MICROALGAE DYNAMIC AND PHYSICOCHEMICAL INFLUENCE IN UPPER EAST WETLAND, PUTRAJAYA NUR NAJIHAH BINTI ABDULLAH 204162 DEPARTMENT OF BIOLOGY FACULTY OF SCIENCE UNIVERSITI PUTRA MALAYSIA 2022/2023
MICROALGAE DYNAMIC AND PHYSICOCHEMICAL INFLUENCE IN UPPER EAST WETLAND, PUTRAJAYA NUR NAJIHAH BINTI ABDULLAH 204162 A project proposal submitted in partial fulfilment of requirement for the Degree of Bachelor of Science in Biology with Education (Honours) DEPARTMENT OF BIOLOGY FACULTY OF SCIENCE UNIVERSITI PUTRA MALAYSIA 2022/2023
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ii ABSTRACT This study assessed the influence of physical properties and the microalgal dynamics in Upper East Wetland, Putrajaya. Physicochemical parameters were examined at three sampling sites (water input: UE3, natural remediation: UE1, and water output: UN1A) between July and September 2022.Physicochemical parameters such as temperature, pH, dissolved oxygen, light intensity, conductivity, also concentrations of ammonia, nitrate and phosphate were studied to determine the correlations with the diversity of microalgae. The significant differences in the concentrations of physicochemical characteristics were analysed using a one-way ANOVA. Analysis of microscopic findings using a compound microscope with a Dino-Eye camera piece and the genus identification for the microalgae found was referred to the online database AlgaeBase.org, freshwater phytoplankton identification book and other research papers. Paleontological Statistics (PAST) software was used to examine the diversity of microalgae with the Shannon-Wiener index, species richness and evenness. Pearson Correlation, Principal Component Analysis (PCA), and Canonical Component Analysis (CCA) were also performed to investigate the association between microalgae and physicochemical parameters at all locations. A total of 25 genera belonging to five classes of microalgae (Cyanophyceae, Chlorophyceae, Bacillariophyceae, Trebouxiophyceae, and Euglenophyceae) were recorded, with Microcystis sp., Ankistrodesmus sp., and Chlorella sp. being the most prevalent. From the Pearson correlation analysis, Cyanophyceae significantly correlates with light intensity and phosphate concentration. Trebouxiophyceae significantly correlates with light intensity and nitrate concentration. Molecular analysis indicated that 18S ribosomal RNA (rRNA) is a viable molecular marker for microalgae identification and should be pursued further. The variety and dominance of microalgal species indicate the trophic level of the site. The trophic characteristics of a lake can be enhanced in the future with monitoring and management to retain its functions and aesthetic values. Keywords: microalgae, constructed wetland, physicochemical parameter, 18S ribosomal RNA gene, molecular marker.
iii ABSTRAK Kajian ini telah dijalankan untuk menilai pengaruh sifat fizikokimia dan dinamik mikroalga di Tanah Lembap Timur Atas, Putrajaya. Parameter fizikokimia telah diperiksa di tiga tapak pensampelan (input air: UE3, pemulihan semula jadi: UE1, dan keluaran air: UN1A) antara Julai dan September 2022. Parameter fizikokimia seperti suhu, pH, oksigen terlarut, keamatan cahaya, kekonduksian, kepekatan ammonia, nitrat dan fosfat juga telah dikaji untuk menentukan korelasi dengan kepelbagaian mikroalga. Perbezaan ketara dalam kepekatan ciri fizikokimia dianalisis menggunakan ANOVA sehala. Analisis mikroskopik menggunakan mikroskop kompaun dengan kamera Dino-Eye dan pengenalpastian genus untuk mikroalga telah dirujuk kepada pangkalan data dalam talian AlgaeBase.org, buku pengenalan fitoplankton air tawar dan kertas penyelidikan lain. Perisian Statistik Paleontologi (PAST) digunakan untuk mengkaji kepelbagaian mikroalga dengan indeks Shannon-Wiener, kekayaan spesies dan kesamarataan. Korelasi Pearson, Analisis Komponen Utama (PCA), dan Analisis Komponen Kanonik (CCA) juga dilakukan untuk mengkaji perkaitan antara mikroalga dan parameter fizikokimia di ketiga-tiga tapak pensampelan. Sebanyak 25 genera yang tergolong dalam lima kelas mikroalga (Cyanophyceae, Chlorophyceae, Bacillariophyceae, Trebouxiophyceae, dan Euglenophyceae) telah direkodkan, dengan Microcystis sp., Ankistrodesmus sp., dan Chlorella sp. menjadi yang paling lazim. Daripada analisis korelasi Pearson, Cyanophyceae mempunyai korelasi yang ketara dengan keamatan cahaya dan kepekatan fosfat. Trebouxiophyceae berkorelasi dengan ketara dengan keamatan cahaya dan kepekatan nitrat. Analisis molekul menunjukkan bahawa RNA ribosom 18S (rRNA) ialah penanda molekul yang sesuai untuk pengenalpastian mikroalga dan perlu diteruskan. Kepelbagaian dan penguasaan spesies mikroalga menunjukkan tahap trofik tapak. Ciri-ciri tropika tasik boleh dipertingkatkan pada masa hadapan dengan pemantauan dan pengurusan bagi mengekalkan fungsi dan nilai estetiknya. Kata kunci: mikroalga, tanah lembap buatan, parameter fizikokimia, gen RNA ribosom 18S, penanda molekul.
iv ACKNOWLEDGEMENT “In the name of Allah, the Most Merciful and the Most Beneficient.” First and foremost, praises and thanks to Allah SWT for His shower of blessing, granting me the strength and ability to complete the final year project successfully. Ya Allah, just like how You've brought me out of darkness over & over again, you gave me the strength to overcome this. I'm limited, but Your Majesty is limitless. I want to express my deep and sincere gratitude to my supervisor, Dr Shahrizim Zulkifly, for his invaluable guidance, support, and helpful counsel. He motivated me to see things positively throughout my research and thesis preparation. I also wish to thank my lab colleagues, Aishah Perkins and Nazurah Mohd Norman, and postgraduate students, Zana Ruhaizat and Afiqah Mohamed, for sharing their expertise, experiences, and support. Their support, encouragement, and comfort are much appreciated and duly noted when times get rough. I would like to thank all my coursemates and friends who have helped me a lot in completing this project. In addition, I would like to thank everyone in the Biology Department at Universiti Putra Malaysia for their generosity and support throughout my studies. Thanks also to my moderator, Dr Wan Mohd Syazwan Wan Solahudin for the evaluation of my presentation and report for my final year project. Lastly, my heartfelt gratitude goes to my family, Mak, Ayah and Adli, for their unwavering support, love, prayers, and presence by my side throughout the project.
