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Lipid productivity as a key characteristic for choosing algalspecies for biodiesel production
Melinda J. Griffiths & Susan T. L. Harrison
Received: 31 July 2008 /Revised and accepted: 12 November 2008 /Published online: 6 January 2009# Springer Science + Business Media B.V. 2008
Abstract Microalgae are a promising alternative sourceof lipid for biodiesel production. One of the mostimportant decisions is the choice of species to use. Highlipid productivity is a key desirable characteristic of aspecies for biodiesel production. This paper reviewsinformation available in the literature on microalgalgrowth rates, lipid content and lipid productivities for 55species of microalgae, including 17 Chlorophyta, 11Bacillariophyta and five Cyanobacteria as well as othertaxa. The data available in the literature are far fromcomplete and rigorous comparison across experimentscarried out under different conditions is not possible.However, the collated information provides a frameworkfor decision-making and a starting point for furtherinvestigation of species selection. Shortcomings in thecurrent dataset are highlighted. The importance of lipidproductivity as a selection parameter over lipid contentand growth rate individually is demonstrated.
Keywords Algal biodiesel . Lipid productivity .
Species selection
Introduction
Biodiesel is currently receiving much attention due to itspotential as a sustainable and environmentally friendly
alternative to petrodiesel. It is generally made by trans-esterification of vegetable oil, primarily from rapeseed,soybean, sunflower, or palm (Ma and Hanna 1999; Xu et al.2006). While biodiesel is a desirable product, the signifi-cant economic and environmental impact of using agricul-tural crops, especially food crops, as a feedstock forbiofuels raises crucial sustainability issues. For example,the increased demand for these crops will affect their pricein the food market, place additional demand on the oftenstrained agricultural system and impose a negative envi-ronmental impact through the additional energy require-ments and eutrophication caused by intensive agriculturalprocesses (Chisti 2008).
Microalgae are a promising alternative source ofvegetable oil. Due to their simple cellular structure, algaehave higher rates of biomass and oil production thanconventional crops (Becker 1994). Some species of algaeproduce large quantities of vegetable oil as a storageproduct, regularly achieving 50% to 60% dry weight aslipid (Sheehan et al. 1998). Hence, algae have been claimedto be up to 20 times more productive per unit area than thebest oil-seed crop (Chisti 2008). Other advantages of algaeare that they can be grown in marginal areas such as on aridland or potentially in the ocean. Many species toleratebrackish or saline waters (Tsukahara and Sawayama 2005).These reduce competition with food crops for agriculturalland and fresh water.
Production of biodiesel from algae is technically, but notyet economically, feasible (Chisti 2008). The majoreconomic bottleneck cited in the literature is algal produc-tivity, followed by labour and harvesting costs (Borowitzka1992). Laboratory yields are reportedly rarely reached inlarge-scale culture, due to issues such as contamination,evaporation, flooding and lack of control over temperatureand light provision in open ponds, as well as difficulties
J Appl Phycol (2009) 21:493–507DOI 10.1007/s10811-008-9392-7
This paper was presented at the 3rd Congress of the InternationalSociety for Applied Phycology, Galway.
M. J. Griffiths : S. T. L. Harrison (*)Centre for Bioprocess Engineering Research,University of Cape Town,Rondebosch,7701 Cape Town, South Africae-mail: [email protected]
with fouling limiting light intensity and oxygen build-up inclosed photobioreactors (Pulz 2001; Lee 2001). Harvestingunicellular algae from solution remains a major challengeand the dilute biomass produced further aggravates the needfor an integrated approach to minimising consumption ofwater and energy as well as downstream processing cost(Benemann et al. 1977).
The first step in developing an algal process is to choosethe algal species. Pulz and Gross (2004) observed that:“successful algal biotechnology mainly depends on choos-ing the right alga with relevant properties for specificculture conditions and products”. Rigorous selection ischallenging owing to the large number of microalgalspecies available, the limited characterisation of these algaeand their varying sets of characteristics. The AquaticSpecies Programme (Sheehan et al. 1998) focussed on theisolation of high lipid content algae for growth in openponds supplemented with CO2 from coal-fired powerstations. They isolated over 3,000 species and screenedseveral of these for lipid production, but a final recommen-dation of species is not provided. More recently, Rodolfi etal. (2008) screened species for high lipid productivity andfound the marine Nannochloropsis and Tetreselmis to beparticularly promising. Little consensus is reported betweenresearch groups on the algal species most suitable forbiodiesel production. Further, the number of strainscommonly exploited in algal biotechnology remains few(Grobbelaar 2000).
Developing a framework to guide choice of appropriatealgal species
A variety of desirable characteristics reported for large-scalealgal culture are summarised in Table 1. A single algalspecies is unlikely to excel in all categories, hence
prioritisation is required. Environmental conditions, avail-able resources and choice of culture system influencespecies choice. For example, the quality of the availablewater supply and the rate of evaporation expected deter-mine the salinity tolerance required of the algae. Somealgae are most productive at high temperatures and brightlight, while growth of others is retarded by full sunlight(Sheehan et al. 1998). Certain algal species cannot begrown reliably outdoors as they are quickly out-competedby faster growing algae. However, those with slowergrowth rates could potentially be maintained in a closedphotobioreactor to facilitate accumulation of higher lipidcontents.
Selection of fast-growing, productive strains, optimisedfor the local climatic conditions is of fundamental impor-tance to the success of any algal mass culture andparticularly for low-value products such as biodiesel. Fastgrowth encourages high biomass productivity. High bio-mass density increases yield per harvest volume anddecreases cost. High growth rate also reduces contamina-tion risk owing to out-competition of slower growers inplanktonic, continuous culture systems. A high content ofthe desired product increases the process yield coefficientand reduces the cost of extraction and purification per unitproduct (Borowitzka 1992). Choosing a species well suitedto the biorefinery approach, for example producing valu-able co-products such as fine chemicals, nutraceuticals or anutrient-rich biomass, contributes to both economic successand environmental sustainability.
