Nitrogen Removal and Lipid Production from Secondary

Wastewater Using Green Alga Chlorella vulgaris

Zhouyang Liu

B.S., Harbin Institute of Technology, 2007

Submitted in fulfillment of the requirements

for the degree of Master of Science

in Environmental Engineering

in the Engineering & Applied Science College of the

University of Cincinnati, 2012

Committee Members: Dr. Joo-Youp Lee (chair)

Dr. Tim Keener

Dr. Mingming Lu

Abstract

Increasing nitrogen discharges into natural water systems have caused more frequent eutrophication and other water quality issues. Microalgae are fast growing photosynthetic microorganisms that can assimilate nitrogen and phosphorus from water. Also, lipid content of certain strains of microalgae could reach over 60%, making microalgae excellent feedstock for biodiesel production. Green alga Chlorella vulgaris was tested for nitrogen removal and lipids production using secondary wastewater from municipal wastewater treatment plant. Around 60% of NH3-Nitrogen was removed after 48 hour, and removal rate was further increased to 75% when carbon dioxide gas was added periodically to control pH. When more active algae seeds were used, NH3-Nigrogen removal rate of 97.1% was achieved. Chlorella vulgaris was also very effective for removing low concentration of phosphate from secondary wastewater. When growing at normal conditions, Chlorella vulgaris contained more polar lipid than neutral lipid.

Total lipid content of Chlorella vulgaris ranged from 10.6% to 14%, and fatty acids were mainly

C16 and C18, making it good biodiesel stock. A freshwater microalgae survey in southwest

Ohio was also conducted to provide useful information for future outdoor algae cultivation. 24 genera of cyanobacteria and 49 genera of eukaryotic algae were identified, with Synechococcus sp. and Cryptomonas sp. being the predominant prokaryotic and eukaryotic microalgae, respectively.

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Acknowledgments

I would like to express my sincere gratitude to Dr. Joo-Youp Lee for his guidance and support throughout this study. I would like to thank Dr. Tim Keener and Dr. Mingming Lu for their helpful suggestions and feedbacks.

I would also like to thank Dr. Daniel Oerther and Dr. Mau-Yi Wu for assisting and teaching me molecular biology techniques, and all the members in the lab especially Jinsoo Kim for their help with my experiments.

Lastly, I want to thank my mother who never lost faith in me, and Zhen who was there with me when I was at the lowest point of my life, for which I am forever grateful.

Table of Contents Chapter 1. Introduction ...... 1

1.1 Nitrogen and Phosphorus Pollution ...... 1

1.2 Nutrients Removal Techniques ...... 2

1.3 Nutrients Removal Using Microalgae ...... 3

1.4 Biofuels from Microalgae ...... 4

1.5 Objective of This Study ...... 6

Chapter 2: Molecular Survey of Freshwater Microalgae in Southwest Ohio Area ...... 7

2.1 Introduction ...... 7

2.2 Materials and Methods ...... 8

2.2.1 Sites Description and Sampling ...... 8

2.2.2 Genomic DNA Extraction ...... 11

2.2.3 PCR Amplification ...... 11

2.2.4 Cloning of rRNA Gene Fragments ...... 12

2.2.5 Sequence Analysis of Clones ...... 13

2.3 Results and Discussions ...... 14

2.3.1 Cyanobacteria ...... 14

2.3.2 Eukaryotic Algae ...... 15

2.3.4 Comparison between Autumn and Winter Samples ...... 18

2.4 Conclusions ...... 22

Chapter 3. Methods and Materials ...... 24

3.1 Algae Cultivation ...... 24

3.2 Gravimetric Analysis of Lipids ...... 25

3.2.1 Collection of Algae Biomass ...... 25

3.2.2 Solvent Extraction of Total Lipids ...... 25

3.2.3 Silica Gel Purification ...... 26

3.2.4 Esterification ...... 26

3.3 GC Analysis ...... 26

3.4 Algae Cell Density Measurement ...... 29

3.5 Ammonia Nitrogen and Phosphorus Analysis ...... 29

3.5.1 Ammonia Nitrogen Measurement ...... 29

3.5.2 Orthophosphate Measurement ...... 30

3.6 Total Inorganic Carbon Measurement ...... 30

Chapter 4. Ammonia Removal Using Chlorella vulgaris ...... 31

4.1 Wastewater Properties ...... 31

4.2 Growth of Chlorella vulgaris in Closed Reactors ...... 33

4.3 Nitrogen Removal Using Chlorella vulgaris ...... 36

4.3.1 Preparation of Algae Seeds ...... 36

4.3.2 Experiment Set-up ...... 36

4.3.3 Nitrogen Removal Without Using CO2 ...... 37

4.3.4 Nitrogen Removal with CO2 ...... 41

Chapter 5. Lipid Analysis ...... 45

5.1 Lipids in Microalgae ...... 45

5.3 Lipid Extraction from Algae ...... 47

5.2 Neutral and Total Lipids in Chlorella vulgaris ...... 48

5.3 Polar Lipids in Chlorella vulgaris ...... 52

Chapter 6. Conclusions ...... 56

References ...... 58

Appendix ...... 65

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Chapter 1. Introduction

1.1 Nitrogen and Phosphorus Pollution

Increasing nitrogen and phosphorus discharge into natural water systems poses great threat to water quality and human health. Excessive nutrients (nitrogen and phosphorus) in aquatic systems lead to more frequent eutrophication, which is a worldwide problem affecting both fresh and coastal marine water resources. In the US, point sources are responsible for more than 50% of the nutrients in rivers and streams in urban areas (Carpenter et al., 1998). Research shows that municipal wastewater treatment plants (WWTPs) account for about 75 percent of the nutrients from point sources (USGS, 1994; EEA 2005).

Nitrogen and phosphorus are the main causes of surface water eutrophication. Freshwater eutrophication leads to excessive growth of phytoplankton, depletion of dissolved oxygen, and has negative impacts on drinking water supply (Smith, 2003). Increased turbidity caused by algae will require extra chlorination, increasing not only the costs but also the levels of disinfection by-products (USEPA, 2001). Unlike freshwater systems where phosphorus is the limiting factor for eutrophication (Schindler, 1977), nitrogen is the most crucial element in coastal marine water systems (Oviatt et al., 1995). Adverse effects of coastal marine eutrophication include blooms of toxic algae, reduced yields of desirable fish species, threats to endangered aquatic species, shifts in marine ecosystem structures (Smith, 2003).

Most wastewater treatment plants include primary treatment and secondary treatment. Primary

wastewater treatment uses screening, sedimentation, and other physical treatment processes to remove floating and suspended solids from raw sewage. Primary treatment can remove about

50% of the BOD but wastewater still contains a high amount of organic pollutants. After the passage of Clean Water Act in 1972, most WWTPs in the U.S. adopted secondary wastewater treatment. Secondary wastewater treatment processes involve biological treatments to remove organic matters from wastewater. The most commonly used approach is using microorganisms in activated sludge to break down organic matters, and a secondary settlement to separate activated sludge from treated wastewater (USEPA, 1998). Advanced wastewater treatment

(tertiary treatment) is not required at all WWTPs, and there are many variations, depending on the characteristics of wastewater and effluent water quality requirements.

1.2 Nutrients Removal Techniques

Biological Nitrogen Removal (BNR) is currently the most widely used technique in WWTPs.

The biological processes involve in nitrogen removal are nitrification and denitrification. First,

- ammonia is converted into nitrite (NO2 ) by autotrophic bacteria group Nitrosomonas under

- aerobic conditions. Then nitrite is oxidized to nitrate (NO3 ) by another bacteria group, in most cases Nitrobacter. Nitrification process alone does remove nitrogen from wastewater.

Denitrification is required to convert nitrate into nitrogen gas by heterotrophic bacteria groups such as Pseudomonas and Alcaligenes under anoxic conditions. Effluents from most wastewater treatment plants (conventional activated sludge treatment including a nitrification step) contain 1-10 mg/L of NH3-N (USEPA, 2011). Organic nitrogen cannot be removed by biological process and only the non-soluble fraction can be removed using solids separations

such as sedimentation and filtration. The summary of removal mechanisms for each form of nitrogen are shown in table 1-1 (Jeyanayagam, 2005).

Table 1-1. Nitrogen removal mechanisms (Jeyanayagam, 2005).

Common Removal Technology Limit

Form of Nitrogen Mechanism (mg/L)

Ammonia-N Nitrification <0.5

Nitrate-N Denitrification 2-Jan

Particulate organic-N Solids separation <1.0

Soluble organic-N None 0.5-1.5

1.3 Nutrients Removal Using Microalgae

BNR requires extra energy, more complex operating regimes, and sometimes external carbon sources are needed for denitrification process. Physical-chemical separation based phosphorus removal process need reagents and produces up to 150% more sludge in volume (Smil, 2000).

