene copy number was not
significant. Furthermore, the dynamics of this gene abundance was similar for
the both reactor types. A significant decline (p<0.001) in this parameter value
on average 2.8 and 2.5 times occurred in TWRs and LWRs, respectively
during the 16 days of the reactors performance. The statistical analyses
revealed a strong negative correlation (Spearman R=-0.87; p<0.001) between
16S rRNA gene copy numbers and chlorophyll a concentrations in LWRs.
While there was a similar trend in the TWRs, the statistical analyses did not
found significant correlation (p>0.05) between these two parameters in these
reactors. This relationship reflects successions of bacterial community in the
LWRs due to changes in origin and forms of available organic matter. Shift in
bacterial community composition is related to decrease in bacterial species
dependent on organic matter supply originating from wastewater, and increase
in abundance of taxa utilizing organic matter released by algae. In addition pH
is growing in the system and there may be selective pressure from the photobioreactor selecting out bacteria better suited to the arising algal community.
Another reason may be that algae and cyanobacteria can inhibit the growth of
nitrifying bacteria (Choi et al., 2010) and as algae concentrations increases the
bacteria will decrease.
27
16S rRNA gene copies mL -1
109
TWR
LWR
8x108
6x108
4x108
2x108
0
0
2
4
6
8
10
12
14
16
Days
Figure 8. Dynamics 16S rRNA gene copy numbers in Paper III.
Abbreviations: LWR – Lake water reactor with 70% wastewater and 30% lake
water; TWR – Tap water reactor with 70% wastewater and 30% tap water.
Bacterial community composition was studied in Paper III. Cyanobacteria
were not diverse in the studied reactors. Dolichospermum macrosporum was
identified as the most abundant cyanobacteria. However the third replicate of
the LWRs diverged from its replicates in having a more diverse community
of cyanobacteria with the most abundant cyanobacteria belonging to the genus
Pseudanabaena.
The most abundant bacteria in both the lake and TWRs belonged to the
phyla Proteobacteria and Bacteroidetes with most dominant families in both
treatments being Sphingobacteriaceae, Cytophagaceae, Flavobacteriaceae,
Comamonadaceae, Planctomycetaceae, Nocardiaceae and Nostocaceae.
Firmicutes abundance decreased in both tap water and LWRs (Fig. 9). The
most abundant Firmicutes families in the wastewater were Ruminococcaceae
Clostridiaceae and Lachnospiraceae, respectively. After the treatment the
dominant Firmicutes families were Bacillaceae, Clostridiaceae and
Peptococcaceae, respectively showing that the algal growth had an impact on
lower taxonomic levels as well.
A similar change occurred in both treatments with phylum Actinobacteria
where the relative abundance was reduced more in the LWRs than in the
TWRs. The most abundant Actinobacteria families in the TWRs were
Nocardiaceae, Conexibacteraceae and Streptomycetaceae. In the LWRs they
were Mycobacteriaceae, Microbacteriaceae and Streptomycetaceae with
significantly lower abundance.
There were also differences in the most dominant families overall (Table
2). In the wastewater samples before treatment Comamonadaceae,
28
Rhodocyclaceae, Burkholderiaceae and Bacteroidaceae were dominant. After
16 days the dominant families in TWRs were Comamonadaceae, unclassified
Burkholderiales, Sphingobacteriaceae and Planctomycetaceae. The dominant
families in the first two replicates of the LWRs were Rhodobacteraceae,
Erythrobacteraceae and Sphingomonadaceae. These were different from the
dominant families in the TWRs and pure wastewater and lake water samples.
In the third lake water reactor the dominant families were Comamonadaceae,
Rhodobacteraceae and Burkholderiaceae differing less from the pure WW.
Both the algal and the bacterial communities were partly similar to those
observed by Krohn-Molt et al. (2013) and Carney et al. (2014) which suggests
a consistency in the composition of mixed community photobioreactors.