An often-overlooked criteria when selecting species isease of harvesting. Harvesting of algal biomass is asignificant capital and operating cost in any algal process,hence it is desirable to select an alga with properties thatsimplify harvesting. Examples include large cell size, highspecific gravity compared to the medium and reliable
Table 1 Desirable characteristics of algae for mass culture
Characteristic Advantages Reference
Rapid growth rate Competitive advantage over competing species;reduces culture area required
Borowitzka (1992)
High product content Higher value of biomass. (Note: use of metabolic energy togenerate product usually leads to slower growth)
Borowitzka (1992)
Growth in extreme environment Reduces contamination and predation. (Note: Limited number of speciescan grow in extreme environments. Can be difficult to maintain conditions)
Borowitzka (1992)
Large cell size, colonial or filamentousmorphology
Reduces harvesting and downstream processing costs Borowitzka (1992)
Wide tolerance of environmentalconditions
Less control of culture conditions required. Growth over range ofseasons and ambient weather conditions
Borowitzka (1992)Grobbelaar (2000)
CO2 tolerance and uptake Greater potential for CO2 sequestration and use of waste CO2 Grobbelaar (2000)Tolerance of shear force Allows cheaper pumping and mixing methods to be used Borowitzka (1992)Tolerance of contaminants Potential growth in polluted water and on flue gases
containing high CO2, NOx and SOx
Zeiler et al. (1995)
No excretion of autoinhibitors Reduces autoinhibition of growth at high biomass densities Grobbelaar (2000)
494 J Appl Phycol (2009) 21:493–507
autoflocculation (Borowitzka 1997). These properties criti-cally influence the process economics for low value productsuntil such time as innovative, cost-effective methods ofharvesting dilute microalgal biomass consisting of cells lessthan 20 μm in diameter (too small for low-cost straining orfiltration (Benemann et al. 1977)) are developed.
An additional algal characteristic for biodiesel productionis the suitability of lipids for biodiesel in terms of the type andamount produced by an algal species, e.g. chain length, degreeof saturation and proportion of total lipid made up bytriglycerides. These influence the quality of biodiesel pro-duced. The majority of lipid-producing algal species have asimilar lipid profile, generally equivalent to vegetable oil fromland plants suitable for biodiesel production (Xu et al. 2006).Botryococcus braunii is a notable exception with extremelylong chain lengths (Banerjee et al. 2002). The proportion ofvarious lipid classes (particularly triglycerides) varies widelywith environmental conditions (Rodolfi et al. 2008), makingit difficult to compare algal species across experimentalconditions (Molina Grima et al. 1994).
Other characteristics critical to success of large-scaleproduction include resistance to contamination, as well astolerance to a wide range of culture parameters such astemperature and salinity. For the purposes of this review,insufficient quantitative information is available in theliterature to be able to compare more than a handful ofthe best-known species on these criteria. The only twocharacteristics relevant to biodiesel production that havebeen measured quantitatively for a wide variety of speciesare growth rate and lipid content.
In this paper, critical review of the information currentlyavailable in the open literature on the lipid productivity ofmicroalgal species is presented to facilitate decision-makingon species selection for biodiesel production. Further, thepaper seeks to highlight gaps in current knowledge.
Methods of data collation and analysis
Gathering data
Growth rates, lipid contents and lipid productivities weregathered from a broad range of literature, spanning a varietyof algal species used across a broad range of purposes.These include fuel production, calorific value as a food orfeed and production of specialty oils and chemicals.
A wide range of reactor configuration, design and scaleare reported under various conditions of nutrient supply,hence, the data gathered was sorted into the followingsubcategories:
& Culture method: laboratory, outdoor pond or outdoorphotobioreactor
& Metabolic mode: photoautotrophic, heterotrophic ormixotrophic
& Nutrient availability: nutrient replete, nitrogen deficientor silicon deficient
Nutrient deficiency, typically nitrogen or silicon defi-ciency, is well known to enhance the lipid content of algae.Lipid content has been reported under both nutrient-repleteand deficient growth conditions (Shifrin and Chisholm1981; Roessler 1990). Nutrient levels have been reduced bydiffering amounts across studies reported. For the purposesof this review, the following definitions are used:
& Nutrient replete: Stoichiometrically balanced nutrientconditions were assumed where no evidence of nutrientreduction or depletion in the medium was provided.
& Nutrient deficient: Specified nutrient was completelyremoved from the culture medium or reduced belowstoichiometric requirements for growth, either bychanging the medium, or maintaining a batch cultureuntil nutrient levels in the culture medium have beenshown to be severely depleted.
This review is restricted to photoautotrophic conditionsas the data for hetero- and mixotrophic conditions weresparse, using a very limited number of species.
All microalgal species currently grown on a large scale,or considered in the context of lipid or biodiesel productionwere initially recorded. The resultant list was refined byexcluding species where reliable data for biomass produc-tivity and lipid content could not be found (e.g. Biddulphia(Odontella) aurita, Chlorococcum, Emiliania huxleyi,Micractinium, Ochromonas danica, Ostreococcus tauri,Pseudokirchneriella subcapitata, Synechocystis aquatilis),and species where the algal lipids produced were unsuitablefor biodiesel (e.g. hydrocarbons produced by Botryococcusbraunii have a chain length of greater than C30 (Banerjee etal. 2002), while vegetable oils currently used for biodieselare mainly C16 and C18 (Harrington 1986)).
Data were collated at species level wherever available. Ina few cases, however, the references used described theorganisms to genus level only, e.g. Amphora (De la Pena2007; Sheehan et al. 1998) and Cylindrotheca (Chisti 2007;Sheehan et al. 1998).
Units of quantification
Typically, lipid content was reported as percentage dryweight (% dw). Where recorded in picograms lipid per cellin the absence of cell weight, it was discarded.
Biomass productivity was usually reported on a volu-metric basis in units of grams per litre per day and on abasis of surface area in units of grams per square metre perday, while growth rates were reported as doubling time (Td)
J Appl Phycol (2009) 21:493–507 495
or specific growth rate. The latter are inter-convertedaccording to Eq. 1.
Td ¼ lnð2Þm
ð1Þ
Biomass productivity in continuous culture is deter-mined as the product of specific growth rate (μ) andbiomass concentration (X). Ideally, for comparison purpo-ses, all biomass productivities should be reported instandard units (e.g. grams per litre per day), withinterconversion as follows:
QV ¼ QA D=1; 000 ¼ mX
where QV is volumetric productivity in grams per litreper day, QA is areal productivity in grams per metresquared per day, D is depth in metres, μ is specific growthrate per day and X is biomass concentration in grams perlitre.
