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Isolation, screening and comprehensive characterization of candidate microalgae for biofuel feedstock production and dairy effluent treatment: A sustainable approach
Bioresource Technology 293 (2019) 121998
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Isolation, screening and comprehensive characterization of candidate microalgae for biofuel feedstock production and dairy effluent treatment: A sustainable approach Ashutosh Pandeya, Sameer Srivastavaa, Sanjay Kumarb, a b
T
⁎
Department of Biotechnology, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, U.P., India School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, U.P., India
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Isolation Screening Phylogenetic analysis Microalgae Dairy effluent Biodiesel
In this study, the indigenous native microalgae were isolated from domestic and dairy effluent (DE) and further screened for DE treatment and lipid accumulation. All the isolated microalgae were examined for their growth adaptability in DE. The growth parameters of 15 isolates were determined and the following six isolates further selected for comprehensive analysis and identified as Desmodesmus sp. ASK01, Chlorella sp. ASK14, Scenedesmus sp. ASK16, Scenedesmus sp. ASK22, Chlorella sp. ASK25 and Chlorella sp. ASK27. The nutrient remediation capacity of six isolates as well as its lipid accumulation potential and biomass composition were determined. The Scenedesmus sp. ASK22 showed the best combined results and promising strain for the DE treatment and biofuel production. Biomass composition of Scenedesmus sp. ASK22 showed an oil accumulation of 30.7% (w/w) and biomass yield 1.22 g L−1. The fatty acid methyl ester (FAME) mainly composed of C15:0, C16:0, C18:0, C18:1 and C18:3.
1. Introduction
for biodiesel production over terrestrial crops and plant sources, such as have higher photosynthetic efficiency, higher nutrient uptake rate, shorter generation time, high lipid content, biomass productivity and flexible cultivation conditions (Karemore et al., 2013). Microalgae cultivation requires an enormous amount of nutrients and freshwater on an industrial scale (Murphy and Allen, 2011). Water is a valuable
The unicellular microalgae have been widely promoted as a green and sustainable bioenergy feedstock over the last few decades and have the potential to replace conventional fuels (petroleum oil, coal and natural gas) (Behera et al., 2015). Microalgae have many advantages
⁎
Corresponding author. E-mail address: [email protected] (S. Kumar).
https://doi.org/10.1016/j.biortech.2019.121998 Received 6 June 2019; Received in revised form 8 August 2019; Accepted 10 August 2019 Available online 13 August 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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0.049). The microalgal cells were maintained in BG11 liquid media at 28 ± 2 °C and illuminated by cool white fluorescent tube light (55 µmol m−2 s−1) with continuous agitation. To grow bacteria free culture of green algal cells, cultures were grown in media supplemented with ampicillin (100 µg/ml) and chloramphenicol (50 µg/ml). After visible mixed algal growth on liquid BG11 media, 100 µL were spread onto a BG11 agar plate under the same growth conditions. Repeated streaking onto fresh BG11 agar plate and microscopic observations were applied until monospecific microalgae bacteria free cultures were obtained individually and separately. Once, pure microalgae strains were isolated singly they were morphologically characterized. Genera of the isolates were identified by microscopic observation and identification of morphological characteristics according to Bellinger and Sigee (2015). Twenty eight green microalgae isolates were maintained regularly in BG11 agar plates at 4 °C.
resource that must be used sensibly and the use of non-potable water for cultivation of microalgae could minimize the use of freshwater (Kumar et al., 2020). Indeed, most of the freshwater consumed is disposed of in the form of wastewater, which is rich in nutrients, posing a serious risk of eutrophication. High artificial media prices and low lipid productivity are critical constraints associated with the production of renewable bioenergy feedstock from microalgae (Allen et al., 2018). Therefore, to compete with petroleum-derived fuels, the efficiency of the process must be improved and made economical at industrial scale (Gendy and El-Temtamy, 2013). To make the cultivation of microalgae more cost-effective, it is recommended to use nutrient laden wastewater as a complex medium in combination with waste CO2 exhaust, which provides a pathway to remove nutrients from wastewater and CO2 sequestration (Acién Fernández et al., 2018; Molazadeh et al., 2019). Commercial production of microalgae biofuel using wastewater as media composition is only possible if three main parameters, i.e. high biomass, lipid productivity and high wastewater tolerance, can be fulfilled (Chen et al., 2015; Show et al., 2017). Appropriate microalgae strain selection is a step towards successful combination of algae-based biofuel production and wastewater treatment (Cobos et al., 2017; Mondal et al., 2017). India is the world's largest dairy producer and consumer. Based on an estimate by the National Dairy Development Board (NDDB), India, it alone contributes about 20% of world milk production at around 176.4 million tonnes in the 2017–2018 (NDDB, 2018). The dairy effluent (DE) is the liquid residue discharge from the dairy industries, which is generated at a scale of 2–3 L DE for each litre of milk processed (Pandey et al., 2019). DE contain large quantities of milk constituents such as casein, lactose, fat, inorganic salts besides detergents and sanitizers which contribute largely towards high BOD and COD (Slavov, 2017). In this regard, there is a need for the isolation and selection of native candidate microalgae strains with potentially high intrinsic lipid content as well as have natural ability to grow efficiently in high strength DE. The use of isolates native to an area may be beneficial in their inherent adaptability to environmental conditions; a potential benefit in developing an energetically efficient platform to produce biofuels. Native microalgae performs better than most other species at commercial scale cultivation using DE as media (Cho et al., 2017; Zhou et al., 2011). The aim of this work was to explore the adequacy of native microalgae isolates for use in the DE treatment process and biodiesel production. Considering the same, the present study focuses on isolation, screening and comprehensive characterization of potential microalgae from DE and other wastewater sources. Further, the promising isolates were studied in detail by analysing their biochemical composition as well as ability of nutrient removal, biomass and lipid production. The information generated from this study allows the selection of the most suitable microalgae strain to develop an integrated system that simultaneously treats DE and generates biomass for use in the production of biofuels.
2.2. Pre-treatment of dairy effluent and their charecterization The raw dairy effluent (RDE) was collected from the inlet of effluent treatment plant of Shyam dairy products, Prayagraj, India. The RDE was filtered to remove suspended particles and characterized for physico-chemical parameters. The DE used in this study was simulated dairy effluent (SDE), which was prepared by mixing 4 g milk powder in 1 L of distilled water. The physicochemical characterization of RDE and SDE were determined using the standard methods of water and wastewater analysis reported by the American Public Health Association (APHA, 1989) unless otherwise specified. Total sugar and protein content in DE were estimated by phenol sulphuric acid method and Lowry method, respectively (DuBois et al., 2002; Lowry et al., 1951). Heavy metals present in the DE were analysed by ICP-OES (Perkin Elmer). The SDE was autoclaved at 121 °C for 15 min and allowed to cool at room temperature before being used for algal cultivation. 2.3. Screening of algal isolates for their growth and lipid accumulation potential in the SDE The candidate microalgae selection was performed in multi-steps. (i) The preliminary adaptation test (cell density evaluation) was determined in SDE followed by growth rate and lipid productivity as shown in Fig. 1. The adaptability test was performed in six well ELISA plate (6 ml working volume). All the algal isolates were grown in SDE medium at temperature 28 ± 2 °C, for 12 days under continuous white fluorescent light illumination (80 µmol m−2 s−1). (ii) The isolates that appeared to be better adapted were scaled up to a 500 ml flask (working volume 300 ml). Samples were withdrawn at alternate days for the determination of growth kinetic parameters. (iii) Based on the kinetic results six potential isolates were scaled up to 1 L flask (working volume 600 ml) and cultured in same conditions as described above. The samples were withdrawn at regular intervals for the determination of chemical oxygen demand (COD), nitrate (N-NO3−1), total phosphorus (TP) removal kinetics, growth kinetics and lipid productivity simultaneously. Finally, the candidate microalgae were screened from above step based on their lipid productivity/lipid yield.
2. Materials and methods 2.1. Wastewater collection, microalgae isolation and purification
2.4. Identification of potential microalgae isolates Water samples used to isolate microalgae were collected aseptically from domestic wastewater lodge area (25°30′12.0″N 81°52′02.7″E) and DE treatment plant of Shyam dairy products (25°38′26.0″ N, 81°83′80.0″ E), Prayagraj (Uttar Pradesh), India. Water samples of 10 ml were inoculated in 250 ml conical flasks containing 100 ml of BG11 medium for microalgae enrichment. The BG11 media comprised of (g L−1) NaNO3 1.5, K2HPO4 0.04, trace elements (MgSO4·7H2O 0.075, CaCl2·2H2O 0.036, Na2CO3 0.02, citric acid 0.006, ferric ammonium citrate 0.006, and EDTA 0.001) and microelement solution (1 ml L−1 that consists of H3BO3 2.86, MnCl2·H2O 1.81, ZnSO4·7H2O 0.222, CuSO4·5H2O 0.079, Na2MoO4·2H2O 0.390, and Co(NO3)2·6H2O
2.4.1. DNA extraction, PCR amplification of 18S rRNA gene and sequencing For the genetic identification of the six selected potential microalgae strains, the total genomic DNA was extracted using the HiPurATM Marine Algal DNA Purification Kit (HiMedia, India). The partial 18S rRNA gene from the genomic DNA of potential isolates ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 was amplified by PCR using the oligonucleotide primers (18SF:5′TCCTGCCAGTAGTCATATGC-3′; 18SR: 5′-TGATCCTCYGCAGG TTCAC-3′) (Grzebyk et al., 1998). The PCR amplification was performed using a 20 µL reaction mixture containing 2
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cultivation, the cultures were harvested by centrifugation at 5000 rpm for 10 min followed by oven drying at 60 °C until constant weight was achieved. The biomass productivity (BP, g L−1 day−1) during the culture period was calculated from the following Eq. (i):
Biomass productivity (BP ) = (Xt − X0 )/(tt − t0)
(i)
−1
where, Xt is the biomass production (g L ) at the end of the growth phase (tt) and X0 is the initial biomass production (g L−1) at t0 (day). The cellular lipid productivity (LP) was calculated as the product of BP and the fractional lipid content (w/w) in the biomass, using following Eq. (ii).
Lipid productivity(LP, mg L−1 d−1) = BP × LC /100 −1
(ii)
−1
where, BP is the biomass productivity (g L d ) and LC is the lipid content (w/w). The following equations were used to calculate the specific growth rate (µ, d−1) and cell doubling time (Td, day).
μ = ln (Wt / Wo)/Δt
(iii)
Td = ln (2)/ μ
(iv)
where, Wt and Wo are the dry cell weight and the end and beginning of batch cultivation. 2.5.2. Chemical oxygen demand (COD), NO3−1 and TP removal The following equations were used to calculate the removal efficiency and removal rate of COD, NO3−1 and TP (Mishra and Mohanty, 2019).