v BGY4959 CERTIFICATION OF APPROVAL DEPARTMENT OF BIOLOGY FACULTY OF SCIENCE UNIVERSITI PUTRA MALAYSIA Name of Student : Nur Najihah binti Abdullah Matric Number : 204162 Programme : Bachelor of Science in Biology with Education (Honours) Session : 2022/2023 Name of Supervisor : Dr Shahrizim bin Zulkifly Title of Research : Microalgae Dynamic and Physicochemical Influence in Upper East Wetland, Putrajaya This is to certify that I have examined the final year project report and all corrections to be made as recommended by the panel of examiners have been carried out. This report complies with the recommended format stipulated in the BGY4959 project guidelines, Department of Biology, Faculty of Science, Universiti Putra Malaysia. Signature and official stamp of Supervisor: Date: 3 February 2023
vi TABLE OF CONTENTS Page ABSTRACT ii ABSTRAK iii ACKNOWLEDGEMENT iv LIST OF TABLES ix LIST OF FIGURES x LIST OF ABBREVIATIONS xii CHAPTER 1 INTRODUCTION 1 1.1 Background of study 1 1.2 Objectives 2 2 LITERATURE REVIEW 3 2.1 Physicochemical parameters 3 2.2 Classification of microalgae 3 2.3 Identification of microalgae 5 2.4 Significance of microalgae 5 2.5 Constructed wetland 6 2.6 Study sites and anthropogenic activities 7 3 METHODOLOGY 9 3.1 Sampling site 9 3.1.1 Location 9 3.2 Duration of sampling 11 3.3 Water sampling 12 3.4 Physicochemical analysis 14 3.4.1 Physical analysis 14 3.4.2 Chemical analysis 15 3.5 Microalgae species identification 16 3.5.1 Microcopic identification analysis 16 3.5.2 Cells enumeration 16 3.6 Molecular analysis 17 3.6.1 Water filtration 17 3.6.2 DNA extraction 18 3.6.3 Polymerase Chain Reaction (PCR) 18 3.6.4 Agarose gel preparation 21 3.6.5 Dyeing and loading samples 21 3.6.6 Gel electrophoresis 21 3.6.7 PCR screening 21 3.7 Data analysis 22
vii 4 RESULTS 23 4.1 Physicochemical analysis 23 4.1.1 pH 26 4.1.2 Temperature 27 4.1.3 Light intensity 28 4.1.4 Dissolved oxygen 29 4.1.5 Conductivity 30 4.1.6 Concentration of phosphates 31 4.1.7 Concentration of nitrate 32 4.1.8 Concentration of ammonia 33 4.2 Microalgae diversity 34 4.2.1 Microscopic identification 34 4.2.2 Shannon–Weiner index 41 4.2.3 Pearson correlation coefficient 43 4.2.4 Principal component analysis 45 4.2.5 Canonical correlation analysis 47 4.3 Amplification of 18S ribosomal RNA 49 5 DISCUSSION 52 5.1 Physicochemical Analysis 52 5.1.1 pH 52 5.1.2 Temperature 54 5.1.3 Light intensity 54 5.1.4 Dissolved oxygen 55 5.1.5 Conductivity 55 5.1.6 Concentration of phosphate 56 5.1.7 Concentration of nitrate 56 5.1.8 Concentration of ammonia 57 5.2 Microalgae diversity 58 5.2.1 Cyanophyceae 58 5.2.2 Chlorophyceae 58 5.2.3 Bacillariophyceae 59 5.2.4 Euglenophyceae 59 5.2.5 Trebouxiophyceae 59 5.3 Trophic level 60 5.4 Molecular analysis 63 5.4.1 DNA extraction 63 5.4.2 PCR optimization 63 5.4.3 PCR components 64 5.4.4 PCR screening and troubleshooting 65 6 SUMMARY, CONCLUSION AND RECOMMENDATIONS FOR FUTURE RESEARCH 66
viii REFERENCES 67 APPENDICES______________________________________________________78 A Physicochemical parameters data from Pusat Perbadanan Putrajaya 76 B Density of microalgae found in UE3, UE1 and UN1A in July 2022 77 C Density of microalgae found in UE3, UE1 and UN1A in August 2022 78 D Density of microalgae found in UE3, UE1 and UN1A in September 2022 79 E One Way ANOVA 80 F Rainfall intensity on selected sites in Putrajaya from July to September 2022. 82 PROFILE OF STUDENT 85
ix LIST OF TABLES TABLE TITLE PAGE Table 3.1 Sampling sites in Upper East Wetland, Putrajaya 10 Table 3.2 Location of anthropogenic activities near the sampling sites 10 Table 3.6 18S rDNA Amplicon Primers used for PCR reaction 19 Table 3.7 Reaction volumes and concentration for PCR mastermix 19 Table 3.8 PCR cycle conditions for 18S rDNA 20 Table 4.1 Physical analysis within the three sites throughout the sampling months 24 Table 4.2 Chemical analysis within the three sites throughout the sampling months 25 Table 4.3 List of microalgae species found in three different sites in July 2022 36 Table 4.4 List of microalgae species found in three different sites in August 2022 37 Table 4.5 List of microalgae species found in three different sites in September 2022 38 Table 4.6 Density of microalgae based on their class at all sites within sampling months 39 Table 4.7 Microalgae indices at Upper East Wetland, Putrajaya from July until September 2022 42 Table 4.8 Pearson correlation coefficients between physicochemical parameters and microalgae class within three sites from July to September 2022 44 Table 5.1 Rainfall intensity from July to September 2022 at Presint 13 53 Table 5.2 Phytoplankton scale for trophic level identification 62