Frequently insufficient information was provided forconversion. The two most common problems were:
& Data given as areal productivity in grams dry weight persquare metre per day without reporting depth of theculture vessel.
& Data presented as a specific growth rate in the absenceof biomass concentration.
Hence, the data in different units have been reportedseparately in Table 3.
Lipid productivity was generally reported in milligramsper litre per day. Where appropriate data were available,lipid productivity was calculated as the product of biomassproductivity and lipid content.
Species names
Algal taxonomy is evolving as molecular methods improve.This has resulted in algal species being re-classified,making collation of information confusing. Speciesreported by a variety of names in the literature aresummarised in Table 2, and their currently acceptedclassification specified.
Analysis of algal species using laboratory data
Data collected for 55 different species under laboratoryconditions are presented in Table 3. Lipid content (in % dw)is given for nutrient-replete, nitrogen (N)-deficient andsilicon (Si)-deficient conditions. Silicon deprivation is onlyrelevant for diatoms (Bacillariophyta and some Ochro-phyta). Biomass growth and productivities are given as Td,volumetric biomass productivity (grams per litre per day)and biomass productivity on the basis of area (grams persquare metre per day). Lipid productivity (milligrams perlitre per day) is presented in two columns:
& Average lipid productivity reported directly in theliterature
& Lipid productivity calculated from laboratory biomassproductivity in grams per litre per day and nutrient-replete lipid content
The overall average values for all species across eachcriterion are shown in the final row.
Lipid content
Lipid content data were readily available and consistentlyreported in the literature. Nutrient limitation, especially Nand Si, is well recognised to influence lipid content (Shifrinand Chisholm 1981; Sheehan et al. 1998). Figure 1 showsthe average laboratory lipid content for green algae(Chlorophyta) and blue-green algae (Cyanobacteria) undernutrient-replete and nitrogen-deficient conditions. Thenutrient-replete lipid content for green algae ranges from13% to 31% dw, with an average of 23%, while theCyanobacteria range is markedly lower, between 5% and13%, with an average of 8%. For green algae, nitrogendeprivation was reported to increase lipid content, with theexception of Chlorella sorokiniana which did not change.The average lipid content of 41% dw reported undernitrogen deprivation shows a twofold increase. In theCyanobacteria, only Oscillatoria showed an increase inlipid content with nitrogen deprivation.
Table 2 Current species names and their previous classification
Current name Previous names
Dunaliella salina (Dunal) Teodoresco Dunaliella bardawilEttlia oleoabundans (S. Chantanachat & H. C. Bold) J. Komárek Neochloris oleoabundansMonodopsis subterranea (J.B. Petersen) Hibberd Monodus subterraneusMonoraphidium minutum (Nägeli) Komárková-Legnerová Ankistrodesmus minutissimusMonoraphidium minutum (Nägeli) Komárková-Legnerová Selenastrum minutumPorphyridium purpureum (Bory de Saint-Vincent) K. Drew & Ross Porphyridium cruentumSelenastrum gracile Reinsch Ankistrodesmus gracilis
496 J Appl Phycol (2009) 21:493–507
Tab
le3
Average
labo
ratory
lipid
content,biom
assandlip
idprod
uctiv
ityfor55
microalgalspeciesandgenera
repo
rted
intheliterature
Species
Taxa
aMediab
Referencesc
Average
from
literature
Literature
Calculated
Nutrient
replete
Ndeficient
Sideficient
Nutrient
replete
Nutrient
replete
Nutrientreplete
Nutrient
replete
Nutrient
replete
%dw
%dw
%dw
Td(h)
gm
−2day−
1gL−1
day−
1mgL−1day−
1mgL−1
day−
1
Amphiprora
hyalina
BM
36;63
2228
3710
Amphora
BM
21;63
5120
4016
0
Anabaenacylin
drica
Cy
F5;
11;17
;31
;47
55
24
Ankistrod
esmus
falcatus
CF
6;59
;63
2432
832
0.46
109
Chaetoceros
calcitrans
OM
6140
0.04
1816
Chaetoceros
muelleri
OM
14;36
;45
;51
;58
;61
;63
;70
1927
3611
0.07
2213
Chlam
ydom
onas
applanata
CF
6518
33
Chlam
ydom
onas
reinha
rdtii
CF
5;67
216
Chlorella
emersonii
CF
329
6319
0.03
8
Chlorella
minutissima
CM
3331
5738
0.03
10
Chlorella
protothecoides
CF
2;33
;74
1323
40
Chlorella
pyrenoidosa
CF
5;11;38
;60
;63
;65
;67
;68
1664
7
Chlorella
sorokinian
aC
F8;
33;44
;60
;61
;72
1818
80.55
4597
Chlorella
vulgaris
CF
5;11;17
;33
;38
;39
;40
;55
;61
;65
;67
2542
1711
0.11
2628
Crypthecodinium
cohnii
DM
3;9;
1525
9
Cyclotella
cryptica
OM
36;63
;65
;70
1834
3813
Cylindrotheca
BM
15;63
2727
7
Dun
aliella
primolecta
Pr
S63
2314
9
Dun
aliella
salin
aPr
S1;
5;6;
47;48
;59
1910
11
Dunaliella
tertiolecta
Pr
S28
;35
;65
;66
1518
11
Ettlia