Microalgae use a large amount of nitrogen and phosphorus for synthesizing proteins, nucleic acids, and phospholipids. Wastewater treatment with microalgae to reduce nitrogen and phosphorus was first described by Oswald (Oswald, 1957). Although there are researches on using microalgae for wastewater treatment, the only commercialized technique is Advanced

Integrated Wastewater Pond Systems (AIWS) by Oswald and Green, LLC (Green et al., 1996).

Compared with conventional methods, the advantages of using microalgae for wastewater treatment include (De la Noüe, 1992):

1) Nutrients removal rates are high;

2) Does not generate additional polluting by-product caused by chemical addition;

3) Algal biomass can be harvested for other uses.

1.4 Biofuels from Microalgae

The depletion of fossil fuels and rising petroleum price has made renewable energy more attractive. There is a growing interest in sustainable and carbon neutral energy as the concerns for energy security and global climate change rise. Biofuels play an important role in the goal to advance alternative energy sources. U.S. plan to increase biofuel production from 4.7 billion gallons in 2007 to 36 billion gallons by 2022 and it is also specified that 21 billion gallons of the

2022 total must be derived from non-cornstarch products. Compared with current productivity of 0.5 billion gallons of soybean biodiesel per year (Department of Energy), other technologies are needed to meet the goal.

One advantage of biodiesel over other biofuels is that it can be used in conventional diesel engines without any modification. Biodiesel also can blend into petrol diesel in any percentage, making it easy to integrate its production into existing petroleum infrastructures. Also, biodiesel provides much more usable energy and lower greenhouse gas emissions compared with bioethanol through life cycle analysis (Hill et al., 2006).

Currently, biodiesel is mainly produced from vegetable oils such as soybean oil, corn oil, canola

oil, and waste cooking oil. In 2007, more than 90% of world’s biodiesel was produced from soybean, rapeseed, and palm oil (Gelder et al., 2008). However, with the low productivity of the terrestrial plants, biodiesel production from these sources will not be able to replace petroleum based liquid fuels. For biodiesel production, feedstocks account for about 80% of the total cost (Demirbas, 2007), therefore inexpensive biomass sources are crucial for biodiesel to compete with petrol diesel.

Microalgae are found in both freshwater and marine environments all over the world. They are fast growing photosynthesizing microorganisms and could double their biomass within one day at optimal conditions. Also, many species are found to be rich in lipids. Depending on species, oil content of microalgae range from 15% to 77% (dry weight) (Chisti, 2007). The concept of producing fuel from algae could date back to 1950s (Meier, 1955). During 1980s and 1990s, the Aquatic Species Program lead by National Renewable Energy Laboratory (NREL) looked into the production of biodiesel from algae. Over 3,000 strains from North America were screened, physiology and genetic engineering studies were conducted, and outdoor mass culture was tested. Although this program was closed at the end of 1990s, it provided a solid foundation for algal biodiesel research. The advantages of producing biodiesel from algae include:

(1) oil yield of microalgae per area is much higher than terrestrial crops;

(2) oil from microalgae will not compete with conventional agriculture;

(3) microalgae cultivation could be combined with carbon dioxide sequestration;

(4) microalgae production could make use of non-arable land and the water demand is lower than terrestrial crops;

(5) residual biomass after oil extraction can be used as feedstock for fermentation or other processes, and

(6) compared with other biofuels, microalgal biodiesel has much higher energy yield and smaller ecological footprint (Groom et al., 2008).

1.5 Objective of This Study

The objective of this study is to test nitrogen removal performance of green alga Chlorella vulgaris in removing nitrogen from synthetic wastewater, and analyze lipids in Chlorella vulgaris to investigate the possibility of producing biofuels at the same time.

This thesis describes the studies using green alga Chlorella vulgaris to removal nitrogen from secondary wastewater. Chapter 2 contains a survey of local algae species in southwest Ohio area. Nitrogen removal experiments and lipids analysis are detailed in Chapters 3, 4 and 5.

The conclusions are described in Chapter 6.

Chapter 2: Molecular Survey of Freshwater Microalgae in

Southwest Ohio Area

2.1 Introduction

For large scale cultivation of microalgae, raceway ponds and tubular photobioreactors are considered two practicable methods (Chisti, 2007). Although photobioreactors have some advantages over open pond systems, such as higher biomass densities, fewer land use, and less contamination. Under current circumstances, the cost of commercial scale algae production using photobioreactors is still too high (Carvalho et al., 2006). Open pond systems are more promising for mass cultivation of microalgae biomass to produce biofuels. One of the challenges that open ponds face is contaminations from other microorganisms (algae, bacteria, and zooplanktons) in the environment. In order to keep indigenous algae species from overtaking desired strains, a survey of algae in natural water bodies will provide useful information for predicting and controlling possible contaminants.

Traditional methods for identification and quantification of algae are morphology based which have several drawbacks:

1) It is difficult to characterize microalgae (< 3 or 5 μm) by simple observation with optical microscopy;

2) Many microalgae do not possess enough morphological characteristics for species characterization;

3) It is a time-consuming procedure.

During the past a couple of decades, the development of molecular techniques has greatly improved our ability to analyze microbial diversity. Because of the high genetic diversity of eukaryotic algae, it is very difficult to target them with a single set of molecular probes. Most researches using molecular methods for algae community diversity study focus on either cyanobacteria or eukaryotic algae or only one class of algae (Fawley et al., 2004; Richards et al.,

2005; Moon-van der Staay et al., 2001; Provan et al., 2004). The purpose of this study is to identify local algae species that could be potential contaminants during outdoor algae cultivation.

In order to include all types of algae the gene we targeted is the small subunit ribosomal RNA gene (16S rDNA in prokaryotes and 18S rDNA in eukaryotes) because of the large number of available sequences in public databases.

2.2 Materials and Methods

2.2.1 Sites Description and Sampling

A total number of fifteen lakes/ponds were selected in southwest Ohio area to take water samples. The selection of the lakes/ponds covers different sizes, depths and types (eutrophic, oligotrophic and semi-oligotrophic). Water samples were collected from fifteen lakes/ponds

(S1-S15) during September 2008-October 2008 and 5 lakes/ponds (S5, S9, S12, S14, S15) in

February 2009 for winter samples. The lakes/ponds sampled twice all had one dominant species in autumn. The sizes of each lakes/ponds and the water depths at each sampling location are shown in Table 2-1.

Table 2-1. Sampling location information.

Sites Area (m2) Depth at sampling site (m)

S1 2900 0.5

S2 11900 0.4

S3 12300 0.5

S4 17500 1.5

S5 9000 3.5

S6 60500 5.0

S7 2900 0.3

S8 17200 2.0

S9 130000 2.0

S10 3700 0.4

S11 88500 6.0

S12 61600 13.0

S13 2000 2.0

S14 31300 5.0

S15 9400 0.3

Figure 2-1. Sampling sites map.

Three to five samples were taken for each lake/pond. Water samples of 200 mL were taken from the mixed water column of the whole depth at each of the sampling locations. Then samples from the same lake/pond were mixed and analyzed as one sample. Water samples were

centrifuged at 10,000 rpm within 6 hours after sampling and preserved at -80 ºC until DNA extraction.

2.2.2 Genomic DNA Extraction

Genomic DNA from each sample was extracted using a Soil DNA Extraction kit (MoBio,

Carlsbad, CA) following the manufacturer’s instruction. Mechanical disruption of the cells was achieved by using a bead beater for 1 minute at 10,000 rpm. After purification with microcentrifuge columns provided by the kit, the genomic DNA was collected in 50μL of sterile

RNAse-free water and quantified using a NanoDrop ND-1000 UV spectrophotometer

(NanoDrop Technologies, Wilmington, DE). Extracted genomic DNA was stored at -20 ºC for further analyses.

2.2.3 PCR Amplification

Two sets of primers were used in this study. For the cyanobacteria which are prokaryotic algae, forward primer CYA359f (5’-GGG GAA TYT TCC GCA ATG GG-3’) and reverse primer

CYA781R (equimolar mixture of CYA781Ra: 5’-GAC TAC TGG GGT ATC TAA TCC CAT T-

3’ and CYA781Rb: 5’-GAC TAC AGG GGT ATC TAA TCC CTT T-3’) were used (Nubel et al.,

1997). The final PCR solutions contained 5 μL of Takara Ex Taq buffer, 4 μL of dNTP (2.5 mM each), 100 μg of bovine serum albumin, 1 uL of each primer (20 pmol/μL), 1 μL of templates,

1.25 units of Ex Taq Polymerase (TAKARA Bio,Madison, WI), and the total volume was brought to 50 μL with RNAse-free water. PCR was performed in a Model 2700 thermal cycler

(Applied Biosystem) and the program included 1 cycle of initial denaturation at 94 ºC for 5 11

minutes, followed by 30 cycles of 94 ºC for 1 minute, 60 ºC for 1 minute, and 72 ºC for 1 minute, followed by a final extension at 72 ºC for 9 minutes.

