- Email: [email protected]
Isolation of a novel microalgae strain Desmodesmus sp. and optimization of environmental factors for its biomass production
Bioresource Technology 148 (2013) 249–254
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Isolation of a novel microalgae strain Desmodesmus sp. and optimization of environmental factors for its biomass production Fang Ji a,e, Rui Hao b, Ying Liu c,e, Gang Li d,e, Yuguang Zhou a,e,⇑, Renjie Dong a,e a
College of Engineering/Biomass Engineering Center, China Agricultural University, PR China College of Food Science and Nutritional Engineering, China Agricultural University, PR China c College of Agriculture and Biotechnology, China Agricultural University, PR China d College of Water Resources and Civil Engineering, China Agricultural University, PR China e Key Laboratory of Clean Production and Utilization of Renewable Energy, Ministry of Agriculture, PR China b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
A new strain of Desmodesmus sp.
EJ15-2 was isolated from fresh water and identified using 18 s rRNA and ITS1 analysis. Temperature, light intensity and light/dark cycle were optimized as 30 °C, 98 lmol/m2/s and 14:10 (L:D). The highest predicted biomass production was 0.758 g/L.
a r t i c l e
i n f o
Article history: Received 11 June 2013 Received in revised form 17 August 2013 Accepted 19 August 2013 Available online 26 August 2013 Keywords: Desmodesmus sp. Biomass production Environmental factors Response surface methodology
a b s t r a c t A novel strain of unicellular green algae was isolated from fresh water samples collected from Yesanpo National Geopark, Laishui County of Hebei Province, China. The morphological and genomic identification of this strain was carried out using 18 s rRNA analysis. This novel strain was identified as Desmodesmus sp. named as EJ15-2. Environmental factors for biomass production of Desmodesmus sp. EJ15-2 grown under autotrophic condition (BG11 medium) was optimized using response surface methodology (RSM). A high correlation coefficient (R2 = 0.923, p 6 0.01) indicated the adaptability of the second-order equation matched well with the growth condition of this strain. The optimal conditions for a relatively high biomass production (up to 0.758 g/L) were at 30 °C, 98 lmol/m2/s and 14:10 (L:D), respectively. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction
⇑ Corresponding author. Address: P.O. Box 50, No. 17 Qinghua Donglu, Haidian District, Beijing 100083, PR China. Tel.: +86 10 62737858; fax: +86 10 62737885. E-mail addresses: [email protected], [email protected] (Y. Zhou). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.08.110
Microalgae, as an important source of the third generation biofuel, can be reproduced rapidly, and can minimize or avoid the occupation of arable land and nutrients used for conventional agriculture (Scott et al., 2010; Bhatnagar et al., 2011). Energy from
250
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
microalgae could be a viable substitute for fossil fuels in the future (Wijffels and Barbosa, 2010). Unicellular eukaryotic microalgae are the products of over 3 billion years of evolution, and are highly diverse (Falkowski et al., 2004). In recent years, a large number of useful microalgae have been isolated (Mishra and Jha, 2009; Schwenzfeier et al., 2011; Yoshida et al., 2006). Microalgae production for fuel purposes varies on its strain and cultivation conditions (Chen et al., 2010). However, it is not economically feasible since it has much higher production costs (Behzadi and Farid, 2007). This requires high biomass production and utilization ratio to reduce its unit production cost (U.S. DOE, 2010). A large quantity of water consumption, which is considered to be one of the most important issues, occupies 10–20% of the total costs of algae production (Subhadra, 2011; Chinnasamy et al., 2010). Harvesting costs contribute 20–30% to the total costs with the majority on cultivation expenses (Demirbas and Demirbas, 2011). It is necessary to isolate new microalgae species which have much higher biomass productivity and stronger adaption to the environment than common species so that the unit production cost could be reduced. Environmental factors have significant effect on microalgae biomass production and the changing conditions cause various influences on the biomass production of different species of microalgae (Li et al., 2011). Light radiation, temperature and pH value will most directly affect productivity (Soletto et al., 2008; Mata et al., 2010; Xenopoulos et al., 2002). The light intensity and photoperiod are the critical components in determining the biomass production of algae cultivation (Parmar et al., 2011). Maximal photosynthetic efficiency is required to attain high biomass production. The light obviously avails the microalgae growth although long high-light periods cause the photodamage which will decrease the photosynthetic efficiency (Grima et al., 1996). In addition, the light/dark cycle strongly depends on the light intensity in the photoperiod. (Barbosa, 2003). The major objective of this work is to isolate a new species of microalgae and figure out its optimal growth conditions, concerning on temperature, pH value, light intensity and photoperiod. The mutual effect of different environmental factors on biomass production by current species of microalgae grown under autotrophic condition was also investigated.
2. Methods 2.1. Isolation, identification and cultivation Desmodesmus sp. EJ15-2 was isolated in September 2011 from freshwater samples, which was collected from Yesanpo National Geopark (39°230 3500 N, 115°420 2700 E), Laishui County of Hebei Province, China. Desmodesmus sp. EJ15-2 was purified by serial dilutions and plate streaking in 1.5% agar Blue-Green (BG-11) medium (Rippka et al., 1979), which was consisted of 1500 mg/L NaNO3, 40 mg/L K2HPO4, 75 mg/L MgSO47H2O, 36 mg/L CaCl22H2O, 6 mg/L citric acid, 6 mg/L ferric ammonium citrate, 1 mg/L EDTANa2, 20 mg/L Na2CO3, and 1 mL/L A5 trace metal solution. The recipe of A5 trace metal solution was: 2.86 g/L H3BO3, 1.86 g/ L MnCl24H2O, 0.22 g/L ZnSO47H2O, 0.39 g/L Na2MoO42H2O, 0.08 g/L CuSO45H2O, and 0.05 g/L Co(NO3)26H2O. The pH value of the medium was titrated to 7.0 with 1 mol/L HCl. Individual colonies were inoculated into liquid BG-11 media within a forced ventilation clean bench (Suzhou Antai Airtech SW-CJ-2FD). All media was autoclaved for sterilization at 121 °C and lasted for 20 min. The seed cultures were grown in 100 mL flasks containing 50 mL of BG-11 medium and incubated at 25 ± 1 °C with illumination, which was provided by fluorescent lights at an irradiance of 80 ± 2 lmol/m2/s (the light/dark periods was 14:10) for 14 d. Peri-