oleoab
unda
nsC
F26
;39
;63
3642
0.46
136
164
Euglena
gracilis
Eg
F5;
16;17
;18
;19
;64
2035
14
Hym
enom
onas
carterae
HM
56;65
2014
41
Isochrysisga
lbana
HM
6;15
;38
;54
;56
;58
;61
;63
2529
2112
0.16
3838
Monod
opsissubterranea
EF
11;17
;41
;57
;61
;65
2513
0.19
3048
Monoraphidium
minutum
CF
6322
528
Nanno
chloris
CM/F
6;15
;39
;58
;63
;65
2830
1232
0.23
7763
Nanno
chloropsis
EM
25;32
;47
;61
;63
;69
3141
290.27
5282
Nanno
chloropsissalin
aE
M50
;63
;65
2746
14
Naviculaacceptata
BF
15;36
;62
;63
3335
4610
Naviculapelliculosa
BF
17;20
;23
;65
2745
345
Naviculasaprop
hila
BF
36;63
2451
499
Nitzschiacommunis
BM
22;36
23
Nitzschiadissipata
BM
36;63
2846
479
Nitzschiafrustulum
BM
5926
Nitzschiapalea
BM
22;63
;65
4740
48
J Appl Phycol (2009) 21:493–507 497
Tab
le3
(con
tinued)
Species
Taxa
aMediab
Referencesc
Average
from
literature
Literature
Calculated
Nutrient
replete
Ndeficient
Sideficient
Nutrient
replete
Nutrient
replete
Nutrientreplete
Nutrient
replete
Nutrient
replete
%dw
%dw
%dw
Td(h)
gm
−2day−
1gL−1day−
1mgL−1
day−
1mgL−1day−
1
Oscillatoria
Cy
F11;17
;55
;63
713
7
Ourococcus
CF
46;65
2750
72
Pavlova
lutheri
HM
54;61
360.21
5075
Pavlova
salin
aH
M61
310.16
4949
Phaeodactylum
tricornu
tum
BM
13;15
;26
;38
;42
;47
2126
2520
0.34
4572
Porph
yridium
purpureum
RM
5;17
;37
;38
;54
;61
1111
0.23
3524
Prymnesium
parvum
HM
4;5
3018
Scenedesmus
dimorphus
CF
5;7;
49;59
2611
Scenedesmus
obliq
uus
CF
5;29
;30
;55
;65
;67
2142
660.12
25
Scenedesmus
quadricaud
aC
F61
180.19
3535
Selena
strum
gracile
CF
6521
28
Skeletonem
acostatum
OM
24;43
;52
;61
;65
1625
160.08
1713
Spirulinamaxima
Cy
S5;
38;71
;73
732
Spirulinaplatensis
Cy
S5;
27;38
;47
;55
1310
1425
Synechococcus
Cy
M5;
6311
975
Tetraselmissuecica
PM
15;42
;54
;61
;63
1726
3628
0.59
3299
Thalassiosira
pseudonana
OM
10;24
;43
;61
;65
1626
120.08
1713
Thalassiosira
weissflo
gii
OM
34;65
2224
14
Tribonem
aO
M11;12
;17
;54
1216
440.51
59
Average
2332
4119
220.23
5052
Blank
indicatesno
inform
ationavailable
aKey
totaxa:CChlorop
hyta,CyCyano
bacteria,D
Dinop
hyta,EEustig
matop
hyta,EgEug
leno
zoa,
HHaptoph
yta,
OOchroph
yta,
PrPrasino
phyta
bKey
tomedia:Ffresh,
Mmarine,
Ssalin
ecKey
toreferences:1Adam
(199
7);2Ahm
adandHellebu
st(199
0);3Apt
andBehrens
(199
9);4Baker
etal.(200
7);5Becker(199
4);6Ben-A
motzandTornabene
(198
5);7Benider
etal.
(200
1);8
BeudekerandTabita
(198
3);9
Bhaud
etal.(19
91);10
Bop
pandLettieri(200
7);11Burlew(195
3);1
2Butterw
icketal.(20
05);13
Ceron
-Garciaetal.(20
00);14
Chelf(199
0);1
5Chisti
(200
7);16
Colem
anet
al.(198
8);17
Collyer
andFog
g(195
4);18
Con
stantopo
ulos
andBloch
(196
7);19
Coo
k(196
6);20
Coo
mbs
etal.(196
7);21
Dela
Pena(200
7);22
Dem
psterand
Som
merfield(199
8);23
Exley
etal.(199
3);24
Fergu
sonet
al.(197
6);25
Fisheret
al.(199
6);26
Gatenby
etal.(200
3);27
Gok
sanet
al.(200
7);28
Goldm
anandPeavey(197
9);29
Grequ
ede
Moraisetal.(20
07);30
Grobb
elaar(200
0);3
1Haury
andSpiller(198
1);3
2HuandGao
(200
3);3
3Illm
anetal.(20
00);34
Ishida
etal.(20
00);35
Janssenetal.(20
01);36
Johansen
etal.(19
87);
37Lee
andBazin
(199
1);38
Lee
(200
1);39
Liet
al.(200
8);40
Liu
etal.(200
8);41
Luet
al.(200
1);42
Maddu
xandJones(196
4);43
Mansour
etal.(200
5);44
Matsukawaet
al.(200
0);45
McG
inniset
al.(199
7);46
McK
nigh
t(198
1);47
Moh
eimani(200
5);48
Moh
eimaniandBorow
itzka(20
06);49
Moo
re(197
5);50
Mou
renteet
al.(199
0);51
Nagle
andLem
ke(199
0);52
OstgaardandJensen(19
82);53
Parrish
andWangersky
(198
7);54
Patiletal.(20
07);55
Piorrecketal.(19
84);56
Price
etal.(19
98);57
Qiang
etal.(19
96);58
Reitanetal.(19
94);59
Renaudet
al.(199
4);60
Richardsonet
al.(196
9);61
Rod
olfiet
al.(200
8);62
Roessler(199
0);63
Sheehan
etal.(199
8);64
Shehata
andKem
pner
(197
7);65
Shifrin
andChisholm
(198
1);66
Siron
etal.
(198
9);67
Sorok
inandKrauss(196
1);68
Spo
ehrandMiln
er(194
9);69
Suenet
al.(198
7);70
Taguchi
etal.(198
7);71
Tom
aselliet
al.(199
7);72
Ugw
uet
al.(200
7);73
VieiraCosta
etal.
(200
2);74
Xuet
al.(200
6)
498 J Appl Phycol (2009) 21:493–507
The average laboratory lipid content for other taxa (includ-ing diatoms, golden algae, Dinophyta, Euglenoza, Haptophyta,Ochrophyta, Prasinophyta and Eustimatophyta) under nutrient-replete, nitrogen-deprived and silicon-deprived conditions aresummarised in Fig. 2. Nitrogen-replete lipid content rangesfrom 11% to 51% dw, with an average of 25%, similar to thatfor green algae. The response to nitrogen deprivation wasvaried. Seventeen of 24 species for which information wasavailable showed an increase in lipid content, with sevenshowing a decrease or no change. The average lipid contentwith nitrogen deprivation was 27%. On silicon deprivation,the average lipid content increased from 24% to 41% dw.