For eukaryotic algae, universal eukaryotic primers (5’-ACC TGG TTG ATC CTG CCA G-3’, 5’-

TGA TCC TTC YGC AGG TTC AC-3’) were used. The final PCR solutions contained 5 μL of

Takara Ex Taq buffer, 4 μL of dNTP (2.5 mM each), 2 μL of each primer (20 pmol/μL), 2 μL of templates, 1.25 units of Ex Taq Polymerase (TAKARA Bio,Madison, WI), and the total volume was brought to 50 μL with RNAse-free water. PCR program included 1 cycle of initial denaturation at 94 °C for 3 minutes, followed by 30 cycles of 94 °C for 45 seconds, 55 °C for 1 minute, and 72 °C for 3 minutes, followed by a final extension at 72 °C for 10 minutes. The length of PCR products were verified by running 1% agarose gel for 45 minutes in 1X TBE buffer (0.1 g NaOH/L; 10.8 g Tris Base/L; 5.5 g Boric Acid/L; 0.74 g EDTA/L; pH=7.0).

2.2.4 Cloning of rRNA Gene Fragments

Cloning was performed using the TOPO TA Cloning Kit (Invitrogen Corp.). Fresh PCR products were purified using QIAquick PCR Purification Kit (Qiagen, Valencia, CA) before cloning. DNA concentrations of purified PCR products were measured by NanoDrop ND-1000

UV spectrophotometer (NanoDrop Technologies, Wilmington, DE). About 20 ng PCR products were ligated with the pCR 2.1-TOPO vector by incubating at room temperature for 5 to 30 minutes (longer incubation time for 18s rRNA PCR products). Then, 4 μL of ligation product was added into chemically competent E. Coli DH5a cells. The E. coli cells were transformed by heat shock at 42 °C for 30 seconds. 1-hour incubation at 37 °C was followed after adding

250 mL room temperature SOC medium. After the incubation, 50 to 80 μL of the cell suspension was transferred to Luria Bertani agar plates (10 g/L tryptone, 10 g/L sodium chloride,

5 g/L yeast extract, 50 μg/mL ampicillin, 10 mg/uL x-gal and 15 g agar). The LB plates were incubated at 37 °C for overnight. For each sample, 100 white colonies were randomly selected and transferred to another LB plate containing only ampicillin for overnight incubation at 37 °C.

The plates were then stored at 4 °C until sequencing.

2.2.5 Sequence Analysis of Clones

For each sample, 96 clones (48 from prokaryotic specific primers and 48 from eukaryotic specific primers) were submitted to Children’s Hospital DNA Core Lab (Cincinnati, OH) for sequencing. Sequencing data was generated using Big Dye sequencing chemistry (Applied

Biosystems, Foster City, California), M13 reverse primers, and an Applied Biosystems PRISM

3730XL DNA Analyzer.

Sequences of low quality were discarded. Sequences were manually cleaned and aligned using

MEGA version 4 software (Tamura et al., 2007) and Greengenes tools (www.greengenes.com).

Potential chimeric sequences detected by Bellerophon (Hugenholtz and Huber, 2003) and

Chimera Check (Cole et al., 2003) were not included in further analyses. Sequences were then submitted to BLAST homology search algorithms in order to assess our sequence similarity to entries available in this public database.

2.3 Results and Discussions

2.3.1 Cyanobacteria

After discarding sequences of low quality and potential chimeras, sequence similarities to entries available in the public database were assessed using BLAST. A total number of 591 sequences belonging to 24 genera were obtained. The summary of results from BLAST are shown in

Table 2-2 and the cyanobacteria community structure of each sampling site is shown in Table A-

1 and Table A-2 (Appendix).

The number of genera of cyanobacteria found in each sample ranged from 1 to 7, and 9 of the samples were at near monoculture status (more than 90% sequences from one species).

Synechococcus sp. was the predominant species, accounting for 58% of the sequences. It was found in 12 out of the 20 sampling sites and was dominant in 10 of them (6 in autumn and 4 in winter). This implies that Synechococcus is a competitive species in natural fresh water bodies and will likely to be a major contaminant in outdoor algae cultivation.

Table 2-2. Complete list of cyanobacteria identified.

Closest Match Number of Sequences Sampling Sites

S2,S3,S4,S6,S8,S9,S10,S11, Synechococcus sp. 341 S12,S13,S14,S15

Planktothrix agardhii 59 S12,S15

Cylindrospermopsis raciborskii 50 S5,S8,S11,S14

S1,S2,S6,S7,S10,S11,S12, Leptolyngbya sp. 42 S13,S14

Microcystis sp. 35 S4,S5,S10,S11,S15

Phormidium sp. 19 S11

Cyanobium sp. 8 S4,S11,S14,S15

Nostoc sp. 6 S6,S7

Synechocystis sp. 6 S10,S13,S14

Cyanobacterium sp. 5 S11,S13

Oscillatoria amphigranulata 4 S1,S2

Gloeothece sp. 4 S12,S15

Dermocarpa violacea 3 S13

Crocosphaera watsonii 3 S7,S9

Woronichinia naegeliana 2 S4

Limnothrix redekei 2 S9,S13

Halomicronema sp. 2 S14

Calothrix sp. 1 S6

Anaerovibrio sp. 1 S6

Rhabdoderma cf. Rubrum 1 S9

Dermocarpella incrassata 1 S10

Plectonema terebrans 1 S14

Snowella litoralis 1 S15

Anabaena sp. 1 S13

2.3.2 Eukaryotic Algae

The same analysis methods were used for eukaryotic sequences. Although there were fewer eukaryotic algae sequences, more species were identified compared with cyanobacteria. A total

number of 257 sequences belonging to 49 genera were obtained. The number of genera found in each sample was also larger, ranging from 3 to 11. The summary of results from BLAST are shown in Table 2-3 and the eukaryotic algae community structure of each sampling site is shown in Table A-3 and Table A-4 (Appendix).

Cryptomonas sp. was the predominant species, accounting for 39% of the sequences. It was found in 15 out of the 20 sampling sites but was dominant only in 6 of them (5 in autumn and 1 in winter). Chlamydomonas sp. was another ubiquitous eukaryotic alga (found in about half of the samples), however, its relative abundance was low.

Table 2-3. Complete list of eukaryotic algae identified.

Closest Match Number of Sequences Sampling Sites

S3,S4,S5,S7,S8,S9,S10,S11,

Cryptomonas sp. 101 S12,S13,S14,S15

Chlamydomonas sp. 15 S1,S3,S5,S9,S10,S11,S12,S14,S15

Synura glabra 15 S9,S14

Tetraselmis kochiensis 9 S9,S12

Plagioselmis nannoplanctica 9 S8,S12,S14

Polarella glacialis 7 S11,S13

Woloszynskia leopoliensis 7 S8,S9,S12,S15

Mallomonas sp. 6 S4,S9,S11,S13

Gymnodinium beii 5 S8,S14,S15

Komma caudata 5 S4,S5,S10

Carteria sp. 4 S9,S14

Ochromonas sp. 4 S1

Ceratium hirundinella 4 S8

Uroglena sp. 4 S1,S4,S8

Chlamydomonad sp. 4 S1,S9,S11

Scenedesmus sp. 3 S10

Pseudopedinella elastica 3 S5,S12

Monas sp. 3 S4,S11,S15

Pteromonas protracta 3 S3,S9

Phacus sp. 3 S11

Leucocryptos marina 3 S12,S14,S15

Paraphysomonas butcheri 3 S4,S5,S14

Stephanodiscus hantzschii 2 S12,S14

Teleaulax amphioxeia 2 S14

Chrysosaccus sp. 2 S5

Spumella sp. 2 S5,S12

Cyclotella menegheniana 2 S9,S13

Aulacoseira distans 2 S8,S10

Euglena tristella 2 S11

Peridinium sp. 2 S8

Pectodictyon pyramidale 2 S10

Phaeoplaca thallosa 2 S7,S12

Phacotus lenticularis 1 S15

Guillardia theta 1 S1

Pseudostaurosiropsis sp. 1 S4

Oocystaceae sp. 1 S10

Wislouchiella planctonica 1 S9

Pseudocharacium americanum 1 S13

Uronema belkae 1 S7

Synedra ulna 1 S12

Poterioochromonas stipitata 1 S7

Oikomonas mutabilis 1 S7

Hydrurus foetidus 1 S12

Fragilaria sp. 1 S12

Karlodinium micrum 1 S12

Paulsenella sp. 1 S14

Chrysochaete britannica 1 S12

Botryococcus sp. 1 S10

Cryptophyta sp. 1 S13

Since universal eukaryotic primers were used, 363 “non-algae” sequences were also obtained and analyzed using BLAST. The results showed that most of the matches belong to protozoa and fungi. Protozoa and other zooplankton feed on algae and could be used as potential agents for controlling the growths of certain types of algae (Itayama et al, 2008; Lance et al., 2006,

Fabbro et al., 2001). The zooplankton community and the algal community in each sampling location were compared, however no significant relationship was found.