x LIST OF FIGURES FIGURE TITLE PAGE Figure 3.1 Location of Upper Wetland Putrajaya 9 Figure 3.2 Site 2 during dry season 11 Figure 3.3 Water sample collected using Van Dorn water sampler 13 Figure 3.4 Water sample collected manually 13 Figure 3.5 Physical analysis conducted using HI98194 Multiparameter (HANNA instrument) and portable light meter 14 Figure 3.6 Chemical analysis conducted using Hach Kit DR900 colourimeter 15 Figure 3.7 Water filtration using filtration pump and membrane filter paper 17 Figure 4.1 pH of water within the three sites throughout the sampling months 26 Figure 4.2 Water temperature within the three sites throughout the sampling months 27 Figure 4.3 Light intensity of three sites throughout the sampling months 28 Figure 4.4 Dissolved oxygen of the three sites throughout the sampling months 29 Figure 4.5 Conductivity of water within the three sites throughout the sampling months 30 Figure 4.6 Phosphate concentration within the three sites throughout the sampling months 31 Figure 4.7 Nitrate concentration within the three sites throughout the sampling months 32 Figure 4.8 Ammonia concentration of Upper East Wetland, Putrajaya within the three sites throughout the sampling months 33 Figure 4.10 Some of microalgae species that represents Bacillariophyceae, Chlorophyceae, Cyanophyceae, Euglenophyceae and Trebouxiophyceae 35 Figure 4.14 Density of microalgae in Upper East Wetland, Putrajaya within the three sites throughout the sampling months 40 Figure 4.15 Principal Component Analysis illustrating the relationship between physicochemical variables and three different sites from July to September 2022 46
xi Figure 4.16 Canonical correlation analysis (CCA) for the eight physicochemical parameters and microalgae class across different sites in different months 48 Figure 4.17 Banding patterns of 18S (rRNA) genes of isolated microalgae from UE1, UE3, and UN1A in July 2022 50 Figure 4.18 Banding patterns of 18S (rRNA) genes of isolated microalgae from UE1, UE3, and UN1A in August 2022 50 Figure 4.19 Banding patterns of 18S (rRNA) genes of isolated microalgae from UE1, UE3, and UN1A in September 2022 51
xii LIST OF ABBREVIATIONS UE3 Upper East 3 UE1 Upper East 1 UN1A Upper North 1A DNA Deoxyribonucleic acid DO Dissolved oxygen PCR Polymerase chain reaction rDNA Ribosomal deoxyribonucleic acid rRNA Ribosomal deoxyribonucleic acid TAE Tris-acetate-EDTA
1 CHAPTER 1 INTRODUCTION 1.1 Background of study Constructed wetland of Putrajaya is one of the most extensive artificial freshwater wetlands in the tropics. It serves many functions to improve the water quality and complies with National Water Quality Standards. Putrajaya Lake and Wetland management considers all of water needs and strives to allocate water equitably to meet all uses and demands. The wetland also satisfies the Sustainable Development Goal 6 of the United Nations, enabling access to clean water and sanitation globally by 2030 (Sustainable Development Goals: Water and Sanitation, 2020). Several active anthropogenic activities near Upper East Wetland Putrajaya in the past years can reduce the water quality. Anthropogenic impacts are responsible for water pollution, increasing nitrogen and phosphorus levels and causing aquatic ecosystems to collapse. It can create an algal bloom in a lake, which increases plant and algae development, lowering oxygen levels in the water. Thus, assessing seasonal variations in physicochemical parameters is very important to identify likely pollution sources, and aggregated monitoring months with comparable features can estimate the degree of the decline in water quality. These physicochemical parameters comprise physical parameters, chemical parameters, and biological parameters. The physical parameters include light intensity, salinity, temperature, pH and dissolved oxygen. In contrast, the chemical parameters are the nutrients, and the biological parameters include the microalgae dynamics. In addition, algae are excellent bioindicators of water quality due to their immediate reaction to water contaminants. Moreover, 18S rRNA molecular marker is competent for identifying various taxonomic groups without requiring time-consuming and complicated procedures. This improved molecular approach can assist in offering more information to identify species and estimate the true diversity of microalgae. Besides, finding specific novel sequences might update our understanding of the distribution of some unique creatures. Furthermore, such an approach is recommended as an effective way for phylogenic investigations in wetland habitats. It might be particularly beneficial for investigating the dynamics of microalgal populations in the context of pollution, such as exposure to anthropogenic activity. However, the previous research on the relationship between the physicochemical parameters and microalgae dynamics in Upper East Wetland, Putrajaya, is limited. Thus, this study helps to further the research and provide more scientific information about physicochemical parameters and microalgae dynamics in Upper East Wetland, Putrajaya. The findings of this study will directly benefit the Management of Putrajaya Lake and Wetland in maintaining good water quality.
2 1.2 Objectives In this study, three objectives were listed below: I. To identify the correlation between the physicochemical parameters and the diversity of microalgae species. II. To compare the diversity of microalgae from the different sites in Upper East Wetland, Putrajaya. III. To evaluate the 18S ribosomal RNA molecular marker for microalgae species in Upper East Wetland, Putrajaya.