Fig. 1. Screening of high lipid producing microalgae for treating dairy effluent (DE).
20 ng of DNA template, 0.2 µM of each oligonucleotide primer, 200 µM dNTP, 1.5 mM MgCl2, 1 × PCR buffer, 1 U of Taq DNA Polymerase (Himedia, India). The following thermal program was used: 95 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 54 °C for 45 s, and elongation at 72 °C for 2 min, followed by final 10 min extension at 72 °C. The PCR product was visualized by electrophoretic separation on 1.0% TAE agarose gel and were purified using a Gene-JET PCR purification kit (Thermo Fisher) before sequencing. Sequencing reactions were performed using the ABI PRISM™ Dye Terminator Cycle Sequencing Kit (Applied Biosystems, USA) with the primers (18SF: 5′TCCTGCCAGTAGTCATATGC-3′; 18SR: 5′-TGATCCTCYGCAGGTTCAC-3′) and analysed in an automatic ABI PRISM 3730 sequencer (Applied Biosystems).
Removal efficiency (%) = [(Ci − Cf )/ Ci] × 100
(v)
Removal rate (mg L−1 d−1) = (Cf − Ci )/(tt − to)
(vi)
where, Ci and Cf are the initial and final concentration of parameter at time to and tt respectively. 2.5.3. Biomass characterizations The carbohydrate and protein in microalgal biomass were estimated by the phenol sulphuric acid and Lowry method, respectively (DuBois et al., 2002; Lowry et al., 1951). Elemental analysis of microalgae was estimated by CHNSO method. To understand the presence of various functional group present in microalgae biomass and oil, Fourier transform infrared spectroscopy (FTIR) was conducted by Nicolet FTIR spectrometer (Madison, USA) as per standard protocol. The thermogravimetric profile of microalgae biomass (MB) and lipid extracted microalgae biomass (LEMB) were studied using thermogravimetric analyser (SII 6300 EXSTAR) in the atmosphere of nitrogen (200 ml L−1). The sample weight loss with rise in temperature was recorded and the TGA data was collected. The high heating value (HHV) and low heating value (LHV) of biomass based on CHNO was calculated by using equation vii and viii (Brown et al., 1994; Miranda et al., 2018).
2.4.2. Phylogenetic analysis To determine the phylogeny of the isolated microalgae strains, their 18S rDNA sequences were aligned with the previously reported sequences of different closely related microalgae species. The 18S rRNA gene sequence obtained in this study and reference sequences available at GenBank were retrieved using BLASTN program and were aligned using ClustalW multiple sequence alignment. Phylogenetic trees were inferred using the Neighbour-Joining method (Saitou and Nei, 1987) and distances were determined according to Kimura 2-model (Kimura, 1980). The robustness of the tree was estimated by bootstrap analysis based on 1000 replications. The MEGA 6.0 program package was used for all analyses (Tamura et al., 2013).
O HHV (MJ Kg−1 db) = 0.3383 × C + 1.422 × ⎛H − ⎞ 8⎠ ⎝
(vii)
H ⎞ × 9.011 LHV (MJ Kg−1 db) = HHV − 2.447 × ⎛ ⎝ 100 ⎠
(viii)
where C, H and O are the weight percentages of carbon, hydrogen, and oxygen in the biomass, respectively.
2.5. Methods of analysis 2.5.1. Growth kinetics analysis The microalgae growth for each isolated strains was estimated by spectrophotometric method (absorbance at 680 nm) at every alternate day up to stationary phase. The dry cell weight (DCW) was estimated by using respective correlation equation, those were prepared for each strain. The DCW was measured gravimetrically by pelleting the cells and then drying for 24 h at a temperature of 60 °C. At the end of batch
2.6. Lipid extraction and fatty acid methyl esters (FAME) analysis Total lipid from dried algal powder was extracted with chloroform: methanol (1:2, v/v) and lipid content was quantified gravimetrically (Bligh and Dyer, 1959). Fatty acid composition analysis of microalgal oil was carried out by using the protocol established by Breuer et al. (2013). 3
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more or less equal to RDE (Table 1). The SDE was used in this study to insure that the physicochemical characteristics being constant throughout the experiment.
2.7. Assessment of the biofuel properties of microalgal oil To assess the FAME profile of the candidate microalgae the following empirical Eqs. (ix)–(xi) were used to evaluate iodine value (IV), saponification value (SV), and cetane number (CN) (Mandotra et al., 2016).
IV =
∑ [(254 × F × D/MW )]
(ix)
SV =
∑ [560 × F /MW ]
(x)
CN = 46.3 + 5458/ SV − (0.225 × IV)
3.2. Isolation and screening of potential microalgae in DE Isolation is a necessary step of bio-process development to obtain a pure culture and presents the first step towards the selection of potential candidate microalgae strain for biodiesel production and wastewater remediation. Besides these, native microalgae can be effectively used for nutrient remediation from wastewater, CO2 sequestration and other value-added products. Chen et al. (2015) reported that the species isolated from wastewater achieved remarkably higher nutrient removal rates than those experiments which were performed using commercial strains. Number of studies has showed that it becomes possible to maximize the nutrients sequestration along with higher biomass and lipid productivity by using native microalgae isolates (Chen et al., 2015; Xin et al., 2010; Zhou et al., 2011). In this study, isolated microalgae strains performances have been studied to determine their robustness and growth yield using SDE as a culture medium. A total of 28 axenic native microalgae strains were isolated from domestic wastewater and DE samples using standard microbiological methods as mentioned in Section 2.1. The cell density of all the microalgae isolates measured after 12 days of batch cultivation in 6 well plates and showed that more than 80% of them can grow in SDE medium. This observation implies that SDE can support microalgae growth requirements and that the strains are fully adapted to it. For the next steps, best performing 15 algal cultures (ASK01, ASK03, ASK07, ASK08, ASK09, ASK11, ASK14, ASK16, ASK17, ASK18, ASK20, ASK21, ASK22, ASK25 and ASK27) were selected (Fig. 2). These were better adapted cultures in SDE medium and considered based on their growth parameters estimation. The growth data was obtained as described above in the Materials and Methods section. Table 2 presents the volumetric cell yield (g L−1) of each adapted culture. The specific growth rate (µ) and doubling time (Td) were calculated and are presented in Table 2. Further, six potential microalgae isolates namely ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 were shortlisted based on their initial adaptability test and growth rate in SDE (Table 2).
(xi)
where, F is the percentage of each fatty acid, D is the number of double bonds, and MW is the molecular weight of the FA. The standard biofuel properties as laid down in ASTM D6751 and EN 14214 were considered for evaluating the data. 3. Results and discussion 3.1. Physicochemical characterisation of the dairy effluent The physicochemical characteristics of RDE collected from DE treatment plant of Shyam dairy products, Prayagraj, India and SDE used in this study is presented in Table 1. The pH was found to be alkaline (8.4 ± 0.2), this may be due the use of detergent for washing. The COD of RDE was 3800 ± 400 mg L−1. The concentration of total phosphorus (TP) and orthophosphate (P-PO4−3) in RDE was found to be 46.1 ± 0.27 mg L−1 and 25.63 ± 2.89 mg L−1, respectively. The nitrate concentration of RDE was 55.5 ± 0.11 mg L−1. Concentration of total sugar and protein was found to be 1.68 ± 0.11 and 373.20 ± 5.47 mg L−1, respectively. In addition, RDE also contained a variety of trace minerals viz., Na, K, Ca, Cu, Fe, Mn, Zn and Ni, which are required for microalgae growth. The characteristics of SDE were Table 1 Physicochemical characteristics of simulated dairy effluent (SDE) used and raw dairy effluent. Characteristics
Unit
Simulated dairy effluent
Raw dairy effluent
pH Conductivity (S/m) Turbidity Total dissolve solids Total Solids Total suspended solids Chemical oxygen demand (COD) Total phosphorus (TP) Ortho phosphate (P-PO4−3) Nitrate (NO3−1) Chloride (Cl−1) Total sugar Protein
– S/m NTU mg L−1 g L−1 g L−1 mg L−1
6.80 ± 0.12 0.1247 ± 0.0035 1623 ± 76 293.36 ± 27.20 2.40 ± 0.04 2.37 ± 0.02 4000 ± 112
8.4 ± 2.4 0.1148 ± 0.05 1453 ± 65 316 ± 35 2.805 ± 0.09 2.489 ± 0.05 3800 ± 400
mg L−1 mg L−1
82.88 ± 1.89 56.28 ± 1.17
46.1 ± 0.27 25.63 ± 2.89
mg L−1 mg L−1 g L−1 g L−1
108.49 ± 2.36 33.98 ± 2.00 2.06 ± 0.01 3.85 ± 0.19
55.5 ± 0.11 63.21 ± 0.52 1.68 ± 0.01 2.63 ± 0.26
mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1
56 ± 8.23 5.3 ± 1.09 37.42 ± 0.98 0.001 ± 0.001 ND ND 3.12 ± 0.4 0.005 ± 0.012 ND 0.095 ± 0.02 ND ND
71.4 ± 14.4 9.1 ± 2.2 56.35 ± 1.35 0.004 ± 0.00 ND ND 0.346 ± 0.31 0.048 ± 0.02 ND 0.056 ± 0.00 ND 0.0145 ± 0.00
Metals Na K Ca Cu Co Cd Fe Mn Pb Zn Cr Ni
3.3. Dairy effluent treatment, growth and lipid productivity of selected potential microalgae isolates Table 3 presents the change in nutrients (COD, N-NO−3 and TP) concentration as well as the removal efficiency of all six potential microalgae isolates in SDE. The isolates ASK01, ASK14, ASK16, ASK22,
ND – below detectable limit. The data are represented as mean value ± standard deviation.