2.3.4 Comparison between Autumn and Winter Samples

Two samples from each of the following sites S5, S9, S12, S14 and S15 in autumn and winter respectively to compare the algal communities change over time. Cyanobacteria and eukaryotic 18

algae distributions of the 10 samples are show in table A-1 to A-4.

Cyanobacteria. The overall data of cyanobacteria distribution in autumn and winter from the five sampling sites are shown in Figure 2-2. For autumn samples, 203 sequences were obtained and BLAST results showed that they belonged to 13 genera. Sampling sites S5, S9, S12, S14 and S15 were all dominated by one genus in autumn: Cylindrospermopsis raciborskii,

Synechococcus sp., Planktothrix agardhii, Synechococcus sp. and Microcystis sp., respectively.

In winter samples, there was a significant decrease in the number of algal species indentified, except for S5 Sample in which no cyanobacteria were detected, all the other 4 sites were dominated by Synechococcus sp. and only 3 genera of cyanobacteria were found.

Halomicronema a Gloeothece sp. sp. Other* 2% 1% 3% Cyanobium sp. 2%

Microcystis sp. Synechococcus sp. 13% 31%

Cylindrospermopsi s raciborskii 19%

Planktothrix agardhii 29%

Leptolyngbya sp. Gloeothece sp. b 1% 1%

Synechococcus sp. 98%

Figure 2-2. Cyanobacteria distribution from sampling sites S5, S9, S13, S14 and S15. a: autumn; b: winter. *: genera with only one sequence was obtained including Crocosphaera watsonii,

Limnothrix redekei, Plectonema terebrans, Rhabdoderma Rubrum, Snowella litoralis, and

Synechocystis sp..

Eukaryotic Algae. In autumn samples from sites S5, S9, S12, S14 and S15, 17 genera of eukaryotic algae were identified. Unlike cyanobacterial community which decreased from 13 genera in autumn to only 3 genera in winter, the total number of eukaryotic algal genera found in winter samples increased to 18. Also, while all the cyanobacteria identified in winter samples were all included in the autumn samples, half of the eukaryotic algae species were not found in autumn samples.

Synura glabra Other* a Stephanodiscus 3% 11% hantzschii 3% Leucocryptos marina 3% Cryptomonas sp. Chlamydomonas 40% 3% Woloszynskia leopoliensis 5% Komma caudata 5% Gymnodinium beii 6% Carteria sp. Tetraselmis sp. 7% 14%

b Other# Teleaulax 11% Spumella sp. amphioxeia 3% 3%

Chrysosaccus sp. Cryptomonas sp. 2% Paraphysomonas 44% sp. 2% Pseudopedinella elastica 4%

Chlamydomonas 5%

Plagioselmis nannoplanctica 10% Synura sp. 16%

Figure 2-3. Eukaryotic algae distribution from sampling sites S5, S9, S13, S14 and S15. a: autumn; b: winter. *: genera with only one sequence was obtained including Chlamydomonad sp., Cyclotella menegheniana, Mallomonas splendens, Monas sp., Phacotus lenticularis,

Pteromonas protracta, and Wislouchiella planctonica.

#: genera with only one sequence was obtained including Phaeoplaca thallosa, Synedra ulna,

Hydrurus foetidus, Karlodinium micrum, Fragilaria sp., Chrysochaete britannica, Woloszynskia pascheri, Leucocryptos marina, and Paulsenella sp..

2.4 Conclusions

From all the 20 water samples (15 for autumn and 5 for winter), 24 genera of cyanobacteria and

49 genera of eukaryotic algae were identified. Synechococcus sp. was found in 12 of the 20 samples and was the predominant cyanobacterium both in autumn and winter. Cryptomonas sp. was the most ubiquitous eukaryotic algae, and the relative abundance remained stable in autumn and winter. Zooplankton and phytoplankton communities were compared but no apparent relationship was found.

Temperature has a major influence on algal community structure. For cyanobacteria, only three genera were identified from winter samples, comparing with 13 genera in autumn. Each of the five sampling sites in autumn was dominated by one genus, however in winter; Synechococcus sp. became dominant in all of them. The influences for eukaryotic algae community were different. Although Cryptomonas sp. remained dominant in winter, the community structure changed greatly and there were more genera of eukaryotic algae identified in winter.

Among all the samples, algal community structures were different from one another and there were no two samples with similar structure. This implies that ecology plays an important part

in controlling algal community structures, making it a powerful tool for regulating the growth of specific species.

Chapter 3. Methods and Materials

3.1 Algae Cultivation

Chlorella vulgaris culture was obtained from UTEX. Culturing medium contains 200 mg/L of ammonia sulfate, 30 mg/L of calcium phosphate monobasic monohydrate, 80 mg/L of magnesium sulfate hetahydrate, 25 mg/L of potassium chloride, 1.5 mg/L of iron chloride, 10 mg/L of potassium phosphate dibasic, 2.86 mg/L of boric acid, 1.81 mg/L of manganese chloride,

0.222 mg/L of zinc sulfate, 0.391 mg/L of sodium molybadate, and 0.079 mg/L of cupric sulfate.

Sodium bicarbonate was used as carbon source. Prior to inoculation, the medium was sterilized by autoclaving at 121 °C for 50 minutes. After adding sodium bicarbonate, the medium was adjusted to pH 7 using 1 N hydrochloric acid.

Fluorescent lights (6,500 K color temperature, Philips) were used as the light source. The light intensity at the surface of the reactors was measured to be around 10,000 lux using a digital lux meter (LX1010BS, Osprey-Talon Company), and a 16 h/8 h light/dark cycle was applied. All algae cultures were checked under light microscope every day to make sure no contamination from other species. Chlorella vulgaris was cultured in closed reactors with working volume of

2 liters in a batch mode. The pH was monitored periodically using a pH probe (Epoxy body pH electrode, Oakton) and adjusted to 7 with 1 N hydrochloric acid.

3.2 Gravimetric Analysis of Lipids

3.2.1 Collection of Algae Biomass

Algae biomass was collected using a bench centrifuge (Type 2010, Thermo-fisher, US) at 4,000 rpm for 10 minutes. Then the biomass was dried in a vacuum oven (Model 280A, Fisher

Scientific, US) at 40 °C under -50 psi until weight was constant. Dried biomass was analyzed within 24 h to minimize lipids loss during storage.

3.2.2 Solvent Extraction of Total Lipids

Total lipids were extracted using solvent extraction modified from Bligh & Dyer method (Bligh

& Dyer, 1959). Lipids extraction was conducted using about 500 mg of dried algae and 30 mL of solvent (chloroform: methanol = 2:1 v/v) for 24 hours with stirring at room temperature.

Then the liquid was centrifuged at 4,000 rpm for 10 minutes to remove algae debris followed by filtration using 0.45 µm filter (Type HA, Millipore, US) to further remove all suspended algae biomass. 10 mL deionized water was added to the filtrate and shacked vigorously for 2 minutes.

Aqueous and organic phases were separated by applying centrifuge at 2,000 rpm for 5 minutes.

The bottom phase (chloroform) was recovered and this wash step was repeated two more times to remove methanol. To determine the weight of total lipids, chloroform phase was transferred to a pre-weighed flask and the solvent was evaporated completely using a rotatory evaporator

(Model: R-II, Buchi, Switzerland) at 40 °C and -55 kPa. The weight of the flask was measured again to calculate total lipid content.

3.2.3 Silica Gel Purification

Neutral lipids were separated by column chromatography using silica gel (70-230 mesh, Alfa

Aesar, US). For every 200 mg of total lipids, 15 g of silica gel and 150 mL of eluent solution

(chloroform) were used. The weight of neutral lipids was determined using the same method described in 3.2.2.

3.2.4 Esterification

Derivatization of fatty acids was achieved using BF3-methanol kit (10% w/w, Sigma-Aldrich).

About 20 mg of fatty acids dissolved in hexane were added to 2 mL of BF3-methanol and heated at 60 °C for 1 hour. Then 1 mL of water and was added to get the esters into the nonpolar solvent. The upper (hexane) layer was carefully removed and dried with anhydrous sodium sulfate (Blau & Halket, 1993).

3.3 GC Analysis

The lipids profile was analyzed using Agilent 6890 Gas Chromatography equipped with J&W

122-7032 DB-WAX capillary column (length: 30m, diameter: 250 µm, film thickness: 0.25 µm) and FID. GC conditions are listed below.

Initial oven temperature: 50 °C

Initial time: 2 min

Ramp rate: 10 °C /min

Maximum temperature: 250 °C

Equilibration time: 2 min

Carrier gas: helium

Flow rate: 2.7 mL/min

Each component was identified through the comparison of retention time with standards

(Supelco 37 Component FAME Mix, Sigma-Aldrich, US). Nonadecanoic acid fatty acid

(C19:0, Sigma-Aldrich, US) was used as internal standard.

Table 3-1: GC standard list and retention time.