odic agitations were performed each day at 9:00, 15:00, and 20:00, respectively. 2.2. DNA analysis The genomic DNA of Desmodesmus sp. EJ15-2 was extracted using the NuClean PlantGen DNA kit (Beijing ComWin Biotech Co., Ltd., China) according to the manufacturer’s instructions. 18 S rDNA genes were Polymeric Chain Reaction (PCR) amplified using the forward (50 TACTGTGAAACTGCGAATGGCTC 30 ) and reverse (50 TGATCCTTCCGCAGGTTCACCTA 30 ) primers (primers synthesized by the Sangon Biotech (Shanghai) Co., Ltd., China). ITS1 genes were amplified using the forward (50 AGTCGTAAC AAGGTTTCCGTAGG 30 ) and reverse (50 TATGCTTAAGTTCAGCGG GTAAT 30 ) primers. All of those primers were designed by DNAMAN (USA) and Primer 5.0 (Canada). PCR products were sequenced by the Life Technologies Corporation (China). Comparisons for similar sequences were carried out using the BLAST Program (NCBI BLAST, USA). 2.3. Biomass production Approximately 50 mL of the culture medium with Desmodesmus sp. EJ15-2 was filtered through a 0.45 lm glass fiber filter (Whatman, USA). The harvested cells were air dried at 80 °C for 24 h. Dried samples were allowed to cool down in a dessicator and weighed (Chae et al., 2006). Generally, dry cell weight (DCW) of microalgae is correlated to the optical density (OD) at the certain wavelength from 450 nm to 680 nm, which means to monitor the abundance of cells containing pigment conveniently (Shen et al., 2008; MacIntyre and Cullen, 2005). In this study, the OD of Desmodesmus sp. EJ15-2 was determined by measuring optical density of 680 nm (OD680) via an ultraviolet photospectrometer. The results were converted to DCW by calculation and the coefficient of OD680 to algae optical density was introduced (Moon, 1983). 2.4. Biomass production in different temperature, light intensity, light/ dark cycle and pH value In order to figure out the environmental limits to the biomass productivity of Desmodesmus sp. EJ15-2 under autotrophic condition, the temperatures used in this study were 15, 20, 25 and 30 °C; the light intensities were 40, 80, 120 and 200 lmol/m2/s, which were controlled by varying the number of fluorescent lamps as well as the distance between the lamps and algae culture; the photoperiodics investigated were 24:0, 18:6, 14:10 and 6:18 h (L:D); and the pH values were gradually adjusted to 5.0, 6.0, 7.0, 8.0, 9.0, and 10.0 with HCl or NaOH. The other conditions were the same as Part 2.1. 2.5. Experimental design and data analysis In order to optimize the environmental factors for the growth of Desmodesmus sp. EJ15-2, central composite design (CCD) and
Table 1 Levels of factors chosen for the experimental design. Level +2 +1 0 1 2
Temperature (°C) (X1)
Light intensity (lmol/ m2/s) (X2)
Light/dark cycle (L:D)(X3)
pH (X4)
30 26 22 18 14
200 160 120 80 40
24:0 19:5 14:10 9:15 4:20
10 9 8 7 6
251
F. Ji et al. / Bioresource Technology 148 (2013) 249–254 Table 2 Random experimental design and results based on RSM for biomass productivity. Experiment no.
Coded level
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Biomass productivity (Y)
X1
X2
X3
X4
Experimental value (g/L)
Predicted value (g/L)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
0.485 ± 0.015 0.602 ± 0.011 0.583 ± 0.019 0.522 ± 0.012 0.638 ± 0.013 0.634 ± 0.008 0.634 ± 0.014 0.613 ± 0.014 0.376 ± 0.013 0.384 ± 0.016 0.477 ± 0.009 0.397 ± 0.006 0.426 ± 0.016 0.447 ± 0.010 0.508 ± 0.008 0.443 ± 0.010 0.605 ± 0.016 0.403 ± 0.009 0.339 ± 0.010 0.542 ± 0.010 0.312 ± 0.027 0.272 ± 0.012 0.483 ± 0.009 0.660 ± 0.012 0.618 ± 0.026 0.622 ± 0.018 0.616 ± 0.003 0.618 ± 0.012 0.618 ± 0.018 0.610 ± 0.020
0.534 0.534 0.534 0.534 0.611 0.611 0.611 0.611 0.396 0.396 0.396 0.396 0.473 0.473 0.473 0.473 0.747 0.471 0.394 0.548 0.323 0.323 0.609 0.609 0.609 0.609 0.609 0.609 0.609 0.609
response surface methodology (RSM) were employed for random experimental design (Deepak et al., 2008). The total number of experiments for four variables was 30 (2k + 2k + n0), where k is the number of independent variables and n0 is the number of repetitions of the experiments at the center point (Can et al., 2006). Totally 24 experiments were augmented with 6 replications at the center values to evaluate the pure error. For statistical calculations, the relation between the coded values and actual values are described as Eq. (1):
xi ¼
Xi X0 DX i
ð1Þ
Where xi is the dimensionless value of an independent variable; Xi is the real value of an independent variable; X0 is the value of Xi at the center point; and DXi is the step change. The response was measured in terms of biomass productivity (Y). The behaviour of the system was determined by assuming a second order polynomial with linear, quadratic and interaction effects as shown by Eq. (2) (Khattar and Shailza,, 2009):
Y ¼ b0 þ
n n n X X X bi X i þ bij X i X j þ bjj X 2j : i¼1
i
ð2Þ
j¼1
Where Y is response; X1, X2, X3 and X4 are input variables; b0 is constant; bi is linear coefficient; bij is interaction coefficient; and bjj is quadratic coefficient. Based on the obtained results (see Part 2.4), the ranges of four factors, temperature (X1), light intensity (X2), light/dark cycle (X3) and pH (X4) were decided with different levels(Table 1). Coded levels, and the experimental values and predicted values of biomass productivity (Y) were shown in Table 2. Estimation of regression coefficients and statistical tests were implemented in the MINITAB Version 15 (Minitab Inc., State College, PA, USA) statistical software based on the RSM. Analysis of variance (ANOVA) was conducted on the coded level of variables
to identify the effects of individual variables. Stepwise deletion approaches of individual non-significant (p < 0.05) terms were conducted to simplify the regression equation by recalculation of the coefficients. At least three replicates were conducted. 3. Results and discussion 3.1. Isolation and identification of microalgae The microalgae EJ15-2 was screened among 21 strains that isolated from different water samples. There were a lot of single, double or quadratic cells surrounded in green cells under the field of optical microscope. The cells were ellipse with smooth surface, 4–6 lm in length, and 3–4 lm in width.The 18 S rRNA gene sequence that amplified from strain EJ15-2 was 1635 bp while the ITS1 was 592 bp. Both of them were with no heterogeneity. The phylogenetic analysis indicated that this strain have a close relationship with Desmodesmus sp. (Fig. 1). 3.2. Biomass production There was a direct correlation between optical density and DCW, which can be described as Eq. (3):
Y ¼ 0:3446X 0:0048
ð3Þ
Where Y is the DCW (g/L); X is the optical density at 680 nm. They have significant correlation coefficient (R2 = 0.993). 3.3. Effect of different experimental factors on Desmodesmus sp. EJ152 biomass production As shown in Fig. 2a, the growth profiles of Desmodesmus sp. EJ15-2 were very similar in 20, 25 and 30 °C, with 0.577, 0.569 and 0.531 g/L at the end of 14 d cultivation, respectively. However,
252
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
Fig. 1. Dendrogram depiting the partial results of a neighbor-joining analysis of ITS1 sequences (MEGA5.10).