Fig. 2. Adaptability check of microalgae growth in SDE. 4
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Table 2 Dry cell weight of the microalgae adapted to SDE. Time (Day)
0 3 5 7 10 12 µ (d−1) Td (day)
Dry cell weight (g L−1) ASK01
ASK03
ASK07
ASK08
ASK09
ASK11
ASK14
ASK16
ASK17
ASK18
ASK20
ASK21
ASK22
ASK25
ASK27
0.074 0.247 0.561 1.089 1.178 1.227 0.23 3.01
0.107 0.164 0.268 0.522 0.647 0.604 0.14 4.95
0.046 0.176 0.261 0.420 0.469 0.499 0.20 3.46
0.052 0.126 0.281 0.468 0.584 0.616 0.21 3.30
0.069 0.136 0.271 0.447 0.572 0.563 0.17 4.08
0.070 0.155 0.350 0.62 0.779 0.830 0.21 3.30
0.026 0.197 0.370 0.747 1.045 1.020 0.31 2.24
0.036 0.140 0.452 0.595 0.666 0.782 0.26 2.67
0.063 0.287 0.430 0.512 0.631 0.563 0.18 3.85
0.097 0.169 0.305 0.597 0.712 0.688 0.16 4.33
0.111 0.177 0.445 0.742 0.975 1.037 0.19 3.65
0.096 0.169 0.311 0.633 0.934 1.061 0.20 3.47
0.038 0.175 0.385 0.810 1.194 1.218 0.29 2.39
0.040 0.249 0.703 1.189 1.447 1.476 0.30 2.30
0.055 0.221 0.467 1.052 1.160 1.285 0.26 2.67
The response values (dry cell weight) were the mean values of triplicates. µ values (bold) represents the higher specific growth rate of the isolates.
ASK25 and ASK27 removed more than 84% of COD with > 280 mg d−1 of removal rate from DE, after 12 days of cultivation. COD is the indirect measure of all forms of organic and inorganic nutrients present in wastewater or DE. All six potential microalgae isolates showed almost similar COD removal efficiency that varied from 84.92% to 91.90%. Decline in the concentration of COD suggests that all six microalgae isolates can efficiently utilize the organic matter as substrate for their growth and source of energy. Microalgae are well known for the removal of all forms of nitrogen (nitrate, nitrite, ammonium and urea) present in wastewater (Delgadillo-Mirquez et al., 2016). In microalgae, nitrate assimilation is performed by two transport and two reduction steps: First, nitrate (NO3−1) is transported into the cell, then a cytosolic nitrate reductase catalyzes nitrate reduction to nitrite, which subsequently is transported into the chloroplast, where the enzyme nitrite reductase catalyzes its reduction to ammonium (Lachmann et al., 2019; Sanz-Luque et al., 2015). Finally, ammonium is incorporated into carbon skeletons by rendering glutamate, through the glutamine synthetase/glutamine oxoglutarate amino transferase or glutamate synthase cycle (Miflin and Lea, 1975). The studied six microalgae isolates removed > 91% of nitrate effectively from SDE. ASK16 and ASK22 completely removed the NO3−1 from the SDE by the end of day 8. Whereas, ASK14 achieved complete removal of NO3−1 by the end of day 12. Chokshi et al. (2016) reported that Acutodesmus dimorphus had completely removed ammonia nitrogen (277.4 mg L−1) and phosphates (5.96 mg L−1) from DE. In another study, the efficiencies of phosphate and nitrate removal by Chlorella pyrenoidosa cultivated in raw and treated DE (in an oxidation pond) were 87 and 60% respectively in the influent and 83 and 49%, respectively, in the effluent (Kothari et al., 2012). As seen in Table 3, TP consumption, which is responsible for energy transfer and nucleic acid formation, was observed in the range between 86 and 92% by ASK01, ASK14, ASK16, ASK 22, ASK25 and ASK27 isolates. A growth characteristic of all six potential microalgae strains in SDE has shown in Table 4. The microalgae isolate ASK25 showed maximum biomass productivity (123.67 mg L−1 d−1) followed by ASK27 (107.08 mg L−1 d−1), ASK01 (102.25 mg L−1 d−1), ASK22 −1 −1 (101.50 mg L d ), ASK14 (90.08 mg L−1 d−1) and ASK16 (65.17 mg L−1 d−1) after 12 days of batch cultivation. Whereas, the ASK22 exhibited maximum lipid accumulation (30.7% w/w) as well as lipid productivity (31.16 mg L−1 d−1) as compared to all other microalgae isolates. From the findings of these experiments, it has been inferred that microalgae isolate ASK22 can be a potential candidate isolate to be further used in RDE treatment and lipid production.
w/w) in ASK22 was found to be significantly more than that reported previously (Chokshi et al., 2016; Daneshvar et al., 2018). ASK22 also showed 31.3% w/w crude protein which was the highest among other five selected microalgae isolates and was evident from its increased elemental nitrogen (7.72%w/w) composition in SDE biomass. However, ASK22 showed the lowest carbohydrate accumulation (26.32% w/w) among all six selected microalgae isolates. The isolate ASK14 exhibited 47.07% w/w of carbohydrate accumulation, which could be considered as a promising substrate for bioethanol production. The elemental composition analysis of biomass of all six potential microalgae isolates obtained from SDE has shown in Table 5. The biomass of all six microalgae isolates have carbon (C) (47.80–52.26%), hydrogen (H) (3.68–5.01%), nitrogen (N) (6.64–7.58%) and oxygen (O) (29.42–34.91%) as major constituents. Sydney et al. (2011) observed that the Botryococcus braunii grown in domestic wastewater contain C 55.33%, H 8.26%, N 2.68% and O 33.55% as major constituents. Similarly, Monoraphidium sp. KMC4 grown in secondary domestic wastewater contains C 48.22%, H 7.66%, N 5.91%, O 38.08% and trace amount of S (0.13%) and the corresponding value of elemental composition for the Monoraphidium sp. KMC4 grown incontrol BG11 medium were 51.04%, 7.94% 5.5%, 35.44% and 0.08%, respectively. These results suggest that the elemental composition of microalgae biomass varies from species to species as well as culture conditions (Mishra and Mohanty, 2019). ASK22 biomass has C 51.28%, N 7.72%, H 5.01% and O 29.42% as major constituents due to higher lipid accumulation (30.7%w/w). This makes ASK22 a potential microalgae feedstock for lipid production. Higher sulfur and nitrogen content in biomass leads to the release of NOx and SOx (Miranda et al., 2018). SOx emission is not expected from fuel derived from microalgae grown in SDE, as sulfur was not detected in the biomass of the six microalgae isolates (Table 5). However, the nitrogen (6.64–7.72%) was found to be marginally higher than that reported in previous studies (Chiranjeevi and Mohan, 2016; Mishra and Mohanty, 2019) which could be due to the higher nitrogen in the SDE that may leads to higher NOx emission. These findings suggest that ASK22 can utilize SDE efficiently as growth medium for their large-scale cultivation that can ultimately be used as a feedstock for sustainable biodiesel production. The heating value (HV) (MJ kg−1 dry weight) is used to describe the total amount of heat energy that can be produced on complete combustion by a unit mass of microalgae. The low heating value (LHV) and high heating values (HHVs) of biomass ranged from 15.32 to 18.14 MJ kg−1 and 16.16–19.24 MJ kg−1, respectively (Table 5). It was found that the LHV and HHV of ASK22 was 18.14 and 19.24 MJ kg−1, respectively, which was identified to be maximum (Table 5). The maximum HV of ASK22 grown in the SDE could be due to the elevated content of lipids and proteins because the main contributions to the HV of cells are from their lipid (38.3 MJ kg−1) and protein (15.5 MJ kg−1) contents (Lardon et al., 2009). Similarly, Mishra and Mohanty (2019) depicted that Monoraphidium sp. KMC4 grown in raw domestic sewage wastewater contained an HHV of 20.33 MJ kg−1, which is close to the ASK22 HHV. These findings suggest that the ASK22 biomass would be used as an
3.4. Biomass characterization of potential microalgae isolates The biomass characteristic of each of the six selected microalgae isolates has summarized in Table 5. Microalgae isolate ASK22 accumulated a high amount of lipids (30.7% w/w) followed by ASK27 (25.8% w/w), ASK16 (24.6% w/w), ASK01 (22.7% w/w), ASK 25 (21.2% w/w) and ASK14 (13.8% w/w). The total lipid content (30.7% 5
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Table 4 Growth characteristics of potential microalgae isolates grown in SDE.
82.88 52.63 40.34 25.98 24.64 21.55 11.00 86.73 5.99
ASK27
A. Pandey, et al.
82.88 41.95 37.75 12.97 8.89 6.03 3.47 95.81 6.62
ASK25
Growth characteristics
82.88 51.62 32.29 22.42 26.43 18.26 10.47 87.36 6.03
82.88 57.44 35.13 28.22 16.55 10.24 7.26 91.24 6.30
Dry cell weight (g L ) Lipid content % (w/w) Biomass productivity (mg L−1 d−1) Lipid productivity (mg L−1 d−1)
ASK14
ASK16
ASK22
ASK25
ASK27
1.23 22.70 102.25
1.08 13.80 90.08
0.78 24.60 65.17
1.23 30.70 101.50
1.48 21.20 123.67
1.29 25.80 107.08
23.21
12.43
16.03
31.16
26.22
27.63
82.88 50.11 22.41 17.82 22.24 20.31 6.71 91.90 6.35 82.88 62.73 40.46 28.69 26.87 18.89 6.67 91.95 6.35
3.5. PCR amplification, phylogenetic analysis and identification
108.49 48.77 42.15 23.30 12.77 5.69 2.98 97.25 8.79
108.49 59.05 31.28 23.47 17.29 12.21 8.86 91.83 8.30
The microscopic examination of six potential microalgae strains presented and revealed their purity and morphology. Based on preliminary morphological identification, the microalgae isolate ASK01, ASK16 and ASK22 were identified as Scenedesmus sp. whereas, microalgae isolate ASK14, ASK25 and ASK27 showed similarity with Chlorella sp. Furthermore, the universal primers were used to amplify the 18S rDNA fragment from six selected microalgae isolates. These amplicons were sequenced and the sequences were submitted in the NCBI database. Gene bank accession numbers of these sequences have been indicated in Fig. 3. The BLAST hits of partial 18S rDNA gene sequences of microalgae isolates ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 indicated their phylogenetic identity with Desmodesmus armatus AB917135 (96%), Chlorella vulgaris KJ 676110 (98.73%), Scenedesmus sp. CPC3 (99%), Scenedesmus abundans X73995 (99%), Chlorella sorokiniana KT852969 (99%) and Chlorella sorokiniana KU948990 (99%), respectively. The results of morphological identification were also supported by sequence based phylogenetic analysis as shown in Fig. 3.
108.49 52.62 17.55 3.86 N.D. N.D. N.D. 100.00 13.56
The response values (COD, N-NO3−1 and TP) were the mean values of triplicates; N.D. – Not detected.
