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: sue.harrison@uct.ac.za

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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