Fig. 2. Comparison of Desmodesmus sp. EJ15-2 dry cell weight under different environmental factors: (a) temperature, (b) pH, (c) light intensity and (d) light/dark cycle.
the biomass production was significantly reduced at 15 °C. It was reported that the optimum growth temperature was 20–25 °C for most algae (Butterwick et al., 2005). The comparison of the cell growth indicated that Desmodesmus sp. EJ15-2 was not sensitive to different pH values (5–10), with the DCW obtained in a small period were 0.453–0.569 g/L (Fig. 2b). As shown in Fig. 2c, the effect of light intensity on the growth of Desmodesmus sp. EJ15-2 was significant. The biomass production
reached the maximum value (0.569 g/L) under 80 lmol/m2/s after 14 d cultivation; and it was far greater than under 40 and 200 lmol/m2/s. An study mentioned that at 56.6 lmol/m2/s, the photosynthetic rate of wild strains of Chlamydomonas reinhardtii increased with the enhance of light intensity until a certain limit (301.9 lmol/m2/s) when the photosynthetic rate began to go down (Murphy and Berberoglu, 2011). The results indicated that photo inhibition took place in Desmodesmus sp. EJ15-2 under strong light.
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
253
Fig. 2d illustrated that the biomass productivity of Desmodesmus sp. EJ15-2 was at 0.567–0.584 g/L after 14 d cultivation under different photoperiods, including 6:18, 14:10 and 18:6 h (light/dark (L:D) cycles). The biomass production plummeted to 0.390 g/L when the photoperiod was extended to 24:0 h at the same temperature, light intensity and pH. Maximum photosynthetic efficiencies can be achieved when the photoperiod approaches the photosynthetic unit turn-over time (Wahidin et al., 2013). It seems that the productivity of photosynthetic cultures might be reduced by extending the lighting hours, which indicates a light inhibition. 3.4. Statistical analysis using RSM Table 2 shows the biomass production corresponding to combined effect of four components in the specified ranges. The results of the second-order response surface model fitting in the form of ANOVA were given in Table 3. The model presented a relatively high coefficient of determination (R2 = 0.923), and given 92.3% variability in the response. The p value below 0.05 indicated that test parameter was significant at 5% level of significance. The regression coefficients, t and p values for all linear, interaction and quadratic effects of the variables were shown in Table 4. It was shown that temperature (X1) and photoperiod (X3) were the most important factors to affect biomass production of Desmodesmus sp. EJ15-2, while light intensity (X2) was of less important, and pH value (X4) was not a significant factor. Optimal regression equations were established by statistically testing each regression coefficient for biomass production with environmental factors (Eq. (4)).
Table 3 Analysis of variance (ANOVA) for the fitted quadratic polynomial model for optimization of biomass productivity. Source
Degree of Sum of Mean of F-value p-value freedom (DF) squares (SS) squares (MS)
Model Linear Square Interaction Residual error Lack of fit Pure error Total
14 4 4 6 14 10 4 29
0.329 0.153 0.162 0.141 0.029 0.029 0.001 –
0.024 0.038 0.040 0.002 0.002 0.003 0.000 –
11.46 18.70 19.70 1.14 – 22.71 – –
0.000 0.000 0.000 0.388 – 0.000 – –
R2 = 0.923.
Table 4 Regression analysis of a full second-order polynomial model for optimization of biomass productivity. Source of variation
Coefficient
Standard error coefficient
t-value
p-value
Significance
Constant X1 X2 X3 X4 X1X2 X1X3 X1X4 X2X3 X2X4 X3X4 X12 X22 X32 X42
0.6090 0.0690 0.0385 -0.0044 0.0112 0.0086 0.0124 0.0092 0.0049 0.0033 0.0231 0.0185 0.0344 0.0715 0.0016
0.0187 0.0092 0.0092 0.0092 0.0092 0.0113 0.0113 0.0113 0.0113 0.0113 0.0113 0.0086 0.0086 0.0086 0.0086
32.544 7.470 4.161 0.473 1.213 0.756 1.099 0.812 0.436 0.293 2.037 2.139 3.975 8.269 0.187
0.000 0.000 0.001 0.643 0.245 0.462 0.290 0.431 0.669 0.774 0.061 0.051 0.001 0.000 0.855
Significant Significant Significant
Significant Significant
Fig. 3. Response surfaces of the environmental factors effects on biomass accumulation by Desmodesmus sp. EJ15-2. The interaction between: (a) temperature and light intensity, (b) temperature and light/dark cycle and (c) light intensity and light/ dark cycle.
254
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
Y ¼ 0:609 0:0344ð0:025x2 3Þ2 0:0715ð0:2x3 2:8Þ2 þ 0:069ð0:25x1 5:5Þ 0:0385ð0:025x2 3Þ
ð4Þ
Where Y is DCW (g/L); X1 is temperature (14 < X1 < 30 °C); X2 is light intensity (40 < X2 < 200 lmol/m2/s); and X3 is light/dark cycle (4 < X3 < 24 h). Thus, based on Eq. (4), the optimal condition for biomass production was at 30 °C, 98 lmol/m2/s, and 14:10 (L:D); and the maximum DCW was 0.758 g/L under this condition after 14 d cultivation. The accuracy of the model was validated with at least three replicates giving the biomass production of 0.741 ± 0.005 g/L, which concurred with the model prediction. The response surfaces of environmental factors for maximum biomass production of Desmodesmus sp. EJ15-2 have been established (Fig. 3a and c) according to the model. As presented in Fig. 3a and Fig. 3b, an increase in biomass production was observed with the enhancement of the temperature within the experimental range. Fig. 3a showed that the effects of temperature and light intensity on the DCW when the other variables were held at constant level. It was observed that the biomass production significantly increased with increasing temperature at a given light intensity. Fig. 3b revealed a uniform trend towards similarly increase in temperature, and its photoperiod was 14:10 (L:D). It can explain that Desmodesmus sp. demonstrates excellent thermo-tolerant performance (Pan et al., 2011), more biomass production under higher temperature compared with other microalgae species. Fig. 3c showed the response contours of biomass production of Desmodesmus sp. EJ15-2 against light intensity with light/dark cycle after 14 d cultivation. The values and signs on regression coefficients suggested that the response was positive for light intensity up to 98 lmol/m2/s and light/dark cycle remain stable at 14:10 h (L:D). The experimental and the predicted values were very close and did not reflect the accuracy and the applicability of RSM. 4. Conclusions A novel green microalgae strain, Desmodesmus sp. EJ 15-2, was isolated and identified from fresh water. Results from this study have demonstrated a process that using RSM to optimize the environmental factors. The optimal conditions for a relatively high biomass production (up to 0.758 g/L) were at 30 °C, 98 lmol/m2/s and 14:10 (L:D), respectively. Acknowledgements This investigation was financially funded by The Chinese National Advanced Technology Development Program (Grant No. 2013AA065802), The Chinese National ‘‘Twelfth Five-Year’’ Plan for Science & Technology Supporting Project (Grant No. 2012BAD47B03), The Chinese Universities Scientific Fund (Grant No. 2013YJ007), The Second Class General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2011M500451), and Beijing Municipal Key Discipline of Biomass Engineering. Reference Barbosa, M., 2003. Microalgal Photobioreactors: Scale-Up and Optimization. Wageningen University, Netherlands. Behzadi, S., Farid, M.M., 2007. Review: examining the use of different feedstock for the production of biodiesel. Asia-Pac. J. Chem. Eng. 2, 480–486. Bhatnagar, A., Chinnasamy, S., Singh, M., Das, K.C., 2011. Renewable biomass production by mixotrophic algae in the presence of various carbon sources and wastewaters. Appl. Energ. 88, 3425–3431. Butterwick, C., Heaney, S.I., Talling, J.F., 2005. Diversity in the influence of temperature on the growth rates of freshwater algae, and its ecological relevance. Freshwater Biol. 50 (2), 291–300.
