Biosorption is an effective method for removing heavy metals from effluent. This work mainly aimed to evaluate the adsorption performance of the widely cultivated novel mushroom, Pleurotus eryngii, for the removal of Cu(II) from single aqueous solutions. Kinetics and equilibria were obtained using a batch technique. The sorption kinetics follows the pseudo-second-order model, whereas the adsorption equilibria are best described by the Langmuir model. The adsorption process is exothermic because both the Langmuir-estimated biosorption capacity and the heat of adsorption estimated from the Temkin model decreased with increasing tested temperature. Based on the adsorption intensity estimated by the Freundlich model and the mean adsorption free energy estimated by the Dubinin–Radushkevich model, the type of adsorption is defined as physical adsorption. The biomass of the macro-fungus P. eryngii has the potential to remove Cu(II) from a large-scale wastewater contaminated by heavy metals, because of its favorable adsorption, short biosorption equilibrium time of 20 min and remarkable biosorption capacity (15.19 mg g−1 as calculated by the Langmuir model). The adsorbed metal-enriched mushroom is a high-quality bio-ore by the virtue of its high metal content of industrial mining grade and easy metal extractability.

INTRODUCTION

Water contamination by heavy metals has been of great concern because of metal toxicity (Pahlavanzadeh et al. 2010; Acheampong et al. 2011; Blázquez et al. 2011; Kikuchi & Tanaka 2012). Copper is one of the metals that are of the most immediate concern (Pahlavanzadeh et al. 2010). Mining wastes, drainage discharges, plating baths, as well as fertilizer, paint, and pigment industry wastewaters contain undesirable amounts of copper ions (Blázquez et al. 2011). Several methods are available to reduce heavy metal concentration in wastewater. Such methods include chemical precipitation, ion exchange, and reverse osmosis. However, high costs restrict the widespread use of these approaches (Ertugay & Bayhan 2010). Compared with conventional physicochemical technologies, biosorption has attracted considerable interest over the last decades for its low cost, effectiveness, and high adsorption efficiency for low-concentration metal ions (Kleinübing et al. 2011; Yang et al. 2011; Kikuchi & Tanaka 2012). Biosorption is a process that utilizes inexpensive non-living biomass to sequester heavy metals through physicochemical adsorption (Kikuchi & Tanaka 2012). Mushroom (fruit body of macro-fungi, e.g., genus Pleurotus) has recently appeared as a potential biomass that can bind heavy metals even from diluted solutions (Ahluwalia & Goyal 2007; Javaid et al. 2011). Mushrooms have macro size, tough texture, and other physical characteristics that are conducive for their development as biosorbents without the need for immobilization or deployment of sophisticated reactor configuration, as in the case of microorganisms (Muraleedharan & Venkobachar 1994; Das et al. 2010). However, efforts to evaluate the absorption capacity of mushroom for the removal of metal ions are limited (Ahluwalia & Goyal 2007; Javaid et al. 2011). Pleurotus eryngii is a type of mushroom that has recently been widely cultivated in China. However, the potential of P. eryngii to remove metal ions remains unknown. This work mainly aimed to evaluate the adsorption performance of the widely cultivated novel mushroom, P. eryngii, for the removal of Cu(II) from single aqueous solutions.

MATERIALS AND METHODS

Preparation of dead fungal biomass

A fresh fruit body of macro-fungus (P. eryngii) was purchased from a local market and was investigated for its potential as a biosorbent. The chemical characteristics of the mushroom biomass were protein (1.6%), carbohydrate (8.5%), oil (0.1%), cellulose (2.7%), K (0.2%), Na (33.4 mg kg−1), Ca (126.8 mg kg−1), Mg (88.6 mg kg−1), and Cu (0.5 mg kg−1). The crude biomass was washed with tap water and distilled water, cut into small pieces, and then oven dried at 313 K for 24 h. The dried biomass was then milled mechanically. The particle size of the biomass was measured by passing successively through sieves of different sizes of 2, 1, 0.595, 0.425, and 0.300 mm. The particles that did not pass through a sieve were again milled until all materials had passed through a sieve of 0.425 mm size. The 0.300–0.425 mm particles were used for the experiments. The surface area of the biomass was 44.88 m2 g−1.