Retention Time Component (min) C6:0 4.748 C8:0 7.509 C10:0 10.096 C11:0 11.296 C12:0 12.443 C13:0 13.527 C14:0 14.567 C14:1 14.911 C15:0 15.557 C15:1 15.894 C16:0 16.514 C16:1 16.731 C17:0 17.424 C17:1 17.64 C18:0 18.309 C18:1n9 18.467 C18:2n6c 18.87 C18:2n6t 18.838

C18:3n6 19.081 C18:3n3 19.361 C20:0 19.973 C20:1n9 20.121 C20:2 20.485 C20:3n6 20.688 C20:3n3 20.857 C20:4n6 20.98 C20:5n3 21.353 C21:0 20.76 C22:0 21.524 C22:1n9 21.675 C22:2 22.025 C22:6n3 23.231 C23:0 22.268 C24:0 23.113 C24:1n9 23.319 C19:0 (IS) 19.156

Figure 3-1. GC spectra of standards.

3.4 Algae Cell Density Measurement

Algae cell density, in terms of algae cell weight per volume of medium, was determined by measuring the optical density at 680 nm using a UV spectrophotometer (UV-1800, Shimadzu).

The relationship between algae dry weight and optical density was previously determined, and the calibration curve is shown in Figure 3-2.

1000 900

800

700 600 500

400 y = 401.13x - 13.922

300 R² = 0.9862 Dry weightDry (mg/L) 200 100 0 0 0.5 1 1.5 2 2.5 Absorbance at 680 nm

Figure 3-2. Chlorella vulgaris biomass dry weight calibration curve.

3.5 Ammonia Nitrogen and Phosphorus Analysis

3.5.1 Ammonia Nitrogen Measurement

Nitrogen concentrations in the form of ammonia were measured with a high performance ammonia ion selective electrode (Orion: 9512HPBNWP, Thermo Scientific, US) using direct calibration method. Samples were filtered through 0.45 µm filter (Type HA, Millipore, US) 29

prior to ammonia analysis to prevent fouling of the electrode membrane. The electrode was calibrated everyday using standards with 10-4, 10-3, and 10-2 M ammonium chloride (Fisher, US), and ammonia concentrations of samples were obtained from calibration curve.

3.5.2 Orthophosphate Measurement

Orthophosphate was measured using Phosper 3 reagent (HACH, US) and a UV-Visible spectrophotometer (UV-1800, Shimadzu Scientific Instruments). A calibration curved was constructed with known concentrations of orthophosphate diluted from orthophosphate standard solution (Ricca Chemical Company). For each measurement, one pillow of Phosper 3 reagent was added to 5 mL of filtered sample and mixed well. After 2 minutes of reaction time, the absorbance at 890 nm wavelength was measured within 5 minutes and orthophosphate concentration was obtained from the calibration curve.

3.6 Total Inorganic Carbon Measurement

2   Total inorganic including carbonate ( CO3 ), bicarbonate ( HCO3 ) and aqueous carbon dioxide

(CO2 (aq)) was measured using acid-base titration method (Ficher & Peters, 1968). 0.01 N of

Hydrochloric acid was used as titrant and the pH change was monitored by a pH meter (Oakton pH 11 series pH meter) The pH meter was calibrated daily using buffer solution 4, 7, and 10

(Fisher Scientific).

Chapter 4. Ammonia Removal Using Chlorella vulgaris

4.1 Wastewater Properties

The mill creek wastewater treatment plant (WWTP) is the largest plant in greater Cincinnati area, handling a maximum of 360 mgd in a combined sewer system. A combined biological phosphorus removal and pre-denitrification biological nitrogen removal process is used at mill creek WWTP (shown in figure 4-1).

Figure 4-1. Biological nutrients removal at Mill creek WWTP (Mill creek WWTP facility plan

2008).

After secondary treatment, the monthly average total kjeldahl nitrogen is 18 mg/L, with 72%

+ being NH3/NH4 nitrogen. Detailed nitrogen and phosphorus concentrations over the year are shown in table 4-1.

Table 4-1. Nitrogen and phosphorus concentrations in the effluent

+ NH3/NH4 -N(mg/L) NO2/NO3-N(mg/L) TKN(mg/L) TP (mg/L)

January 12.08 2.41 17.2 1.0

February 9.05 2.15 12.8 1.0

March 13.15 2.75 17.4 1.1

April 12.14 0.57 15.9 1.0

May 13.91 1.61 17.1 0.9

June 17.15 2.22 21.2 0.6

July 18.65 1.82 22.1 0.8

August 13.46 2.00 20.6 0.1

September 11.99 2.27 20.4 0.5

October 9.90 2.83 16.7 0.5

November 17.09 4.34 22.9 1.2

December 8.54 2.58 13.5 0.8

Average 13.09 2.30 18.2 0.8

A sample collected from the secondary effluent was analyzed and the total inorganic carbon was

58.6±0.28 mg/L, which is sufficient for Chlorella vulgaris to grow. Modified Shuiseng-4 medium that matched nitrogen, phosphorus, and total inorganic carbon levels was used as synthetic wastewater in the experiments. The goal of this study is to remove 90% of nitrogen

within 48 hours, and to further investigate the possibility of biofuel production using green alga

Chlorella vulgaris.

4.2 Growth of Chlorella vulgaris in Closed Reactors

+ Closed bioreactors used in this research minimize the loss of NH3/NH4 and CO2 (q) into the air from culturing medium, providing more accurate tracking of nitrogen and carbon. However, one possible drawback of this form of bioreactor for algae cultivation is photorespiration caused by accumulating oxygen produced from algae during photosynthesis:

6CO2 + 12H2O + light → C6H12O6 + 6O2 + 6H2O

This process is catalyzed by ribulose-1,5-bisphosphate (RuBP) carboxylase/oxygenase

(RuBisCo), and CO2 and O2 are mutually competitive on this enzyme (Figure 4-2).

Figure 4-2. C3 carbon fixation and photorespiration pathways. (Bloom, 2010) 33

Under normal conditions, this reaction is in favor of CO2. But when oxygen is at a high level, oxygenation rate will increase, lowering carbon fixation efficiency. The effect of oxygen on photosynthesis can be estimated by comparing photosynthesis rates at different oxygen levels.

It was found that net photosynthesis in C3 plants is decreased by about 30% in 21% oxygen comparing with 2% oxygen level (Chollet and Ogren, 1975; Heskth, 1967). As C3 plants, green algae use the same carbon fixation and photorespiration pathways. In fact, photorespiratory activity was first observed in Chlorella (Warburg, 1920).

In this study, experiments were conducted in closed photobioreactors running in batch mode and using bicarbonate as carbon source. However, for large scale application and cultivation of microalgae, open systems are considered more economical. It is not clear whether the growth of Chlorella vulgaris will be affected by oxygen when culturing in closed reactors. A test was conducted to compare the growth of Chlorella vulgaris in closed and open reactors. Two types of reactors were used (Figure 4-3). Bottles with screw caps were used for closed reactors and there was no gas exchange during the cultivation. For open reactors, foam stoppers were used to minimize contamination and provide gas exchange with the atmosphere. Both reactors had working volumes of 500 mL and head spaces of 50 mL. Other cultivation parameters such as medium, algae seeds, light intensity, and light/dark cycle were all kept the same.

250

200

150

Closed Reactor

100 Open Reactor Cell Density (mg/L) Density Cell

0 0 50 100 150 200 250 300 Time (hour)

Figure 4-3. Growth of Chlorella vulgaris in closed and open reactors.

The initial algal biomass concentrations in both types of reactors were 16 mg/L. No lag phase was observed, and cell densities in closed and open reactors reached maximum at 206.4±2.4 mg/L and 226.0±4.7 mg/L after about 9 days. During the exponential phase, cell densities in closed reactors were higher than open reactors. But the maximum cell densities in open reactors were higher, possibly due to extra carbon dioxide obtained from gas transfer. The specific growth rates at exponential phase were 0.01172±0.00012 h-1 and 0.01106±0.00002 h-1 for closed and open reactors respectively. This indicates that under the conditions used in this study, Chlorella vulgaris does not show any photorespiratory effects. All the following experiments were conducted in closed photobioreactors.

4.3 Nitrogen Removal Using Chlorella vulgaris

4.3.1 Preparation of Algae Seeds

Algae seeds were prepared by growing Chlorella vulgaris in closed 2 L glass bottles with shuisheng-4 medium. Composition of the medium, light conditions and other parameters are described in Chapter 3. Sodium bicarbonate was used as the only carbon source at an initial concentration of 1000 mg/L. Chlorella vulgaris culture was obtained from UTEX. Algae were harvested by centrifugation after cell densities reached over 200 mg/L to be used as inoculum for later nitrogen removal experiments.

Two types of algae seeds were prepared. Seed 1 was grown in shuisheng-4 medium without adjusting pH, with initial pH being around 8.2. The same medium for seed 2 was adjusted to pH 7 with hydrochloric acid after adding sodium bicarbonate. Other conditions for these two types of seeds were all kept the same.

