optimization of growth operational conditions for co2 biofixation by native synechocystis sp

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Research ArticleReceived: 17 June 2010 Revised: 29 October 2010 Accepted: 29 November 2010 Published online in Wiley Online Library: 11 January 2011

(wileyonlinelibrary.com) DOI 10.1002/jctb.2568

Optimization of growth operational conditionsfor CO2 biofixation by native Synechocystis sp.Lorena Martınez, Vanesa Redondas, Ana-Isabel Garcıa and Antonio Moran∗

Abstract

BACKGROUND: A fundamental step in assessing the viability of a CO2 biofixation system based on microalgae is to identifythe maximum CO2 biofixation yield that can be achieved for this microorganism when it is cultivated under optimumoperational growth conditions. Response surface methodology was applied to determine optimum culture conditions for CO2biofixation by a recently isolated freshwater cyanobacterium Synechocystis sp. The strain was cultivated in a 1 L bubble columnphotobioreactor, in semicontinuous mode.

RESULTS: Statistical analysis showed that temperature (from 22 to 39 ◦C), pH (from 7.2 to 8.8) and light intensity (from 928to 2272 µE m−2 s−1), in addition to some of their interactions, had a significant effect on CO2 biofixation. An optimum CO2biofixation rate of 2.07 gCO2 L−1culture day−1 was found within the experimental region, at an average light intensity 686 µEm−2 s−1, pH 7.2 and temperature 35.3 ◦C.

CONCLUSIONS: Based on these results, it is concluded that Synechocystis sp. presents a good tolerance to both high temperatureand light intensity, characteristics which facilitate its application in outdoor CO2 biofixation systems.c© 2011 Society of Chemical Industry

Keywords: photosynthetic CO2 biofixation; optimum operational culture conditions; cyanobacteria; Synechocystis; response surfacemethodology

INTRODUCTIONThe recent rise in atmospheric concentrations of CO2 is considereda worldwide environmental and economic problem. One ofthe present technologies for overcoming this problem is CO2

biofixation with microalgae. A microalgae biofixation system isrecommended, in particular, for CO2 reduction from flue gases,a reduction system which should be placed outdoors and nextto emission sources. This kind of system for CO2 reduction isboth natural and environment friendly. In addition, it offers afree inorganic carbon source for microalgae growth. A furtheradvantage of a microalgae CO2 biofixation system is that itcan use a free source of inorganic nutrients resulting fromwastewater remediation. The two conditions, a free source ofnutrients combined with ecological wastewater treatment, arenecessary to improve the economic feasibility of the process.Obviously, biofixation does not resolve long term storage of CO2

and it does not mitigate CO2 in the atmosphere. The key to abiofixation system with microalgae is to transform this microalgaebiomass into renewable energy, thus making a significant impacton reduction of CO2 emissions.

The first step in determining the viability of a CO2 biofixationsystem based on microalgae is to identify the maximum possibleyield for the microorganism, i.e. the maximum CO2 biofixation thatcan be achieved by that microorganism when it is cultivated underoptimum growth conditions. It is first necessary to ensure thatthe CO2 and inorganic nutrients required for photoautotrophicmicroorganism culture are always available in sufficient quantityin the culture medium. Then, optimization of CO2 biofixation canproceed by operating under optimal conditions as regards other

growth factors. The most important of these are light availability,pH and temperature.1 The optimum light that a photosyntheticmicroorganism receives, its growth temperature and its toleranceto these factors, depend on physiological characteristics which arespecific to each organism and affected by environmental factorsin their natural habitat.2 Temperature has a strong influence onthe thermodynamic coefficients of several biosynthetic reactions.As a result, the temperature/growth rate ratio increases untilan optimum value is achieved, and then it slows down.3 Lightis the source of energy for photosynthetic organisms and itshould be provided according to the P/I curve (photosyntheticactivity/light intensity) specific to each species.4 As regards pH,this affects the inorganic carbon equilibrium, i.e. the proportion ofCO2/HCO−

2/H2CO3 in a culture medium as well as the oxidationstate of some inorganic nutrients and micronutrients. Therefore,each phototrophic microorganism needs a specific pH level atwhich its growth rate will be optimal, and this will depend onthe chemical species they assimilate most easily. When growthconditions are optimal, the growth rate will be optimum.4,5 Itis very important to process development to understand theoptimal culture conditions, particularly the chemical and physicalparameters, due to the impact of these on the economics andapplicability of the process.

∗ Correspondence to: Antonio Moran, Natural Resources Institute, University ofLeon, Avda Portugal, 41, 24071, Leon, Spain. E-mail: [email protected]

Natural Resources Institute, University of Leon, Avda Portugal, 41, 24071, Leon,Spain

J Chem Technol Biotechnol 2011; 86: 681–690 www.soci.org c© 2011 Society of Chemical Industry

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www.soci.org L Martınez et al.