108.49 54.16 24.86 17.67 11.23 8.92 N.D. 100.00 9.04 108.49 70.96 52.23 33.67 7.15 2.20 2.44 97.75 8.84 4000.00 2923.08 1421.54 542.98 623.70 647.78 323.88 91.90 306.34 4000.00 2769.23 1421.54 723.98 873.18 809.72 603.16 84.92 283.07 4000.00 2615.38 1827.69 769.23 566.80 485.83 380.00 90.50 292.85 4000.00 3384.62 1624.62 904.98 1164.24 1012.15 485.83 87.85 292.85 4000.00 3076.92 1726.15 1247.40 995.48 971.66 420.00 92.92 309.72 4000.00 2769.23 1726.15 769.23 956.34 566.80 485.83 87.85 292.85 0 2 4 6 8 10 12 Nutrient removal (%) Removal rate (mg/d)
ASK01
alternative source of biofuel feedstock and the SDE can be used as a growth medium for sustainable biofuel feedstock production.
108.49 79.23 24.42 27.62 N.D. N.D. N.D. 100.00 13.56
ASK14 ASK01 ASK22 ASK14 ASK01 ASK27 ASK14 ASK01
COD (mg L−1)
ASK16
ASK22
ASK25
N-NO3−1 (mg L−1)
ASK16
Nutrients removal Time (Day)
Table 3 Chemical oxygen demand (COD), Nitrate and phosphate (TP) concentration of selected microalgal isolate over 12 days.
ASK25
ASK27
TP (mg L−1)
ASK16
ASK22
−1
Microalgae isolates
3.6. FAME analysis of candidate microalgae ASK22 Fig. 4 depicts the relative fatty acid percentage in Scenedesmus sp. ASK22 calculated through GC–MS analysis. FAME analysis revealed the presence of C15:0 (2.02%), C16:0 (29.23%), C18:0 (13.5%), C18:1 (46.2%) and C18:3 (9.50) that ensures it’s suitability for biodiesel production as these fatty acids have better stability to oxidation and fluidity (Knothe, 2012; Mandotra et al., 2016). The composition of fatty acid and the types of fatty acids produced are considered important for biodiesel quality (Minhas et al., 2016). Quality depends primarily on the unsaturation ratio as unsaturated fatty acids enhance cold flow properties while saturated fatty acids maintain good oxidative stability. In fact, oleic acid is one of the most desirable fuel oil components since it gives a good balance between the properties of cold flow and stability of oxidation (Hoekman et al., 2012). The quality of fuel oil has been reported to depend on high oleic acid content as well as ignition point, combustion heat, cold filter plug point, oxidative stability, viscosity, lubricity and is determined by the fatty acid profile composition. The profile of fatty acids obtained for Scenedesmus sp. ASK22 was found to be suitable and potential candidate for use in biodiesel production which has been cultivated using SDE as growth medium. A few other lipid properties, namely CN, IV, and SV, were analysed based on FAME profiling to check the suitability of Scenedesmus sp. ASK22 as biofuel producer. As per EN14214 standard the CN should not exceed 120 g I2/ 100 g or be less than 47 g I2/100 g (Sun et al., 2011). Biodiesel CN is used to rate the combustion quality. Hence, the higher CN value indicates the shorter the ignition time. Whereas, the iodine value (IV) denotes the degree of unsaturation of the microalgal oil and should be less than 120 (EN 14214:2012). A high IV indicates low oxidative stability of the fuel (Knothe, 2012). Biodiesel derived from Scenedesmus sp. 6
Bioresource Technology 293 (2019) 121998
C8.47H9.06NO4.17 C8.16H6.32NO3.94 C8.07H8.39NO3.56 C7.76H9.01NO3.35 C7.94H7.54NO4.19 C8.04H7.87NO3.82 16.61 16.09 18.65 19.13 16.10 17.31 16.71 16.17 18.75 19.24 16.19 17.40 22.70 13.80 24.60 30.70 21.20 25.80
Fig. 4. Fatty acid profiles of candidate microalgal isolates.
ASK22 biomass met the CN (56.59) and IV (67.64) standards laid down in the USA (ASTM D6751) and those in Europe (EN 14214), making this strain optimally suitable for biodiesel production.
33.18 47.07 34.82 26.32 43.16 29.90
26.88 23.01 28.64 31.30 24.73 26.96
47.80 48.91 52.26 51.28 49.56 50.13
4.30 3.68 4.58 5.01 3.96 4.31
6.64 7.06 7.58 7.72 7.33 7.31
ND ND ND ND ND ND
31.37 31.56 30.64 29.42 34.91 31.99
C22.73H40.32NO10.35 24.31 24.49 36.14 8.80
32.62
55.33
8.26
2.84
ND
33.6
C7.35H13.60NS0.05O5.53 16.32 16.46 20.30 19.70
44.40
43.18
6.72
6.85
0.75
43.32
C6.2H11.15NS0.06O3.55 26.50 27.50
28.50
47.10
7.13
8.86
1.27
19.52 19.68 38.97
C10.83H20.01NS0.01O5.64 22.08 20.27 31.40 31.04
33.70
51.04
7.94
5.50
0.08
35.44
22.26 20.44 38.08 0.13 5.91 7.66 30.07 30.37
35.81
48.22
LHV HHV
Lipid (% w/w) Protein (% w/w) Carbohydrate (% w/w)
Fig. 3. Evolutionary relationships of taxa. The evolutionary history was inferred using the Neighbour-Joining method (Saitou and Nei, 1987). The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches (Felsenstein, 1985). The evolutionary distances were computed using the Kimura 2-parameter method (Kimura, 1980). Evolutionary analyses were conducted in MEGA6 (Tamura et al., 2013).
Scenedesmus obliquus
Domestic wastewater SDE SDE SDE SDE SDE SDE
The infrared spectra (500–4000 cm−1) of the Scenedesmus sp. ASK22 MB were very much similar to the spectra of Scenedesmus sp. ASK22 LEMB. The region in the spectra from 3100 to 2800 cm−1 indicates the presence of lipid in the sample and was due to the symmetrical and asymmetrical stretching vibration of –CH2 groups (Miranda et al., 2016; Nautiyal et al., 2014). These –CH2 groups form the backbone of lipids and show the vibrations particularly at 2922.58 cm−1 (Forfang et al., 2017). The intensities of the peaks in this region of the spectra for the MB and LEMB of Scenedesmus sp. ASK22 were compared and a decrease in intensity was observed in the LEMB which implies that the lipid was successfully extracted from the Scenedesmus sp. ASK22 MB. Soybean oil
Botryococcus braunii LEM14 ASK01 ASK14 ASK16 ASK22 ASK25 ASK27
Domestic wastewater BG11
Monoraphidium sp. KMC4 Monoraphidium sp. KMC4 Scenedesmus obliquus
Municipal wastewater BG11
Medium
3.7. FTIR analysis of Scenedesmus sp. ASK22 MB, LEMB and oil
Microalgae
Table 5 Microalgal biomass composition of selected microalgal strains, cultured in SDE.
Carbon (% w/w)
Hydrogen (% w/w)
Nitrogen (% w/w)
Sulfer (% w/w)
Oxygen (% w/w)
Heating value (MJ Kg−1 db)
Empirical equation
C9.52H17.97NS0.01O5.64
References
Mishra and Mohanty (2019) Mishra and Mohanty (2019) Ansari et al. (2019) Ansari et al. (2019) Sydney et al. (2011) This study This study This study This study This study This study
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reinforced the suitability of Scenedesmus sp. ASK22 for biodiesel production.
is currently a major feedstock for production of biodiesel (Huang et al., 2012). The FTIR analysis of oil extracted from Scenedesmus sp. ASK22 and soybean oil also performed and the result suggests that the algal oil is like soybean oil except a peak in the range of 730–400 cm−1 in the soybean oil which could be due to the aliphatic groups (Kothari et al., 2013). The peaks in the range of 1743–1734 cm−1 correspond to > C] O stretching in both (Scenedesmus sp. ASK22 oil and Soybean oil) (Forfang et al., 2017). Scenedesmus sp. ASK22 oils show peaks in the region of 1437–1460 cm−1, which indicates the presence of the methyl ester moiety (Nautiyal et al., 2014). The presence of these peaks in the Scenedesmus sp. ASK22 oil proved the presence of free fatty acids in algal oil which further strengthens the fact that Scenedesmus sp. ASK22 cultivated in SDE has the potential for biodiesel feedstock.
Acknowledgments A.P. would like to thank Ministry of Human Resource Development, India for their financial support as PhD fellowship. The author (SK) is also thankful to MNNIT Allahabad for funding the research under the MNNIT seed grant. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.121998.
3.8. Thermal analysis of Scenedesmus sp. ASK22 using TG/DTG References The thermogravimetric analysis of both MB and LEMB of Scenedesmus sp. ASK22 obtained from SDE medium was done in an inert (nitrogen) and oxidative (air) atmosphere. LEMB comprises considerable amounts of residual lipid, carbohydrate, protein, nitrogen, phosphorus and other micronutrients that make it a prospective feedstock for biofuels (Mishra and Mohanty, 2019). In this regard, the feasibility of Scenedesmus sp. ASK22 LEMB has been studied as a feedstock for bioenergy. The TG and DTG curve showed three distinct decomposition stages in MB and LEMB. In the initial step, loss of weight was observed up to 140 °C and 165 °C for MB and LEMB, respectively. This sudden weight loss was caused by the release of the volatile compounds. In the next step, the initial temperature was set at 140 °C and 165 °C. There was a marked increase in weight loss reaching a maximum weight loss of 11.49% min−1 and 14.84% min−1 at 297.0 °C and 296.0 °C for MB and LEMB, respectively. After this point, the rate of weight loss decreased steadily until 390.2 °C for MB and 395.4 °C for LEMB. In the next section there was slight increase in weight loss observed at 409.9 °C and 431.8 °C for MB and LEMB, respectively. After that, a continuous weight loss was observed, until the burnout temperature 530.5 °C and 559.5 °C for MB and LEMB, respectively. At higher temperature, some weight loss can occur possibly due to CaCO3 decomposition and metal reduction (Miranda et al., 2018). The TG and DTG curves of the sample (MB and LEMB) analysed in an inert (nitrogen) atmosphere. The initial temperature was set at 170 °C. From the initial temperature, there a marked increase in weight loss was observed, reaching a maximum of 11.49% min−1 and 24.80% min−1 for MB and LEMB, respectively at 296 °C–297 °C. Higher molecular weight compounds like carbohydrates, protein, and lipids in microalgae experience cracking and depolymerization reactions due to the continuous heat supply at this stage and this is considered as the active pyrolytic zone. Due to the decomposition of highly thermally stable compounds, char formation occurs at the final stage. Furthermore, it showed a second peak in the DTG curve which is lower than the first peak. This may be due to the difference in chemical composition of MB and LEMB. Based on the above results, MB and LEMB of Scenedesmus sp. ASK22 grown in SDE maintained satisfactory thermal stability for the development of valuable by-products (hydrocarbon gas and bio-oil) during reuse.
