Bioresource Technology 98 (2007) 3344–3353
Comparative study of biosorption of heavy metals using different
types of algae
E. Romera, F. González *, A. Ballester, M.L. Blázquez, J.A. Muñoz
Dpto. Ciencia de los Materiales e Ingenierı́a Metalúrgica, Facultad de C. Quı́micas, Universidad Complutense, Ciudad Universitaria, 28040 Madrid, Spain
Received 11 July 2006; received in revised form 11 September 2006; accepted 17 September 2006
Available online 10 July 2007
Abstract
Sorption capacity of six different algae (green, red and brown) was evaluated in the recovery of cadmium, nickel, zinc, copper and lead
from aqueous solutions. The optimum sorption conditions were studied for each monometallic system. The optimum pH was 6 for the
recovery of Cd, Ni and Zn, and less than 5 for Cu and Pb. The best results were obtained with the lowest biomass concentration used
(0.5 g/L). Experimental data fitted a Langmuir model very well according to the following sequence of the sorption values:
Pb > Cd P Cu > Zn > Ni. The brown algae achieved the lowest metal concentration levels in solution; the best results were obtained
with Fucus spiralis. Finally, a software computer program was used to simulate the process by comparison of theoretical with experimental results and show minimum differences between both types of data.
Ó 2006 Published by Elsevier Ltd.
Keywords: Algae; Biosorption; Heavy metals; Simulation
1. Introduction
Biosorption is an innovative technology that employs
inactive and dead biomass for the recovery of heavy metals
from aqueous solutions. As an alternative to traditional
methods, its promising results are now being considered
for application by the scientific community. In this context,
research and development of new biosorbent materials has
focused especially on algae, due to its high sorption capacity and its availability in almost unlimited amounts (Klimmek et al., 2001). However, the latest publications in this
field have considered mainly other types of biomass (especially fungi and bacteria) instead of algae.
It seems likely that algae do not form a homogeneous
group within the vegetal kingdom. They are divided into
several evolutionary pathways completely independent: a
‘‘red pathway’’ with red algae (Rhodophyta), a ‘‘brown
pathway’’ with brown algae (inter alia, Chromophyta) and
*
Corresponding author. Tel.: +34 91 394 43 35; fax: +34 91 394 43 57.
E-mail address: [email protected] (F. González).
0960-8524/$ - see front matter Ó 2006 Published by Elsevier Ltd.
doi:10.1016/j.biortech.2006.09.026
a ‘‘green pathway’’ that includes green algae (Chlorophyta)
along with mosses, ferns and several plants. Differences
between these types of algae are mainly in the cell wall,
where sorption takes place.
Research in the field of biosorption has mostly concerned itself with brown algae (Holan et al., 1993; Chong
and Volesky, 1995; Matheickal and Yu, 1999; Matheickal
et al., 1999; Yu et al., 1999; Leusch et al., 1995) and to a
less extent with green (Dönmez et al., 1999; Aksu et al.,
1999, 1997) and red algae (Holan and Volesky, 1994).
The cell walls of brown algae generally contain three
components: cellulose, the structural support; alginic acid,
a polymer of mannuronic and guluronic acids and the corresponding salts of sodium, potassium, magnesium and
calcium; and sulphated polysaccharides. As a consequence,
carboxyl and sulphate are the predominant active groups in
this kind of algae. Red algae also contain cellulose, but
their interest in connection with biosorption lies in the presence of sulphated polysaccharides made of galactanes (agar
and carragenates). Green algae are mainly cellulose, and a
high percentage of the cell wall are proteins bonded to
polysaccharides to form glycoproteins. These compounds
E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
contain several functional groups (amino, carboxyl, sulphate, hydroxyl, . . .) which could play an important role
in the biosorption process.
The aim of the present work was to evaluate the sorption capacity of six different algae, Codium vermilara, Spirogyra insignis, Asparagopsis armata, Chondrus crispus,
Fucus spiralis and Ascophyllum nodosum, in respect of five
different heavy metals: cadmium, nickel, zinc, copper and
lead.