Preparation of Cu(II) solutions

The stock solution (1,000 mg dm−3) of Cu(II) was prepared by dissolving a known quantity of copper chloride (CuCl2•2H2O) in deionized water, after which the concentration of Cu(II) in the stock solution was measured. The stock solution was finally diluted to obtain the desired solutions. All chemicals used in this study were of analytical grade.

Biosorption experiments

Biosorption experiments were conducted in 250 mL conical flasks. Exactly 50 mL of Cu(II) solutions with the desired concentrations was equilibrated with the biosorbent for 360 min in a temperature-controlled water bath shaker at 120 r min−1 (adsorbent concentration = 2 g dm−3). The pH values of the solutions were all 5.5 and were adjusted by adding 0.1 mol dm−3 of HCl and NaOH solutions. The samples were filtered, and the Cu(II) concentration of the filtrate was measured by flame atomic absorption spectrometry (TAS990, P General China). The kinetic studies were performed in 1,000 mL conical flasks containing 600 mL of Cu(II) solutions. The samples of 5 mL solution were withdrawn at scheduled time intervals. The uptake of Cu(II) by biosorption at time t(qt) was calculated as follows: 
formula
1
 
formula
2
where i is the ith sampling, n is the total number of sampling times during time t, qi is the uptake of Cu(II) from the previous (i-1)th sampling to the ith sampling, Ci is the Cu(II) concentration (mg dm−3) of the ith withdrawn sample, V is initial volume of the solution (i.e., 0.6 dm3 in this study), v is sampling volume at a time (i.e., 0.005 dm3 in this study), and W is the mass of the biosorbent (g). The equilibrium uptake of Cu(II) (qe) was calculated as (Khan et al. 2012) 
formula
3
where Ce is the equilibrium Cu(II) concentration (mg dm−3) in the solution and V is volume of the solution (dm3).

Nonlinear regression analysis

All model parameters were evaluated by nonlinear regression using Origin 7.0 (OriginLab Corporation, USA). The correlation coefficient (R2), standard error (SE), and average percent errors (APEs) were used to evaluate the goodness of fit. SE and APE were calculated according to the methods described by Das et al. (2010).

RESULTS AND DISCUSSION

Blank and control studies revealed that the adsorption of Cu(II) onto the walls of the conical flasks was negligible and that the macro-fungus did not leach any metal into the aqueous solution.

Kinetic studies

The kinetics of adsorption is necessary for selecting the optimum operating conditions for the full-scale batch process. Cu(II) biosorption by P. eryngii biomass was very rapid during the early 5 min, and equilibrium was established after contact for approximately 20 min between the biomass and metal solution. Such rapid metal uptake is an advantage for the application of P. eryngii in water treatment practice (Zhu et al. 2008; Moussous et al. 2012). The duration of all biosorption tests in this study was set to 360 min, a contact time sufficient to reach equilibrium and stable qe values. The kinetics of biosorption of Cu(II) on P. eryngii were studied using two models: the pseudo-first-order and pseudo-second-order models.

Pseudo-first-order kinetic model

The Lagergren pseudo-first-order model is a kinetic model based on adsorption capacity (Nemr 2009). This model is expressed as the following linear equation (Nemr 2009; Ertugay & Bayhan 2010): 
formula
4
where k1 is the rate constant of pseudo-first-order adsorption (min−1). The fitted parameters are listed in Table 1. The correlation coefficients were low, and the fitted error values of APE percentage were high. Moreover, the experimental qe values did not agree with the estimated ones obtained from the linear plots of log (qeqt) versus t. All these results indicate that the pseudo-first-order model is unsuitable for modeling the adsorption of Cu(II) onto P. eryngii.
Table 1

Pseudo-first-order and pseudo-second-order rate constants, calculated and experimental qe values for different initial Cu(II) concentrations