The samples studied in Paper III were also examined for the presence and
relative abundance of pathogens. Both treatments successfully reduced the
overall amount of pathogens in the wastewater. On average, the LWRs
performed better in reducing the overall amount of pathogens resulting in a
76.1±9.0% reduction. This may be due to the difference in the community;
however since the pH was considerably higher in the LWR, it is also likely to
have had an effect on the pathogen population survival.
29
Table 2. The ten most abundant bacterial families (by average proportion)
in the tap water reactors (TWR) and lake water reactors (LWR) based on the
rRNA reads annotated using the M5 non-redundant protein database (M5NR)
in MG-RAST. Proportions in the wastewater (WW) and lake water (LW) used
are displayed as comparison.
No.
Family
TWR
WW
LW
1
Comamonadaceae
0.10±0.06
0.18
2
unclassified Burkholderiales 0.06±0.01
0.04
3
Sphingobacteriaceae
0.04±0.01
0.01
4
Planctomycetaceae
0.04±0.01
0.004
5
Flavobacteriaceae
0.03±0.01
0.03
6
Burkholderiaceae
0.03±0.01
0.04
7
Cytophagaceae
0.03±0.01
0.01
8
Rhodobacteraceae
0.03±0.004
0.02
9
Sphingomonadaceae
0.3±0.02
0.01
10
Xanthomonadaceae
0.02±0.002
0.01
No.
Family
LWR
WW
LW
1
Rhodobacteraceae
0.22±0.15
0.01
0.01
2
Sphingomonadaceae
0.08±0.05
0.003
0.01
3
Comamonadaceae
0.07±0.07
0.20
0.23
4
Erythrobacteraceae
0.06±0.05
0.001
0.002
5
Cytophagaceae
0.03±0.02
0.005
0.01
6
Acetobacteraceae
0.03±0.02
0.002
0.004
7
Flavobacteriaceae
0.03±0.01
0.02
0.06
8
Rhizobiaceae
0.03±0.01
0.01
0.01
9
Planctomycetaceae
0.02±0.02
0.002
0.004
10
Bradyrhizobiaceae
0.02±0.003
0.01
0.01
30
Figure 9. Heat-maps based on the rRNA reads annotated using SILVA
SSU database of prokaryote phyla in the lake water (LW), wastewater (WW1,
WW2), tap water reactor (TWR1-3) and lake water reactor (LWR1-3)
samples. Colour intensity (white to red) shows relative abundance of the
specific phyla in the sample groups.
31
4.3 Functional analysis of the metagenomes
In Paper III functional analysis of reactors’ metagenomes based on 28
different subsystems of the SEED project (Overbeek et al., 2005) was
performed to get an overview of what functional genes are present in the
photobioreactors. There were several functional differences between lake
water and wastewater communities. Lake water was higher in phages,
prophages, transposable elements and plasmids, and more importantly in
photosynthesis subsystems. Wastewater however, was higher in virulence,
motility, chemotaxis and nitrogen metabolism subsystems compared to the
samples from the TWRs and LWRs. LWRs had higher relative abundance of
subsystems related to photosynthesis, vitamins, cofactors, prosthetic groups
and pigments subsystems. The TWRs were higher in virulence, disease and
defence and nitrogen metabolism subsystems. Higher abundance of vitamin
biosynthesis genes in the LWRs was due to higher proportion of B-group
vitamins, particularly pyridoxine, biotine and folate related genes.
There was also a difference in the bacteria contained cobalamin and
coenzyme B12 synthesis genes between the two reactor types. The B12
vitamin (cobalamin) is very important for algal growth and is acquired through
a symbiosis with bacteria (Croft et al., 2005). In the TWR the most dominant
bacteria containing these genes were unidentified by MG-RAST. The second
and third highest cobalamin and coenzyme B12 gene abundance was in
Methylibium petroleiphilum and Leptothrix cholodnii, both from the
Burkholderiales order. In the LWR, it was Rhodobacter sphaeroides from the
Rhodobacterales order, contributing mostly to the cobalamin and coenzyme
B12 synthesis genes. This means that there was a distinct difference in the
bacteria providing the B12 vitamin to the algae. It is possible that this also had
an effect on the growth rate of the algae.