2. Methods
Two algae from each main division (green, brown and
red) were selected for the experiments. Table 1 shows the
identification of each alga (Cabioch et al., 1995; Van den
Hoek et al., 1995; Graham and Wilcox, 1998).
All biomass were marine except S. insignis, harvested
from fresh water. The samples of C. vermilara, C. crispus,
F. spiralis and A. nodosum were collected on the northern
Atlantic coast of Spain and the red alga A. armata on the
Mediterranean coast of Malaga, in southern Spain. Finally,
S. insignis was collected at Valmayor dam (Madrid, Spain).
Sample preparation consisted in a preliminary visual
cleaning of impurities followed by several washes with distilled water. The overflow collected in each wash contained
small particles of biomass substantially more difficult to
settle, and it was centrifuged at 5000 rpm for 10–15 min.
Then, after removal of the clear liquid, the pellet was mixed
with previously-washed biomass to avoid large losses of it,
and the whole system dried to constant weight in an oven
at 60 °C. Once dry, samples were ground to the adequate
particle size for biosorption tests (<0.5 mm). The specific
surface area of the biomass, 0.48 m2/g, was determined
by adsorption of N2 at the temperature of liquid nitrogen
using an automatic micrometer.
Synthetic dissolutions were prepared with distilled water
from stock solutions of 1000 mg/L of Cd2+, Cu2+, Ni2+,
Pb2+ and Zn2+ from the corresponding sulphate salts,
except for lead where nitrate was used. All reagents were
of analytical grade. For the tests with nitrate, the initial
pH was adjusted with nitric acid and ammonia as alkaline
reagent. Both reagents were diluted in distilled water in the
appropriate proportions for each case. For all other tests,
1% or 10% v/v diluted sulphuric acid and chemically pure
NaOH dissolved in distilled water at a concentration of
1 g/L were used.
3345
The tests were performed in 100 mL Erlenmeyer flasks
placed on a multiple stirred plate. Samples were removed
at different times and centrifuged at 5000 rpm. In the overflow, the final pH was measured and metal concentration
was determined by atomic absorption spectrophotometry.
The procedure for calculating the isotherms for the
thirty monometallic systems studied (five metals and six
different algae) was as follows: once pH and biomass concentration were optimum, five tests were performed for
each metal with each biomass at different initial metal concentrations (10, 25, 50, 100 and 150 mg/L), using 100 mL
of solution in all cases.
The experiments were controlled by analysing metal
concentration and measuring the pH before the start and
at the end of each test. Each experiment lasted 120 min,
time enough to follow the biosorption kinetics and to reach
equilibrium conditions in all cases.
3. Results and discussion
3.1. Influence of pH
In the biosorption phenomenon, the pH value affects
two aspects (Guibal et al., 1994): metal ion solubility and
biosorbent total charge, since protons can be adsorbed or
released. This behaviour will depend on the functional
groups present on the alga cell wall, which in turn determine the acidity constant. Therefore, the pH value of the
medium affects the system’s equilibrium state, according
to the following equations:
B H () B þ Hþ
ð1Þ
where Ka is given by
Ka ¼
½B ½Hþ ½B H
pK a pH ¼ log
ð2Þ
½B H
½B ð3Þ
For pH values lower than pKa, equilibrium (1) shifts to the
left, consuming protons and increasing pH until its value
equals pKa. When the pH of the medium is higher than
pKa, the opposite will happen. This effect is shown in
Fig. 1 for all the biomass used.
The effect of this variable and its optimum value, at
which the biomass presents the highest sorption capacity
(q, expressed in mg or mmol of metal/g of biomass), were
Table 1
Identification of the six members of group algae
Division
Class
Order
Family
Codium vermilara
Spirogyra insignis
Asparagopsis
armata
Chondrus crispus
Fucus spiralis
Ascophyllum nodosum
Green algae
(Chlorophyta)
Chlorophyceae
Codiales
Codiaceae
Green algae
(Chlorophyta)
Chlorophyceae
Zygnematales
Zygnemataceae
Red algae
(Rhodophyta)
Rhodophyceae
Bonnemaisoniales
Bonnemaisoniaceae
Red algae
(Rhodophyta)
Rhodophyceae
Gigartinales
Gigartinaceae
Brown algae
(Chromophyta)
Phaeophyceae
Fucales
Fucaceae
Brown algae
(Chromophyta)
Phaeophyceae
Fucales
Fucaceae
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E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
Cadmium
7
pH
6
3
5
4
4
5
6
3
2
0
30
60
90
120
150
Time (min)
Fig. 1. pH evolution as a function of time using Asparagopsis armata as
biosorbent.