Traditional process optimization has been accomplished fol-lowing one-dimensional search methods, successively varyingone variable at a time. However, the application of such meth-ods for optimization of variables is not only time-consuming, butcan also lead to drawing erroneous conclusions from the resultssince, among other reasons, interactions between factors can bemissed.6 Statistical experimental designs minimize the errors pro-duced when determining the effects of each factor, by facilitatingefficient, simultaneous and systematic variation of all factors. Afterdetermining the relationships between factors and estimated re-sponses, statistical optimization enables identification of optimalconditions for a process.7 One of the most frequently applied toolsfor statistical optimization is response surface methodology (RSM).

Our experimental design was focused on a selected experi-mental region, where previous evidence had indicated a possibleoptimum point.8 Synechocystis sp. is a native strain that has recentlybeen isolated and has only been identified at genus level, thus littleis known of its behaviour at different available light intensities,pHs and temperatures. The choice of a native species rather thanother, better known, species was based on the research projectspecifications, aimed at investigating local resources.9 Previousstudies have reported maximum growth rates and CO2 biofixationat irradiated light intensities of 1200 to 2400 µE m−2 s−1 for batchcultures of this cyanobacterium.10 However, the experimental re-gion limits for pH and temperature are unknown. Therefore, inorder to select the range for these two factors, we conducted aliterature search, and found that maximal growth rates for non-extreme environment microalgae and cyanobacteria lie within thetemperature range 25 to 35 ◦C11 – 15 and pH range 7.5 to 8.5.5,16,17

The aim of the present study was to find optimal conditions ofthe cell culture factors pH, temperature and available light in orderto maximize CO2 biofixation by Synechocystis sp. autotrophiccultures, using statistical tools as experimental design andresponse surface methodology.

EXPERIMENTALOrganism and culture mediumThe strain studied in this research is a native cyanobacteriumSynechocystis sp., isolated and identified at genus level atthe Natural Resources Institute of Leon, Spain.18 This nativeSynechocystis sp. strain was considered a good CO2 biofixationorganism, due to its specific growth rate, as high as 0.073 h−1, andits good light utilization efficiency.9

Semicontinuous culture method for CO2 biofixationdeterminationContinuous cultivation is the most stable and reliable means ofdetermining CO2 photoautotrophic biofixation as it comprises all

the growth stages of the microorganisms, from the lag phaseto the stabilization phase, permitting observation of lag phaseduration, the point at which highest growth rates occur, theproduction/excretion of metabolites, etc. However, this kind ofculture presents two constraints for the purpose of the presentstudy. First, the light available to cells changes continuouslyas biomass increases and an independent culture must be runin order to obtain a single result, which can take from 6 to10 days (Fig. 1). Furthermore, two further continuous cultures arerequired in order to provide replicates for statistical analysis.In contrast, by carrying out the experiment in semicontinuousmode, once steady state has been achieved each growth slopeobserved within the dilution period represents a replicate sinceit has been developed under the same conditions, includingavailable light. The second constraint of a continuous culture isthat the uptake of CO2 is different along the growth curve. Inthis study, only the highest growth rate phase is of interest, sinceoptimum CO2 biofixation is achieved with young cyanobacteriagrowing exponentially. By diluting the culture, nutrients andlight were replenished, facilitating cell growth and divisionand so producing a young cell culture. Based on the previousstatements, semicontinuous mode was selected to accomplishthis study.

The inoculum of Synechocystis sp. was grown under laboratoryconditions, in a 100 mL bubbled conical flask, with controlledinjection of 5%-in-air CO2, 16/8 photoperiod and on Mann andMyers culture medium.19 In all runs, this inoculum was added tothe photobioreactor at an initial concentration of 0.5±0.1 biomassdry weight L−1 culture.

All cultures carried out for this study were run in a semi-continuous mode for 7 days by daily dilution of the volumeof culture necessary to maintain the initial biomass concentra-tion of 0.5 g L−1. The dilution was prepared by replacing thesame volume of biomass suspension with sterile Mann and Myersmedium. In these experimental semicontinuous cultures, follow-ing daily dilution cells started growing from the same biomassconcentration, with the same light availability. By cellular divi-sion, biomass concentration should increase as light availabilityis reduced, due to the self-shading effect.20,21 After a growthslope of 24 h, the culture was diluted to the initial biomassconcentration of 0.5 g L−1. If all other culture conditions remainconstant, cells should present the same pattern of growth andlight reduction each day. When a repeated growth and light re-duction pattern had been observed on consecutive days, a steadystate was considered to have been reached. In this study, oncesteady state was observed, samples were taken on the following3 days, and these were considered three pseudo-replicates of theexperiment.

Num

ber

of c

ells

Time of culture Time of culture

Lag phase

Exponential phase

Stationary phase

Exponential phase

CONTINUOUS CUTURE SEMI-CONTINUOUS CUTURE

Num

ber

of c

ells

1 growth 6-10 day scurve = 1 replicate 1 day growth period = 1 replicate

Figure 1. Continuous and semi-continuous culture growth.