The effect of nutrient depletion is further demonstratedin Fig. 3, showing the shift in lipid contents with Ndeficiency for Chlorophyta (Fig. 3a) and other taxa(Fig. 3b). Most of the Chlorophyta have a lipid contentbetween 20% and 30% dw under N-sufficient conditions.Under N deprivation, a shift in lipid content to the right isclearly seen with resultant contents from 18% to 64%. Thediatoms and other taxa have a wider distribution under N-sufficient conditions. The varied response to nutrientdeficiency is demonstrated by the resultant bimodaldistribution, with one species dropping below 10% and anincrease in the number of species between 40% and 50%
0
10
20
30
40
50
60
70
80
90
100
Ankis
trodesm
us f
alc
atus
Chla
mydom
onas a
ppla
nata
Chla
mydom
onas r
ein
hardtii
Chlo
rella e
mersonii
Chlo
rella m
inutis
sim
a
Chlo
rella p
rotothecoid
es
Chlo
rella p
yrenoid
osa
Chlo
rella s
orokin
iana
Chlo
rella v
ulg
aris
Ettlia o
leoabundans
Monoraphid
ium
min
utum
Nannochlo
ris
Ourococcus
Scenedesm
us a
cum
inatus
Scenedesm
us d
imorphus
Scenedesm
us o
bliquus
Scenedesm
us q
uadric
auda
Anabaena c
ylindric
a
Oscilla
toria
Spirulina m
axim
a
Spirulina p
latensis
Synechococcus
Lip
id c
on
ten
t (%
dw
)
Nitrogen replete
Nitrogen deprived
Cyanobacteria
Fig. 1 Average laboratory lipidcontent under nutrient-replete(open circles) and nitrogen-de-prived (filled circles) conditionsfor Chlorophyta and Cyanobac-teria. Error bars show the min-imum and maximum recordedvalues for each species (solidlines nitrogen replete, dashedlines nitrogen deprived)
0
10
20
30
40
50
60
70
80
90
100
Am
phip
rora h
yalina
Am
phora
Chaetoceros c
alc
itrans
Chaetoceros m
uelleri
Crypthecodin
ium
cohnii
Cyclo
tella c
ryptic
a
Cylindrotheca
Dunaliella p
rim
ole
cta
Dunaliella s
alina
Dunaliella t
ertio
lecta
Eugle
na g
racilis
Hym
enom
onas c
arterae
Isochrysis
galb
ana
Monodopsis
subterranea
Nannochlo
ropsis
Nannochlo
ropsis
salina
Navic
ula
acceptata
Navic
ula
pellic
ulo
sa
Navic
ula
saprophila
Nitzschia
com
munis
Nitzschia
dis
sip
ata
Nitzschia
frustulu
m
Nitzschia
pale
a
Pavlo
va lutheri
Pavlo
va s
alina
Phaeodactylu
m t
ric
ornutum
Porphyrid
ium
purpureum
Prym
nesiu
m p
arvum
Skele
tonem
a c
ostatum
Tetraselm
is s
uecic
a
Thala
ssio
sira p
seudonana
Thala
ssio
sira w
eis
sflogii
Trib
onem
a
Lip
id c
on
ten
t (%
dw
)
Nitrogen replete
Nitrogen deficient
Silicon deficient
Fig. 2 Average laboratory lipidcontent under nutrient-replete,nitrogen-deprived and silicon-deprived conditions for Dino-phyta, Eustigmatophyta, Eugle-nozoa, Haptophyta, Ochrophytaand Prasinophyta. Error barsshow the minimum and maxi-mum recorded values for eachspecies (solid lines nitrogen re-plete and silicon deprived,dashed lines nitrogen deprived)
J Appl Phycol (2009) 21:493–507 499
dw lipid. Species grown under Si deficiency have lipidcontent between 30% and 50% dw.
Average nutrient-replete and nitrogen-deficient lipid con-tent, respectively are 22% and 36% for freshwater species and24% and 28% for marine and saline species. The greaternitrogen-deprived lipid content for freshwater species islargely because most green algae (which show a greaterresponse to nitrogen deprivation) are freshwater species.
Biomass productivity
Typical algal growth rates are reported in Figs. 4 and 5 as theaverage doubling time for each species. A high doublingtime corresponds to a low specific growth rate. The averagedoubling time for green algae is 24 h, corresponding to a μof 0.69 day−1. The average doubling time for Cyanobacteriais 17 h (μ=0.96 day−1), and for other taxa 18 h (μ=0.92 day−1). The average doubling time calculated for
the top 20% of Chlorophyta, Cyanobacteria and other taxawith respect to growth rate all lie in the range 7 to 8 h(μ=2.08–2.38 day−1).
When species are grouped according to culture environ-ment rather than taxa, no dominant trend in growth rates orlipid contents is seen. Average doubling time for freshwaterspecies is 20 h (μ=0.83 day−1) while that for marine orsaltwater species is 19 h (μ=0.88 day−1).
Lipid productivity
Average lipid productivities collected from literature (lit.)are summarised in Fig. 6 and supplemented with calculated(calc.) lipid productivity, where available. Lipid productiv-ity was calculated as the product of average nutrient-repletelipid content and biomass productivity in grams per litre perday. Productivities under nutrient-deprived conditions werenot included due to a lack of reported growth rates underthese conditions. The overall average lipid productivity forall species from literature was 50 mg L−1 day−1 comparedwith a calculated value of 52 mg L−1 day−1. No obvioustrend in lipid productivity with taxonomy or cultureenvironment (fresh versus marine) was noted.
From Fig. 6, three species stand out as having very highlipid productivities, above 100 mg L−1 day−1: Amphora(160 mg L−1 day−1 lit.), Ettlia oleoabundans (formerlyNeochloris oleoabundans) (136 lit. and 164 calc. mg L−1
day−1) and Ankistrodesmus falcatus (109 mg L−1 day−1 calc.).The lipid productivity for Amphora is the average of values
for three different strains, known as AMPHO27, AMPHO45and AMPHO46, collected by M. Sommerfield, 1986–1987(Sheehan et al. 1998). This may account for the wide spreadof recorded lipid productivities (63 to 345 mg L−1 day−1, asshown by the error bars in Fig. 6). These values, along withthose for the Cyanobacteria Synechococcus, which, rathersurprisingly given its low lipid content, has a relatively highlipid productivity (75 mg L−1 day−1), were measured by NileRed staining as triolein equivalents (Sheehan et al. 1998).According to Sheehan et al. (1998), a major problem withNile Red is that species vary widely in their ability to take upthis lipophilic dye. This limits the accuracy of these measure-ments. There has been no rigorous comparison of Nile Redstaining and lipid quantitation across species.