Chondrus crispus
q (mmol/g)
0.4
Cd
0.3
Ni
0.2
Zn
Cu
0.1
Pb
0
2
3
4
5
6
7
pH
Fig. 2. Metal uptake capacity as a function of pH using Chondrus crispus.
investigated in a series of tests at whole pH numbers
between 2 and 6 (below 2, the high proton concentration
minimizes metal sorption, and above 6 metal precipitation
is favoured), depending on the metal, and keeping constant
the biomass concentration and the initial metal concentration in solution at 1 g/L and 50 mg/L respectively.
The influence of the pH value was very similar for the six
algae employed. Fig. 2 shows that sorption uptake by
C. crispus alga was also low at low pH metal, since equilibrium (1) shifted to the left. At higher pH, the number of
negatively charged active sites increased, facilitating a
higher electrical attraction to positively charged metal ions.
The optimum pH values for each biomass and metal are
shown in Table 2. The optimum sorption pH of cadmium,
nickel and zinc for the six biomass studied was 6. For copper, the value ranged between 4 and 5, while for lead it was
between 3 and 5 depending on the type of biomass, with the
lowest values corresponding to brown algae (F. spiralis and
A. nodosum). Coinciding with these differences, it was
observed that at the highest initial pH, the solution in contact with the biomass raised the pH during the test to close
to the precipitation pH of metal until reaching the corresponding value of pKa. This meant that the decrease of
metal in solution observed was possibly not due to biosorption alone. In this case, the only way to assure that the
decrease of metal concentration was due to biosorption
alone was to start the test from a lower initial pH.
For a better evaluation of the experimental data on the
influence of the pH value, a series of blank tests were performed without metal in solution, where the biomass was
in contact with acid solutions prepared with sulphuric
and nitric acid in distilled water. The pH values used ranged from 1 to 6. From these tests, the amount of protons
consumed by the biomass can be calculated as a function
of the initial pH value, and in turn the possible competition
between protons and metal cations for the active biomass
sites.
From the blank tests, the pH value of maximum proton
sorption capacity was set at 3 or 4 in all cases. This value
reasonably agrees with the pKa of carboxyl groups in biomolecules (Gardea-Torresday et al., 1996; Seki and Suzuki,
1998; Figueira et al., 1999), which according to the literature are the main anchoring sites on the cell wall (Akthar
et al., 1996; Kim et al., 1995; Leusch et al., 1995; Lau
et al., 1999).
3.2. Influence of biomass concentration
The biomass concentration is another important variable during metal uptake. At a given equilibrium concentration, the biomass takes up more metal ions at lower
than at higher cell densities (Mehta and Gaur, 2001). It
has been suggested that electrostatic interactions between
cells can be a significant factor in the relationship between
biomass concentration and metal sorption. In this connection, at a given metal concentration, the lower the biomass
concentration in suspension, the higher will be the metal/
biosorbent ratio and the metal retained by sorbent unit,
unless the biomass reaches saturation. High biomass concentrations can exert a shell effect, protecting the active
sites from being occupied by metal. The result of this is a
lower specific metal uptake, that is, a smaller amount of
metal uptake per biomass unit. Fig. 3 shows this influence
Table 2
Values of optimum pH and biomass concentration (g/L) for the sorption of each metal with different biomass
Codium vermilara
Spirogyra insignis
Asparagopsis armata
Chondrus crispus
Fucus spiralis
Ascophyllum nodosum
Cadmium
Nickel
pH
Biomass con.
pH
Biomass con.
pH
Zinc
Biomass con.
pH
Copper
Biomass con.
pH
Lead
Biomass con.