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Optimization of CO2 biofixation by native Synechocystis sp. www.soci.org

Figure 2. Diagram of the photobioreactor and auxiliary systems designed for determining the influence of different light intensities on Synechocystis spgrowth rate.

Culture system and culture conditionsSynechocystis sp. were cultured in a bubble column photobioreac-tor made of glass, with 1 L working volume (300 mm high, 80 mmdiameter). Figure 2 shows a schematic diagram of the photobiore-actor and auxiliary elements providing continuous aeration, pHcontrol and controlled addition of CO2. Air was bubbled continu-ously at a rate of 0.4 L min−1 L−1. Pure CO2 was provided from acommercial cylinder and mixed with air for a final concentrationof 10% CO2 in air, which was supplied to the photobioreactoron demand. CO2 injection ‘on demand’ means that the CO2 elec-tronic control valve opened whenever the pH meter detected adrop in pH, normally caused by uptake of dissolved inorganiccarbon by cyanobacterial cells. CO2 then flowed to the gas mixerand entered the reactor in a 10% concentration in air. As CO2

was dissolved in the culture medium, the pH started to rise untilthe set value was again reached, and the CO2 valve closed. Thisoperational mode enabled simultaneous control of pH and theinorganic source. The photobioreactor surface was illuminatedby means of a set of fluorescent lamps (33 W, 2150 lumen), in-stalled on a vertical light reflector called the illumination unit.Each incident light intensity on the photobioreactor surface, Iext ,was attained by adjusting distance and number of illuminationunits.

Synechocystis sp. cultures were illuminated following a 16/8photoperiod and the photobioreactor was installed in a thermo-static chamber. Temperature control was attained by changingthe set point of the chamber and by placing air fans aroundthe photobioreactor when needed. During dark hours, reac-tor temperature equalled chamber set point, between 20 and25 ◦C, depending on the experiment. The influence of temper-ature on respiration rates during dark hours was considerednegligible.

Homogeneous culture samples were collected every day and atthe same time, light intensity at the centre of the photobioreactor,Iint , was measured using a spherical radiometer (QuantumScalar Laboratory PAR Irradiance Sensor QSL-2100, BiosphericalInstruments, EEUU).

Analytical methodsMonitoring of cultures under different conditions of temperature,incident light intensity and pH, was conducted by daily measure-ment of biomass concentration (Cb), total organic carbon (TOC)and average light intensity at the centre of the reactor (Iav). Eachmeasurement was carried out in triplicate. Biomass concentrationwas determined as dry weight by filtering 10 mL of culture througha 0.45 µm Whatman filter, and washing with 20 mL 0.5 N HCl todissolve precipitated salts. The filtrate was dried at 105 ◦C for 24 h.For TOC measurements,22 1 mL 0.1 HCl was added to 10 mL ofculture sample to bring pH down 4, ensuring that all dissolvedinorganic carbon was converted into CO2. After that, samples weresparged with O2 for 2 min in order to desorb CO2 and measured byan automatic infrared analyzer TOC-5000 (Simadzu Corporation,Japan).

Daily CO2 biofixation (F) was determined from measuredTOC,23,24 as shown in Equation (1):

Fi = TOCi − TOCi−1

(ti − ti−1)· 44

12(1)

where TOCi is the total organic carbon concentration measured attime ti , in gC L−1 culture, and ti is the time when the daily samplewas measured, in h.

Average light intensity inside the reactor, Iav , was determinedas described by Molina-Grima et al.:25

Iav = Iext

Ka · p · Cb· [1 − exp(−Ka · p · Cb)] (2)

where Iav is the average light intensity available to cells, µEm−2 s−1, p is the length of light path inside the culture where Iav

was determined, m, Cb is biomass concentration, in g dry weightL−1, and Ka is the extinction coefficient, kg m−2.

The extinction coefficient, which depends mainly on biomassconcentration, was calculated for Synechocystis sp. when it wascultured in the tubular photobioreactor as described above (Fig. 2),following Acien-Fernandez and coworkers.20

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During culture, the inorganic carbon taken up by the cyanobac-teria comes from the CO2 bubbled in the culture medium. Sincethese were semicontinuous cultures, CO2 biofixation was mea-sured as the organic carbon that cells accumulated between dailyinitial status (Cb = 0.5 g L−1) and the final status reached after agrowth period of 24 h, where the status was influenced by cul-ture variables pH, temperature and incident light intensity. Duringcarbon dioxide uptake, i.e. as biomass concentration increased,the average light intensity available to cells, Iav , changed from themaximum value at initial status, time i –1, to the minimum valueat final status, time i. Thus, average light intensity during eachgrowth slope (Iav,s,i) related to fixed CO2 during this growth slope(Fi) was determined as:

Iav,s,i = Iav,i−1 + Iav,i

2(3)

Experimental design and response surface methodologyResponse surface methodology (RSM) comprises a set of math-ematical and statistical techniques useful for improving andoptimizing a response of interest, by identifying the optimal set offactors or independent variables affecting the response. In recentyears, RSM has been proposed as a cost effective tool for the de-sign and development of new products and processes, reducingtime and materials in experimentation.26,27 Once the indepen-dent variables influencing the response under study have beenidentified, optimization of the response comprises the followingsteps:28 (1) designing a set of experiments that will yield ade-quate measurements of the response of interest; (2) determininga mathematical model that best predicts the data obtained fromthe design chosen in (1); and (3) finding the optimal settings thatwill lead to the maximum value of the response.