The high lipid productivity of Amphora is the product of ahigh reported lipid content (average 51% dw) and a modestgrowth rate (Td=20 h). The lipid productivities of E.oleoabundans and A. falcatus are a function of their highgrowth rates (0.46 g L−1 day−1) and lipid contents of 36%and 24% dw, respectively. The next most productive strainsare the marine Tetraselmis suecica (99 mg L−1 day−1) andthe freshwater C. sorokiniana (97 mg L−1 day−1), both withvery high growth rates (0.59 and 0.55 g L−1 day−1,respectively) and below average lipid content (17% and
(a)
0
2
4
6
8
10
12
14
0-10 10-20 20-30 30-40 40-50 50-60 60-70
0-10 10-20 20-30 30-40 40-50 50-60 60-70
Lipid content (% dw)
Nu
mb
er o
f sp
ecie
s N replete
N replete
N deficient
(b)
0
2
4
6
8
10
12
14
Lipid content (% dw)
Nu
mb
er o
f sp
ecie
s
N deficient
Si deficient
Fig. 3 Number of algal species in each lipid content category undernutrient-sufficient, nitrogen and silicon-deficient conditions. a Chloro-phyta, b Dinophyta, Euglenozoa, Haptophyta, Ochrophyta, Prasinophyta
500 J Appl Phycol (2009) 21:493–507
18% dw). These are followed by Nannochloropsis (82 mgL−1 day−1), Nannochloris (77 mg L−1 day−1), Pavlovalutheri (75 mg L−1 day−1) and Phaeodactylum tricornutum(72 mg L−1 day−1). These four species are reported to haveaverage to good growth rates (0.27, 0.23, 0.21 and 0.34 gL−1 day−1, respectively) and average or above lipid content(31%, 28%, 36% and 26% dw, respectively).
In Fig. 7, the impact of biomass productivity and lipidcontent on lipid productivity under nutrient-replete condi-tions is analysed through correlation. A general correlation isdemonstrated between lipid productivity and biomass pro-ductivity. All species with a high biomass productivity(above 0.4 g L−1 day−1) and most those above 0.2 g L−1
day−1 have a high lipid productivity, greater than 60 mg L−1
day−1. However, the top three biomass producers are not thetop lipid producers, indicating that lipid content is also afactor. Lipid content does not correlate directly with lipidproductivity, further indicating that lipid content alone is nota good indicator of suitability for biodiesel production. Thespecies with a high lipid productivity (>60 mg L−1 day−1)range in lipid content from 11% dw to 51%. Further, specieswith a high lipid content (>40%) vary in lipid productivitybetween 17 and 160 mg L−1 day−1.
Figure 8 shows lipid content as a function of doublingtime. Contrary to the popular belief that the large metabolicdemand of high lipid content necessitates slow growth rate,there appears to be no significant correlation between these.The two species with the highest lipid contents (51% and33% dw) maintain average to good doubling times of 20 and10 h, respectively. In order to exploit nutrient limitation tomaximise lipid content and productivity, a clear relationshipis needed. As rigorous data on nutrient deficiency andgrowth rate are not available across sufficient species, this isnot included in the review.
Comparison of laboratory data with outdoor pondand photobioreactor data
Table 4 shows the average literature data for microalgalspecies grown outdoors in open ponds or closed photo-bioreactors. Information was found for only 19 of the 55species reported in Table 3. The ratio of productivityoutdoors to productivity in the laboratory is compared.Lipid content in outdoor ponds was very similar to thatunder laboratory conditions. The overall average lipidcontent for the 20 species in outdoor ponds was 110% ofthat in the laboratory. Overall biomass productivities ingrams per square metre per day and grams per litre per daywere 130% and 96%, respectively of that under laboratoryconditions. Productivity in outdoor photobioreactors was onaverage five times higher than in the laboratory. This iscontrary to the expectation that productivities achieved inoutdoor ponds are generally lower than those in thelaboratory. This may be due to the limited available datasetor because conditions (e.g. of light) are not comparable.
Discussion
Most promising species in terms of lipid productivity
Analysis of lipid content, biomass productivity and theircombination to yield lipid productivity has been conductedacross literature data collected on 55 algal species undernutrient-replete conditions. These data, summarised in Fig. 6,highlight the following species for high lipid productivity:Amphora, E. oleoabundans, A. falcatus, C. sorokiniana andT. suecica. Average lipid productivities reported for theserange from 97 to 160 mg L−1 day−1. While these species
0
10
20
30
40
50
60
70
80
90
100
Ankis
trodesm
us falc
atus
Chla
mydom
onas r
ein
hardtii
Chlo
rella e
mersonii
Chlo
rella m
inutis
sim
a
Chlo
rella p
rotothecoid
es
Chlo
rella p
yrenoid
osa
Chlo
rella s
orokin
iana
Chlo
rella v
ulg
aris
Monoraphid
ium
min
utum
Nannochlo
ris
Ourococcus
Scenedesm
us a
cum
inatus
Scenedesm
us d
imorphus
Anabaena c
ylindric
a
Oscilla
toria
Spirulina m
axim
a
Spirulina p
latensis
Synechococcus
Do
ub
lin
g t
ime (
ho
urs)
Cyanobacteria
Fig. 4 Average doubling timefor Chlorophyta and Cyanobac-teria. Error bars represent high-est and lowest recorded values
J Appl Phycol (2009) 21:493–507 501
have exhibited promise with respect to lipid productivity, theincompleteness of the dataset (lipid productivity onlyavailable or discernable for 25 of the 55 species studied ona volumetric basis to allow direct comparison) and thevarying extent of optimisation imply that other species maybe added to this grouping as data become available.
Lipid productivity is the product of lipid content andbiomass productivity, hence, it is dependent on both, butlipid content has not been shown to be a reliable indicatorof lipid productivity, whereas a more dominant correlationwas observed between biomass and lipid productivity.