 Pseudo-first-order Pseudo-second-order 
 C0 = 30 mg L−1 C0 = 60 mg L−1 C0 = 30 mg L−1 C0 = 60 mg L−1 
R2 0.9650 0.5980 0.9996 0.9978 
SE 0.11 0.36 0.24 0.39 
APE (%) 42.29 63.80 3.07 4.60 
k1 0.0513 0.0372   
k2   0.0509 0.0429 
qe-estimated 2.11 1.63 3.68 5.87 
qe-experiment 3.53 5.47 3.53 5.80 
 Pseudo-first-order Pseudo-second-order 
 C0 = 30 mg L−1 C0 = 60 mg L−1 C0 = 30 mg L−1 C0 = 60 mg L−1 
R2 0.9650 0.5980 0.9996 0.9978 
SE 0.11 0.36 0.24 0.39 
APE (%) 42.29 63.80 3.07 4.60 
k1 0.0513 0.0372   
k2   0.0509 0.0429 
qe-estimated 2.11 1.63 3.68 5.87 
qe-experiment 3.53 5.47 3.53 5.80 

Pseudo-second-order kinetic model

The pseudo-second-order kinetic model is expressed below (Ertugay & Bayhan 2010; Ding et al. 2012) 
formula
5
where k2 is the rate constant of pseudo-second-order sorption (g mg−1 min−1). The plots of t/qt against t showed a good linear relationship (Figure 1), with high regression coefficients (R2 > 0.997), low standard error value, and low APE percentage (Table 1). Furthermore, as indicated in Table 1, the calculated theoretical qe values inferred from the pseudo-second-order model agreed well with the detected experimental values. Thus, the pseudo-second-order model is well suited to model the biosorption kinetics of Cu(II) onto P. eryngii. Other researchers have also reported the suitability of the pseudo-second-order model for the adsorption of heavy metal ions by macro-fungus biomass (Ertugay & Bayhan 2010) and other biosorbents (Ding et al. 2012; Bakyayita et al. 2014).
Figure 1

Pseudo-second-order kinetics for Cu(II) ion adsorption onto P. eryngii. Varying initial metal concentrations (C0, 30 and 60 mg dm−3) were used. Adsorbent concentration = 2 g dm−3, temperature = 303 K, pH = 5.5, agitating rate = 120 r min−1.

Figure 1

Pseudo-second-order kinetics for Cu(II) ion adsorption onto P. eryngii. Varying initial metal concentrations (C0, 30 and 60 mg dm−3) were used. Adsorbent concentration = 2 g dm−3, temperature = 303 K, pH = 5.5, agitating rate = 120 r min−1.

Adsorption isotherms

Isotherm data analysis is essential for developing an equation which will accurately represent adsorption characteristics at equilibrium and could be useful for designing the use of adsorbent (Nemr 2009; Das et al. 2010). In addition, the adsorption isotherm can be used to describe the interaction between solute and adsorbent and is thus critical to the optimization of the operating procedure (Nemr 2009; Das et al. 2010). In the present study, Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich (D–R) adsorption isotherm models were used to describe the adsorption equilibrium at various temperatures.

Langmuir isotherm

The Langmuir isotherm model can be applied in the following nonlinear form (Langmuir 1916): 
formula
6
where Qm is the maximum adsorption capacity of biosorbent (mg g−1) and KL is the adsorption equilibrium constant (dm3 mg−1). As shown in Table 2, the Langmuir model exhibited the best fit to the experimental adsorption data by having higher R2 and lower error values (SE and APE) than the other models. This finding suggests a homogeneous and monolayer mode of adsorption. The estimated Qm decreased with increasing temperature, indicating that the Cu(II) adsorption process is exothermic in nature. The results are in accordance with the previously reported results on the adsorption of Ag(I) by another macro-fungus Pleurotus platypus (Das et al. 2010).
Table 2

Isotherm constants of various adsorption models for Cu(II) adsorption on P. eryngii at different temperatures