3 CHAPTER 2 LITERATURE REVIEW 2.1 Physicochemical parameters Microalgae have a rapid response to changes in environmental conditions, such as changes in physicochemical parameters (Oyeku & Mandal, 2020). Physicochemical parameters include the physical, chemical, and biological parameters. Algae diversity and successions often act as the bioindicator for water quality; variations in succession can be linked to changes in the aquatic habitat and physicochemical attributes, for instance, the impact of nutrient runoffs into lakes and wetland (Mohd. Sabkie et al., 2020). The study made by Ma et al. (2020) has measured pH, temperature, turbidity, conductivity, total dissolved solids, total suspended solids, total alkalinity, biological oxygen demand, chemical oxygen demand, dissolved oxygen, total organic carbon, sulphate, nitrate, and phosphate for physicochemical parameters. Specific algae grew by their physicochemical requirements, enabling development and succession (Mohd. Sabkie et al., 2020). For example, the rate of cyanobacteria growth will increase as the light intensity increases compared to the growth rate of other algae (Loogman, 1982). Temperature also causes an increase in the diversity and population density of algae (Bigham et al., 2019). Next, a higher concentration of nutrients causes the increased phytoplankton concentration at the study sites (Sharip et al., 2016). Several microalgae are sensitive to alkalinity and phosphate phosphorus concentration, such as Stichococcus bacillaris, Staurastrum rotula, and Sphaeroplea sp. (Chaidir et al., 2019). Sensitivity in this context suggests that when the concentration or levels of the parameters increased or dropped, the number of organisms declined or grew oppositely (Chaidir et al., 2019). 2.2 Classification of microalgae The algae from all over the world are registered on the website algaeBASE.org. (Chaidir et al., 2019). There are 13 phyla of algae that have been described in AlgaeBase based on morphological and molecular identification, which are Cyanobacteria, Rhodophyta, Glaucophyta, Charophyta, Chlorophyta, Cryptophyta, Haptophyta, Ochrophyta Choanozoa, Loukozoa, Metamonada, Percolozoa and Myzozoa (Guiry, 2012). However, there are six primary families of microalgae in the lake: Bacillariophyceae, Chlorophyceae, Cyanophyceae, Xanthophyceae, Dinophyceae and Euglenophyceae (Tiwari & RC, 2018). Microalgae are a diverse collection of photoautotrophic eukaryotic protists and prokaryotic cyanobacteria. Microalgae come in a wide range of sizes, shapes, and forms (Correa et al., 2017). The size, shape, and presence of microalgae in water bodies differ significantly depending on the microalgae species (Chaidir et al., 2019). Phylum Chlorophyta is a unicellular and multicellular organism with chlorophylls a and b in a single chloroplast and can be found in freshwater, marine, and terrestrial habitats (Heimann & Huerlimann, 2015). This group has many thylakoid membranes surrounded by a plastid outer membrane made from the membrane of integral protein (Borowitzka et al., 2016). Chlorophyte plastid genomes have been sequenced, and they range in size from 118 to 204 kb, containing 94–107 genes. Besides, starch granules of Chlorophyta composed of amylose and amylopectin can develop inside the chloroplast, particularly under unfavourable growth circumstances (Safi et al.,
4 2014). Unicellular members of the phylum Chlorophyta such as Chlorella vulgaris, Dunaliella salina, and Haematococcus pluvialis, are utilized in the commercial sector (Heimann & Huerlimann, 2015). Organisms of Chlorophyta are usually known as green algae. The most common group of Chlorophyta is the Chlorophyceae, the most morphologically varied class with biflagellate and quadriflagellate motile cells with different flagellar apparatus configurations (Gault & Marler, 2009). Next, cyanobacteria are one of the common microalgae that can be found in the freshwater habitat (Borowitzka et al., 2016). Cyanobacteria are blue-green due to the presence of phycocyanin, an antioxidant that has been used in cosmetic and pharmaceutical sectors (Gault & Marler, 2009). Other than that, Anabaena sp. and Lyngbya sp. can be found in the freshwater habitat in Jeli, Gunung Stong, and Kelantan (Rajkumar & Sobri Takriff, 2016). Anabaena sp. can produce more than one type of cyanotoxins (Gault & Marler, 2009). In addition, most cyanobacteria form relationships with other organisms by anchoring or attaching to the substrate or being endophytic (Uyeda et al., 2016). The endosymbiosis of cyanobacteria with the primary eukaryotic host can induce the formation of a primary plastid (Manoylov, 2014). This primary plastid is vital to differentiate between the phyla of microalgae. Bacillariophyceae, also known as diatoms, are a distinct type of algae distinguished by their yellow-brown colouring under a light microscope and the presence of thick silica cell wall (Bellinger & Sigee, 2015). They can be found in marine and freshwater habitats as non-flagellate single cells, simple colonies, or chains of cells (Heimann & Huerlimann, 2015). It contains plastids with periplasmic endoplasmic reticulum and girdle lamellae (Bellinger & Sigee, 2015). On the outside of the plastid, there are chysolaminarin and lipid food stores (Bellinger & Sigee, 2015). The frustule is a unique cell wall of opaline silicon dioxide and organic coatings (Bellinger & Sigee, 2015). Next, the Dinoflagellates are mainly unicellular biflagellate microalgae, while some of them lack flagella and are connected, such as Stylodinium sp. (Bellinger & Sigee, 2015). They are primarily found in marine systems' surface waters and can be found in the freshwater habitat (Bellinger & Sigee, 2015). Dinoflagellates possess chlorophylls a and c but are often golden or olive-brown due to the high concentration of carotene and the accessory pigment, xanthophyll peridinin (Lee, 2018). Pyrenoids are abundant, and starch is the primary storage product (Bellinger & Sigee, 2015). Yellow-green algae, Xantophyceae, are non-motile, single-celled, or colonial algae with a yellow or fresh green look (Bellinger & Sigee, 2015). Yellow-green algae are environmentally constrained to the small aquatic environment and damp soils. Carbohydrate is stored in the form of oil droplets or chrysolaminarin granules (Bellinger & Sigee, 2015). Walls are made chiefly of pectin or pectic acid, frequently two spliced, overlapping parts that split into 'H' shapes fragments after filament dissociation (Bellinger & Sigee, 2015). Euglenophyta is unicellular microalgae with 40 genera, most of which are found in freshwater (Bellinger & Sigee, 2015). Cells are generally motile, either by flagella or in non-flagellate cells, through the body's capacity to change form (Lee, 2018). Furthermore, the production of paramylon as a reserve storage material for euglena (Bellinger & Sigee, 2015). A pellicle or surface coat gives the cell a striated appearance and can be found immediately under the plasmalemma (Bellinger & Sigee, 2015). It comprises interlocking protein strips that wrap helically around the cell (Lee, 2018).