4.4 Nutrient dynamics and metal removal
4.4.1 Carbon
Carbon concentrations before and after the experiments were measured in
Paper II and III. As the algae took up CO2, Total Organic Carbon (TOC)
concentrations increased in all the reactors. DOC however showed a similar
decrease, meaning the carbon present in the water phase was taken up by the
algae. In the best performing LWR and WWR reactors presented in Paper II,
carbon was reduced 62.4% and 57.0%, respectively. Similar dynamics were
apparent in experiments presented in Paper III, with LWRs having a higher
increase in TOC concentration due to higher algal growth.
4.4.2 Nitrogen and phosphorous
In Paper I the nutrient removal was quite poor due to the low amount of
algae growth. In the tested wastewater/lake water mixtures, the best
32
performance in ammonium removal was seen in the 70% lake water and 30%
waste water mixture with 95% purification efficiency (PE). In the mixtures
with 50% wastewater and 50% lake water and 70% wastewater and 30% lake
water the PEs were 75% and 67%, respectively. In the same samples nitrate
concentration increased 3 and 8 times, respectively. This shows that most of
the ammonium removal was due to nitrification. TP removal was insignificant
throughout the experiment.
In Paper II there was a reduction in the ammonium concentration
throughout the experimental period and an increase in the NO3-N
concentration. After 12 days, the concentration of NH4-N was below 0.01 mg
L-1 in all the reactors. NO3-N increased to 1.6 mg L-1 in LWR and TWR and
to 1.8 mg L-1 in the WWR. After 16 days, NO3-N concentrations were <0.005,
0.16 and 0.28 mg L-1 in LWR, TWR and WWR, respectively.
After all three of the experiments in Paper II, the TP concentrations were
below 0.05 mg L-1 in the water phase of all reactors
In Paper III the PEs were improved over the previous 2 papers, most likely
due to a higher algal growth rate and therefore a higher uptake of nutrients. In
the beginning of both experiments there was a fast reduction in the ammonium
concentration. The reactors in the first experiment started out with an average
NH4-N concentration of 34.9±2.4 mg L-1 and in the second experiment
39.1±1.1 mg L-1. In both cases, the concentration of NH4-N was below 0.01
mg L-1 after 4 days which means that >99.9% of the ammonium had been
removed. NO3-N concentration increased compared to the initial
concentration however it stayed below 0.7 mg L-1 in both experiments
showing that the nitrate resulting from nitrification was quickly assimilated
by the algae. The TP concentration in both of the reactors was low already in
the beginning due to the way wastewater is handled in the plant. There was
however a big difference in the dynamics after the start of the experiment. In
the water reactor TP concentration increased, most likely due to bacterial
breakdown of complexes. After the algae started to grow exponentially, TP
concentration stabilized and started a slow decrease.
ANOVA showed a significant difference (p<0.001) between the TWRs and
LWRs in both cases with NO3 and TP.
The LWRs performed better at removing TP than the reactors with water
added to wastewater. There was no initial growth in the total TP concentration
as the algae started their growth sooner. After the 8th day the concentration
was below 0.01 mg L-1.
The results found in Papers I-III are mostly similar to what other authors
have found in wastewater treating photobioreactors with Termini et al., (2011)
reporting 90-99% reduction in ammonium and Riaño et al., (2012) more than
99%. The growth in nitrate concentration seen in Papers I-II is probably due
to the high concentration of nitrifying bacteria commonly found in wastewater
(Harms et al., 2003). Because the reactors are aerobic, the bacteria can quickly
nitrify the ammonium before the algae start to grow. Similar results have been
33
reported by Karya et al., (2013) who showed that 81-85% of the ammonium
in a wastewater photo-bioreactor was removed by nitrification not by the
uptake of algae.