Can, M.Y., Kaya, Y., Algur, O.F., 2006. Response surface optimization of the removal of nickel from aqueous solution by cone biomass of Pinus sylvestris. Bioresour. Technol. 97 (14), 1761–1765. Chae, S.R., Hwang, E.J., Shin, H.S., 2006. Single cell protein production of Euglena gracilis and carbon dioxide fixation in an innovative photo-bioreactor. Bioresour. Technol. 97, 322–329. Chen, C., Yeh, K., Su, H., Lo, Y., Chen, W., Chang, J., 2010. Strategies to enhance cell growth and achieve high level oil production of a Chlorella vulgaris isolate. Biotechnol. Progr. 26, 679–686. Chinnasamy, S., Bhatnagar, A., Hunt, R.W., Das, K.C., 2010. Microalgae cultivation in a wastewater dominated by carpet mill effluents for biofuel applications. Bioresour. Technol. 101, 3097–3105. Deepak, V., Kalishwaralal, K., Ramkumarpandian, S., Venkatesh Babu, S., Senthilkumar, S.R., Sangiliyandi, G., 2008. Optimization of media composition for Nattokinase production by Bacillus subtilis using response surface methodology. Bioresour. Technol. 99 (17), 8170–8174. Demirbas, A., Demirbas, M.F., 2011. Importance of algae oil as a source of biodiesel. Energ. Conv. Manage. 52, 163–170. Falkowski, P.G., Katz, M.E., Knoll, A.H., Quigg, A., Raven, J.A., Schofield, O., Taylor, F.J.R., 2004. The evolution of modern eukaryotic phytoplankton. Science 305, 354–360. Grima, E.M., Sevilla, J.M.F., Perez, J.A.S., Camacho, F.G., 1996. A study on simultaneous photolimitation and photoinhibition in dense microalgal cultures taking into account incident and averaged irradiances. J. Biotechnol. 45, 59–69. Khattar, J.I.S., Shailza, 2009. Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology. Process Biochem. 44 (1), 118–121. Li, Y., Zhou, W., Hu, B., Min, M., Chen, P., Ruan, R.R., 2011. Integration of algae cultivation as biodiesel production feedstock with municipal wastewater treatment: Strains screening and significance evaluation of environmental factors. Bioresour. Technol. 102 (23), 10861–10867. MacIntyre, H.L., Cullen, J.J., 2005. Using Cultures To Investigate The Physiological Ecology Of Microalgae. In: Andersen, R.A. (Ed.), Algal Culturing Techniques. Elsevier Academic Press, Burlington, pp. 287–326. Mata, M.T., Martins, A.A., Caetano, N.S., 2010. Microalgae for biodiesel production and other applications: a review. Renew. Sustain. Energy Rev. 14 (1), 217– 232. Mishra, A., Jha, B., 2009. Isolation and characterization of extracellular polymeric substances from micro-algae Dunaliellasalina under salt stress. Bioresour. Technol. 100 (13), 3382–3386. Moon, N.J., 1983. Inhibition of the growth of acid tolerant yeasts by acetate, lactate and propionate and their synergistic mixtures. J. Appl. Bacteriol. 55 (3), 453– 460. Murphy, T.E., Berberoglu, H., 2011. Effect of algae pigmentation on photobioreactor productivity and scale-up: A light transfer perspective. J. Quant. Spectrosc. Radiat. 112 (18), 2826–2834. Pan, Y.Y., Wang, S.T., Chuang, L.T., Chang, Y.W., Chen, C.N.N., 2011. Isolation of thermo-tolerant and high lipid content green microalgae: Oil accumulation is predominantly controlled by photosystem efficiency during stress treatments in Desmodesmus. Bioresour. Technol. 102 (22), 10510–10517. Parmar, A., Singh, N.K., Pandey, A., 2011. Gnansounou, E., Madamwar, D., Cyanobacteria and microalgae: A positive prospect for biofuels. Bioresour. Technol. 102 (22), 10163–10172. Rippka, R., Deruelles, J., Waterbury, J.B., Herdman, M., Stanier, R.Y., 1979. Generic assignments, strain histories and properties of pure cultures of Cyanobacteria. J. Gen. Microbiol. 111, 1–61. Schwenzfeier, A., Wierenga, P.A., Gruppen, H., 2011. Isolation and characterization of soluble protein from the green microalgae Tetraselmis sp. Bioresour. Technol. 102 (19), 9121–9127. Scott, S.A., Davey, M.P., Dennis, J.S., Horst, I., Howe, C.J., Lea-Smith, D.J., Smith, A.G., 2010. Biodiesel from algae: challenges and prospects. Curr. Opin. Biotechnol. 21, 277–286. Shen, Y., Yuan, W., Pei, Z., Mao, E., 2008. Culture of microalga Botryococcus in livestock wastewater. Trans. ASABE. 51 (4), 1395–1400. Soletto, D., Binaghi, L., Ferrari, L., Lodi, A., Carvalho, J.C.M., Zilli, M., Converti, A., 2008. Effects of carbon dioxide feeding rate and light intensity on the fed-batch pulse feeding cultivation of Spirulina platensis in helical photobioreactor. Biochem. Eng. J. 39 (2), 369–375. Subhadra, B.G., 2011. Water management policies for the algal biofuel sector in the Southwestern United States. Appl. Energ. 88 (10), 3492–3498. U.S. DOE, 2010. National algal biofuels technology roadmap. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Biomass Program. Wahidin, S., Idris, A., Shaleh, S.R.M., 2013. The influence of light intensity and photoperiod on the growth and lipid content of microalgae Nannochloropsis sp. Bioresour. Technol. 129, 7–11. Wijffels, R.H., Barbosa, M.J., 2010. An outlook on microalgal biofuels. Science 329, 796–799. Xenopoulos, M.A., Frost, P.C., Elser, J.J., 2002. Joint effects of UV radiation and phosphours supply on algal growth rate and elemental composition. Ecology 83 (2), 423–435. Yoshida, N., Ikeda, R., Okuno, T., 2006. Identification and characterization of heavy metal-resistant unicellular alga isolated from soil and its potential for phytoremediation. Bioresour. Technol. 97 (15), 1843–1849.