5 2.3 Identification of microalgae Taxonomic classifications can identify microalgae species through morphological and molecular identifications (Manoylov, 2014). The morphological identification used a microscope to observe the microalgae, whereas the molecular markers needed gene regions to determine the algae species (Manoylov, 2014). Previous taxonomic research on microalgae that conduct primary production in aquatic environments is based on the organisms' behavioural, metabolism, and morphological properties (Sonmez et al., 2022). Nowadays, they can be identified through morphological and genetic identification. For morphological identification, the shape, size, and colour of microalgae found are compared to the morphological microalgae species on the website AlgaeBase (2022). Round, elliptical, rectangular, trapezoidal, rod, cylinder, star, and form combinations are some of the shapes of microalgae (Chaidir et al., 2019). For instance, the isolated microalgae were green, unicellular, and spherical or subspherical under light microscopy. (El-Sheekh et al., 2020). The molecular identification procedures commonly utilised are DNA extraction, PCR amplification using 18s rRNA primer for eukaryotic microalgae while electrophoresis, PCR product purification, and sequencing for 16S rRNA primer prokaryotic microalgae (Chaidir et al., 2019). The 18s rRNA primer is a universal primer and is often used to identify the microalgae up to the genus level (Khaw et al., 2020). 2.4 Significance of microalgae Microalgae have a wide range of additional commercially relevant and valuable goods. Many attempts are being made to mass-produce biodiesel without regard for the environmental consequences (Hossain et al., 2020). Freshwater algae, blue-green algae, and diatoms are cultured for biofuel production (Phang et al., 2015). Based on the past research, when compared to biodiesel from first-generation feedstocks such as coconut, palm, and soybean biodiesel in Malaysia, biodiesel from microalgae species such as Spirulina platensis, Chlorella protothecoides, and others had much higher density, viscosity, initial boiling point, total acid number, cetane number, flash point, calorific value, and diesel index (Hossain et al., 2020). Other than that, most microalgae have a potent lipid peroxidation inhibition; they are utilised as alternatives for dangerous synthetic antioxidants and alternative sources of chemicals preventing food quality degradation and retaining nutritional value (Natrah et al., 2007). Microalgae such as Isochrysis galbana, Chaetoceros calcitrans, Chlorella vulgaris, and Nannochloropsis oculata serve as the natural antioxidants as they have shown stronger antioxidant activity α-tocopherol (Natrah et al., 2007). Algae can be used in wastewater treatment for several purposes, including removing coliform bacteria, minimising chemical, and biological oxygen demand, removing nitrogen and phosphorus, and removing heavy metals (Abdel-Raouf et al., 2012). The more excellent pollutant removal capability was attributed to algae's oxygen enrichment and rise in dissolved oxygen during photosynthesis (Zhao et al., 2016). In standard wastewater treatment, organic materials are frequently supplemented in such wastewater to boost bacterial nutrient removal efficiency as a source of energy. On the other hand, using microalgae sunlight, soluble inorganic carbon dioxide, nitrogen, and other nutrients increase their cell numbers while purifying wastewater (Al-Jabri et al., 2021). The wastewater contains nutrients like nitrogen and phosphate, which are necessary for microalgal cell development (Jasni et al., 2021). The capacity of
6 phototrophic microorganisms to deliver oxygen to aerobic organic pollution degraders and increase nutrient and pathogen removal is the basis of microalgae-based wastewater treatment (Al-Jabri et al., 2021). Some of which microalgae are used to treat the municipal wastewater, such as Chlorella sp., are used to purify the municipal wastewater (Kamarudin et al., 2015). Other than that, the cultivation of Chlorella vulgaris is 96.3% proven efficient in removing copper, and Scenedesmus sp. is 97% proven efficient in removing nickel in the wastewater (Al-Jabri et al., 2021). Besides, microalgae also act as an indicator of a healthy aquatic ecosystem. The composition and structure of microalgae are essential markers of environmental health (Rajkumar & Sobri Takriff, 2016). Algae are used as natural indicators of environmental conditions because they tend to form algae blooms in bodies of water and their ability to develop rapidly (Khalil et al., 2021). They are a great source of nutrition for plankton and may also be used to detect eutrophication and water components like chlorophyll and carotenoid (Xu et al., 2020). Outbreaks of hazardous or poisonous microalgae, such as red tides and algal blooms, are significant marine pollution hazards which can cause a severe impact on the economy to the aquaculture sector (Xu et al., 2020). Microalgae are particularly sensitive to ecological changes at tiny spatial scales (Rajkumar & Sobri Takriff, 2016). 2.5 Constructed wetland Wetlands are the transitional region between water and land where sediments and heavy metals are trapped before the wastewater is released into the mainstream (Mohd. Sabkie et al., 2020). Natural wetlands are formed by three significant aspects of geology, hydrology, and biology, whereas constructed wetlands involve the technical aspect made by humans (Kanungo et al., 2017). Constructed wetlands give protection against soil corrosion and flooding and provide a haven for other organisms such as wildlife and aquatic organisms (Mohd. Sabkie et al., 2020). Most importantly, constructed wetlands serve a significant role in treating wastewater and maintaining water quality (Smart Putrajaya, 2021). The constructed wetland effectively removed nutrients, solids, five-day biochemical oxygen demand, chlorophyll-a, and thermotolerant coliforms, primarily during the dry season (TravainiLima & Sipaúba-Tavares, 2012). This is due to higher concentrations in treatment inflows and lower hydraulic loading rates compared to rates during the rainy season (Travaini-Lima & Sipaúba-Tavares, 2012). In addition, constructed wetland can remove the water contaminant through numerous processes, including sedimentation, photolysis, hydrolysis, microbial degradation, absorption, degradation, and plant uptake (Li et al., 2021). The natural water filtration of constructed wetlands comprises wetland vegetation, soils, and associated microbial assemblages (Maiga et al., 2017). One of the most noticeable characteristics of constructed wetlands is the presence of macrophytes, which can distinguish them from misunderstand as unplanted soil filters or lagoons (Vymazal, 2013). The wetland vegetation can slow down the flow of the stream, providing a microenvironment inside the water columns and supplying the attachment sites for microbial activity (Davis, 1994). Besides, the microorganisms will assist the wetland plants in breaking down and transforming the pollutants (Davis, 1994). There are several wetland plant species that were planted in Upper West Wetland of Putrajaya, such as Cyperus Compactus, Eleochlaris variegata, Scirpus mucronatus and Fuirena umbellata.