6
6
6
6
6
6
0.5
1
0.5
0.5
0.5
0.5
6
6
6
6
6
6
0.5
1
0.5
0.5
0.5
0.5
6
6
6
6
6
6
0.5
1
0.5
0.5
0.5
0.5
5
4
5
4
4
4
0.5
1
0.5
0.5
0.5
0.5
5
5
4
4
3
3
0.5
0.5
0.5
0.5
0.5
0.5
E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
Ascophyllum nodosum
q (mmol/g)
0.8
Cd
0.6
Ni
0.4
Zn
Cu
0.2
Pb
3347
concentrations. This does not occur with lead since this is
the metal with the highest affinity for the biomass, as will
be shown later. Such conclusions have been supported by
several researchers (Itoh et al., 1975; De Rome and Gadd,
1987; Modak and Natarajan, 1995; Sandau et al., 1996;
Veglió and Beolchini, 1997; Dönmez et al., 1999; Hammaini et al., 1999).
0
0
1
0.5
1.5
2
2.5
3.3. Sorption isotherms
Biomass conc. (mmol/L)
Fig. 3. Metal uptake capacity as a function of biomass concentration
using Ascophyllum nodosum.
using A. nodosum as sorbent. Similar results were obtained
with the other algae tested.
In the tests, the initial metal concentration (50 mg/L)
and the pH value (the optimum for each system tested)
were kept constant. The biomass concentration was tested
at 0.5, 1.0 and 2.0 g/L.
Table 2 shows that, except for S. insignis and several
metals, the maximum sorption capacity corresponded to
the lowest biomass concentration of the three used (0.5 g/
L), irrespective of the type of alga or metal, which confirms
the loss of biosorbent effectiveness at high concentrations.
The difference for S. insignis can be due to the weakness
of electrostatic attraction forces produced at low biomass
A Langmuir isotherm was then obtained by plotting the
values of sorption capacity (q) versus equilibrium metal
concentration (Ce) for the six biosorbents tested (Fig. 4).
Next, Ce/q was plotted versus Ce to fit the experimental
data to a straight line. With this linear plot of a Langmuir
equation, qmax and b (inverse of K) values can be determined immediately according to (Feng and Aldrich, 2004)
Ce
Ce
K
¼
þ
q
qmax qmax
Effectiveness was greatest in the sorbent with the highest
values of qmax and b (or the lowest values of K).
The fit of experimental data to a Langmuir model was
evaluated by the regression coefficient R2 (Table 3). This
coefficient is defined by the following expression:
Spirogyra insignis
Chondrus crispus
0.4
0.8
Cd
0.3
Ni
0.2
Zn
Cu
0.1
Cd
q (mmol/g)
q (mmol/g)
ð4Þ
Pb
0.6
Ni
Zn
Cu
0.4
0.2
Pb
0
0
0
0.5
1
1.5
2
2.5
3
0
0.5
1
Ce (mmol/L)
2.5
1
Cd
Ni
0.3
Zn
Cu
0.2
0.1
Pb
q (mmol/g)
q (mmol/g)
2
Ascophyllum nodosum
Codium vermilara
0.4
0
Cd
0.8
Ni
Zn
Cu
Pb
0.6
0.4
0.2
0
0
0.5
1
1.5
2
2.5
3
0
0.5
1
Ce (mmol/L)
Aaparagopsis armata
0.2
Cu
Pb
0.1
0
0.5
1
1.5
Ce (mmol/L)
2
2.5
3
q (mmol/g)
Cd
Ni
Zn
0.3
0
1.5
2
Ce (mmol/L)
0.4
q (mmol/g)
1.5
Ce (mmol/L)
Fucus spiralis
1.2
1
0.8
0.6
0.4
0.2
0
Cd
Ni
Zn
Cu
Pb
0
0.5
1
1.5
2
2.5
Ce (mmol/L)
Fig. 4. Langmuir sorption isotherms for the five metals tested with each type of alga.
3
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E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
Pb > Cu P Cd > Ni > Zn. This sequence was very similar
to the order of affinities of the biomass for the metal (given
by Langmuir constant b: Pb > Cd P Cu > Ni > Zn) and
also decreased for each of the studied biomass expressed in
L/mmol in Table 4. Only in those cases where values were
very similar was that order of affinities altered to any degree
(C. crispus and A. nodosum).