4.3.2 Experiment Set-up

Synthetic wastewater was prepared by adjusting total inorganic, nitrogen, and total phosphorus concentrations in shuisheng-4 medium to match the levels in secondary wastewater from Mill

Creek WWTP. Based on previous researches, it requires relatively high algae concentration to remove nitrogen efficiently from wastewater within a short period of time (48 hours). Hence, the initial algae concentration was set at around 350 mg/L. Pictures of the closed batch photobioreactors are shown in Figure 4-4.

Figure 4-4. Picture of the experiment set up for the closed batch photobioreactors.

Light intensity at the surface of the reactors was 10,000 lux by controlling the distance between fluorescent light and reactors, and a 16 h/8 h light/dark cycle was used to simulate summer sunlight conditions. Samples were taken every 12 hours to measure cell density, ammonia, total inorganic carbon, and total phosphorus using methods described in Chapter 3.

4.3.3 Nitrogen Removal without Using CO2

- All algae can use CO2 either directly from dissolved CO2 (aq) or from HCO3 via dehydration in

water for autotrophic growth: 6CO2 12H2O  light (energy)  C6H12O6  6O2  6H2O .

Dissolved CO2 concentration in water is controlled by Henry’s law, and since CO2 is close to

“ideal” gas thus at atmospheric pressure, the dissolvability of CO2 is very low. Using CO2 (aq) as only carbon source could provide sufficient for some algae species with high surface to

volume ratios or with relatively low growth rates (Larsson and Axelsson, 1999; Johnston et al.,

1992). For microalgae with high growth rates such as Chlorella vulgaris, other sources of carbon are needed to keep up with fast photosynthesis rate. As CO2 dissolves in water, it will

- 2- react with water to form carbonic acid (H2CO3), and further dissociate into HCO3 and CO3 :

*     2 H2CO3  H  HCO3 , HCO3  H CO3 . The distribution of carbon species is

- controlled by pH. At pH 6-8, which is the optimal range for most algae, HCO3 is the

- predominant carbon form. Chlorella vulgaris has been found to be able to utilize HCO3 with the help of carbonic anhydrase (CA) which catalyses the dehydration of bicarbonate:

  HCO 3  OH  CO 2 (Robertson, 1943).

As described in 4.3.1, algae seed 1 was used in this set of experiments. The initial concentration of total inorganic carbon, ammonia, and orthophosphate in the synthetic wastewater were 67.36 mg/L, 16.65 mg/L and 1.40 mg/L, respectively. The initial algae concentration was set at 365.8 mg/L. Growth of Chlorella vulgaris showed no lag phase, and cell density continued to increase over the whole 48 hours period with a final concentration of

444.2 mg/L (Figure 4-5). Since sodium bicarbonate was the only carbon source, TIC decreased gradually as algae biomass increased. NH3-N decreased in the same trend as TIC, with higher uptake rates at 0-12 h and 24-48 h. The final NH3-N removal from synthetic wastewater was

59.6% after 48 h. It should be noted that at the end of the experiment, there were still moderate levels of TIC and NH3-N left in the synthetic wastewater, indicating that there were no carbon or nitrogen limitation (Figure 4-5, Figure 4-6). For phosphorus uptake, PO4-P dropped rapidly and

was below detection limit after 24 h (Figure 4-8). Although there was phosphorus limitation,

Chlorella vulgaris growth was not affected.

500 480

460

440 420 400 380

360 Cell Density Cell (mg/L) 340 320 300 0 12 24 36 48 Time (hour)

Figure 4-5. Growth of Chlorella vulgaris from seed 1 without CO2 addition.

10 Total InorganicTotal Carbon (mg/L) 0 0 12 24 36 48 Time (hour)

Figure 4-6. Uptake of TIC by Chlorella vulgaris from seed 1 without CO2 addition.

18 16 14 12

10 N (mg/L) N - 8

NH3 6 4 2 0 0 12 24 36 48 Time (hour)

Figure 4-7. Uptake of NH3-N by Chlorella vulgaris from seed 1 without CO2 addition.

1.8 1.6 1.4

1.2

1 P (mg/L) P

- 0.8

PO4 0.6 0.4 0.2 0 0 12 24 36 48 Time (hour)

Figure 4-8. Uptake of PO4-P by Chlorella vulgaris from seed 1 without CO2 addition.

4.3.4 Nitrogen Removal with CO2

In order to achieve higher nitrogen removal rates, one approach is to use nitrogen starved

Chlorella vulgaris. Previous studies showed that nitrogen starved algae were about 30% more efficient in removing ammonia than algae growing in normal conditions. However, it is difficult to prepare and store large amounts of nitrogen starved algae at large scale, making this

- approach impractical in real wastewater treatment scenario. As HCO3 being consumed by

  - carbonic anhydrase catalyzed reaction: HCO3  CO2 OH , hydroxyl ion (OH ) will be released and pH will increase to values less favorable for Chlorella vulgaris growth. To keep pH at optimal range (7-8), CO2 (g) was bubbled into reactors until pH reached 7.0 every 12 hours. In addition, two types of seed were used: seed 1 was the same one used in last experiment; seed 2 was prepared while controlling pH and should have higher activity.

Without using CO2, the growth rate of Chlorella vulgaris from seed 1 in the last experiment was

-1 0.0048 h . With CO2 for pH controlling, growth rate for seed 1 and seed 2 increased greatly to

0.0071 h-1 and 0.010 h-1, indicating that pH is an important parameter for Chlorella vulgaris.

For seed 2, cell density reached maximum at 36 h and started to drop slowly due to nutrients limitation (Figure 4-9). Although the purpose of CO2 addition was to control pH, it did provide extra carbon source. As a result, TIC decreased much slower, as shown in Figure 4-10.

600

550

500

450 Run 1

400 Run 2 Cell Density Cell (mg/L) 350

300 0 12 24 36 48 Time (hour)

Figure 4-9. Growth of Chlorella vulgaris from seed 1 &2 with CO2 addition. Run 1: using seed

1; Run 2: using seed 2.

40 Run 1 30 Run 2 20

10 Total InorganicTotal Carbon (mg/L) 0 0 12 24 36 48 Time (hour)

Figure 4-10. Uptake of TIC by Chlorella vulgaris from seed 1 &2 with CO2 addition. Run 1: using seed 1; Run 2: using seed 2.

The consumption of NH3-N from synthetic wastewater by Chlorella vulgaris is shown in Figure

4-11. For seed 1, with the addition of CO2, NH3-N removal rate after 48 h increased from

59.6% to 75.6%. For seed 2, the goal of 90% nitrogen removal was achieved after 36 h, and removal rate reached 97.1 % after 48 h. This result was in accordance with biomass growth rate. For microalgae, nitrogen is mainly used for protein metabolism; therefore, nitrogen uptake rate is directly related with biomass growth rate. To efficiently remove nitrogen, it is helpful to select strains with high growth rates and seeds should have high activity. Due to low PO4-P concentration in the synthetic wastewater and fast biomass growth rate, phosphorus consumption rates were rapid and no PO4-P was detected after 12 h. Still, phosphorus deficiency did not show any noticeable influence on Chlorella vulgaris growth.

15 N (mg/L) N

- Run 1 10

Run 2 NH3

0 0 12 24 36 48 Time (hour)

Figure 4-11. Uptake of NH3-N by Chlorella vulgaris from seed 1 &2 with CO2 addition. Run 1: using seed 1; Run 2: using seed 2.

1.8 1.6 1.4

1.2

1 P (mg/L) P - 0.8 Run 1

PO4 0.6 Run 2 0.4 0.2 0 1 2 3 4 5 Time (hour)

Figure 4-12. Uptake of PO4-P by Chlorella vulgaris from seed 1 &2 with CO2 addition. Run 1: using seed 1; Run 2: using seed 2.

Chapter 5. Lipid Analysis

5.1 Lipids in Microalgae

Microalgae produce different types of lipids, including fatty acyls, sterols, fat-soluble vitamins, glycerides, glycolipids, phospholipids, and others. The main biological functions of lipids include energy storage, and as structural components of cell membranes (Guschina A. &

Harwood L., 2006). Under normal growth conditions, most fatty acids produced by algae are used to synthesize glycerol-based membrane lipids. These lipids are polar, consisting mainly glycolipids and phospholipids. For green algae, major components of polar lipids include phospholipids: Phosphatidylethanolamine (PA), Phosphatidylcholine (PC), Phosphatidylinositol

(PI), Phosphatidylglycerol (PG); and glycolipids: monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), Sulfoquinovosyldiacyglycerol (SQDG) (Siegenthaler and

Murata, 1998; Gunstone et al., 1994). The structures of these common polar lipids are shown in

Figure 5-1.

Figure 5-1. Structures of common polar lipids in green algae.

Under environmental stresses, algae tend to convert fatty acids into triacylglycerides (TAG), which are the main feedstocks for current biodiesel production. Researches have been done to investigate the effects of various stress conditions include nutrients (nitrogen, phosphorus and silica) starvation, temperature, light intensity, and physiological status on TAG content of algae.