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Isolation, screening and comprehensive characterization of candidate microalgae for biofuel feedstock production and dairy effluent treatment: A sustainable approach Ashutosh Pandeya, Sameer Srivastavaa, Sanjay Kumarb, a b
T
⁎
Department of Biotechnology, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, U.P., India School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, U.P., India
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Isolation Screening Phylogenetic analysis Microalgae Dairy effluent Biodiesel
In this study, the indigenous native microalgae were isolated from domestic and dairy effluent (DE) and further screened for DE treatment and lipid accumulation. All the isolated microalgae were examined for their growth adaptability in DE. The growth parameters of 15 isolates were determined and the following six isolates further selected for comprehensive analysis and identified as Desmodesmus sp. ASK01, Chlorella sp. ASK14, Scenedesmus sp. ASK16, Scenedesmus sp. ASK22, Chlorella sp. ASK25 and Chlorella sp. ASK27. The nutrient remediation capacity of six isolates as well as its lipid accumulation potential and biomass composition were determined. The Scenedesmus sp. ASK22 showed the best combined results and promising strain for the DE treatment and biofuel production. Biomass composition of Scenedesmus sp. ASK22 showed an oil accumulation of 30.7% (w/w) and biomass yield 1.22 g L−1. The fatty acid methyl ester (FAME) mainly composed of C15:0, C16:0, C18:0, C18:1 and C18:3.
1. Introduction
for biodiesel production over terrestrial crops and plant sources, such as have higher photosynthetic efficiency, higher nutrient uptake rate, shorter generation time, high lipid content, biomass productivity and flexible cultivation conditions (Karemore et al., 2013). Microalgae cultivation requires an enormous amount of nutrients and freshwater on an industrial scale (Murphy and Allen, 2011). Water is a valuable
The unicellular microalgae have been widely promoted as a green and sustainable bioenergy feedstock over the last few decades and have the potential to replace conventional fuels (petroleum oil, coal and natural gas) (Behera et al., 2015). Microalgae have many advantages
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Corresponding author. E-mail address: [email protected] (S. Kumar).
https://doi.org/10.1016/j.biortech.2019.121998 Received 6 June 2019; Received in revised form 8 August 2019; Accepted 10 August 2019 Available online 13 August 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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0.049). The microalgal cells were maintained in BG11 liquid media at 28 ± 2 °C and illuminated by cool white fluorescent tube light (55 µmol m−2 s−1) with continuous agitation. To grow bacteria free culture of green algal cells, cultures were grown in media supplemented with ampicillin (100 µg/ml) and chloramphenicol (50 µg/ml). After visible mixed algal growth on liquid BG11 media, 100 µL were spread onto a BG11 agar plate under the same growth conditions. Repeated streaking onto fresh BG11 agar plate and microscopic observations were applied until monospecific microalgae bacteria free cultures were obtained individually and separately. Once, pure microalgae strains were isolated singly they were morphologically characterized. Genera of the isolates were identified by microscopic observation and identification of morphological characteristics according to Bellinger and Sigee (2015). Twenty eight green microalgae isolates were maintained regularly in BG11 agar plates at 4 °C.
resource that must be used sensibly and the use of non-potable water for cultivation of microalgae could minimize the use of freshwater (Kumar et al., 2020). Indeed, most of the freshwater consumed is disposed of in the form of wastewater, which is rich in nutrients, posing a serious risk of eutrophication. High artificial media prices and low lipid productivity are critical constraints associated with the production of renewable bioenergy feedstock from microalgae (Allen et al., 2018). Therefore, to compete with petroleum-derived fuels, the efficiency of the process must be improved and made economical at industrial scale (Gendy and El-Temtamy, 2013). To make the cultivation of microalgae more cost-effective, it is recommended to use nutrient laden wastewater as a complex medium in combination with waste CO2 exhaust, which provides a pathway to remove nutrients from wastewater and CO2 sequestration (Acién Fernández et al., 2018; Molazadeh et al., 2019). Commercial production of microalgae biofuel using wastewater as media composition is only possible if three main parameters, i.e. high biomass, lipid productivity and high wastewater tolerance, can be fulfilled (Chen et al., 2015; Show et al., 2017). Appropriate microalgae strain selection is a step towards successful combination of algae-based biofuel production and wastewater treatment (Cobos et al., 2017; Mondal et al., 2017). India is the world's largest dairy producer and consumer. Based on an estimate by the National Dairy Development Board (NDDB), India, it alone contributes about 20% of world milk production at around 176.4 million tonnes in the 2017–2018 (NDDB, 2018). The dairy effluent (DE) is the liquid residue discharge from the dairy industries, which is generated at a scale of 2–3 L DE for each litre of milk processed (Pandey et al., 2019). DE contain large quantities of milk constituents such as casein, lactose, fat, inorganic salts besides detergents and sanitizers which contribute largely towards high BOD and COD (Slavov, 2017). In this regard, there is a need for the isolation and selection of native candidate microalgae strains with potentially high intrinsic lipid content as well as have natural ability to grow efficiently in high strength DE. The use of isolates native to an area may be beneficial in their inherent adaptability to environmental conditions; a potential benefit in developing an energetically efficient platform to produce biofuels. Native microalgae performs better than most other species at commercial scale cultivation using DE as media (Cho et al., 2017; Zhou et al., 2011). The aim of this work was to explore the adequacy of native microalgae isolates for use in the DE treatment process and biodiesel production. Considering the same, the present study focuses on isolation, screening and comprehensive characterization of potential microalgae from DE and other wastewater sources. Further, the promising isolates were studied in detail by analysing their biochemical composition as well as ability of nutrient removal, biomass and lipid production. The information generated from this study allows the selection of the most suitable microalgae strain to develop an integrated system that simultaneously treats DE and generates biomass for use in the production of biofuels.
2.2. Pre-treatment of dairy effluent and their charecterization The raw dairy effluent (RDE) was collected from the inlet of effluent treatment plant of Shyam dairy products, Prayagraj, India. The RDE was filtered to remove suspended particles and characterized for physico-chemical parameters. The DE used in this study was simulated dairy effluent (SDE), which was prepared by mixing 4 g milk powder in 1 L of distilled water. The physicochemical characterization of RDE and SDE were determined using the standard methods of water and wastewater analysis reported by the American Public Health Association (APHA, 1989) unless otherwise specified. Total sugar and protein content in DE were estimated by phenol sulphuric acid method and Lowry method, respectively (DuBois et al., 2002; Lowry et al., 1951). Heavy metals present in the DE were analysed by ICP-OES (Perkin Elmer). The SDE was autoclaved at 121 °C for 15 min and allowed to cool at room temperature before being used for algal cultivation. 2.3. Screening of algal isolates for their growth and lipid accumulation potential in the SDE The candidate microalgae selection was performed in multi-steps. (i) The preliminary adaptation test (cell density evaluation) was determined in SDE followed by growth rate and lipid productivity as shown in Fig. 1. The adaptability test was performed in six well ELISA plate (6 ml working volume). All the algal isolates were grown in SDE medium at temperature 28 ± 2 °C, for 12 days under continuous white fluorescent light illumination (80 µmol m−2 s−1). (ii) The isolates that appeared to be better adapted were scaled up to a 500 ml flask (working volume 300 ml). Samples were withdrawn at alternate days for the determination of growth kinetic parameters. (iii) Based on the kinetic results six potential isolates were scaled up to 1 L flask (working volume 600 ml) and cultured in same conditions as described above. The samples were withdrawn at regular intervals for the determination of chemical oxygen demand (COD), nitrate (N-NO3−1), total phosphorus (TP) removal kinetics, growth kinetics and lipid productivity simultaneously. Finally, the candidate microalgae were screened from above step based on their lipid productivity/lipid yield.
2. Materials and methods 2.1. Wastewater collection, microalgae isolation and purification
2.4. Identification of potential microalgae isolates Water samples used to isolate microalgae were collected aseptically from domestic wastewater lodge area (25°30′12.0″N 81°52′02.7″E) and DE treatment plant of Shyam dairy products (25°38′26.0″ N, 81°83′80.0″ E), Prayagraj (Uttar Pradesh), India. Water samples of 10 ml were inoculated in 250 ml conical flasks containing 100 ml of BG11 medium for microalgae enrichment. The BG11 media comprised of (g L−1) NaNO3 1.5, K2HPO4 0.04, trace elements (MgSO4·7H2O 0.075, CaCl2·2H2O 0.036, Na2CO3 0.02, citric acid 0.006, ferric ammonium citrate 0.006, and EDTA 0.001) and microelement solution (1 ml L−1 that consists of H3BO3 2.86, MnCl2·H2O 1.81, ZnSO4·7H2O 0.222, CuSO4·5H2O 0.079, Na2MoO4·2H2O 0.390, and Co(NO3)2·6H2O
2.4.1. DNA extraction, PCR amplification of 18S rRNA gene and sequencing For the genetic identification of the six selected potential microalgae strains, the total genomic DNA was extracted using the HiPurATM Marine Algal DNA Purification Kit (HiMedia, India). The partial 18S rRNA gene from the genomic DNA of potential isolates ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 was amplified by PCR using the oligonucleotide primers (18SF:5′TCCTGCCAGTAGTCATATGC-3′; 18SR: 5′-TGATCCTCYGCAGG TTCAC-3′) (Grzebyk et al., 1998). The PCR amplification was performed using a 20 µL reaction mixture containing 2
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cultivation, the cultures were harvested by centrifugation at 5000 rpm for 10 min followed by oven drying at 60 °C until constant weight was achieved. The biomass productivity (BP, g L−1 day−1) during the culture period was calculated from the following Eq. (i):
Biomass productivity (BP ) = (Xt − X0 )/(tt − t0)
(i)
−1
where, Xt is the biomass production (g L ) at the end of the growth phase (tt) and X0 is the initial biomass production (g L−1) at t0 (day). The cellular lipid productivity (LP) was calculated as the product of BP and the fractional lipid content (w/w) in the biomass, using following Eq. (ii).
Lipid productivity(LP, mg L−1 d−1) = BP × LC /100 −1
(ii)
−1
where, BP is the biomass productivity (g L d ) and LC is the lipid content (w/w). The following equations were used to calculate the specific growth rate (µ, d−1) and cell doubling time (Td, day).
μ = ln (Wt / Wo)/Δt
(iii)
Td = ln (2)/ μ
(iv)
where, Wt and Wo are the dry cell weight and the end and beginning of batch cultivation. 2.5.2. Chemical oxygen demand (COD), NO3−1 and TP removal The following equations were used to calculate the removal efficiency and removal rate of COD, NO3−1 and TP (Mishra and Mohanty, 2019).