In addition, for the same equilibrium metal concentration, the sorption capacity of each biomass (q) also followed a similar sequence in almost all cases, in
accordance with the descending order of qmax for each biosorbent (Table 4).
These results corroborate the hypothesis that the binding of metal to active sites of the cell wall is closely related
to some intrinsic metal property, such as ionic radii and
electronegativity of atoms (Chong and Volesky, 1996;
Tobin et al., 1984).
On the other hand, brown algae (F. spiralis and A. nodosum) showed higher sorption capacity than any other algae
(Table 4). At worst they were able to retain twice as much
metal as any of the other tested algae. The presence of alginates in the cell wall of brown algae could be responsible
for such behaviour by anchoring the metal to the biomass
(Fourest and Volesky, 1997; Davis et al., 2000).
The sequence obtained as a function of the type of alga
was: brown > red > green. Of the red algae, A. armata,
with an intermediate sorption capacity, behaved similarly
to green algae, whereas C. crispus was closer to brown
algae. This relates to the fact that the latter contains carragenates in its composition, which behave similarly to the
alginates in brown algae and are responsible for metal
uptake by the biomass.
From this it would seem that, besides the biomass itself
or the type of alga used, the sorption phenomenon fundamentally depends on the type of metal employed.
Table 3
Regression coefficients for each Langmuir isotherm
Codium vermilara
Spirogyra insignis
Asparagopsis armata
Chondrus crispus
Ascophyllum nodosum
Fucus spiralis
Pn
2
Nickel
Zinc
Copper
Lead
0.99
0.99
0.96
0.99
0.99
0.99
0.99
0.97
0.99
0.99
0.99
0.99
0.96
0.95
0.99
0.99
0.99
0.99
0.99
0.97
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.99
0.99
2
R ¼ 1 Pi¼1
n
ðY obsi Y cali Þ
i¼1 ðY obsi
where
Yobs
Y obs
Ycalc
Cadmium
ð5Þ
Y obs Þ2
values of qe obtained experimentally
average values of qe obtained experimentally
values of qe calculated from the model
The closer the value is to 1, the better the fit to the
model. The data shown in Table 3 indicate an almost perfect fit. Table 4 shows the values of constants qmax and b in
moles to allow comparison of sorption data for each metal.
Fig. 4 shows, in general, that for the five metals tested,
when the metal concentration equilibrium increased, the
value of sorption capacity also increased, but only up to
a limiting value. That value represents the maximum
amount of metal that the biomass can retain when all bond
sites have been occupied.
The metal that was best taken up by the biomass was lead.
S. insignis, C. vermilara and A. armata were able to reduce
the equilibrium metal concentration for each metal to a similar extent to other biomass, but they attained a higher sorption capacity. For each metal, the value of Ce decreased,
following the same sequence for all the biomass, as follows:
Table 4
Values of Langmuir constants for the five monometallic systems tested with each biomass: Codium vermilara (1), Spirogyra insignis (2), Asparagpsis armata
(3), Chondrus crispus (4), Ascophyllum nodosum (5) and Fucus spiralis (6)
Algae
Cadmium
(mg/g)
Values of qmax
1
21.8
2
22.9
3
32.3
4
75.2
5
87.7
6
114.9
Nickel
(mmol/g)
0.19
0.20
0.29
0.67
0.78
1.02
Cadmium
(L/mg)
Values of b
1
2
3
4
5
6
0.10
0.12
0.09
0.06
0.15
0.11
Zinc
(mg/g)
(mmol/g)
(mg/g)
13.2
17.5
17.1
37.2
43.3
50.0
0.22
0.30
0.29
0.63
0.74
0.85
23.8
21.1
21.6
45.7
42.0
53.2
Nickel
(L/mmol)
11.15
13.59
10.61
6.37
17.34
12.67
(L/mg)
0.09
0.04
0.12
0.05
0.13
0.13
Copper
(mmol/g)
0.36
0.32
0.33
0.70
0.64
0.81
Zinc
(L/mmol)
5.34
2.57
7.23
2.83
7.90
7.92
(L/mg)
0.03
0.04
0.07
0.07
0.22
0.11
(mg/g)
16.9
19.3
21.3
40.5
58.8
70.9
Lead
(mmol/g)
0.27
0.30
0.33
0.64
0.93
1.12
Copper
(L/mmol)
1.80
2.58
4.92
4.63
14.38
6.94
(L/mg)
0.14
0.09
0.13
0.04
0.16
0.17
(mg/g)
(mmol/g)
63.3
51.5
63.7
204.1
178.6
204.1
0.30
0.25
0.31
0.98
0.86
0.98
Lead
(L/mmol)
(L/mg)
(L/mmol)
8.92
5.51
8.37
2.47
10.39
10.87
0.11
0.57
0.04
0.01
0.09
0.13
23.45
117.87
9.10
2.08
19.15
26.72
E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
3.4. Behaviour of algae in comparison with other types of
biomass
A comparative overall analysis of the results obtained