Different algae species appear to require different stress conditions for TAG accumulation to occur, either single or multiple conditions working together (Hu et al., 2008). A simplified triacyglycerol biosynthesis via glycerol pathway in algae is shown in Figure 5-2. The synthesis pathways of fatty acids and TAG are very similar between algae and higher plants. One major difference is higher plants only accumulate TAG in specific tissues (mostly seeds and fruits), so

when lipids are harvested from these plants, TAG is the main content (usually over 95%).

Since microalgae are single cell microorganisms, lipids extracted using common methods also contain large amounts of membrane lipids (polar lipids) which are not suitable for biodiesel production with current techniques used biofuel industry.

Figure 5-2. Triacyglycerol biosynthesis pathway in algae. (Modified from Roessler et al., 1994)

5.3 Lipid Extraction from Algae

Solvent extraction has been widely used for extracting lipids from microalgae, following Blygh

& Dyer method or Folch method (Blygh & Dyer, 1959; Folch et al., 1957). To ensure the accuracy of following lipid quantifications and to reflect lipids in the samples, extraction methods should be able to extract all the lipids from microalgae. However, these methods were developed for extracting lipids from general plants and animal tissues, and microalgae have rigid cell walls, so lipids may not be completely extracted using these methods.

Over the years, modifications were made and evaluated on the methods mentioned above. The amounts of total lipids extracted from many algae were increased by adding HCl to the chloroform-methanol extraction solvents. The increases of total lipids were mainly due to the release of more membrane polar lipids. But for algae with high neutral lipids such as

Botryococcus braunii, no additional lipid could be detected with the help of acid (Dubinsky &

Aaronson, 1979). The effects of different cell destruction methods prior to solvent extraction were tested (Lee et al., 1998). Using bead beater resulted 1.96 times higher total lipids than direct extraction with chloroform/methanol only. French press and lyophilization also increased lipid recovery by about 30%. Due to the high toxicity of chloroform, performances of alternative lipid extraction solvent systems were evaluated (Lee et al., 1998; Guckert et al.,

1988; Jensen et al., 2003). Solvent systems such as hexane/isopropanol, dichloroethane/methanol, acetone/dichloromethane, and hexane/diethyl ether were tested.

Some solvent systems such as hexane/isopropanol has better extraction of neutral lipids, and extraction efficiencies could be optimized for specific samples to get results close to chloroform/methanol system, but chloroform/methanol always have highest total lipid recovery.

5.2 Neutral and Total Lipids in Chlorella vulgaris

Total lipids and neutral lipid profiles of Chlorella vulgaris before and after 48h nitrogen removal experiments are shown in Table 5-1. For neutral lipid profiles, fatty acids between C14 to C24 were detected, with C16:0, C18:0, C18:2 and C18:3n3 being the most abundant components, accounting for over 80% weights of all fatty acids. This result is similar with other studies on

Chlorella vulgaris (Cheng et al., 2010; Petkov & Garcia, 2007; Podojil et al., 1978). In terms

of carbon chain lengths, these profiles are similar with vegetable oils, which are the major biodiesel feedstocks currently. In addition, polyunsaturated fatty acids (PUFAs) and fatty acid methyl esters with multiple unsaturated ponds are susceptible to oxidation during long term storage thus are less suitable for biodiesel. While for Chlorella vulgaris, PUFAs with more than 3 unsaturated bonds were very low (between 1.5% and 4.9%), indicating that neutral lipids from Chlorella vulgaris are suitable for biodiesel. Although algae from seed 2 contained slightly higher C16:0 and C18:0, the lipid profiles from these different seeds remained consistent.

Table 5-1. Neutral lipids profile and total lipids of Chlorella vulgaris before and after 48h nitrogen removal experiments.

Seed 1 Seed 2 After After After Before with CO2 without CO2 Before with CO2 (wt%) (wt%) (wt%) (wt%) (wt%) C14:0 0.9 ND ND 0.37 ND C15:1 1.2 ND ND ND ND C16:0 39.4 25.8 22.2 47.4 28.3 C16:1 ND 1.8 1.2 ND ND C17:0 1.1 ND 1.0 ND ND C17:1 5.2 13.5 10.1 4.6 11.3 C18:0 20.6 5.8 9.0 26.6 9.3 C18:1 0.8 1.7 3.2 0.8 1.3 C18:2 9.2 15.1 14.7 4.3 13.0 C18:3n6 1.8 ND 6.0 ND ND C18:3n3 15.3 31.0 27.6 12.3 32.6 C20:3n6 1.2 ND ND ND ND C20:4n6 1.8 ND 1.6 2.0 2.0 C20:5n3 0.8 ND ND ND ND C22:6n3 0.8 1.5 1.5 ND ND C22:2 ND ND 1.8 1.3 ND C24:0 ND ND ND ND 2.3 Total lipids(mg/g) 108.5 107.9 105.9 139.8 124.1

During the 48h nitrogen removal experiment, neutral lipid profiles from both seeds changed in the same trend. Four main fatty acid components had dramatic changes: C16:0 and C18:0 decreased by 39.6% and 64.5%, respectively; C18:2 and C18:3n3 increased by 131.9% and

127.9%, respectively. Other fatty acids also changed but did not have major influence on lipid profiles due to their relatively low contents. The overall trend can be summarized as saturated fatty acids decreased, unsaturated fatty acids increased, and carbon chains became longer. A simplified schematic of fatty acids synthesis is shown in Figure 5-3 (Gurr et al., 2002).

Unsaturated fatty acids cannot be produced from de novo synthesis and can only be generated via modifications of saturated fatty acids. This indicates that during the 48 h nitrogen removal period, the main metabolic activity of Chlorella vulgaris involving lipids was modification of existing fatty acids rather than the synthesis of new fatty acids. Although unsaturated fatty acids increased (especially C18:3n3) after nitrogen removal, making it less favorable to be used as biodiesel feedstock, these unsaturated bonds can be easily reduced by partial catalytic hydrogenation or other commonly used techniques in vegetable oil industry (Jang et al., 2005).

Figure 5-3. A simplified schematic of fatty acids synthesis (Gurr et al., 2002).

Total lipids contents obtained from these different conditions ranged from 10.6% to 14.0%. It should be noted that phosphorus deficiency did not increase total lipid content. There was a slightly decrease in total lipid per unit algae biomass due to rapid growth, but the total lipid productivity (= algal cell density × lipid content) always increased, as shown in Figure 5-4.

With high lipid content and fast growth, total lipid per volume of seed 2 increased from 49.6 mg/L to 71.5 mg/L in 48 h. If all the secondary wastewater is treated by Chlorella vulgaris, based on this rate and the average wastewater flow at Mill Creek WWTP of 128 MGD, it could produce around 1400 gallons of total lipid per day.

a 160.0 140.0 120.0

100.0

80.0 Before 48h algae) 60.0 After 48h 40.0

20.0 Total Lipid Lipid Total Content dry(mg/g 0.0 Seed 1 (w/o Seed 1 (with Seed 2 (with CO2) CO2) CO2)

b 80.0 70.0 60.0

50.0

40.0 Before 48h

medium) 30.0 After 48h 20.0

10.0 Total Lipid Productivity (mg/L Productivity(mg/L Lipid Total 0.0 Seed 1 (w/o Seed 1 (with Seed 2 (with CO2) CO2) CO2)

Figure 5-4. Total lipid content per gram of dry algae and total lipid productivity per volume synthetic wastewater before and after 48h nitrogen removal experiment.

5.3 Polar Lipids in Chlorella vulgaris

Chlorella vulgaris growing in Shuisheng-4 medium under normal condition were analyzed for lipid and fatty acid compositions. Total lipid extraction method was the same as described in 52

Chapter 3. For column chromatography step, elution sequence of 150 mL chloroform, 600 mL acetone, and 150 mL methanol was used to collect neutral lipids, glycolipids, and phospholipids, respectively (Figure 5-5). Then the content of each lipid class was determined gravimetrically using the same method as neutral lipid analysis. Fatty acid compositions were also determined using GC after esterification.

Figure 5-5. Lipids separation procedures using silica gel column chromatography with step elution.

Total lipid was comprised of 62% glycolipid, 18% of neutral lipid, and 16% phospholipid.

Considering there was no nutrients deficiency or other stressed conditions, this result was comparable with other reports (Hodgson et al., 1991; Piorreck 1984). Fatty acid composition of each lipid class is summarized in Figure 5-6. Most fatty acids in glycolipid and phospholipid were in the range of C16 to C18, and major components (C16:0, C18:0, C18:2 and C18:3n3) were the same as neutral lipid.

From figure 5-1 we know that although there are many different types of polar lipids, their basic structures are similar, with a polar group connected to a diglycerol. They can be converted to fatty acid esters through esterification reaction as shown in Figure 5-6. With proper treatments,

Polar lipids should also be able be used as biodiesel feedstocks. Since lipids from microalgae growing under normal conditions generally contain more polar lipids than neutral lipids, it will be important to find ways to utilize these polar lipids.