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Isolation of a novel microalgae strain Desmodesmus sp. and optimization of environmental factors for its biomass production Fang Ji a,e, Rui Hao b, Ying Liu c,e, Gang Li d,e, Yuguang Zhou a,e,⇑, Renjie Dong a,e a
College of Engineering/Biomass Engineering Center, China Agricultural University, PR China College of Food Science and Nutritional Engineering, China Agricultural University, PR China c College of Agriculture and Biotechnology, China Agricultural University, PR China d College of Water Resources and Civil Engineering, China Agricultural University, PR China e Key Laboratory of Clean Production and Utilization of Renewable Energy, Ministry of Agriculture, PR China b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
A new strain of Desmodesmus sp.
EJ15-2 was isolated from fresh water and identified using 18 s rRNA and ITS1 analysis. Temperature, light intensity and light/dark cycle were optimized as 30 °C, 98 lmol/m2/s and 14:10 (L:D). The highest predicted biomass production was 0.758 g/L.
a r t i c l e
i n f o
Article history: Received 11 June 2013 Received in revised form 17 August 2013 Accepted 19 August 2013 Available online 26 August 2013 Keywords: Desmodesmus sp. Biomass production Environmental factors Response surface methodology
a b s t r a c t A novel strain of unicellular green algae was isolated from fresh water samples collected from Yesanpo National Geopark, Laishui County of Hebei Province, China. The morphological and genomic identification of this strain was carried out using 18 s rRNA analysis. This novel strain was identified as Desmodesmus sp. named as EJ15-2. Environmental factors for biomass production of Desmodesmus sp. EJ15-2 grown under autotrophic condition (BG11 medium) was optimized using response surface methodology (RSM). A high correlation coefficient (R2 = 0.923, p 6 0.01) indicated the adaptability of the second-order equation matched well with the growth condition of this strain. The optimal conditions for a relatively high biomass production (up to 0.758 g/L) were at 30 °C, 98 lmol/m2/s and 14:10 (L:D), respectively. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction
⇑ Corresponding author. Address: P.O. Box 50, No. 17 Qinghua Donglu, Haidian District, Beijing 100083, PR China. Tel.: +86 10 62737858; fax: +86 10 62737885. E-mail addresses: [email protected], [email protected] (Y. Zhou). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.08.110
Microalgae, as an important source of the third generation biofuel, can be reproduced rapidly, and can minimize or avoid the occupation of arable land and nutrients used for conventional agriculture (Scott et al., 2010; Bhatnagar et al., 2011). Energy from
250
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
microalgae could be a viable substitute for fossil fuels in the future (Wijffels and Barbosa, 2010). Unicellular eukaryotic microalgae are the products of over 3 billion years of evolution, and are highly diverse (Falkowski et al., 2004). In recent years, a large number of useful microalgae have been isolated (Mishra and Jha, 2009; Schwenzfeier et al., 2011; Yoshida et al., 2006). Microalgae production for fuel purposes varies on its strain and cultivation conditions (Chen et al., 2010). However, it is not economically feasible since it has much higher production costs (Behzadi and Farid, 2007). This requires high biomass production and utilization ratio to reduce its unit production cost (U.S. DOE, 2010). A large quantity of water consumption, which is considered to be one of the most important issues, occupies 10–20% of the total costs of algae production (Subhadra, 2011; Chinnasamy et al., 2010). Harvesting costs contribute 20–30% to the total costs with the majority on cultivation expenses (Demirbas and Demirbas, 2011). It is necessary to isolate new microalgae species which have much higher biomass productivity and stronger adaption to the environment than common species so that the unit production cost could be reduced. Environmental factors have significant effect on microalgae biomass production and the changing conditions cause various influences on the biomass production of different species of microalgae (Li et al., 2011). Light radiation, temperature and pH value will most directly affect productivity (Soletto et al., 2008; Mata et al., 2010; Xenopoulos et al., 2002). The light intensity and photoperiod are the critical components in determining the biomass production of algae cultivation (Parmar et al., 2011). Maximal photosynthetic efficiency is required to attain high biomass production. The light obviously avails the microalgae growth although long high-light periods cause the photodamage which will decrease the photosynthetic efficiency (Grima et al., 1996). In addition, the light/dark cycle strongly depends on the light intensity in the photoperiod. (Barbosa, 2003). The major objective of this work is to isolate a new species of microalgae and figure out its optimal growth conditions, concerning on temperature, pH value, light intensity and photoperiod. The mutual effect of different environmental factors on biomass production by current species of microalgae grown under autotrophic condition was also investigated.
2. Methods 2.1. Isolation, identification and cultivation Desmodesmus sp. EJ15-2 was isolated in September 2011 from freshwater samples, which was collected from Yesanpo National Geopark (39°230 3500 N, 115°420 2700 E), Laishui County of Hebei Province, China. Desmodesmus sp. EJ15-2 was purified by serial dilutions and plate streaking in 1.5% agar Blue-Green (BG-11) medium (Rippka et al., 1979), which was consisted of 1500 mg/L NaNO3, 40 mg/L K2HPO4, 75 mg/L MgSO47H2O, 36 mg/L CaCl22H2O, 6 mg/L citric acid, 6 mg/L ferric ammonium citrate, 1 mg/L EDTANa2, 20 mg/L Na2CO3, and 1 mL/L A5 trace metal solution. The recipe of A5 trace metal solution was: 2.86 g/L H3BO3, 1.86 g/ L MnCl24H2O, 0.22 g/L ZnSO47H2O, 0.39 g/L Na2MoO42H2O, 0.08 g/L CuSO45H2O, and 0.05 g/L Co(NO3)26H2O. The pH value of the medium was titrated to 7.0 with 1 mol/L HCl. Individual colonies were inoculated into liquid BG-11 media within a forced ventilation clean bench (Suzhou Antai Airtech SW-CJ-2FD). All media was autoclaved for sterilization at 121 °C and lasted for 20 min. The seed cultures were grown in 100 mL flasks containing 50 mL of BG-11 medium and incubated at 25 ± 1 °C with illumination, which was provided by fluorescent lights at an irradiance of 80 ± 2 lmol/m2/s (the light/dark periods was 14:10) for 14 d. Peri-