In this study, comparison of lipid productivities acrossalgal species has been restricted to laboratory lipid contentunder nutrient-sufficient conditions. It is clearly illustratedthat lipid content may be enhanced by nutrient stress. Lipidcontent reported under N- and Si-deficient conditions wereon average 138% and 168% of nutrient-sufficient lipidcontents reported, respectively. The response to nutrientdeficiency has been shown to vary across species. InChlorophyta, nitrogen stress correlates well with increasedlipid content (Fig. 3a), whereas the response of other taxa tonitrogen stress is more varied (Fig. 3b). A positive increase
0
10
20
30
40
50
60
70
80
90
100
Am
phip
rora
hya
lina
Am
phor
a
Cha
etoc
eros
mue
lleri
Cry
pthe
codi
nium
coh
nii
Cyc
lote
lla c
rypt
ica
Cyl
indr
othe
ca
Dun
alie
lla s
alin
a
Dun
alie
lla te
rtio
lect
a
Eug
lena
gra
cilis
Hym
enom
onas
car
tera
e
Isoc
hrys
is g
alba
na
Nan
noch
loro
psis
Nav
icul
a ac
cept
ata
Nav
icul
a pe
llicu
losa
Nav
icul
a sa
prop
hila
Nitz
schi
a co
mm
unis
Nitz
schi
a di
ssip
ata
Pha
eoda
ctyl
um tr
icor
nutu
m
Por
phyr
idiu
m p
urpu
reum
Pry
mne
sium
par
vum
Ske
leto
nem
a co
stat
um
Tetr
asel
mis
sue
cica
Tha
lass
iosi
ra p
seud
onan
a
Tha
lass
iosi
ra w
eiss
flogi
i
Trib
onem
a
Do
ub
ling
tim
e (h
ou
rs)
Fig. 5 Average doubling time for other taxa. Error bars represent highest and lowest recorded values
0
50
100
150
200
250
300
350
Am
phor
a
Ettl
ia o
leoa
bund
ans
Ank
istr
odes
mus
falc
atus
Nan
noch
loris
Syn
echo
cocc
us
Trib
onem
a
Nan
noch
loro
psis
Pav
lova
luth
eri
Pav
lova
sal
ina
Nitz
schi
a pa
lea
Pha
eoda
ctyl
um tr
icor
nutu
m
Chl
orel
la s
orok
inia
na
Isoc
hrys
is g
alba
na
Sce
nede
smus
qua
dric
auda
Por
phyr
idiu
m p
urpu
reum
Tetr
asel
mis
sue
cica
Mon
odop
sis
subt
erra
nea
Chl
orel
la v
ulga
ris
Sce
nede
smus
obl
iquu
s
Cha
etoc
eros
mue
lleri
Cha
etoc
eros
cal
citr
ans
Tha
lass
iosi
ra p
seud
onan
a
Ske
leto
nem
a co
stat
um
Chl
orel
la m
inut
issi
ma
Chl
orel
la e
mer
soni
i
Lip
id p
rod
uct
ivit
y (m
g.L
-1.d
ay-1
)
Fig. 6 Average literature (darkgrey bars) and calculated (lightgrey) values for biomass pro-ductivity (milligrams per litreper day). Error bars show theminimum and maximumrecorded lipid productivity forliterature values and propagationof error for calculated values
502 J Appl Phycol (2009) 21:493–507
in lipid content with silicon deficiency is reported acrossthe diatoms. The translation of an increase in lipid contentinto an increase in lipid productivity is dependent on thedegree of growth retardation caused by the nutrientdeficiency. The response of biomass productivity to nutrientlimitation has been shown to vary widely between species(Rodolfi et al. 2008). Enhanced lipid content and growthretardation under nutrient deficiency often counterbalanceone another. However, there are cases where nitrogendeprivation has been shown to improve lipid productivityin the short term, e.g. Nannochloropsis (Rodolfi et al.2008). Furthermore, two-stage culture with initial optimi-sation of biomass and final optimisation of lipid content
benefits volumetric lipid yield. Currently, insufficientlyrigorous data on the impact of nutrient limitation onbiomass productivity limit comparison of lipid productivityunder nutrient deficiency on a species basis.
From a practical perspective, it should be noted thatAmphora, Navicula, Cylindrotheca and Nitzschia are allbenthic diatoms (Chen 2007), living in the lowest level of abody of water, often attached to the substrate bottom.Certain species may be able to be grown planktonicallywith sufficient agitation. If they require culture on solidsubstrates, this may render them unsuitable for large-scaleproduction due to harvesting complexity.
The importance of reporting lipid productivity
Lipid productivity is a critical variable for evaluating algalspecies for biodiesel production. It is, however, under-utilised as illustrated by only being reported for 20 of the55 species presented here. Lipid productivity can becalculated as the product of biomass productivity (gramsdry weight per litre per day) and lipid content (% dw) togive an indicator of oil produced on a basis of both volumeand time. Lipid content reported in the absence of growthrate or biomass productivity does not allow rational speciesselection for lipid production, as faster growing speciesmay demonstrate lipid productivity greater than those witha very high lipid content. A high lipid content may,however, improve the efficiency of biomass processing(Rodolfi et al. 2008). Using the indicator of lipidproductivity across the duration of the production phase isseen to be particularly important under conditions of
0
10
20
30
40
50
60
70
80
0 20 40 60
Lipid content (% dw)
Do
ub
ling
tim
e (h
ou
rs)
Fig. 8 Doubling time versus lipid content under nutrient-repleteconditions
(a)
0
20
40
60
80
100
120
140
160
180
0.0 0.2 0.4 0.6 0.8
Biomass productivity (g.L -1 .day -1)
Lip
id p
rod
uct
ivit
y (m
g.L
-1.d
ay-1
)
(b)
0
20
40
60
80
0
120
140
160
180
0 20 40 60Lipid content (% dw)
Lip
id p
rod
uct
ivit
y (m
g.L
-1.d
ay-1
)
Fig. 7 Correlation of lipid productivity (average of lit. and calc.) withbiomass productivity (a) and lipid content (b) under nutrient-repleteconditions
J Appl Phycol (2009) 21:493–507 503
Tab
le4
Average
lipid
contentandbiom
assprod
uctiv
ityfrom
literatureformicroalgaegrow
nin
outdoo
rpo
ndsandph
otob
ioreactors
undernu
trient-replete
cond
ition
s
Species
Taxa
Media
References
Outdo
orpo
nds
Outdo
orPBR
Outdo
orpo
nd/labo
ratory
PBR/labo
ratory
Average
from
literature
Calculatedratio
ofaverages
Lipid
content
Biomass
prod
uctiv
ityBiomass
prod
uctiv
ityBiomass
prod
uctiv
ityLipid
content
Biomass
prod
uctiv
ityBiomass
prod
uctiv
ityBiomass
prod
uctiv
ity%
dwgm
−2day−
1gL−1
day−
1gL−1
day−
1Ratio
Ratio
Ratio
Ratio
Amph
ora
BM
6340
390.79
0.98
Ana
baenacylin
drica
Cy
F46
0.05
Ankistrod
esmus
falcatus
CF
630.18
0.38
Cha
etoceros
muelleri
OM
6326
260.18
1.39
2.50
Chlorella
pyreno
idosa
CF
38;63
143.27
Chlorella
vulgaris
CF
11;55
161.49
Cyclotella
cryptica
OM
6324
271.35
Dun
aliella
salin
aPr
S47