with each biomass was carried out after quantifying the
biosorbent capacity of the six algae under study. This study
took into consideration data given in literature relating to
other types of more widely-investigated biomass, such as
bacteria and fungi (Kapoor and Viraraghavan, 1995; Pillichshammer et al., 1995; Pagnanelli et al., 2003; Veit
et al., 2005; Yan and Viraraghavan, 2003; Zouboulis
et al., 2004). Figs. 5 and 6 show graphically the values of
3349
qmax and b obtained from Langmuir isotherms and the
average reference values of bacteria and fungi.
In all cases, algae equalled or improved the best results
of qmax obtained with fungi and bacteria.
Brown algae (F. spiralis and A. nodosum) and the red
alga C. crispus attained the highest values of qmax for all
metals, well above those obtained with any of the other
algae. In the former case, recovery values of 1 mmol
metal/g biomass were obtained, while in the latter case values scarcely reached 0.3 mmol/g.
The similar behaviour of C. crispus and brown algae has
already been explained in terms of their carragenates
Fig. 5. Values of qmax for the six algae studied and average values of fungi and bacteria.
Fig. 6. Values of b for the six algae studied.
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E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
content, which are gelifying compounds that enhance processes of this kind.
A. armata (red alga), S. insignis and C. vermilara (green
algae) behaved similarly in terms of maximum sorption
capacity, but worse than C. crispus and brown algae.
One of the reasons why green algae show lower levels of
metal recovery could be that there is less probability of
having two adjacent carboxylic groups at the right distance
to allow the metal bond between them, such as happens
with alginates (Schiewer and Wong, 2000).
Fig. 6 represents biomass affinity (expressed by the value
of b) versus different metal cations. All algae showed similar affinities for each metal tested, except for lead where
each biomass seemed to behave differently. This could be
explained by a different biomass bonding mechanism than
in the other metals. In any event, the affinity for lead was
much higher than for any other metal, especially with green
and brown algae.
This difference would have been more significant if the
value for S. insignis with lead (117 L/mmol) had been considered, but this value was left out for a better comprehension of the other results. Singh et al. (2000) obtained a
greater uptake of Pb using Spirogyra as biosorbent and dissolutions of Pb2+, Cd2+, Zn2+ and Cu2+ and suggested that
lead anchored to the biomass due to its high affinity for certain active sites, which in turn were different to those taking
up Cu2+ or Zn2+. Infrared spectroscopy revealed less functional groups on the surface than with other algae. This
could account for the fact that qmax was not as high as
expected from the high value of b, the same as occurred
in the present work.
The reason why some biomass show high affinity for a
given metal and low sorption capacity, or vice versa, may
be related to the degree of affinity of a specific biomass
for each metal. Although the total amount of metal
anchored on its surface will also depend on the number
of active sites present and on how easily they can be
accessed (Hashim and Chu, 2004).
Therefore, when selecting the most appropriate biosorbent for a concrete situation it would be advisable to establish working conditions. It may be of more interest to
recover the metal irrespective of the equilibrium concentration reached, or on the contrary the priority may be to
reduce effluent pollution levels to within the legally-permitted range. In addition, if the interest was the recovery of
large amounts of metal while achieving low equilibrium
concentrations at the same time, the biosorbent used
should have a high sorption capacity compatible with high
values of b. However, if the idea was to recover the maximum amount of metal regardless of the equilibrium concentration reached, the biomass with the highest value of
qmax should be selected even although the value of b was
lower; in our case, this would be achieved with C. crispus.