7 Furthermore, algae also portrayed an essential role in constructed wetlands as they have a large capacity for wastewater treatment (Zhao et al., 2016). Closterium parvulum, Nitzschia longissima, Stauroneis anceps., Lyngbya confervoides, and Staurastrum sp. can be found in the constructed wetlands of USM Engineering Campus (Shaharuddin et al., 2019). Staurastrum sp., Scenedesmus sp., and Peridinium sp. are most frequently found in the Putrajaya Lake and Wetlands during the sampling period during the study of Mohd. Sabkie et al. (2020). One of the most important features of constructed wetlands is the well-structured components. This will include several constructed wetlands zones, including an inlet zone, macrophyte, open water, ZII zone, and outlet zone (Putrajaya Lake and Wetland Management and Operational System, 2019b). The inlet zone enables the water to flow into the cell of wetlands and can reduce the impact of soil erosion (Putrajaya Lake and Wetland Management and Operational System, 2019b). The macrophyte zone provides sustainable microbial biofilms in aquatic plants that aid in nutrient transformation, organic flocculation, pollutant filtration, sedimentation, and oxygenation (Putrajaya Lake and Wetland Management and Operational System, 2019b). The outlet zone can capture extremely contaminated flows for recovery or recycling, maintain specific water regimes for improved habitat diversity, encourage particular species of flora and fauna, and manage water levels for water plant establishment (Putrajaya Lake and Wetland Management and Operational System, 2019b). 2.6 Study sites and anthropogenic activities Putrajaya Wetland are essential as it aids in flood prevention, wildlife conservation, ecotourism, recreation, research and education, and soil erosion protection (Smart Putrajaya, 2021). Putrajaya Wetland is located at the centre of Putrajaya City and situated at the northern part of the Putrajaya Lake (Hj Wan Ali @ Yaacob et al., 2015). The Putrajaya wetland is a constructed wetland system with five arms and each with 23 cells. Putrajaya Wetland provides 60% of the lake's water flow (Hj Wan Ali @ Yaacob et al., 2015). Those arms are Upper North Wetland, Upper East Wetland, Upper West Wetland, Lower East Wetland and Upper Bisa Wetland. Except for Upper Bisa, all the components eventually flow to the Central Wetland, totalling 24 cells before the water enters Putrajaya Lake (Putrajaya Lake and Wetland Management and Operational System, 2019b). From the past study, several groups of algae can be found abundantly throughout their samplings: Scenedesmus sp., Staurastrum sp. and Peridinium sp (Mohd. Sabkie et al., 2020). This study will be conducted in the Upper East Wetland of Putrajaya, which includes three different sampling sites, which are UE3 (Site 1), UE1 (Site 2) and UN1A (Site 3). The water will flow through UE3 to UN1A to the central wetland. Furthermore, the Upper East Wetland of Putrajaya are free water surface constructed wetland. It included emergent macrophytes consisting of a shallow sealed basin with 20 to 30 cm of rooting soil and 20 to 40 cm of water depth. Near the study site, the Upper East Wetland, several anthropogenic activities have been conducted actively: the golf course and residential area. Palm Garden Golf Club is located near Site 1, which acts as the inlets of Upper East Wetland, Putrajaya. Residential of Presint 12, Putrajaya is located near Site 2. Site 2 acts as a natural bioremediation system in which it purifies contaminants from the water inflow before it reaches Putrajaya Lake. Anthropogenic activities can lead to changes in the hydrological cycle, thus affecting water quality degradation (Camara, Jamil & Abdullah, 2019). Water quality decline in urban settings is caused by a variety of activities,
8 including residential, industrial, commercial operations, and recreational activities (Camara, Jamil & Abdullah, 2019). Agricultural runoff from fertilizer-rich lands, such as crops and livestock farms, plant nurseries, and golf courses, is the most common source of water pollutants (Othman et al., 2014). For instance, the concentration of chemical oxygen demand (COD) rose dramatically from upstream to downstream, while the application of fertilizers and pesticides to the turfgrass of the golf course exceeded the limit inside the golf course and downstream (Luong & Dung, 2019). Other than that, there was an increase in dissolved oxygen downstream from the golf course on the river, implying that the enhanced bloom of algae provided dissolved oxygen to the river (King et al., 2007). Next, residential, industrial, commercial, and recreational activities are the primary contributors to water quality degradation in urbanized environments (Camara, Jamil & Abdullah, 2019). Pollutants that are not properly managed can cause serious environmental and health problems, especially in larger districts or cities (Stefanakis et al., 2014). Furthermore, household wastes, also known as municipal solid wastes, are generated in daily activities from residential, office, or service buildings (Stefanakis et al., 2014). Even though the constructed wetland is designed as natural filtration to remove the pollutants and reduce the nutrients such as phosphorus and nitrates, the discharge of these nutrients into sensitive water environments can increase the growth of algae and unwanted aquatic macrophytes (Abdel-Raouf et al., 2012). This may lead to eutrophication and unwanted competition within the marine ecosystem.
9 CHAPTER 3 METHODOLOGY 3.1 Sampling site 3.1.1 Location Upper East Wetland of Putrajaya was assessed at three different sites (Figure 3.1). UE3 is Site 1 with latitude 2°57'39"N, and longitude 101°42'27''E, UE1 is Site 2 with latitude 2°57'36"N, and longitude 101°42'23''E and UN1A is Site 3 with latitude 2°57'30"N, and longitude 101°41'56''E. Water flows from Site 1 to Site 2 and Site 3 and finally to Central Wetland. The distance between Site 2 and Site 1 is about 714.11 m, and the distance between Site 1 to Site 3 is about 356.68 m. The total distance from Site 2 to Site 3 is 1.00 km (3,287.43 ft). Moreover, the anthropogenic activity conducted near the study sites are less than 1 km. The distance is estimated using Google Earth features. The distance between Site 1 to Palm Garden Golf Club is about 697.65 m. The distance between the Site 2 to residential area, Presint 12, Putrajaya is about 31.24 m. Lastly, Site 3 is an open place, which in Tandop Wetland Putrajaya. The distance between Site 3 and the road track is about 23.2 m. Figure 3.1: Location of Upper Wetland Putrajaya (Google Earth, 2022). UE1 UE3 UN1A
10 Table 3.1: Sampling sites in Upper East Wetland, Putrajaya. Site Location Latitude Longitude Site 1 UE3 2°57'39"N 101°42'27''E Site 2 UE1 2°57'36"N 101°42'23''E Site 3 UN1A 2°57'29"N 101°41'59''E. Table 3.2: Location of anthropogenic activities near the sampling sites. Location Latitude Longitude Palm Garden Golf Club 2°57'56"N 101°42'49''E Presint 12, Putrajaya 2°57'36"N 101°42'20''E Tandop Wetland Putrajaya 2°57'29"N 101°41'58''E.