Fig. 1. Screening of high lipid producing microalgae for treating dairy effluent (DE).
20 ng of DNA template, 0.2 µM of each oligonucleotide primer, 200 µM dNTP, 1.5 mM MgCl2, 1 × PCR buffer, 1 U of Taq DNA Polymerase (Himedia, India). The following thermal program was used: 95 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 54 °C for 45 s, and elongation at 72 °C for 2 min, followed by final 10 min extension at 72 °C. The PCR product was visualized by electrophoretic separation on 1.0% TAE agarose gel and were purified using a Gene-JET PCR purification kit (Thermo Fisher) before sequencing. Sequencing reactions were performed using the ABI PRISM™ Dye Terminator Cycle Sequencing Kit (Applied Biosystems, USA) with the primers (18SF: 5′TCCTGCCAGTAGTCATATGC-3′; 18SR: 5′-TGATCCTCYGCAGGTTCAC-3′) and analysed in an automatic ABI PRISM 3730 sequencer (Applied Biosystems).
Removal efficiency (%) = [(Ci − Cf )/ Ci] × 100
(v)
Removal rate (mg L−1 d−1) = (Cf − Ci )/(tt − to)
(vi)
where, Ci and Cf are the initial and final concentration of parameter at time to and tt respectively. 2.5.3. Biomass characterizations The carbohydrate and protein in microalgal biomass were estimated by the phenol sulphuric acid and Lowry method, respectively (DuBois et al., 2002; Lowry et al., 1951). Elemental analysis of microalgae was estimated by CHNSO method. To understand the presence of various functional group present in microalgae biomass and oil, Fourier transform infrared spectroscopy (FTIR) was conducted by Nicolet FTIR spectrometer (Madison, USA) as per standard protocol. The thermogravimetric profile of microalgae biomass (MB) and lipid extracted microalgae biomass (LEMB) were studied using thermogravimetric analyser (SII 6300 EXSTAR) in the atmosphere of nitrogen (200 ml L−1). The sample weight loss with rise in temperature was recorded and the TGA data was collected. The high heating value (HHV) and low heating value (LHV) of biomass based on CHNO was calculated by using equation vii and viii (Brown et al., 1994; Miranda et al., 2018).
2.4.2. Phylogenetic analysis To determine the phylogeny of the isolated microalgae strains, their 18S rDNA sequences were aligned with the previously reported sequences of different closely related microalgae species. The 18S rRNA gene sequence obtained in this study and reference sequences available at GenBank were retrieved using BLASTN program and were aligned using ClustalW multiple sequence alignment. Phylogenetic trees were inferred using the Neighbour-Joining method (Saitou and Nei, 1987) and distances were determined according to Kimura 2-model (Kimura, 1980). The robustness of the tree was estimated by bootstrap analysis based on 1000 replications. The MEGA 6.0 program package was used for all analyses (Tamura et al., 2013).
O HHV (MJ Kg−1 db) = 0.3383 × C + 1.422 × ⎛H − ⎞ 8⎠ ⎝
(vii)
H ⎞ × 9.011 LHV (MJ Kg−1 db) = HHV − 2.447 × ⎛ ⎝ 100 ⎠
(viii)
where C, H and O are the weight percentages of carbon, hydrogen, and oxygen in the biomass, respectively.
2.5. Methods of analysis 2.5.1. Growth kinetics analysis The microalgae growth for each isolated strains was estimated by spectrophotometric method (absorbance at 680 nm) at every alternate day up to stationary phase. The dry cell weight (DCW) was estimated by using respective correlation equation, those were prepared for each strain. The DCW was measured gravimetrically by pelleting the cells and then drying for 24 h at a temperature of 60 °C. At the end of batch
2.6. Lipid extraction and fatty acid methyl esters (FAME) analysis Total lipid from dried algal powder was extracted with chloroform: methanol (1:2, v/v) and lipid content was quantified gravimetrically (Bligh and Dyer, 1959). Fatty acid composition analysis of microalgal oil was carried out by using the protocol established by Breuer et al. (2013). 3
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more or less equal to RDE (Table 1). The SDE was used in this study to insure that the physicochemical characteristics being constant throughout the experiment.
2.7. Assessment of the biofuel properties of microalgal oil To assess the FAME profile of the candidate microalgae the following empirical Eqs. (ix)–(xi) were used to evaluate iodine value (IV), saponification value (SV), and cetane number (CN) (Mandotra et al., 2016).
IV =
∑ [(254 × F × D/MW )]
(ix)
SV =
∑ [560 × F /MW ]
(x)
CN = 46.3 + 5458/ SV − (0.225 × IV)
3.2. Isolation and screening of potential microalgae in DE Isolation is a necessary step of bio-process development to obtain a pure culture and presents the first step towards the selection of potential candidate microalgae strain for biodiesel production and wastewater remediation. Besides these, native microalgae can be effectively used for nutrient remediation from wastewater, CO2 sequestration and other value-added products. Chen et al. (2015) reported that the species isolated from wastewater achieved remarkably higher nutrient removal rates than those experiments which were performed using commercial strains. Number of studies has showed that it becomes possible to maximize the nutrients sequestration along with higher biomass and lipid productivity by using native microalgae isolates (Chen et al., 2015; Xin et al., 2010; Zhou et al., 2011). In this study, isolated microalgae strains performances have been studied to determine their robustness and growth yield using SDE as a culture medium. A total of 28 axenic native microalgae strains were isolated from domestic wastewater and DE samples using standard microbiological methods as mentioned in Section 2.1. The cell density of all the microalgae isolates measured after 12 days of batch cultivation in 6 well plates and showed that more than 80% of them can grow in SDE medium. This observation implies that SDE can support microalgae growth requirements and that the strains are fully adapted to it. For the next steps, best performing 15 algal cultures (ASK01, ASK03, ASK07, ASK08, ASK09, ASK11, ASK14, ASK16, ASK17, ASK18, ASK20, ASK21, ASK22, ASK25 and ASK27) were selected (Fig. 2). These were better adapted cultures in SDE medium and considered based on their growth parameters estimation. The growth data was obtained as described above in the Materials and Methods section. Table 2 presents the volumetric cell yield (g L−1) of each adapted culture. The specific growth rate (µ) and doubling time (Td) were calculated and are presented in Table 2. Further, six potential microalgae isolates namely ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 were shortlisted based on their initial adaptability test and growth rate in SDE (Table 2).
(xi)
where, F is the percentage of each fatty acid, D is the number of double bonds, and MW is the molecular weight of the FA. The standard biofuel properties as laid down in ASTM D6751 and EN 14214 were considered for evaluating the data. 3. Results and discussion 3.1. Physicochemical characterisation of the dairy effluent The physicochemical characteristics of RDE collected from DE treatment plant of Shyam dairy products, Prayagraj, India and SDE used in this study is presented in Table 1. The pH was found to be alkaline (8.4 ± 0.2), this may be due the use of detergent for washing. The COD of RDE was 3800 ± 400 mg L−1. The concentration of total phosphorus (TP) and orthophosphate (P-PO4−3) in RDE was found to be 46.1 ± 0.27 mg L−1 and 25.63 ± 2.89 mg L−1, respectively. The nitrate concentration of RDE was 55.5 ± 0.11 mg L−1. Concentration of total sugar and protein was found to be 1.68 ± 0.11 and 373.20 ± 5.47 mg L−1, respectively. In addition, RDE also contained a variety of trace minerals viz., Na, K, Ca, Cu, Fe, Mn, Zn and Ni, which are required for microalgae growth. The characteristics of SDE were Table 1 Physicochemical characteristics of simulated dairy effluent (SDE) used and raw dairy effluent. Characteristics
Unit
Simulated dairy effluent
Raw dairy effluent
pH Conductivity (S/m) Turbidity Total dissolve solids Total Solids Total suspended solids Chemical oxygen demand (COD) Total phosphorus (TP) Ortho phosphate (P-PO4−3) Nitrate (NO3−1) Chloride (Cl−1) Total sugar Protein
– S/m NTU mg L−1 g L−1 g L−1 mg L−1
6.80 ± 0.12 0.1247 ± 0.0035 1623 ± 76 293.36 ± 27.20 2.40 ± 0.04 2.37 ± 0.02 4000 ± 112
8.4 ± 2.4 0.1148 ± 0.05 1453 ± 65 316 ± 35 2.805 ± 0.09 2.489 ± 0.05 3800 ± 400
mg L−1 mg L−1
82.88 ± 1.89 56.28 ± 1.17
46.1 ± 0.27 25.63 ± 2.89
mg L−1 mg L−1 g L−1 g L−1
108.49 ± 2.36 33.98 ± 2.00 2.06 ± 0.01 3.85 ± 0.19
55.5 ± 0.11 63.21 ± 0.52 1.68 ± 0.01 2.63 ± 0.26
mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1
56 ± 8.23 5.3 ± 1.09 37.42 ± 0.98 0.001 ± 0.001 ND ND 3.12 ± 0.4 0.005 ± 0.012 ND 0.095 ± 0.02 ND ND
71.4 ± 14.4 9.1 ± 2.2 56.35 ± 1.35 0.004 ± 0.00 ND ND 0.346 ± 0.31 0.048 ± 0.02 ND 0.056 ± 0.00 ND 0.0145 ± 0.00
Metals Na K Ca Cu Co Cd Fe Mn Pb Zn Cr Ni
3.3. Dairy effluent treatment, growth and lipid productivity of selected potential microalgae isolates Table 3 presents the change in nutrients (COD, N-NO−3 and TP) concentration as well as the removal efficiency of all six potential microalgae isolates in SDE. The isolates ASK01, ASK14, ASK16, ASK22,
ND – below detectable limit. The data are represented as mean value ± standard deviation.
Fig. 2. Adaptability check of microalgae growth in SDE. 4
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Table 2 Dry cell weight of the microalgae adapted to SDE. Time (Day)
0 3 5 7 10 12 µ (d−1) Td (day)
Dry cell weight (g L−1) ASK01
ASK03
ASK07
ASK08
ASK09
ASK11
ASK14
ASK16
ASK17
ASK18
ASK20
ASK21
ASK22
ASK25
ASK27
0.074 0.247 0.561 1.089 1.178 1.227 0.23 3.01
0.107 0.164 0.268 0.522 0.647 0.604 0.14 4.95
0.046 0.176 0.261 0.420 0.469 0.499 0.20 3.46
0.052 0.126 0.281 0.468 0.584 0.616 0.21 3.30
0.069 0.136 0.271 0.447 0.572 0.563 0.17 4.08
0.070 0.155 0.350 0.62 0.779 0.830 0.21 3.30
0.026 0.197 0.370 0.747 1.045 1.020 0.31 2.24
0.036 0.140 0.452 0.595 0.666 0.782 0.26 2.67
0.063 0.287 0.430 0.512 0.631 0.563 0.18 3.85
0.097 0.169 0.305 0.597 0.712 0.688 0.16 4.33
0.111 0.177 0.445 0.742 0.975 1.037 0.19 3.65
0.096 0.169 0.311 0.633 0.934 1.061 0.20 3.47
0.038 0.175 0.385 0.810 1.194 1.218 0.29 2.39
0.040 0.249 0.703 1.189 1.447 1.476 0.30 2.30
0.055 0.221 0.467 1.052 1.160 1.285 0.26 2.67
The response values (dry cell weight) were the mean values of triplicates. µ values (bold) represents the higher specific growth rate of the isolates.