In any case, the ideal solution would be to find biosorbents with high sorption capacity and high values of b
simultaneously (Langmuir, 1918; Holan et al., 1995; Davis
et al., 2003). In this sense, brown algae seem to be ahead in
processes of this kind since they generally present higher or
at least acceptable values of both parameters. In the present study, for instance, F. spiralis was the alga with the best
sorption capacity of all the metals tested and consequently
was the most effective of the six biosorbents used.
3.5. Simulation of the process
The good fit of the experimental data to a Langmuir
model makes it possible to set up metal-biomass equilibria
of the type
Sorbent ðBÞ þ Metal ðMeÞ () Sorbent–Metal ðBMeÞ
Equilibrium B
Me
BMe
ðqmax qÞ
Ce
q
ð6Þ
where
B
Me
BMe
final sorbent amount at equilibrium (number of
bond sites not occupied by the metal which are vacant)
final metal amount in solution once equilibrium is
reached
metal amount bonded with the sorbent at equilibrium (number of bond sites occupied by metal at
equilibrium)
The equilibrium constant for such a reaction coincides
with Langmuir affinity constant (b). Therefore, once equilibrium was defined, it was attempted to predict the biomass behaviour versus each metal.
The idea was to find out what actually happens when the
biomass is brought into contact with a metal solution
under specific conditions. The known data for each system
were: the equilibrium constant or affinity constant (b) of
the Langmuir model and the maximum biomass uptake
or total number of active sites available for the metal on
the biomass surface.
Data corresponding to the equilibrium conditions were
calculated and compared with experimental data using a
chemical speciation computer program PHREEQCI 6.2
(Charlton et al., 1997). The value of the metal concentration left in solution at equilibrium is entered and the program calculates both the amount of metal retained by the
biomass (qe or number of active sites available for that
metal occupied under such conditions) and the number of
unoccupied sites at equilibrium (qmax q). This assumption is of particular interest since government restrictions
are constantly being tightened to reduce pollution levels
in sewage.
To that end the reactions between the biomass and each
metal with its corresponding equilibrium constant (b), as
determined from the Langmuir model, were uploaded to
the program database.
For instance, the simulation reactions for the brown
alga F. spiralis (Fucusspi) and heavy metals cadmium and
copper were as follows:
E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
3351
Table 5
Comparison between experimental data and the estimated by the program for each monometallic system under study
C0 (mg/L)
(qe qp)100/qmax (%)
Cadmium
10
25
50
100
150
Spirogyra insignis
Codium vermilara
Asparagopsis armata
Chondrus crispus
Ascophyllum nodosum
Fucus spiralis
19.6
1.3
5.6
0.3
5.4
9.6
2.3
0.3
7.2
3.0
7.3
8.3
7.9
10.0
10.6
0.6
2.7
7.8
3.8
6.1
0.3
3.9
2.4
5.6
1.8
5.1
1.8
2.1
1.8
6.1
Nickel
10
25
50
100
150
12.2
4.9
3.5
1.3
11.1
7.7
5.7
7.0
5.3
1.4
2.7
3.2
1.3
4.8
1.7
0.7
4.6
7.5
2.4
6.6
2.0
1.0
7.5
4.9
1.4
8.1
3.2
0.4
0.8
3.8
Zinc
10
25
50
100
150
16.7
6.8
1.0
1.8
15.8
10.6
2.9
4.6
14.7
8.7
8.3
4.9
0.2
8.6
4.6
2.4
5.9
1.1
10.5
4.8
5.4
2.0
9.0
3.9
1.4
15.8
10.9
0.9
4.2
8.6
Copper
10
25
50
100
150
25.3
10.0
4.7
4.3
11.6
4.5
4.6
2.2
5.9
2.7
3.5
7.0
1.0
6.3
2.9
2.9
5.1
7.1
5.4
10.6
2.0
0.1
10.5
2.2
3.8
9.2
6.5
8.9
1.8
3.9
Lead
10
25
50
100
150
28.0
3.5
4.6
1.9
0.9
11.4
0.2
9.5
2.4
2.1
2.0
2.9
0.1
0.0
4.6
0.4
0.0
1.7
4.1
4.1
3.2
2.5
2.9
0.3
3.0
1.0
6.5
1.7
0.8
3.5
Fucusspi þ Cd þ 2 ¼ FucusspiCd þ 2
log k 4:1026
#ðk ¼ 12; 667Þ
Fucusspi þ Cu þ 2 ¼ FucusspiCu þ 2
log k 4:0363
#ðk ¼ 10; 872Þ
By specifying the equilibrium conditions, the program
was able to determine the values of Fucusspi, FucusspiCu+2 and FucusspiCd2+. Thus the program was able
to reproduce the experimental results very well, with a
good correlation between experimental data and data calculated by the program.