The design of the experiment and the order of the mathematicalmodel should be selected at the same time. If curvature of theresponse surface is suspected, this indicates that there is evidenceof a response increment as a variable is increased to a maximumvalue and after that, the response starts to decline. Thus, the modelshould be at least of second order.29 As explained earlier, optimumgrowth and CO2 biofixation for a photosynthetic microorganismwill be found at specific temperature, pH and irradiance values.When operational growth conditions depart from their optimumvalues, CO2 biofixation will fall. This suggests that a second-orderdesign and model is a minimum requirement. A second-ordermodel represents an easy approach because it has only onestationary point.26 If statistical analysis reveals that the second-order model is not suitable to explain the observed response, ahigher order model should be tried.

It should be noted that a second-order model also presents amajor constraint: to conclude the study, the optimal point mustbe found. However, it is frequently difficult to limit a successfulexperimental region, especially with recent isolates. In this case,

a second-order model was extremely useful since it indicatedthe correct search direction, i.e. where to move the experimentalregion in order to find the optimal point.

In this study, optimum values of culture conditions pH,temperature and irradiance were sought in order to locateSynechocystis sp. optimum CO2 biofixation by means of a23 second-order central composite design (CCD).8 This designconsisted of 17 experimental runs, in which the level of cultureconditions (natural variables) were fixed as specified in Table 1.Corresponding levels of coded variables8 are also presented(Table 2).

Response data observed in the 17 runs were fitted to a second-order polynomial equation by multiple regression procedure. Thesecond-order model equation for three variables is:

Y = β0 + β1X1 + β2X2 + β3X3 + β12X1X2 + β13X1X3 + β23X2X3

+ β11X12 + β22X2

2 + β33X32 (4)

where Xi represents the independent variables which affect theresponse Y , Y is the predicted response, β0 is the independentcoefficient (constant), βi are the coefficients of the first-order termsdue to direct effects, and βij are the coefficients for squared termsdue to interactions between variables.

To evaluate to what extent the model Equation (4) describes theeffect of growth parameters (available light intensity, temperatureand pH) on the experimental response (CO2 biofixation), an F-test should be run30 to determine how the response is affectedby varying the level of the factors. A non-statistically significantmodel cannot be used. The quality of fit for a statistically significantmodel is defined by the multiple relation coefficient R. When themodel contains a number of terms, the coefficient Radj is designedto determine the fit of a complex model.8 However, it shouldbe noted that any model with acceptable statistical parametersis valid if it describes the logical behaviour of the system understudy.

Regression analysis and a least squares method were used todetermine coefficients for Equation (4), their standard error and itssignificance.

By solving the experimental values of variables Xi , thecoordinates of the stationary point Xs = (Xs1, Xs2, Xs3) can befound. The sign of the eigenvalues of the matrix containing theestimated coefficients for second-order terms determines whetherthe stationary point is a maximum, minimum, or saddle point. Ifthe stationary point is a maximum, the optimization procedure iscompleted. In the case of the stationary point being a saddle point,the maximum is located beyond the experimental region. Thus,it cannot be found with this design and the experimental regionmust be moved according the direction given by the second-ordermodel. However, if one still wishes to find the optimum responsewithin the limits of the region, a ridge analysis method must befollowed in order to determine the best operability point.31

Table 1. Natural variables, coded variables and levels for the central composite experimental design

Level

Natural independent variable Coded variable −1.68 −1 0 +1 +1.68

External light intensity, Iext (µE m−2 s−1) X1 928 1200 1600 2000 2272

pH X2 7.16 7.50 8 8,50 8.84

Temperature (◦C) X3 21.6 25 30 35 38.4


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Table 2. Matrix for the 23 central composite design for the study of CO2 biofixation, in coded and actual levels of independent variables

Actual level of natural variables Coded level of variables

Run Iext µE m−2 s−1 pH Temperature ◦C X1 X2 X3

1 1200 7.50 25 −1 −1 −1 Factorial portion

2 2000 7.50 25 +1 −1 −1

3 1200 7.50 35 −1 −1 +1

4 2000 7.50 35 +1 −1 +1

5 1200 8.50 25 −1 +1 −1

6 2000 8.50 25 +1 +1 −1

7 1200 8.50 35 −1 +1 +1

8 2000 8.50 35 +1 +1 +1

9 1600 8 30 0 0 0 Central portion

10 1600 8 30 0 0 0

11 1600 8 30 0 0 0

12 2272 8 30 +1.68 0 0 Axial portion

13 1600 8 21.6 0 0 −1.68

14 1600 8.84 30 0 +1.68 0

15 928 8 30 −1.68 0 0

16 1600 8 38.4 0 0 +1.68

17 1600 7.16 30 0 −1.68 0

Statistical analysisMultiple regression analysis of experimental data sets for theresponse variable was performed using Origin 6.1 software(Northampton, USA). Three-dimensional plots and iterative pro-cedures for ridge analysis were executed by means of Matlab R2008a software (Natik, USA).