odic agitations were performed each day at 9:00, 15:00, and 20:00, respectively. 2.2. DNA analysis The genomic DNA of Desmodesmus sp. EJ15-2 was extracted using the NuClean PlantGen DNA kit (Beijing ComWin Biotech Co., Ltd., China) according to the manufacturer’s instructions. 18 S rDNA genes were Polymeric Chain Reaction (PCR) amplified using the forward (50 TACTGTGAAACTGCGAATGGCTC 30 ) and reverse (50 TGATCCTTCCGCAGGTTCACCTA 30 ) primers (primers synthesized by the Sangon Biotech (Shanghai) Co., Ltd., China). ITS1 genes were amplified using the forward (50 AGTCGTAAC AAGGTTTCCGTAGG 30 ) and reverse (50 TATGCTTAAGTTCAGCGG GTAAT 30 ) primers. All of those primers were designed by DNAMAN (USA) and Primer 5.0 (Canada). PCR products were sequenced by the Life Technologies Corporation (China). Comparisons for similar sequences were carried out using the BLAST Program (NCBI BLAST, USA). 2.3. Biomass production Approximately 50 mL of the culture medium with Desmodesmus sp. EJ15-2 was filtered through a 0.45 lm glass fiber filter (Whatman, USA). The harvested cells were air dried at 80 °C for 24 h. Dried samples were allowed to cool down in a dessicator and weighed (Chae et al., 2006). Generally, dry cell weight (DCW) of microalgae is correlated to the optical density (OD) at the certain wavelength from 450 nm to 680 nm, which means to monitor the abundance of cells containing pigment conveniently (Shen et al., 2008; MacIntyre and Cullen, 2005). In this study, the OD of Desmodesmus sp. EJ15-2 was determined by measuring optical density of 680 nm (OD680) via an ultraviolet photospectrometer. The results were converted to DCW by calculation and the coefficient of OD680 to algae optical density was introduced (Moon, 1983). 2.4. Biomass production in different temperature, light intensity, light/ dark cycle and pH value In order to figure out the environmental limits to the biomass productivity of Desmodesmus sp. EJ15-2 under autotrophic condition, the temperatures used in this study were 15, 20, 25 and 30 °C; the light intensities were 40, 80, 120 and 200 lmol/m2/s, which were controlled by varying the number of fluorescent lamps as well as the distance between the lamps and algae culture; the photoperiodics investigated were 24:0, 18:6, 14:10 and 6:18 h (L:D); and the pH values were gradually adjusted to 5.0, 6.0, 7.0, 8.0, 9.0, and 10.0 with HCl or NaOH. The other conditions were the same as Part 2.1. 2.5. Experimental design and data analysis In order to optimize the environmental factors for the growth of Desmodesmus sp. EJ15-2, central composite design (CCD) and
Table 1 Levels of factors chosen for the experimental design. Level +2 +1 0 1 2
Temperature (°C) (X1)
Light intensity (lmol/ m2/s) (X2)
Light/dark cycle (L:D)(X3)
pH (X4)
30 26 22 18 14
200 160 120 80 40
24:0 19:5 14:10 9:15 4:20
10 9 8 7 6
251
F. Ji et al. / Bioresource Technology 148 (2013) 249–254 Table 2 Random experimental design and results based on RSM for biomass productivity. Experiment no.
Coded level
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Biomass productivity (Y)
X1
X2
X3
X4
Experimental value (g/L)
Predicted value (g/L)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
0.485 ± 0.015 0.602 ± 0.011 0.583 ± 0.019 0.522 ± 0.012 0.638 ± 0.013 0.634 ± 0.008 0.634 ± 0.014 0.613 ± 0.014 0.376 ± 0.013 0.384 ± 0.016 0.477 ± 0.009 0.397 ± 0.006 0.426 ± 0.016 0.447 ± 0.010 0.508 ± 0.008 0.443 ± 0.010 0.605 ± 0.016 0.403 ± 0.009 0.339 ± 0.010 0.542 ± 0.010 0.312 ± 0.027 0.272 ± 0.012 0.483 ± 0.009 0.660 ± 0.012 0.618 ± 0.026 0.622 ± 0.018 0.616 ± 0.003 0.618 ± 0.012 0.618 ± 0.018 0.610 ± 0.020
0.534 0.534 0.534 0.534 0.611 0.611 0.611 0.611 0.396 0.396 0.396 0.396 0.473 0.473 0.473 0.473 0.747 0.471 0.394 0.548 0.323 0.323 0.609 0.609 0.609 0.609 0.609 0.609 0.609 0.609
response surface methodology (RSM) were employed for random experimental design (Deepak et al., 2008). The total number of experiments for four variables was 30 (2k + 2k + n0), where k is the number of independent variables and n0 is the number of repetitions of the experiments at the center point (Can et al., 2006). Totally 24 experiments were augmented with 6 replications at the center values to evaluate the pure error. For statistical calculations, the relation between the coded values and actual values are described as Eq. (1):
xi ¼
Xi X0 DX i
ð1Þ
Where xi is the dimensionless value of an independent variable; Xi is the real value of an independent variable; X0 is the value of Xi at the center point; and DXi is the step change. The response was measured in terms of biomass productivity (Y). The behaviour of the system was determined by assuming a second order polynomial with linear, quadratic and interaction effects as shown by Eq. (2) (Khattar and Shailza,, 2009):
Y ¼ b0 þ
n n n X X X bi X i þ bij X i X j þ bjj X 2j : i¼1
i
ð2Þ
j¼1
Where Y is response; X1, X2, X3 and X4 are input variables; b0 is constant; bi is linear coefficient; bij is interaction coefficient; and bjj is quadratic coefficient. Based on the obtained results (see Part 2.4), the ranges of four factors, temperature (X1), light intensity (X2), light/dark cycle (X3) and pH (X4) were decided with different levels(Table 1). Coded levels, and the experimental values and predicted values of biomass productivity (Y) were shown in Table 2. Estimation of regression coefficients and statistical tests were implemented in the MINITAB Version 15 (Minitab Inc., State College, PA, USA) statistical software based on the RSM. Analysis of variance (ANOVA) was conducted on the coded level of variables
to identify the effects of individual variables. Stepwise deletion approaches of individual non-significant (p < 0.05) terms were conducted to simplify the regression equation by recalculation of the coefficients. At least three replicates were conducted. 3. Results and discussion 3.1. Isolation and identification of microalgae The microalgae EJ15-2 was screened among 21 strains that isolated from different water samples. There were a lot of single, double or quadratic cells surrounded in green cells under the field of optical microscope. The cells were ellipse with smooth surface, 4–6 lm in length, and 3–4 lm in width.The 18 S rRNA gene sequence that amplified from strain EJ15-2 was 1635 bp while the ITS1 was 592 bp. Both of them were with no heterogeneity. The phylogenetic analysis indicated that this strain have a close relationship with Desmodesmus sp. (Fig. 1). 3.2. Biomass production There was a direct correlation between optical density and DCW, which can be described as Eq. (3):
Y ¼ 0:3446X 0:0048
ð3Þ
Where Y is the DCW (g/L); X is the optical density at 680 nm. They have significant correlation coefficient (R2 = 0.993). 3.3. Effect of different experimental factors on Desmodesmus sp. EJ152 biomass production As shown in Fig. 2a, the growth profiles of Desmodesmus sp. EJ15-2 were very similar in 20, 25 and 30 °C, with 0.577, 0.569 and 0.531 g/L at the end of 14 d cultivation, respectively. However,
252
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
Fig. 1. Dendrogram depiting the partial results of a neighbor-joining analysis of ITS1 sequences (MEGA5.10).