The program also required specification of the physicochemical parameters of the solution (pH, temperature,
equilibrium metal concentration, ionic species in solution,
etc.) and the characteristics of the biomass itself (qmax, specific surface area and mass used).
Table 5 shows the difference between the percentage of
active sites occupied by the metal [(qe/qmax) Æ 100] in experiments with different initial concentrations for each metal
and the percentage simulated by the program [(qp/
qmax) Æ 100] for each biomass tested. Of course, the smaller
the difference between the two values, the better the program’s predictive accuracy.
In general, there was a remarkable similarity between
experimental data and those obtained using the program
even though identical data were compared. In the most
unfavourable cases, all coinciding with low initial metal
concentrations, deviations were around 10% of the estima-
tion of biomass active sites occupied by metal species,
which is not a significant figure in the simulation of a process. This may be the result of stronger competition by protons for the active sites on the algae cell walls, since the
presence of metallic species in solution was hardly significant in these cases. Thus, the program furnished excellent
data and came very close to the actual situation when an
effluent contaminated with each of the five metals studied
is treated with this type of biomass. This therefore confirms
the viability of the program and its ability to estimate the
value of q or the percentage of active sites occupied by
the metal at equilibrium, with good reliability for all the
biomass assayed.
4. Conclusions
The effectiveness of the selected algae as biosorbent
material was confirmed. Sorption capacity depended on
the pH and the biomass concentration. The optimum pH
value for recovery of Cd, Ni and Zn was 6 for the six algae
under study. The optimum sorption pH for Cu ranged
from 4 to 5 and for Pb from 3 to 5. Reducing the biomass
concentration increased the sorption capacity, and in many
cases values were higher at the lowest concentration (0.5 g/
L).
Experimental data fitted a Langmuir equation very
well. The regression coefficient was higher than 0.95 for
the thirty monometallic systems studied; therefore, the
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E. Romera et al. / Bioresource Technology 98 (2007) 3344–3353
equilibrium reactions between each biomass and the different metals are probably
B þ Me () B Me
where the b constant is the equilibrium constant of such
model.
In all cases sorption capacity was greater with lead followed by cadmium. The sorption values for nickel, copper
and zinc were very similar and the general sequence was:
lead > copper P cadmium > zinc > nickel.
Green and red algae, without carragenates in their composition, presented similar values of qmax for all metals. In
all cases, these values were much lower than those registered with C. crispus and brown algae, which besides being
very high for all metals reduced the metal equilibrium concentration to very low levels. In any case, the best results
were achieved with F. spiralis.
All algae showed similar affinities for each metal tested,
but lead behaved differently in each case because the mechanism of bonding to the biomass was also different from
the other metals. The sequence of affinities between each
biomass and the different metals was as follows: lead >
cadmium P copper > nickel > zinc.
In general, the six algae studied achieved more effective
biosorption of the five metals than the average values registered for bacteria and fungi.
The PHREEQCI program proved to be a very useful
tool for predicting the behaviour of the biomass once equilibria were defined.
Acknowledgements
The authors wish to express their gratitude to the Spanish Ministry of Science and Technology for funding this
work. Thanks are also given to Eduardo Costas from Complutense University of Madrid and Antonio Flores from
University of Malaga.
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