11 3.2 Duration of sampling Water sampling in the dry seasons (Figure 3.2) was on 28th July 2022, 25th August 2022, and 22nd September 2022. Even though the east-coast dry season in Malaysia is from March until September, there was heavy rain during the sampling period. Figure 3.2: Site 2 during dry season.
12 3.3 Water sampling Water sampling at three stations using a Van Dorn water sampler to collect triplicate of 500 ml water samples (Figure 3.3) from May, June, and July to analyse physicochemical parameters and algae diversity in Upper East Wetland. Water samples were taken from the subsurface, 0.5 m depth, at all the sites (Pranab Gogoi et al., 2019). However, specific sites required taking the water samples manually (Figure 3.4) as the water level was shallow. The next step is a measurement of water transparency on the surface level using a Secchi disk (Mohd. Sabkie et al., 2020). Next, for chemical analysis, the water samples were stored in an opaque container, and upon reaching the lab, the water samples were stored temporarily in the fridge. For microscopic analysis, ten drops of glutaraldehyde were added to the water samples for preservation.
13 Figure 3.3: Water sample collected using Van Dorn water sampler. Figure 3.4: Water sample collected manually.
14 3.4 Physicochemical analysis 3.4.1 Physical analysis Temperature, pH, conductivity, and dissolved oxygen (DO) were measured in situ (Maurya RR, 2015) by using HI98194 Multiparameter (HANNA instrument) and light intensity will be measured by using a portable light meter (Figure 3.5). Figure 3.5: Physical analysis conducted using HI98194 Multiparameter (HANNA instrument) and portable light meter.
15 3.4.2 Chemical analysis The water samples collected were stored in opaque container in the laboratory for chemical analysis. Concentrations of nitrate-nitrogen, ammonia, and phosphate were analysed ex-situ by using the Hach Kit DR900 colourimeter (Figure 3.6) according to the manufacturer’s protocol (Mohd. Sabkie et al., 2020). Figure 3.6: Chemical analysis conducted using Hach Kit DR900 colourimeter.
16 3.5 Microalgae species identification 3.5.1 Microcopic identification analysis The water samples for microscopic identification were preserved using 10 drops of glutaraldehyde. The microalgae in the water samples were identified using the compound microscope with a Dino-Eye camera piece and connected to the laptop that has been installed with Dino Capture 2.0 software (Mohd. Sabkie et al., 2020). Next, the genus identification for the microalgae found was referred to online database AlgaeBase.org, freshwater phytoplankton identification book and other research papers. 3.5.2 Cells enumeration The microalgae from each water samples were counted and enumerated via Sedgwick Rafter Counting Slide by site and by month. The formula (Hötzel & Croome, 1999) used is as follows: [ −1 ] = × 1,000 3 × × N = number of cells/units counted A = area of field (mm²) D = depth of a field (Sedgwick rafter counting chamber depth (mm) F = number of fields counted
17 3.6 Molecular analysis 3.6.1 Water filtration The water samples were filtered using filtration pump and membrane filter paper with pore size of 0.2m (Whatman filter No. 1) for each replicate with a total of 27 samples. The filter apparatus and membrane filters were sterilized beforehand using autoclave. Then, the membrane filter paper then was stored at –80°C until DNA extraction which was conducted within 2 months of storage (Djurhuus et al., 2017). Figure 3.7: Water filtration using filtration pump and membrane filter paper.
18 3.6.2 DNA extraction The DNA samples were extracted per purification of DNA from soil and sediment protocol from Macherey-Nagel and by using Macherey-Nagel NucleoSpin Soil DNA isolation kit for DNA from soil. DNA extraction require an organic extraction, nonorganic and adsorption method (Gupta, 2019). Freezing is a common approach of preserving samples collected for DNA analyses (Straube & Juen, 2013). Then, the DNA samples then were stored at –80°C until PCR was conducted. 3.6.3 Polymerase Chain Reaction (PCR) The DNA sample templates were amplified through PCR utilising a Thermal Cycler. Figure 3.3 displays the sequences of the forward and reverse 18S Amplicon PCR primers. Figure 3.4 and Figure 3.5 provide the components of each Thermo Scientific Phusion Plus Green PCR Master Mix and the 18S rDNA amplification protocol, respectively.
19 Table 3.3: 18S rDNA Amplicon Primers used for PCR reaction. Primer Sequence (5’ to 3’) 18S Forward Primer ATAACAGGTCTGTGATGCCCT 18S Reverse Primer CCTTCYGCAGGTTCACCTAC Table 3.4: Reaction volumes and concentration for PCR mastermix. Component Volume Microalgae DNA sample 2.5 l Amplicon PCR Forward Primer 18S 5 l Amplicon PCR Reverse Primer 18S 5 l Thermo Scientific Phusion Plus Green PCR Master Mix 12.5 l Total 25 l
20 Table 3.5: PCR cycle conditions for 18S rDNA. Initial denaturation 94 C 2 minutes 30 cycles 98 C 63 C 68 C 10 seconds 30 seconds 1 minutes Final extension 72 C 10 minutes Hold 10 C
21 3.6.4 Agarose gel preparation All PCR results were examined by 1% gel electrophoresis in 2x Tris-acetate-EDTA (TAE) buffer following Polymerase Chain Reaction (PCR). In a 250 ml Erlenmeyer flask, 1.2 g of agarose gel and 60 ml of 2x TAE buffer were combined to create a solution of agarose gel and TAE buffer. The flask was then heated for 2 minutes in a microwave. The solution in the flask was cooled by passing the bottom under cold water while gently spinning it, and then 5 l of FloroSafe DNA Stain was added to the solution. The agarose was then put into the gel-casting tray, which was equipped with combs. The agarose gel then solidified in few minutes. 3.6.5 Dyeing and loading samples Once the gel had solidified, 2x TAE buffer solution was poured into submerging the gel for electrophoresis completely. To allow DNA samples to sink into the gel wells, 1.5 l of Thermo Scientific 6X TriTrack DNA Loading Dye was added to each DNA sample from the PCR product, and DNA samples were mixed with the loading dye, then loaded into each gel well. The Thermo Scientific GeneRuler 1 kb DNA Ladder, which serves as a marker, was loaded to the beginning of the gel, and Thermo Scientific GeneRuler 100bp DNA Ladder was loaded to the end of the gel to determine the approximate size of the DNA samples separated by gel electrophoresis. The negative control was loaded into the second last well to prevent false positive results. 3.6.6 Gel electrophoresis The loaded samples in the gel were run at 90V for 70 minutes until the dye line was approximately 75-80% of the way down the gel. The DNA was negatively charged and will move towards the positive electrode. 3.6.7 PCR screening Under UV light, the DNA band of the samples was seen and compared to the DNA Hyper Ladder in the first and the last lane, which serves as a marker to determine the size of the DNA fragments.