Polar Lipids Alcohol Esters

Figure 5-6. Reaction that converts polar lipids to biodiesel.

Figure 5-7. Lipids composition and fatty acids composition. (a) Lipid composition of Chlorella vulgaris growing in Shuisheng-4 medium under normal condition. (b) Fatty acid composition of neutral lipid. (c) Fatty acid composition of glycolipid. (d) Fatty acid composition of phospholipid.

Chapter 6. Conclusions

From all the 20 water samples (15 for autumn and 5 for winter), 24 genera of cyanobacteria and

49 genera of eukaryotic algae were identified. Synechococcus sp. was found in 12 of the 20 samples and was the predominant cyanobacterium both in autumn and winter. Cryptomonas sp. was the most ubiquitous eukaryotic algae, and the relative abundance remained stable in autumn and winter. Each sample had its unique algae community structure. Zooplankton and phytoplankton communities were compared but no apparent relationship was found.

Temperature has a major influence on algal community structure. For cyanobacteria, only three genera were identified from winter samples, comparing with 13 genera in autumn. Each of the five sampling sites in autumn was dominated by one genus, however in winter; Synechococcus sp. became dominant in all of them. Temperature influence for eukaryotic algae community was different. Although Cryptomonas sp. remained dominant in winter, the community structure changed greatly and there were more genera of eukaryotic algae identified in winter.

Nitrogen consumption rate by Chlorella vulgaris was directly related to growth rate. Chlorella vulgaris removed 59.6% of NH3-N in 48 h without using external carbon source. With periodic

CO2 addition every 12 h to maintain pH between 7 to 8, Chlorella vulgaris growth rate increased

-1 -1 from 0.0048 h to 0.0071 h , and NH3-H removal rate increased to 75.6%. When more active

-1 seeds were used, growth rate further increased to 0.01 h and NH3-N removal rate reached

97.1%. Chlorella vulgaris is also very effective for removing low concentration of phosphorus, and short period of phosphorus deficiency did not affect its growth rate.

Total lipid content of Chlorella vulgaris ranged from 10.6% to 14.0%. GC analysis showed that 56

C16:0, C18:0, C18:2 and C18:3n3 were main fatty acid components, indicating that lipids extracted from Chlorella vulgaris will be suitable for biodiesel feedstock. After 48 h nitrogen removal experiment, major changes in fatty acid profile were: C16:0 and C18:0 decreased by

39.6% and 64.5%, respectively; C18:2 and C18:3n3 increased by 131.9% and 127.9%, respectively. Total lipid obtained from Chlorella vulgaris growing under normal conditions contained more polar lipids than neutral lipid (78% polar and 18% neutral). Fatty acids from these polar lipids were also suitable for making biodiesel.

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Appendix

Table A-1: Cyanobacteria distribution at each sampling site for autumn samples

Sampling Site Cyanobacteria Distribution

S1 Oscillatoria amphigranula ta 17%

Leptolyngbya sp. 83%

S2 Oscillatoria amphigranul ata 9%

Leptolyngbya sp. Synechococc 32% us sp. 59%

Synechococc us sp. 100%

S4 Woronichinia Cyanobium naegeliana sp. 4% 5%

Microcystis aeruginosa 14%

Synechococc us sp. 77%

S5 Microcystis aeruginosa 7%

Cylindrosper mopsis raciborskii 93%

S6 Synechococ cus sp. 20%

Leptolyngby a sp. 10% Nostoc sp. 50% Anaerovibri o sp. Calothrix 10% sp. 10%

Leptolyngbya sp. 25%

Crocosphaera watsonii 50% Nostoc sp. 25%

S8 Cylindrosper mopsis raciborskii 2%

Synechococc us sp. 98%

S9 Rhabdoder Crocosphae ma Rubrum ra watsonii 2% Limnothrix 2% redekei 3%

Synechococ cus sp. 93%

S10 Synechococcu Microcystis s sp. sp. 8% 4% Dermocarpell a incrassata 4% Synechocystis sp. 9% Leptolyngbya sp. 75%

S11 Synechococcus Cyanobium sp. Microcystis sp. sp. 5% 3% 3% Cyanobacteriu m sp. Leptolyngbya 5% sp. 6% Cylindrosperm Phormidium opsis sp. raciborskii 51% 27%

S12

Planktothrix agardhii 100%

S13 Leptolyngbya sp. Synechocystis Synechococcus 8% 23% sp. Anabaena sp. 8% 8% Limnothrix redekei 7% Dermocarpa violacea Cyanobacteriu 23% m sp. 23%

S14 Cyanobium sp. Synechocystis Plectonema 4% sp. terebrans 4% 4% Cylindrosperm opsis raciborskii 3% Halomicronem a sp. 7% Synechococcus sp. 78%

S15 Synechococcus Snowella sp. litoralis 2% 2% Gloeothece sp. 7%

Cyanobium sp. 7%

Planktothrix Microcystis sp. agardhii 55% 27%

Table A-2: Cyanobacteria distribution at selected sampling sites for winter samples.

Sampling Site Cyanobacteria Distribution

S5 Not Detected

Synechococcus sp. 100%

S12 Gloeothece Leptolyngby sp. a sp. 2% 2%

Synechococc us sp. 96%

S14 Leptolyngbya sp. 4%

Synechococcus sp. 96%

S15

Synechococcus sp. 100%

Table A-3. Eukaryotic algae distribution at each sampling site for autumn samples.

Sampling Site Eukaryotic Algae Distribution

S1 Uroglena sp. 13%

Guillardia theta 12%

Ochromonas sp. Chlamydomona 50% d sp. 25%

S2 Not Detected

S3 Cryptomonas sp. 20%

Chlamydomonas sp. 40%

Pteromonas protracta 40%

S4 Paraphysomonas Pseudostaurosiro butcheri psis sp. 4% 4% Monas sp. 4% Uroglena americana 8%

Mallomonas sp. 13%

Cryptomonas sp. 67%

S5 Chlamydomonas 17%

Cryptomonas 33% Komma caudata 50%

S6 Not Detected

S7 Phaeoplaca thallosa Oikomonas 9% mutabilis 9% Poterioochromon as sp. 9%

Uronema belkae 9% Cryptomonas 64%

S8 Cryptomonas Gymnodinium 7% beii 7% Aulacoseira Ceratium distans hirundinella 7% 29% Plagioselmis nannoplanctica 7% Uroglena sp. 7% Woloszynskia leopoliensis Peridinium sp. 22% 14%

S9 Wislouchiella Chlamydomonad Cyclotella planctonica sp. menegheniana 4% 4% Woloszynskia 4% leopoliensis 4% Mallomonas splendens Tetraselmis 4% kochiensis Pteromonas 31% protracta 4%

Carteria radiosa 7% Cryptomonas Synura glabra 31% 7%

S10 Oocystaceae sp. Komma caudata 5% 5% Botryococcus sp. 5%

Pectodictyon pyramidale 10%

Chlamydomonas 10% Cryptomonas Scenedesmus 50% abundans 15%

S11 Polarella Chlamydomonas glacialis sp. 7% 7%

Monas sp. 7% Cryptomonas sp. Chlamydomonad 29% sp. 7%

Mallomonas caudata Phacus sp. 7% Euglena sp. 22% 14%

S12 Stephanodiscus hantzschii Woloszynskia 25% leopoliensis 25%

Leucocryptos Tetraselmis sp. marina 25% 25%

S13 Cyclotella Mallomonas meneghiniana akrokomos 8% 8%

Cryptophyta sp. 8%

Pseudocharacium americanum 9% Polarella glacialis Cryptomonas sp. 50% 17%

S14 Stephanodiscus hantzschii 13%

Gymnodinium beii 12% Cryptomonas sp. 50%

Carteria sp. 25%

S15 Leucocryptos Woloszynskia marina leopoliensis Chlamydomonas 5% 5% incerta 5% Monas sp. 5%

Phacotus lenticularis 6% Cryptomonas sp. Gymnodinium beii 58% 16%

Table A-4. Eukaryotic algae distribution at selected sampling sites for winter samples.

Sampling Site Eukaryotic Algae Distribution

S5 Paraphysomona Spumella sp. s imperforata 3% Chrysosaccus 3% sp. 5% Pseudopedinell a elastica 5% Chlamydomona s 8%

Cryptomonas sp. 76%

S9 Not Detected

S12 Chrysochaete Woloszynskia britannica pascheri 7% 7%

Fragilaria sp. 7% Plagioselmis nannoplanctica Karlodinium 33% micrum 7%

Hydrurus foetidus 7% Cryptomonas Spumella sp. pyrenoidifera 7% Pseudopedinell 6% Phaeoplaca a elastica Synedra ulna thallosa 7% 6% 6%

S14 Leucocryptos Paraphysomona Paulsenella sp. marina s butcheri 4% 4% 4% Chlamydomona s sp. Teleaulax 4% amphioxeia 7%

Plagioselmis nannoplanctica 11% Synura sp. 48%

Cryptomonas sp. 18%

S15 Not Detected