RESULTS AND DISCUSSIONEffects of average light intensity, pH and temperature on CO2biofixationFigure 3 gives one example of the 17 semicontinuous cultures(experimental run number 4), performed for CO2 optimizationversus light intensity, pH and temperature. As can be seen in Fig. 3,the first 4 days comprised the acclimatization stage. During thistime, reactor volume was washed at least twice, depending on thedilution rate imposed by the initial biomass concentration, fixedat 0.5 g L−1. After the acclimatization period, similar daily growthslopes can be observed. From this point onwards, it was possibleto assume that the cells had acclimatized to the imposed cultureconditions (pH, temperature and available light intensity) set foreach experimental run. The CO2 biofixation achieved during each1 day growth slope represented a pseudo-replicate for this run.Three replicates were carried out for each run. The results depictedin Table 3 are average CO2 biofixation and standard deviationcalculated from the three pseudo-replicates for each run.

As observed in Fig. 3, biomass concentration changed overthe 1 day period, from an initial value of 0.5 g L−1 to a finalvalue of approximately 1.5 g L−1. For each run, the value forfinal biomass concentration reached after a 1 day growth periodvaried, as it depended on the different culture conditions ofthe treatment or experimental run. However, it varied within anarrow range, from 0.90 to 1.60 g L−1. This indicates that underconstant external irradiance, Iext , increasing biomass concentrationover a 1 day period promoted a change in light intensity insidethe phototobioreactor. Consequently, average light intensity,

Iav , varied from the highest value, just after dilution, to thelowest value, after a 1 day period. These variations for eachday of culture can be observed in Fig. 3. At constant externalirradiance, average light intensity depends mainly on biomassconcentration (Equation (2)). As biomass concentration varieddaily in a similar way for all experimental runs, all of them wereequally attenuated and therefore, Iav increased or decreased atthe same rate as Iext for each treatment. As a result, the effectof Iav on growth and CO2 biofixation was determined by Iext . Itwas for this reason that the optimization study was conductedusing external irradiance as the independent variable. An analysisof the influence of Iav on CO2 biofixation is described later in thisarticle.

Table 3 shows CO2 biofixation obtained for the 17 runs. The besttreatment corresponded to run number 4, where a CO2 biofixationof 1.90 ± 0.9 g L−1 day−1. was found at 2000 µE m−2 s−1, pH 7.5and 35 ◦C. The results given in Table 3 indicate that CO2 biofixationincreased with temperature at a given level of irradiance and pH,as can be seen by comparing runs 5 and 7, where a temperaturerise from 25 to 35 ◦C produced a 31% increase in CO2 biofixation.This can also be observed in experiments 2 and 4, where the sameincrement in temperature increased response by 81%. However,CO2 biofixation at high pH (8.5) and low temperatures (25 ◦C)indicated some kind of inhibition as irradiance increased, an effectobserved by comparing experiments 5 and 6. In general, the bestresults for CO2 biofixation were found at the highest temperaturesand irradiances. As regards pH, CO2 biofixation was better at lowpH combined with high irradiance.

The multiple regression analysis of CO2 biofixation results, sum-marized in Table 3, determined linear and quadratic coefficients,which define the following second-order response polynomialequation, in coded variables:

Y = −1.226 + 0.116X1 − 0.0632X2 + 0.315X3

− 0.137X1X2 + 0.062X1X3 − 0.075X2X3 − 0.091X12

+ 0.015X22 + 0.021X3

2 (5)


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Run n° 4

0.00

1.00

2.00

3.00

4.00

0 1 2 3 4 5 6 7 8

time (d)

Cb

(g

/l) /

F (

g/l/

d)

0

200

400

600

800

1000

Iav

(mic

roE

/m2 /

s)

Cb CO2 biofixation Iav

Steady state: pseudo-replicatesAcclimatization stage

Figure 3. Semicontinuous culture carried out under culture conditions specified for run 4, showing biomass concentration, average light intensity andCO2 biofixation. Biomass concentration (Cb) in g L−1 and CO2 biofixation (F) in g L−1 day−1.