Fig. 2. Comparison of Desmodesmus sp. EJ15-2 dry cell weight under different environmental factors: (a) temperature, (b) pH, (c) light intensity and (d) light/dark cycle.
the biomass production was significantly reduced at 15 °C. It was reported that the optimum growth temperature was 20–25 °C for most algae (Butterwick et al., 2005). The comparison of the cell growth indicated that Desmodesmus sp. EJ15-2 was not sensitive to different pH values (5–10), with the DCW obtained in a small period were 0.453–0.569 g/L (Fig. 2b). As shown in Fig. 2c, the effect of light intensity on the growth of Desmodesmus sp. EJ15-2 was significant. The biomass production
reached the maximum value (0.569 g/L) under 80 lmol/m2/s after 14 d cultivation; and it was far greater than under 40 and 200 lmol/m2/s. An study mentioned that at 56.6 lmol/m2/s, the photosynthetic rate of wild strains of Chlamydomonas reinhardtii increased with the enhance of light intensity until a certain limit (301.9 lmol/m2/s) when the photosynthetic rate began to go down (Murphy and Berberoglu, 2011). The results indicated that photo inhibition took place in Desmodesmus sp. EJ15-2 under strong light.
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
253
Fig. 2d illustrated that the biomass productivity of Desmodesmus sp. EJ15-2 was at 0.567–0.584 g/L after 14 d cultivation under different photoperiods, including 6:18, 14:10 and 18:6 h (light/dark (L:D) cycles). The biomass production plummeted to 0.390 g/L when the photoperiod was extended to 24:0 h at the same temperature, light intensity and pH. Maximum photosynthetic efficiencies can be achieved when the photoperiod approaches the photosynthetic unit turn-over time (Wahidin et al., 2013). It seems that the productivity of photosynthetic cultures might be reduced by extending the lighting hours, which indicates a light inhibition. 3.4. Statistical analysis using RSM Table 2 shows the biomass production corresponding to combined effect of four components in the specified ranges. The results of the second-order response surface model fitting in the form of ANOVA were given in Table 3. The model presented a relatively high coefficient of determination (R2 = 0.923), and given 92.3% variability in the response. The p value below 0.05 indicated that test parameter was significant at 5% level of significance. The regression coefficients, t and p values for all linear, interaction and quadratic effects of the variables were shown in Table 4. It was shown that temperature (X1) and photoperiod (X3) were the most important factors to affect biomass production of Desmodesmus sp. EJ15-2, while light intensity (X2) was of less important, and pH value (X4) was not a significant factor. Optimal regression equations were established by statistically testing each regression coefficient for biomass production with environmental factors (Eq. (4)).
Table 3 Analysis of variance (ANOVA) for the fitted quadratic polynomial model for optimization of biomass productivity. Source
Degree of Sum of Mean of F-value p-value freedom (DF) squares (SS) squares (MS)
Model Linear Square Interaction Residual error Lack of fit Pure error Total
14 4 4 6 14 10 4 29
0.329 0.153 0.162 0.141 0.029 0.029 0.001 –
0.024 0.038 0.040 0.002 0.002 0.003 0.000 –
11.46 18.70 19.70 1.14 – 22.71 – –
0.000 0.000 0.000 0.388 – 0.000 – –
R2 = 0.923.
Table 4 Regression analysis of a full second-order polynomial model for optimization of biomass productivity. Source of variation
Coefficient
Standard error coefficient
t-value
p-value
Significance
Constant X1 X2 X3 X4 X1X2 X1X3 X1X4 X2X3 X2X4 X3X4 X12 X22 X32 X42
0.6090 0.0690 0.0385 -0.0044 0.0112 0.0086 0.0124 0.0092 0.0049 0.0033 0.0231 0.0185 0.0344 0.0715 0.0016
0.0187 0.0092 0.0092 0.0092 0.0092 0.0113 0.0113 0.0113 0.0113 0.0113 0.0113 0.0086 0.0086 0.0086 0.0086
32.544 7.470 4.161 0.473 1.213 0.756 1.099 0.812 0.436 0.293 2.037 2.139 3.975 8.269 0.187
0.000 0.000 0.001 0.643 0.245 0.462 0.290 0.431 0.669 0.774 0.061 0.051 0.001 0.000 0.855
Significant Significant Significant
Significant Significant
Fig. 3. Response surfaces of the environmental factors effects on biomass accumulation by Desmodesmus sp. EJ15-2. The interaction between: (a) temperature and light intensity, (b) temperature and light/dark cycle and (c) light intensity and light/ dark cycle.
254
F. Ji et al. / Bioresource Technology 148 (2013) 249–254
Y ¼ 0:609 0:0344ð0:025x2 3Þ2 0:0715ð0:2x3 2:8Þ2 þ 0:069ð0:25x1 5:5Þ 0:0385ð0:025x2 3Þ
ð4Þ
Where Y is DCW (g/L); X1 is temperature (14 < X1 < 30 °C); X2 is light intensity (40 < X2 < 200 lmol/m2/s); and X3 is light/dark cycle (4 < X3 < 24 h). Thus, based on Eq. (4), the optimal condition for biomass production was at 30 °C, 98 lmol/m2/s, and 14:10 (L:D); and the maximum DCW was 0.758 g/L under this condition after 14 d cultivation. The accuracy of the model was validated with at least three replicates giving the biomass production of 0.741 ± 0.005 g/L, which concurred with the model prediction. The response surfaces of environmental factors for maximum biomass production of Desmodesmus sp. EJ15-2 have been established (Fig. 3a and c) according to the model. As presented in Fig. 3a and Fig. 3b, an increase in biomass production was observed with the enhancement of the temperature within the experimental range. Fig. 3a showed that the effects of temperature and light intensity on the DCW when the other variables were held at constant level. It was observed that the biomass production significantly increased with increasing temperature at a given light intensity. Fig. 3b revealed a uniform trend towards similarly increase in temperature, and its photoperiod was 14:10 (L:D). It can explain that Desmodesmus sp. demonstrates excellent thermo-tolerant performance (Pan et al., 2011), more biomass production under higher temperature compared with other microalgae species. Fig. 3c showed the response contours of biomass production of Desmodesmus sp. EJ15-2 against light intensity with light/dark cycle after 14 d cultivation. The values and signs on regression coefficients suggested that the response was positive for light intensity up to 98 lmol/m2/s and light/dark cycle remain stable at 14:10 h (L:D). The experimental and the predicted values were very close and did not reflect the accuracy and the applicability of RSM. 4. Conclusions A novel green microalgae strain, Desmodesmus sp. EJ 15-2, was isolated and identified from fresh water. Results from this study have demonstrated a process that using RSM to optimize the environmental factors. The optimal conditions for a relatively high biomass production (up to 0.758 g/L) were at 30 °C, 98 lmol/m2/s and 14:10 (L:D), respectively. Acknowledgements This investigation was financially funded by The Chinese National Advanced Technology Development Program (Grant No. 2013AA065802), The Chinese National ‘‘Twelfth Five-Year’’ Plan for Science & Technology Supporting Project (Grant No. 2012BAD47B03), The Chinese Universities Scientific Fund (Grant No. 2013YJ007), The Second Class General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2011M500451), and Beijing Municipal Key Discipline of Biomass Engineering. Reference Barbosa, M., 2003. Microalgal Photobioreactors: Scale-Up and Optimization. Wageningen University, Netherlands. Behzadi, S., Farid, M.M., 2007. Review: examining the use of different feedstock for the production of biodiesel. Asia-Pac. J. Chem. Eng. 2, 480–486. Bhatnagar, A., Chinnasamy, S., Singh, M., Das, K.C., 2011. Renewable biomass production by mixotrophic algae in the presence of various carbon sources and wastewaters. Appl. Energ. 88, 3425–3431. Butterwick, C., Heaney, S.I., Talling, J.F., 2005. Diversity in the influence of temperature on the growth rates of freshwater algae, and its ecological relevance. Freshwater Biol. 50 (2), 291–300.