4.4.3 Metals
Metal concentrations were described in Papers I and III. In Paper I the
mixtures with 30% added lake water were successfully able to reduce Cu, Zn
and Ba concentrations. In all the samples mixed with inflowing wastewater,
there was a growth in Al concentration. With As Cd, Cr and Pb there was no
noticeable change in the samples as the concentrations were below detection
levels even before the experiment.
With Paper III only Cr, Co, Ni, Cu, Zn, As, Cd and Pb were studied as these
were determined to be the more important metals in the treatment process.
They were chosen due to their prevalence in the wastewater used and amount
of literature on their removal with algae.
Co and Zn concentration was significantly (p<0.001) reduced both in
mixtures (Fig. 9) with water and with lake water however there was no
significant difference between the mixtures. The average reduction for Co was
75.6±2.2% in the water reactors and 56.5±11.7% in the LWRs. For Zn the
respective reductions were 63.6±22.7% and 82.1±3.9%. Cr, Ni and Cu
concentrations showed a significant increase in mixtures with water (p<0.01,
p<0.05 and p<0.01 respectively). In the mixtures with lake water Cr and Cu
showed no significant change while Ni showed a statistically significant
reduction (p<0.05). As, Cd and Pb showed no statistically significant changes
in any of the mixtures nor was there a significant difference weather water or
lake water was added.
34
5 Conclusions
The algae grew successfully in a wastewater medium (Paper I-III) and it
was found that the best performance was in a reactor with a mixture of 30%
lake water to 70% wastewater (Paper I). The impact of addition of lake water
to photo-bioreactor on the performance of reactors was dependent on the
season when lake water was obtained. Sampling the lake water for inoculum
during a warmer season means higher and faster algal growth (Paper II).
The reactors were effective in removing ammonium and phosphorous from
the wastewater. After 16 days of cultivation, both nitrogen and phosphorous
levels in the water phase were below effluent standards in Sweden (Paper IIII). While 16 days is too long for a full-scale system to be effective, the
treatment time can be reduced by moving to a semi-continuous or a continuous
system or by optimizing the growth.
Overall, the concentration of algae in the photobioreactors increased as the
bacterial population decreased. By day 8 bacterial 16S rDNA gene copy
numbers had reached a stable base and the chlorophyll concentration started
rising. Adding lake water to the mixture had a noticeable effect as that reactor
outperformed both the WWR and TWRs (Paper II-III).
The most dominant algae in the photobioreactors studied were
Scenedesmus, Desmodesmus and Chlorella, which are commonly seen in
wastewater treating photobioreactors and can be potentially used for the
production of biogas and biodiesel (Paper II-III).
The most abundant bacterial phyla were Proteobacteria and Bacteroidetes.
This information is in line with previous research and reinforces our
understanding of the most important species in mixed culture photobioreactor
communities. (Paper III).
The LWRs had more abundant subsystems dealing with photosynthesis,
vitamins synthesis and cofactors and fewer controlling virulence and nitrogen
metabolism than the TWRs and pure wastewater. Bacterial community in
LWRs was more involved in synthesis of vitamins essential for auxotrophic
algae growth. (Paper III).
35
6 Future work
There is a need to go more deeply in to the microbial processes in
wastewater treating photobioreactors as the microbial and algal communities
seem to have a strong effect on each other and on the growth and nutrient
dynamics. With detailed information on the biological processes present in the
reactors, full scale wastewater treating photobioreactors could be better
modelled and optimized for the required purpose. With the current data we
can at least say that nitrogen metabolism and vitamin production by the
bacteria present do affect the algal growth.
To improve the process and understand what is happening to the
community and the pollutants, there are many more interesting parameters and
questions to study:
● Heavy metal dynamics.
● Moving from a batch reactor to a semi-continuous/continuous
system.
● Moving the process to a larger scale.
● Studying the metagenome data to find interesting functional
genes and interest points to analyse in the future.
Because we need to understand how the processes we study in the lab
translate to a full scale system, we have a pilot-scale plant in Västerås, Sweden
in the planning stage.
36
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