Table 3. CO2 biofixation obtained for each experimental run. Average and standard deviations are calculated from three pseudo-replicates

Natural variables Coded variables Response

Run Iext µE m−2 s−1 pHTemperature

◦C X1 X2 X3

Observed CO2biofixation (g L−1 day−1)

Predicted CO2biofixation (g L−1 day−1)

1 1200 7.50 25 −1 −1 −1 0.9 ± 0.13 0.65

2 2000 7.50 25 +1 −1 −1 1.05 ± 0.04 1.04

3 1200 7.50 35 −1 −1 +1 1.32 ± 0.29 1.31

4 2000 7.50 35 +1 −1 +1 1.90 ± 0.09 1.94

5 1200 8.50 25 −1 +1 −1 0.97 ± 0.15 0.95

6 2000 8.50 25 +1 +1 −1 0.75 ± 0.13 0.78

7 1200 8.50 35 −1 +1 +1 1.27 ± 0.16 1.31

8 2000 8.50 35 +1 +1 +1 1.43 ± 0.17 1.39

9 1600 8 30 0 0 0 1.22 ± 0.16 1.23

10 1600 8 30 0 0 0 1.11 ± 0.15 1.23

11 1600 8 30 0 0 0 1.35 ± 0.10 1.23

12 2272 8 30 +1.68 0 0 1.16 ± 0.15 1.16

13 1600 8 21.6 0 0 −1.68 0.78 ± 0.15 0.76

14 1600 8.84 30 0 +1.68 0 1.15 ± 0.18 1.16

15 928 8 30 −1.68 0 0 0.80 ± 0.21 0.77

16 1600 8 38.4 0 0 +1.68 1.85 ± 0.18 1.82

17 1600 7.16 30 0 −1.68 0 1.41 ± 0.17 1.37

and when Equation (5) is transformed into natural variables, wehave:

Y = −11.418 + 0.007Iext − 0.909pH + 0.201T

− 6.84 × 10−4IextpH + 3.10 × 10−5IextT − 2.98 × 10−2pHT

− 6.16 × 10−7Iext2 + 0.060pH2 + 7.93 × 10−4T2 (6)

The analysis of variance (ANOVA) is presented in Table 4. Thestatistical significance of the model was evaluated using the F-test,which indicated that the model from Equation (5) was statisticallysignificant at a 95% confidence level, where a P-value of less than0.05 (P < 0.05) indicates that the model is significant. This resultconfirmed the hypothesis of a second-order model and it was notnecessary to look for a higher order model.

The fit of the model was measured by the coefficient of multipledetermination R2. With a value of 0.974 for R2 (see Table 5), themodel (Equation (5)) provided good prediction of the observedresponse. According to R2, 97.4% of response variability can beexplained by the effect of the model variables, and only 2.6% of the

Table 4. Analysis of variance for the model in Equation (5)

SourceDegrees of

freedomSum ofsquares

Meansquares F statistic P-value

Model 10 1.52738 0.16971 24.25613 0.00043a

Error 6 0.04198 0.007

Total 16 1.56936

a Statistically significant at 95%.

response was not explained by the model. The residuals plot forthe model and observed response values are presented in Fig. 4. Ahomogeneous distribution of residuals around the axis, showingno patterns or trends, is an indication of good model adequacy.26

The analysis of variance presented in Table 5 determined whichcoefficients of the model were statistically significant (P < 0.05)and therefore, affected the response: the lower the P value, thegreater the effect of the parameter on the response.8 Thus, it can


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Table 5. Coded coefficients and significance (P-values) determinedby parameter estimation from the fit of response surface model to thedata in Table 3

Variable Coefficients t statistic P-value

β0 1.22583 25.4232 <0.0001a

X1: Iext 0.11642 5.1411 0.0021a

X2: pH −0.0632 −2.7911 0.0315a

X3: T 0.31518 11.2657 <0.0001a

X1X2: IextpH −0.13675 −4.6241 0.0036a

X1X3: IextT 0.062 2.0965 0.0809

X2X3: pHT −0.0745 −2.5192 0.0453a

X12: IextIext −0.09141 −3.5850 0.0116a

X22: pHpH 0.01488 0.5837 0.5807

X32: TT 0.02138 0.6902 0.5159

R2 0.974

R2adj 0.933

a Statistically significant at 95%.

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.40 0.80 1.20 1.60 2.00

observations (g/l/d)

resi

dual

s

Figure 4. Plot of residuals for the estimated model in Equation (5).

be seen that all linear coefficients were statistically significant. Thesign of lineal coefficients indicates that an increase in irradianceand/or temperature increased the value of the response. A contraryeffect was caused by lineal variation of pH. These results agreewith previous observations recorded for experimental data. Theanalysis also demonstrated the existence of interactions amongthe three independent variables: the coefficients for interactionsIext –pH, pH–T , and Iext – Iext were statistically significant at a 95%confidence level. The interaction Iext −Iext was not a true interactionbetween factors, but rather indicated that a change in Iext had aquadratic effect on the response. P-values for linear and quadraticcoefficients indicated that temperature, external irradiance andthe interaction between Iext and pH were the variables which mostaffected the response, i.e. CO2 biofixation by Synechocystis sp.The statistical analysis enabled us to confirm that CO2 biofixationdepends on culture conditions such as light intensity availablefor cells, pH and temperature. It should be noted that pH alonedid not influence Synechocystis sp. growth as much as when itwas combined with irradiance. Coefficients for pH, Iext and theirinteraction Iext –pH confirmed that low pH and high irradianceimproved CO2 biofixation, as can be observed from the resultspresented in Table 3. A P-value close to 0.05 determined for thepH–T interaction indicated that this polynomial term modifiedCO2 biofixation in a lesser degree than other significant variablesand interactions.