 293 K 303 K 313 K 
Langmuir 
Qm (mg g−115.19 10.04 9.74 
KL 0.01 0.02 0.01 
R2 0.994 0.982 0.978 
 SE 0.21 0.20 0.32 
 APE (%) 6.45 4.48 6.89 
Freundlich 
KF ((mg g−1) (mg L−1)−1/n0.32 0.61 0.38 
n 1.44 1.86 1.67 
R2 0.984 0.962 0.975 
 SE 0.34 0.29 0.34 
 APE (%) 11.68 7.58 5.05 
Temkin 
b 908.44 1,093.17 1,355.73 
BT 2.68 2.30 1.92 
AT (dm3 mg−10.15 0.19 0.19 
R2 0.980 0.987 0.956 
 SE 0.38 0.16 0.45 
 APE (%) 10.78 2.60 10.47 
D–R 
Qm (mg g−17.43 6.35 5.91 
KDR (mol2 kJ−2107.30 61.23 99.02 
E (kJ mol−10.0683 0.0904 0.0711 
R2 0.919 0.904 0.827 
 SE 0.76 0.48 0.89 
 APE (%) 27.74 12.36 23.63 
 293 K 303 K 313 K 
Langmuir 
Qm (mg g−115.19 10.04 9.74 
KL 0.01 0.02 0.01 
R2 0.994 0.982 0.978 
 SE 0.21 0.20 0.32 
 APE (%) 6.45 4.48 6.89 
Freundlich 
KF ((mg g−1) (mg L−1)−1/n0.32 0.61 0.38 
n 1.44 1.86 1.67 
R2 0.984 0.962 0.975 
 SE 0.34 0.29 0.34 
 APE (%) 11.68 7.58 5.05 
Temkin 
b 908.44 1,093.17 1,355.73 
BT 2.68 2.30 1.92 
AT (dm3 mg−10.15 0.19 0.19 
R2 0.980 0.987 0.956 
 SE 0.38 0.16 0.45 
 APE (%) 10.78 2.60 10.47 
D–R 
Qm (mg g−17.43 6.35 5.91 
KDR (mol2 kJ−2107.30 61.23 99.02 
E (kJ mol−10.0683 0.0904 0.0711 
R2 0.919 0.904 0.827 
 SE 0.76 0.48 0.89 
 APE (%) 27.74 12.36 23.63 

The range of equilibrium concentrations used in the study was 8–98 mg L−1.

The Langmuir parameters can be used to predict the affinity between the adsorbate and adsorbent using the dimensionless separation factor RL (Oguz 2005; Acheampong et al. 2011), which can be described as follows: 
formula
7
where KL is the Langmuir adsorption equilibrium constant. The 0 < RL < 1 indicates favorable adsorption, and RL > 1 denotes unfavorable adsorption (Oguz 2005; Acheampong et al. 2011). The RL values were found in the range of 0.47–0.90, 0.32–0.81, and 0.39–0.84 at 293, 303, and 213 K, respectively, indicating that the adsorption of Cu(II) is favorable for the biosorbent property of P. eryngii. In addition, it has remarkable biosorption capacity compared with various other biomasses (Appendix Table A1, available online at http://www.iwaponline.com/wst/071/511.pdf).

Freundlich isotherm

The Freundlich isotherm model was chosen to estimate the adsorption intensity of Cu(II) ions on the P. eryngii surface. The Freundlich model assumes that adsorption occurs on a heterogeneous surface accompanied by the interactions between adsorbed molecules (Nemr 2009). This model has the following nonlinear form (Freundlich 1906): 
formula
8
where KF and n are constants related to adsorption affinity(Nemr 2009; Xu et al. 2009). A value of n between 1 and 10 indicates a favorable adsorption process (Goldberg et al. 2005; Murugesan et al. 2011; Khan et al. 2012). Also, n > 1 indicates that the adsorption is favored by the physisorption process (Murugesan et al. 2011). In this study, the n values (1.44–1.86) were higher than 1.0 but less than 10 (Table 2), illustrating that Cu(II) ions are favorably adsorbed by P. eryngii, which is consistent with the former findings on the separation factor RL. The adsorption is favored by the physisorption process. Interestingly, the variation of those constants related to adsorption affinity (KL, KF, and n) with temperature was not regular (Table 2). Similar results were also found in other biomasses (Wang et al. 2009), while the constant of adsorption capacity (Qm) increased regularly with the rising temperature. This indicates that adsorption capacity is controlled by not only the affinity characteristics but also other factors. Deep studies are still required to investigate the adsorption mechanisms and environmental factors influencing adsorption capacity.