22 3.7 Data analysis One way ANOVA was conducted to study the significant differences between the concentration of physicochemical parameters such as nutrients between each site by using SPSS 27.0 software. Pearson correlation analysis was used to determine the relationship between the algae succession and the physicochemical parameters (Tiwari & RC, 2018) by using SPSS 27.0 software. The species diversity index and species relative abundance of microalgae was calculated by using Shannon–Weiner index (H′) (Kumar et al., 2020) by using formula as below: = −∑[( ) × log( )] where: H = Shannon diversity index pi = proportion of individuals of i-th species in a whole community Other than that, the conclusions derived from physical and chemical parameter analyses neither indicate the correlations between the various physicochemical properties nor provide the grouping of samples with comparable characteristics. Therefore, multivariate statistical techniques, such as principal component analysis (PCA), are needed to observe physical and chemical parameter patterns and extract additional information from the enormous amount of acquired heterogeneous data (Mwove et al., 2018). Next, Canonical correlation analysis (CCA) was performed to investigate the potential association between physical and chemical characteristics and microalgae in sites (Chia et al., 2011).
23 CHAPTER 4 RESULTS 4.1 Physicochemical analysis Assessment of physicochemical analyses such as pH, temperature, light intensity, dissolved oxygen, conductivity, the concentration of phosphates, concentration of nitrates and concentration of ammonia was shown in Table 4.1 and Table 4.2. The comparison of each physicochemical variable between each site month was shown in Figure 4.1 to Figure 4.8.
2 Table 4.1: Physical analysis within the three sites throughout t superscripts are significantly different at p<0.05) Month Site Temperature pH July Site 1 31.33±0.475e 6.92±0.035a Site 2 30.22±0.265e 6.89±0.096a Site 3 27.86±0.038cde 7.02±0.105a August Site 1 29.71±0.330 cde 7.06±0.046a Site 2 31.07±0.140 de 6.85±0.000a Site 3 28.57±0.193abc 7.13±0.067a September Site 1 30.64±0.135d 6.93±0.047a Site 2 29.17±0.598abc 6.88±0.151a Site 3 28.09±0.08a 7.02±0.267a
4 the sampling months. (Means and standard error with different Mean ± Standard Error Dissolved Oxygen Light Intensity Conductivity 4.43±0.023a 44.67±10.269a 48±0.000a 4.71±0.062a 1797.25±172.103bc 48±0.816a 4.83±0.065a 304.67±110.637a 62.33±7.311a 3.66±0.892a 91.67±8.969a 62±1.732a 5.34±0.030 a 1923.5±54.500c 48±0.000a 4.49±0.312a 420±33.045a 70.67±0.333a 4.61±0.344a 199±53.780 a 45.67±0.882b 5.51±0.799a 1193.67±273.868b 59±16.503a 4.25±0.583a 120.33±2.404a 76.33±1.453c
2 Table 4.2: Chemical analysis within the three sites throughout t superscripts are significantly different at p<0.05) Month Site Concentration of Nitrate (mgL-1 ) July Site 1 1.27±0.067b Site 2 11.38±1.516a Site 3 1.27±0.067b August Site 1 0.87±0.433b Site 2 1.40±0.100b Site 3 0.46±0.422b September Site 1 1.23±0.067b Site 2 0.7±0.265b Site 3 1.23±0.067b
5 the sampling months. (Means and standard error with different Mean ± Standard Error Concentration of Phosphate (mgL-1 ) Concentration of Ammonia (mgL-1 ) 0.22±0.020 abc 0.29±0.141a 0.22±0.064bcd 0.05±0.018a 0.45±0.037cde 0.29±0.141a 0.31±0.041ab 0.09±0.012a 0.27±0.000bcd 0.09±0.025a 0.13±0.067e 0.16±0.069a 0.00±0.003bcde 0.18±0.068a 0.06±0.009a 0.06±0.012a 0.02±0.012de 0.18±0.068a
26 4.1.1 pH Figure 4.1 below shows the pH concentration for sampling for July, August, and September 2022 at three different sites in Upper East Wetland, Putrajaya. The pH value was ranged between 6.54 and 7.46. However, there is no significant difference between all sites from July to September 2022 (p>0.05). Figure 4.1: pH of water within the three sites throughout the sampling months. (Error bars denote standard error with n=3)
27 4.1.2 Temperature Figure 4.2 below shows the temperature value for three different sites in July, August, and September 2022. The highest mean value was in August 2022 at Site 2 (31.33 ± 0.48 °C), and the lowest was at Site 2 in September 2022 (27.86 ± 0.04 °C). The temperature of Site 2 and Site 3 has increased in August 2022; however, it has decreased significantly (p<0.05) in September 2022. Figure 4.2: Water temperature within the three sites throughout the sampling months. (Error bars denoted standard error with n=3)
28 4.1.3 Light intensity Figure 4.3 below shows the light intensity for three different sites in July, August, and September. The lowest mean value in August 2022 at Site 2 (44.67 ± 10.37 cd), and the highest was in July at Site 1 (1923.50 ± 54.50 cd). The light intensity of all sites has reduced significantly (p<0.05) from July to September 2022. The standard error for July 2022 at Site 2 and Site 3 is very high, but the difference of light intensity between all sites during sampling months is significant (p<0.05). Figure 4.3: Light intensity of three sites throughout the sampling months. (Error bars denote standard error with n=3)