It is very interesting to observe that there was no interactionbetween light intensity available to cells, and temperature(P = 0.0809 > 0.05). Several published studies have reported

a combined effect of light intensity and temperature onphotosynthetic microorganism growth. Some authors have statedthat at low temperatures, a high irradiance level can leadto a level of photoinhibition, which would not occur athigher temperatures.2,32 This was demonstrated by Ugwu andcoworkers,33 who came to the conclusion that when lightintensity was at its optimum value, temperature changes producedan adverse effect on Chlorella sorokiniana growth. Sanchezet al.,5 observed that at high irradiances, Scenedesmus almeriensisproductivity dropped drastically from 33 ◦C onwards. In contrast,at low irradiances, interaction between light intensity availableto cells and temperature was much less severe, as little changein productivity was found for the same range of temperatures.5

In the present study, the fact that no interaction was observedbetween light intensity available to cells, Iav , and temperaturedoes not imply that such an effect did not exist. It is possible thatit was not found in the experimental region covered here becausethe irradiances and temperatures used were not sufficiently high.In previous experiments, we have observed that when a lowbiomass concentration culture (0.20 g L−1) was irradiated withIext = 2200 µE m−2 s−1, a temperature rise up to 45 ◦C led to anirreversible drop in photosynthetic efficiency in only 5 h (results notshown). The combined effect of high irradiance and temperaturestress has already been observed for a Synechocystis sp. strain,PCC 6803, by Asadulghani and co-workers. In fact, temperaturesover 45 ◦C are thought to be lethal to the cyanobacterium strainSynechocystis sp. PCC 6803.34

Optimum average light intensity, pH and temperaturefor maximum CO2 biofixationThree-dimensional response surface plots are presented for twovariables, fixing the third variable at its central level (Fig. 5). Theplots represent the second-order polynomial equation in naturalvariables (Equation (6)). The response surfaces show the importanteffect that temperature (Fig. 5(c)) and light intensity (Fig. 5(a))have on CO2 biofixation, especially at low pH levels, as discussedearlier. The plot presents a maximum CO2 biofixation versus lightintensity, which can be appreciated throughout the whole range oftemperatures and the apparent interaction between light intensityand temperature can also be observed (see Fig. 5(b)), as predictedby its P-value in Table 5. The effect of pH on response was not verycritical at low light intensities but became more important at highlight intensities (Fig. 5(a)).

The calculated stationary point and its eigenvalues (λ1 =−1.6321, λ2 = 1.2189 and λ3 = 1.3759) indicated the presenceof a saddle point in the experimental region. After following aniterative procedure, the predicted best operability point was found,located at Iext = 1807, µE m−2 s-1−1, pH=7.20 and T = 35.3 ◦C,where maximum CO2 biofixation of 2.01 g L−1 day−1 was attainedin the experimental region.

Response surface analysis gave the optimum temperaturefor the experimental region as 35.3 ◦C, a little higher than thenormal optimum temperatures for microalgae, indicating that theSynechocystis strain has good tolerance to high temperatures.This property makes it a suitable strain for outdoor cultures,where temperatures can be extremely high at noon. This resultagrees with those found by other authors, who characterizedSynechocystis PCC 6803 as a mesophilic cyanobacterium showingan optimal temperature of 32 ◦C, and able to grow at temperaturesbetween 15 and 45 ◦C. Beyond this temperature range, its growthrate suffered a severe drop.35 Moreover, according to Fig. 5(b) and5(c), it would seem that a further increase in the temperature


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Figure 5. Three-dimensional response surface plots described by thefitted second-order model for Synecocystis sp. CO2 biofixation (F,gCO2 L−1 day−1) vs external light intensity (Iext , µE m−2 s−1), pH andtemperature (T , ◦C). (a) pH vs Iext and fixed temperature. (b) Temperaturevs Iext and fixed pH. (c) Temperature vs pH and fixed Iext .

limits beyond the experimental region continued to improveSynechocystis CO2 biofixation. Another Synechocystis, the aqualitis,has been cultivated in outdoor photobioreactors, maintaining theculture temperature at 40 ± 3 ◦C.36 It should also be rememberedthat the low temperature of our Synechocystis cultures led to animportant reduction in its CO2 biofixation rate. In these cases, theprovision of some kind of system for culture heating would berecommended. This culture requirement is easily solved since theapplication of a CO2 biofixation system with microalgae is aimed atlarge industrial plants, such as power plants, concrete production,etc., where residual heat is always available. Thus, a Synechocystissp. culture would imply the additional benefit of profitable use ofresidual energy.