ASK25 and ASK27 removed more than 84% of COD with > 280 mg d−1 of removal rate from DE, after 12 days of cultivation. COD is the indirect measure of all forms of organic and inorganic nutrients present in wastewater or DE. All six potential microalgae isolates showed almost similar COD removal efficiency that varied from 84.92% to 91.90%. Decline in the concentration of COD suggests that all six microalgae isolates can efficiently utilize the organic matter as substrate for their growth and source of energy. Microalgae are well known for the removal of all forms of nitrogen (nitrate, nitrite, ammonium and urea) present in wastewater (Delgadillo-Mirquez et al., 2016). In microalgae, nitrate assimilation is performed by two transport and two reduction steps: First, nitrate (NO3−1) is transported into the cell, then a cytosolic nitrate reductase catalyzes nitrate reduction to nitrite, which subsequently is transported into the chloroplast, where the enzyme nitrite reductase catalyzes its reduction to ammonium (Lachmann et al., 2019; Sanz-Luque et al., 2015). Finally, ammonium is incorporated into carbon skeletons by rendering glutamate, through the glutamine synthetase/glutamine oxoglutarate amino transferase or glutamate synthase cycle (Miflin and Lea, 1975). The studied six microalgae isolates removed > 91% of nitrate effectively from SDE. ASK16 and ASK22 completely removed the NO3−1 from the SDE by the end of day 8. Whereas, ASK14 achieved complete removal of NO3−1 by the end of day 12. Chokshi et al. (2016) reported that Acutodesmus dimorphus had completely removed ammonia nitrogen (277.4 mg L−1) and phosphates (5.96 mg L−1) from DE. In another study, the efficiencies of phosphate and nitrate removal by Chlorella pyrenoidosa cultivated in raw and treated DE (in an oxidation pond) were 87 and 60% respectively in the influent and 83 and 49%, respectively, in the effluent (Kothari et al., 2012). As seen in Table 3, TP consumption, which is responsible for energy transfer and nucleic acid formation, was observed in the range between 86 and 92% by ASK01, ASK14, ASK16, ASK 22, ASK25 and ASK27 isolates. A growth characteristic of all six potential microalgae strains in SDE has shown in Table 4. The microalgae isolate ASK25 showed maximum biomass productivity (123.67 mg L−1 d−1) followed by ASK27 (107.08 mg L−1 d−1), ASK01 (102.25 mg L−1 d−1), ASK22 −1 −1 (101.50 mg L d ), ASK14 (90.08 mg L−1 d−1) and ASK16 (65.17 mg L−1 d−1) after 12 days of batch cultivation. Whereas, the ASK22 exhibited maximum lipid accumulation (30.7% w/w) as well as lipid productivity (31.16 mg L−1 d−1) as compared to all other microalgae isolates. From the findings of these experiments, it has been inferred that microalgae isolate ASK22 can be a potential candidate isolate to be further used in RDE treatment and lipid production.
w/w) in ASK22 was found to be significantly more than that reported previously (Chokshi et al., 2016; Daneshvar et al., 2018). ASK22 also showed 31.3% w/w crude protein which was the highest among other five selected microalgae isolates and was evident from its increased elemental nitrogen (7.72%w/w) composition in SDE biomass. However, ASK22 showed the lowest carbohydrate accumulation (26.32% w/w) among all six selected microalgae isolates. The isolate ASK14 exhibited 47.07% w/w of carbohydrate accumulation, which could be considered as a promising substrate for bioethanol production. The elemental composition analysis of biomass of all six potential microalgae isolates obtained from SDE has shown in Table 5. The biomass of all six microalgae isolates have carbon (C) (47.80–52.26%), hydrogen (H) (3.68–5.01%), nitrogen (N) (6.64–7.58%) and oxygen (O) (29.42–34.91%) as major constituents. Sydney et al. (2011) observed that the Botryococcus braunii grown in domestic wastewater contain C 55.33%, H 8.26%, N 2.68% and O 33.55% as major constituents. Similarly, Monoraphidium sp. KMC4 grown in secondary domestic wastewater contains C 48.22%, H 7.66%, N 5.91%, O 38.08% and trace amount of S (0.13%) and the corresponding value of elemental composition for the Monoraphidium sp. KMC4 grown incontrol BG11 medium were 51.04%, 7.94% 5.5%, 35.44% and 0.08%, respectively. These results suggest that the elemental composition of microalgae biomass varies from species to species as well as culture conditions (Mishra and Mohanty, 2019). ASK22 biomass has C 51.28%, N 7.72%, H 5.01% and O 29.42% as major constituents due to higher lipid accumulation (30.7%w/w). This makes ASK22 a potential microalgae feedstock for lipid production. Higher sulfur and nitrogen content in biomass leads to the release of NOx and SOx (Miranda et al., 2018). SOx emission is not expected from fuel derived from microalgae grown in SDE, as sulfur was not detected in the biomass of the six microalgae isolates (Table 5). However, the nitrogen (6.64–7.72%) was found to be marginally higher than that reported in previous studies (Chiranjeevi and Mohan, 2016; Mishra and Mohanty, 2019) which could be due to the higher nitrogen in the SDE that may leads to higher NOx emission. These findings suggest that ASK22 can utilize SDE efficiently as growth medium for their large-scale cultivation that can ultimately be used as a feedstock for sustainable biodiesel production. The heating value (HV) (MJ kg−1 dry weight) is used to describe the total amount of heat energy that can be produced on complete combustion by a unit mass of microalgae. The low heating value (LHV) and high heating values (HHVs) of biomass ranged from 15.32 to 18.14 MJ kg−1 and 16.16–19.24 MJ kg−1, respectively (Table 5). It was found that the LHV and HHV of ASK22 was 18.14 and 19.24 MJ kg−1, respectively, which was identified to be maximum (Table 5). The maximum HV of ASK22 grown in the SDE could be due to the elevated content of lipids and proteins because the main contributions to the HV of cells are from their lipid (38.3 MJ kg−1) and protein (15.5 MJ kg−1) contents (Lardon et al., 2009). Similarly, Mishra and Mohanty (2019) depicted that Monoraphidium sp. KMC4 grown in raw domestic sewage wastewater contained an HHV of 20.33 MJ kg−1, which is close to the ASK22 HHV. These findings suggest that the ASK22 biomass would be used as an
3.4. Biomass characterization of potential microalgae isolates The biomass characteristic of each of the six selected microalgae isolates has summarized in Table 5. Microalgae isolate ASK22 accumulated a high amount of lipids (30.7% w/w) followed by ASK27 (25.8% w/w), ASK16 (24.6% w/w), ASK01 (22.7% w/w), ASK 25 (21.2% w/w) and ASK14 (13.8% w/w). The total lipid content (30.7% 5
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Table 4 Growth characteristics of potential microalgae isolates grown in SDE.
82.88 52.63 40.34 25.98 24.64 21.55 11.00 86.73 5.99
ASK27
A. Pandey, et al.
82.88 41.95 37.75 12.97 8.89 6.03 3.47 95.81 6.62
ASK25
Growth characteristics
82.88 51.62 32.29 22.42 26.43 18.26 10.47 87.36 6.03
82.88 57.44 35.13 28.22 16.55 10.24 7.26 91.24 6.30
Dry cell weight (g L ) Lipid content % (w/w) Biomass productivity (mg L−1 d−1) Lipid productivity (mg L−1 d−1)
ASK14
ASK16
ASK22
ASK25
ASK27
1.23 22.70 102.25
1.08 13.80 90.08
0.78 24.60 65.17
1.23 30.70 101.50
1.48 21.20 123.67
1.29 25.80 107.08
23.21
12.43
16.03
31.16
26.22
27.63
82.88 50.11 22.41 17.82 22.24 20.31 6.71 91.90 6.35 82.88 62.73 40.46 28.69 26.87 18.89 6.67 91.95 6.35
3.5. PCR amplification, phylogenetic analysis and identification
108.49 48.77 42.15 23.30 12.77 5.69 2.98 97.25 8.79
108.49 59.05 31.28 23.47 17.29 12.21 8.86 91.83 8.30
The microscopic examination of six potential microalgae strains presented and revealed their purity and morphology. Based on preliminary morphological identification, the microalgae isolate ASK01, ASK16 and ASK22 were identified as Scenedesmus sp. whereas, microalgae isolate ASK14, ASK25 and ASK27 showed similarity with Chlorella sp. Furthermore, the universal primers were used to amplify the 18S rDNA fragment from six selected microalgae isolates. These amplicons were sequenced and the sequences were submitted in the NCBI database. Gene bank accession numbers of these sequences have been indicated in Fig. 3. The BLAST hits of partial 18S rDNA gene sequences of microalgae isolates ASK01, ASK14, ASK16, ASK22, ASK25 and ASK27 indicated their phylogenetic identity with Desmodesmus armatus AB917135 (96%), Chlorella vulgaris KJ 676110 (98.73%), Scenedesmus sp. CPC3 (99%), Scenedesmus abundans X73995 (99%), Chlorella sorokiniana KT852969 (99%) and Chlorella sorokiniana KU948990 (99%), respectively. The results of morphological identification were also supported by sequence based phylogenetic analysis as shown in Fig. 3.
108.49 52.62 17.55 3.86 N.D. N.D. N.D. 100.00 13.56
The response values (COD, N-NO3−1 and TP) were the mean values of triplicates; N.D. – Not detected.