350.30
1.80
Isochrysisga
lban
aH
M38
;63
2228
0.96
0.90
2.44
6.13
Mon
odop
sissubterranea
EF
41;57
0.99
5.18
Mon
orap
hidium
minutum
CF
630.28
Nan
nochloropsis
EM
47;61
;63
2115
1.95
0.69
7.33
Nan
nochloropsissalin
aE
M63
1625
0.58
1.76
Pha
eoda
ctylum
tricornu
tum
BM
13;38
;47
0.07
1.85
0.20
5.53
Porph
yridium
purpureum
RM
38;63
0.18
0.36
0.78
1.60
Scenedesmus
obliq
uus
CF
3048
Spirulinamaxima
Cy
S38
0.25
Spirulinaplatensis
Cy
S38
;47
;55
110.10
1.02
0.44
Tetraselmissuecica
PM
6322
191.29
0.68
Average
2624
0.17
1.33
1.10
1.30
0.96
5.15
Blank
indicatesno
inform
ationavailable.Ratioshave
been
generatedby
dividing
outdoo
rvalues
bycorrespo
ndinglabo
ratory
values
inTable
3.Keysto
taxa,media
andreferences
asin
Table3
PBRph
otob
ioreactors
504 J Appl Phycol (2009) 21:493–507
nutrient deprivation known to enhance lipid content inseveral algae species, while frequently decreasing growthrate (Illman et al. 2000).
Limitations
This review summarises information available in open sourceliterature on the lipid productivity of microalgae, with the aimof facilitating the choice of algal species for large-scalebiodiesel production. While such review is essential to informspecies selection, constraints are recognised.
The body of literature on algal culture and algal lipidcontent has been collected for a variety of purposes, includingthe production of food, feed, fuel and fine chemicals, as wellas taxonomic, toxicity and environmental studies. Experi-ments have been conducted under different conditions, withdifferent equipment and protocols, using a variety of strains,nutrient levels, temperature, pH, media composition, light,culture vessels and locations. Many studies do not presentoptimised data, hence, absolute comparison cannot be made,but the data can be used to identify key species with potentialfor further experimental work.
A further constraint is that algal taxonomic definition haslagged behind other systems. The species is not alwaysidentified correctly in studies, with classification oftenrestricted to a genus level. A single species can have avariety of strains with widely different characteristics.
Lipid productivity is presented on a range of bases, mosttypically on a basis of volume or area. Reactor geometry isrequired for their inter-conversion. Similarly, algal growthmay be presented as a productivity or growth rate. Biomassconcentration, key for their inter-conversion, is frequentlynot reported. Hence, complete reporting of culture con-ditions on reporting productivities is key to maximisingdata available for comparison.
Lipid contents and biomass and lipid productivities,although key characteristics for biodiesel production, arenot the only characteristics to be considered to ensure acost-effective and feasible biodiesel production process.Resistance to contamination, tolerance of operating con-ditions such as light, temperature, ionic strength and fluegas toxins, nutrient requirements, as well as ease ofharvesting and downstream processing, also impact thesuccess of large-scale culture. However, insufficient pub-lished information currently exists to enable comparisonacross these aspects of a variety of species.
The road ahead
Owing to the limitations discussed above, emanating fromthe level of development of the current algal literature, thisreview provides a starting point for further investigation. Torefine selection, species must be tested either under their
optimal conditions, or the expected operational conditionsat large scale. A set of standard conditions as close to thoseexpected in the large-scale culture facility as possible couldbe used to screen species in the laboratory. This wouldselect for species ideal for that particular location andsystem. Alternatively, the culture conditions of selectedpromising species could be optimised individually toprovide a measure of the maximum biological productivityexpected. The culture system would then be designed tomimic these conditions.
In conclusion, this paper demonstrates the role ofinformation available in the literature in providing early-stage guidance on species selection. However, these dataremain incomplete, demanding further experimental study,particularly with respect to characterising optimum growthconditions, measuring growth rates under conditions thatenhance lipid content (e.g. nitrogen deprivation), measuringlipid productivity under outdoor conditions (i.e. fluctuatingtemperatures and light intensities), determining the resil-ience of species by measuring the range of environmentalconditions (e.g. pH, temperature, nutrient levels, CO2
levels, light, etc.) within which the algae remains produc-tive and determining ease of algal cell harvesting (e.g. byflocculation, filtration or sedimentation).
To ensure maximum value of the data presented, it isnecessary that growth rates be reported in standard units.Here, volumetric lipid and biomass productivity are recom-mended, the former being the product of biomass productivityand lipid content while the latter is the product of specificgrowth rate and biomass concentration. Sufficient informationshould be provided for their inter-conversion.
The final choice of algal species is governed by theculture system used, resources available, location andprevailing environmental conditions, as well as the scopeand aims of the individual project in question. In addition tothe key indicator of lipid productivity, characteristics suchas ease of cultivation and harvesting are vital to the successof any large-scale algae culture facility and a sufficient datainventory of these factors remains to be generated.
Acknowledgements This work is based upon research supported bythe South African Research Chair Initiative of the Department ofScience and Technology and the National Research Foundation. Thefinancial assistance of the National Research Foundation (NRF)towards this research is hereby acknowledged. Opinions expressedand conclusions arrived at are those of the authors and are notnecessarily to be attributed to the NRF.
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