Can, M.Y., Kaya, Y., Algur, O.F., 2006. Response surface optimization of the removal of nickel from aqueous solution by cone biomass of Pinus sylvestris. Bioresour. Technol. 97 (14), 1761–1765. Chae, S.R., Hwang, E.J., Shin, H.S., 2006. Single cell protein production of Euglena gracilis and carbon dioxide fixation in an innovative photo-bioreactor. Bioresour. Technol. 97, 322–329. Chen, C., Yeh, K., Su, H., Lo, Y., Chen, W., Chang, J., 2010. Strategies to enhance cell growth and achieve high level oil production of a Chlorella vulgaris isolate. Biotechnol. Progr. 26, 679–686. Chinnasamy, S., Bhatnagar, A., Hunt, R.W., Das, K.C., 2010. Microalgae cultivation in a wastewater dominated by carpet mill effluents for biofuel applications. Bioresour. Technol. 101, 3097–3105. Deepak, V., Kalishwaralal, K., Ramkumarpandian, S., Venkatesh Babu, S., Senthilkumar, S.R., Sangiliyandi, G., 2008. Optimization of media composition for Nattokinase production by Bacillus subtilis using response surface methodology. Bioresour. Technol. 99 (17), 8170–8174. Demirbas, A., Demirbas, M.F., 2011. Importance of algae oil as a source of biodiesel. Energ. Conv. Manage. 52, 163–170. Falkowski, P.G., Katz, M.E., Knoll, A.H., Quigg, A., Raven, J.A., Schofield, O., Taylor, F.J.R., 2004. The evolution of modern eukaryotic phytoplankton. Science 305, 354–360. Grima, E.M., Sevilla, J.M.F., Perez, J.A.S., Camacho, F.G., 1996. A study on simultaneous photolimitation and photoinhibition in dense microalgal cultures taking into account incident and averaged irradiances. J. Biotechnol. 45, 59–69. Khattar, J.I.S., Shailza, 2009. Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology. Process Biochem. 44 (1), 118–121. Li, Y., Zhou, W., Hu, B., Min, M., Chen, P., Ruan, R.R., 2011. Integration of algae cultivation as biodiesel production feedstock with municipal wastewater treatment: Strains screening and significance evaluation of environmental factors. Bioresour. Technol. 102 (23), 10861–10867. MacIntyre, H.L., Cullen, J.J., 2005. Using Cultures To Investigate The Physiological Ecology Of Microalgae. In: Andersen, R.A. (Ed.), Algal Culturing Techniques. Elsevier Academic Press, Burlington, pp. 287–326. Mata, M.T., Martins, A.A., Caetano, N.S., 2010. Microalgae for biodiesel production and other applications: a review. Renew. Sustain. Energy Rev. 14 (1), 217– 232. Mishra, A., Jha, B., 2009. Isolation and characterization of extracellular polymeric substances from micro-algae Dunaliellasalina under salt stress. Bioresour. Technol. 100 (13), 3382–3386. Moon, N.J., 1983. Inhibition of the growth of acid tolerant yeasts by acetate, lactate and propionate and their synergistic mixtures. J. Appl. Bacteriol. 55 (3), 453– 460. Murphy, T.E., Berberoglu, H., 2011. Effect of algae pigmentation on photobioreactor productivity and scale-up: A light transfer perspective. J. Quant. Spectrosc. Radiat. 112 (18), 2826–2834. Pan, Y.Y., Wang, S.T., Chuang, L.T., Chang, Y.W., Chen, C.N.N., 2011. Isolation of thermo-tolerant and high lipid content green microalgae: Oil accumulation is predominantly controlled by photosystem efficiency during stress treatments in Desmodesmus. Bioresour. Technol. 102 (22), 10510–10517. Parmar, A., Singh, N.K., Pandey, A., 2011. Gnansounou, E., Madamwar, D., Cyanobacteria and microalgae: A positive prospect for biofuels. Bioresour. Technol. 102 (22), 10163–10172. Rippka, R., Deruelles, J., Waterbury, J.B., Herdman, M., Stanier, R.Y., 1979. Generic assignments, strain histories and properties of pure cultures of Cyanobacteria. J. Gen. Microbiol. 111, 1–61. Schwenzfeier, A., Wierenga, P.A., Gruppen, H., 2011. Isolation and characterization of soluble protein from the green microalgae Tetraselmis sp. Bioresour. Technol. 102 (19), 9121–9127. Scott, S.A., Davey, M.P., Dennis, J.S., Horst, I., Howe, C.J., Lea-Smith, D.J., Smith, A.G., 2010. Biodiesel from algae: challenges and prospects. Curr. Opin. Biotechnol. 21, 277–286. Shen, Y., Yuan, W., Pei, Z., Mao, E., 2008. Culture of microalga Botryococcus in livestock wastewater. Trans. ASABE. 51 (4), 1395–1400. Soletto, D., Binaghi, L., Ferrari, L., Lodi, A., Carvalho, J.C.M., Zilli, M., Converti, A., 2008. Effects of carbon dioxide feeding rate and light intensity on the fed-batch pulse feeding cultivation of Spirulina platensis in helical photobioreactor. Biochem. Eng. J. 39 (2), 369–375. Subhadra, B.G., 2011. Water management policies for the algal biofuel sector in the Southwestern United States. Appl. Energ. 88 (10), 3492–3498. U.S. DOE, 2010. National algal biofuels technology roadmap. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Biomass Program. Wahidin, S., Idris, A., Shaleh, S.R.M., 2013. The influence of light intensity and photoperiod on the growth and lipid content of microalgae Nannochloropsis sp. Bioresour. Technol. 129, 7–11. Wijffels, R.H., Barbosa, M.J., 2010. An outlook on microalgal biofuels. Science 329, 796–799. Xenopoulos, M.A., Frost, P.C., Elser, J.J., 2002. Joint effects of UV radiation and phosphours supply on algal growth rate and elemental composition. Ecology 83 (2), 423–435. Yoshida, N., Ikeda, R., Okuno, T., 2006. Identification and characterization of heavy metal-resistant unicellular alga isolated from soil and its potential for phytoremediation. Bioresour. Technol. 97 (15), 1843–1849.