108.49 54.16 24.86 17.67 11.23 8.92 N.D. 100.00 9.04 108.49 70.96 52.23 33.67 7.15 2.20 2.44 97.75 8.84 4000.00 2923.08 1421.54 542.98 623.70 647.78 323.88 91.90 306.34 4000.00 2769.23 1421.54 723.98 873.18 809.72 603.16 84.92 283.07 4000.00 2615.38 1827.69 769.23 566.80 485.83 380.00 90.50 292.85 4000.00 3384.62 1624.62 904.98 1164.24 1012.15 485.83 87.85 292.85 4000.00 3076.92 1726.15 1247.40 995.48 971.66 420.00 92.92 309.72 4000.00 2769.23 1726.15 769.23 956.34 566.80 485.83 87.85 292.85 0 2 4 6 8 10 12 Nutrient removal (%) Removal rate (mg/d)
ASK01
alternative source of biofuel feedstock and the SDE can be used as a growth medium for sustainable biofuel feedstock production.
108.49 79.23 24.42 27.62 N.D. N.D. N.D. 100.00 13.56
ASK14 ASK01 ASK22 ASK14 ASK01 ASK27 ASK14 ASK01
COD (mg L−1)
ASK16
ASK22
ASK25
N-NO3−1 (mg L−1)
ASK16
Nutrients removal Time (Day)
Table 3 Chemical oxygen demand (COD), Nitrate and phosphate (TP) concentration of selected microalgal isolate over 12 days.
ASK25
ASK27
TP (mg L−1)
ASK16
ASK22
−1
Microalgae isolates
3.6. FAME analysis of candidate microalgae ASK22 Fig. 4 depicts the relative fatty acid percentage in Scenedesmus sp. ASK22 calculated through GC–MS analysis. FAME analysis revealed the presence of C15:0 (2.02%), C16:0 (29.23%), C18:0 (13.5%), C18:1 (46.2%) and C18:3 (9.50) that ensures it’s suitability for biodiesel production as these fatty acids have better stability to oxidation and fluidity (Knothe, 2012; Mandotra et al., 2016). The composition of fatty acid and the types of fatty acids produced are considered important for biodiesel quality (Minhas et al., 2016). Quality depends primarily on the unsaturation ratio as unsaturated fatty acids enhance cold flow properties while saturated fatty acids maintain good oxidative stability. In fact, oleic acid is one of the most desirable fuel oil components since it gives a good balance between the properties of cold flow and stability of oxidation (Hoekman et al., 2012). The quality of fuel oil has been reported to depend on high oleic acid content as well as ignition point, combustion heat, cold filter plug point, oxidative stability, viscosity, lubricity and is determined by the fatty acid profile composition. The profile of fatty acids obtained for Scenedesmus sp. ASK22 was found to be suitable and potential candidate for use in biodiesel production which has been cultivated using SDE as growth medium. A few other lipid properties, namely CN, IV, and SV, were analysed based on FAME profiling to check the suitability of Scenedesmus sp. ASK22 as biofuel producer. As per EN14214 standard the CN should not exceed 120 g I2/ 100 g or be less than 47 g I2/100 g (Sun et al., 2011). Biodiesel CN is used to rate the combustion quality. Hence, the higher CN value indicates the shorter the ignition time. Whereas, the iodine value (IV) denotes the degree of unsaturation of the microalgal oil and should be less than 120 (EN 14214:2012). A high IV indicates low oxidative stability of the fuel (Knothe, 2012). Biodiesel derived from Scenedesmus sp. 6
Bioresource Technology 293 (2019) 121998
C8.47H9.06NO4.17 C8.16H6.32NO3.94 C8.07H8.39NO3.56 C7.76H9.01NO3.35 C7.94H7.54NO4.19 C8.04H7.87NO3.82 16.61 16.09 18.65 19.13 16.10 17.31 16.71 16.17 18.75 19.24 16.19 17.40 22.70 13.80 24.60 30.70 21.20 25.80
Fig. 4. Fatty acid profiles of candidate microalgal isolates.
ASK22 biomass met the CN (56.59) and IV (67.64) standards laid down in the USA (ASTM D6751) and those in Europe (EN 14214), making this strain optimally suitable for biodiesel production.
33.18 47.07 34.82 26.32 43.16 29.90
26.88 23.01 28.64 31.30 24.73 26.96
47.80 48.91 52.26 51.28 49.56 50.13
4.30 3.68 4.58 5.01 3.96 4.31
6.64 7.06 7.58 7.72 7.33 7.31
ND ND ND ND ND ND
31.37 31.56 30.64 29.42 34.91 31.99
C22.73H40.32NO10.35 24.31 24.49 36.14 8.80
32.62
55.33
8.26
2.84
ND
33.6
C7.35H13.60NS0.05O5.53 16.32 16.46 20.30 19.70
44.40
43.18
6.72
6.85
0.75
43.32
C6.2H11.15NS0.06O3.55 26.50 27.50
28.50
47.10
7.13
8.86
1.27
19.52 19.68 38.97
C10.83H20.01NS0.01O5.64 22.08 20.27 31.40 31.04
33.70
51.04
7.94
5.50
0.08
35.44
22.26 20.44 38.08 0.13 5.91 7.66 30.07 30.37
35.81
48.22
LHV HHV
Lipid (% w/w) Protein (% w/w) Carbohydrate (% w/w)
Fig. 3. Evolutionary relationships of taxa. The evolutionary history was inferred using the Neighbour-Joining method (Saitou and Nei, 1987). The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches (Felsenstein, 1985). The evolutionary distances were computed using the Kimura 2-parameter method (Kimura, 1980). Evolutionary analyses were conducted in MEGA6 (Tamura et al., 2013).
Scenedesmus obliquus
Domestic wastewater SDE SDE SDE SDE SDE SDE
The infrared spectra (500–4000 cm−1) of the Scenedesmus sp. ASK22 MB were very much similar to the spectra of Scenedesmus sp. ASK22 LEMB. The region in the spectra from 3100 to 2800 cm−1 indicates the presence of lipid in the sample and was due to the symmetrical and asymmetrical stretching vibration of –CH2 groups (Miranda et al., 2016; Nautiyal et al., 2014). These –CH2 groups form the backbone of lipids and show the vibrations particularly at 2922.58 cm−1 (Forfang et al., 2017). The intensities of the peaks in this region of the spectra for the MB and LEMB of Scenedesmus sp. ASK22 were compared and a decrease in intensity was observed in the LEMB which implies that the lipid was successfully extracted from the Scenedesmus sp. ASK22 MB. Soybean oil
Botryococcus braunii LEM14 ASK01 ASK14 ASK16 ASK22 ASK25 ASK27
Domestic wastewater BG11
Monoraphidium sp. KMC4 Monoraphidium sp. KMC4 Scenedesmus obliquus
Municipal wastewater BG11
Medium
3.7. FTIR analysis of Scenedesmus sp. ASK22 MB, LEMB and oil
Microalgae
Table 5 Microalgal biomass composition of selected microalgal strains, cultured in SDE.
Carbon (% w/w)
Hydrogen (% w/w)
Nitrogen (% w/w)
Sulfer (% w/w)
Oxygen (% w/w)
Heating value (MJ Kg−1 db)
Empirical equation
C9.52H17.97NS0.01O5.64
References
Mishra and Mohanty (2019) Mishra and Mohanty (2019) Ansari et al. (2019) Ansari et al. (2019) Sydney et al. (2011) This study This study This study This study This study This study
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reinforced the suitability of Scenedesmus sp. ASK22 for biodiesel production.
is currently a major feedstock for production of biodiesel (Huang et al., 2012). The FTIR analysis of oil extracted from Scenedesmus sp. ASK22 and soybean oil also performed and the result suggests that the algal oil is like soybean oil except a peak in the range of 730–400 cm−1 in the soybean oil which could be due to the aliphatic groups (Kothari et al., 2013). The peaks in the range of 1743–1734 cm−1 correspond to > C] O stretching in both (Scenedesmus sp. ASK22 oil and Soybean oil) (Forfang et al., 2017). Scenedesmus sp. ASK22 oils show peaks in the region of 1437–1460 cm−1, which indicates the presence of the methyl ester moiety (Nautiyal et al., 2014). The presence of these peaks in the Scenedesmus sp. ASK22 oil proved the presence of free fatty acids in algal oil which further strengthens the fact that Scenedesmus sp. ASK22 cultivated in SDE has the potential for biodiesel feedstock.
Acknowledgments A.P. would like to thank Ministry of Human Resource Development, India for their financial support as PhD fellowship. The author (SK) is also thankful to MNNIT Allahabad for funding the research under the MNNIT seed grant. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.121998.
3.8. Thermal analysis of Scenedesmus sp. ASK22 using TG/DTG References The thermogravimetric analysis of both MB and LEMB of Scenedesmus sp. ASK22 obtained from SDE medium was done in an inert (nitrogen) and oxidative (air) atmosphere. LEMB comprises considerable amounts of residual lipid, carbohydrate, protein, nitrogen, phosphorus and other micronutrients that make it a prospective feedstock for biofuels (Mishra and Mohanty, 2019). In this regard, the feasibility of Scenedesmus sp. ASK22 LEMB has been studied as a feedstock for bioenergy. The TG and DTG curve showed three distinct decomposition stages in MB and LEMB. In the initial step, loss of weight was observed up to 140 °C and 165 °C for MB and LEMB, respectively. This sudden weight loss was caused by the release of the volatile compounds. In the next step, the initial temperature was set at 140 °C and 165 °C. There was a marked increase in weight loss reaching a maximum weight loss of 11.49% min−1 and 14.84% min−1 at 297.0 °C and 296.0 °C for MB and LEMB, respectively. After this point, the rate of weight loss decreased steadily until 390.2 °C for MB and 395.4 °C for LEMB. In the next section there was slight increase in weight loss observed at 409.9 °C and 431.8 °C for MB and LEMB, respectively. After that, a continuous weight loss was observed, until the burnout temperature 530.5 °C and 559.5 °C for MB and LEMB, respectively. At higher temperature, some weight loss can occur possibly due to CaCO3 decomposition and metal reduction (Miranda et al., 2018). The TG and DTG curves of the sample (MB and LEMB) analysed in an inert (nitrogen) atmosphere. The initial temperature was set at 170 °C. From the initial temperature, there a marked increase in weight loss was observed, reaching a maximum of 11.49% min−1 and 24.80% min−1 for MB and LEMB, respectively at 296 °C–297 °C. Higher molecular weight compounds like carbohydrates, protein, and lipids in microalgae experience cracking and depolymerization reactions due to the continuous heat supply at this stage and this is considered as the active pyrolytic zone. Due to the decomposition of highly thermally stable compounds, char formation occurs at the final stage. Furthermore, it showed a second peak in the DTG curve which is lower than the first peak. This may be due to the difference in chemical composition of MB and LEMB. Based on the above results, MB and LEMB of Scenedesmus sp. ASK22 grown in SDE maintained satisfactory thermal stability for the development of valuable by-products (hydrocarbon gas and bio-oil) during reuse.
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