As can be seen in the three-dimensional plots in Fig. 5(a) and4(b), high light intensity was necessary in order to improve theresponse. Figure 5(c) shows that optimum pH in the experimentalregion was strongly dependent on operating temperature. This

plot (Fig. 5(c)) also shows an inverse relationship between pH andtemperature: the highest CO2 biofixation values were located athigh temperatures and low pH. The response improvement at lowpH and high temperatures may reflect a preference of Synechocystissp. for a low carbon dioxide concentration in its culture medium,since the amount of dissolved CO2 at high temperatures and lowpHs is lower than at low temperatures and high pHs. The injectionof 10% CO2 in air selected for this study, at low temperatures andhigh pHs, might have created a concentration of dissolved carbondioxide which was too high for Synechocystis sp to grow well. Thissuggests that the parameter influencing response it is not pH, butrather dissolved inorganic carbon concentration, determined byalkalinity.

Results for optimum external irradiance in the experimentalregion should be evaluated as a function of average light intensity,Iav , which represents the true light intensity that cells receivedinside the reactor and which can be compared and applied toany kind of reactor and operation mode.25 Average light intensitywas calculated as described in Equation (2) for the initial and finalconcentration of the daily growth slope. Figure 5 represents thethree growth slopes for biomass concentration and average lightintensity, found during best operational culture conditions. Theaverage light intensity of growth slope, Iav,s , was calculated asdescribed in Equation (3) for each replicate. Thus, it was foundthat the best operational average light intensity required to reachoptimum CO2 biofixation by Synechocystis sp. in the experimentalregion studied was 686 ± 70 µE m−2 s−1, as a medium value.

Comparing our result for best operational light inten-sity – optimum light intensity in our experimental region – poseddifficulties as regards both the external light intensity parameterand the average light intensity parameter, for different reasons.As discussed earlier, the results of external light intensity, Iext , canonly be compared for the same strain, biomass concentration andreactor design. By contrast, average light intensity, which is thecomparable parameter, is difficult to find in studies of Synechocys-tis strains. When our result is compared with that reported forother species, the optimum average light intensity found herefor Synechocystis sp. was much higher: 391 µE m−2 s−1 for Sele-nastrum capricornutum,37 between 170 and 200 µE m−2 s−1 forHaematococcus pluvialis38 or between 200 and 300 µE m−2 s−1 forPhaeodactilum tricornutum,39 all of which are microalgae strains.From this it can be concluded that the cyanobacterium Syne-chocystis shows good tolerance to high light intensities, a featureensuring its robustness for application in outdoor cultures.

Verification of predicted maximum CO2 biofixation valueThe estimated maximum CO2 biofixation in the experimentalregion was verified by running an additional culture under thebest operational conditions as calculated by the ridge analysis:Iext = 1807 µE m−2 s−1, pH=7.20 and T = 35.3 ◦C. The threesteady state days, or three pseudo-replicates for this culture, areshown in Fig. 6, where a CO2 biofixation of 2.07±0.04 g L−1 day−1

was achieved. This experimental value is very close to the2.01 g L−1 day−1 that was estimated by the model. It confirmsthat the estimated operational point for maximum CO2 biofixationin the experimental region was correct and that the polynomialmodel explained the observed CO2 biofixation response as afunction of the independent variables studied.

Determination of the best operational conditions in theexperimental region improved CO2 biofixation by 9% over thosefound for best treatment (see Table 3). When the behaviour ofthe response surface plots (Fig. 5) was examined, it could be


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seen that the model predicted response values of approximately2.5 g L−1 day−1 at temperatures close to 40 ◦C and external lightintensities of 2500 µE m−2 s−1. Although the model was only validfor the experimental region studied, it nevertheless indicates thedirection in which to search in order to identify the absoluteoptimum, which will be located at higher temperatures andirradiances than those studied here.

CONCLUSIONSIn the present study, maximum CO2 biofixation by the recentlyisolated cyanobacterium Synechocystis sp. was studied as afunction of the primary culture conditions; available light intensity,pH and temperature. The combination of an experimental designwith response surface methodology has proved to be a practicaltool for determining the total effect (linear and combinedinteractions) of different culture conditions on Synechocystis sp.CO2 biofixation.

The response surface model predicted an optimum value forCO2 biofixation within the experimental region studied of 2.01 gof CO2 L−1 day−1, by setting culture conditions at an average lightintensity of 686 µE m−2 s−1, pH of 7.2 and temperature of 35.3 ◦C.The adequacy of the methodological procedure was confirmedfollowing additional and independent experimentation underoptimum conditions in the experimental region, where a CO2

biofixation value of 2.07 g L−1 day−1, very close to the optimumestimated by the model, was achieved.

The search direction suggested by the model in order tolocate the absolute optimum was to move the experimentalregion to higher temperatures and light intensities. Consequently,we can affirm that Synechocystis sp. shows good tolerance tohigh light intensities and irradiances. The combination of bothtolerances indicates that the recently isolated cyanobacteriumstrain Synechocystis sp. has excellent potential for outdoor CO2

biofixation culture systems.

ACKNOWLEDGEMENTSThis work was supported by IDOM International, Spain.

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optimization of growth operational conditions for co2 biofixation by native synechocystis sp

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