Temkin isotherm

The Temkin isotherm assumes that the decrease in the heat of adsorption is linear rather than logarithmic, as implied in the Freundlich isotherm. The Temkin equation has generally been used in the following form (Nemr 2009): 
formula
9
where R is the universal gas constant (8.314 J mol−1 K−1); T is the absolute temperature (K); the constant b (J mol−1) is related to the heat of adsorption (Nemr 2009); and BT = (RT)/b and AT (dm3 mg−1) represent the heat of adsorption and equilibrium binding constant, respectively (Başar 2006). The constant obtained for Temkin isotherms at various temperatures in Table 2 shows that BT decreased with increasing temperature, indicating exothermic adsorption (Başar 2006). These results agree with the former results from the Langmuir equation.

D–R isotherm

The D–R equation was chosen to estimate the porosity and the apparent free energy of adsorption (Liu & Liu 2008; Nemr 2009). This model has been generally applied as follows (Nemr 2009): 
formula
10
 
formula
11
where KDR (mol2 kJ−2) is a constant related to the adsorption energy, Qm (mg g−1) is the theoretical monolayer saturation capacity, and ɛ is the Polanyi potential. The mean adsorption free energy E (kJ mol−1) can be calculated as follows: 
formula
12
The mean adsorption free energy gives information about chemical and physical adsorption (Başar 2006). In this study, the calculated E was in the range of 0.068–0.090 kJ mol−1 (Table 2), which is lower than the energy range of the adsorption reaction at 8–16 kJ mol−1 (Oguz 2005; Başar 2006). The type of adsorption of Cu(II) on the P. eryngii biomass is thus defined as physical adsorption (Oguz 2005; Başar 2006). In addition, we discuss the treatment and disposal of the adsorbed biomass. When 2 g dm−3P. eryngii was exposed to >50 mg dm−3 Cu(II) solution, the qe reached 4.5–6.7 mg g−1. According to the China National Standard of ‘Specifications of copper, lead, zinc, silver, nickel, and molybdenum mineral exploration (DZ/T 0214–2002)’, the lowest industrial mining grade for Cu in a sulfide ore is 0.4% (4 mg g−1) for surface mining, and the average mining grade is 0.4–0.6% (4–6 mg g−1). The copper content in the P. eryngii biomass exceeded the lowest industrial mining grade and reached the average mining grade. The adsorbed metal-enriched macro-fungus P. eryngii can be disposed as a high-quality bio-ore, and the adsorbed metal can be easily recovered.

CONCLUSIONS

The fungal species P. eryngii has been identified as an efficient biosorbent for removing Cu(II) from single aqueous solution. This fungus has exhibited fast adsorption and remarkable biosorption capacity. The kinetic studies proved that the pseudo-second-order kinetic is the applicable model. The Langmuir model exhibited the best fit to the experimental adsorption data. The maximum adsorption capacity was 15.19 mg g−1, as calculated by the Langmuir model. The type of adsorption is physical and exothermic in nature. The adsorbed metal-enriched macro-fungus P. eryngii is a high-quality bio-ore by virtue of its high Cu content of industrial mining grade and easy metal recovery.

ACKNOWLEDGEMENTS

This work was supported by Open Project of Key Laboratory of Biochemistry & Molecular Biology in Universities of Shandong, Weifang science and technology development project (201301010 and 20121340), and Shandong spark program (2011XH06027).

REFERENCES

REFERENCES
Acheampong
M. A.
Pereira
J. P. C.
Meulepas
R. J. W.
Lens
P. N. L.
2011
Biosorption of Cu(II) onto agricultural materials from tropical regions
.
Journal of Chemical Technology & Biotechnology
86
(
9
),
1184
1194
.
Ahluwalia
S. S.
Goyal
D.
2007
Microbial and plant derived biomass for removal of heavy metals from wastewater
.
Bioresource Technology
98
(
12
),
2243
2257
.
Bakyayita
G. K.
Norrström
A. C.
Nalubega
M.
Kulabako
R. N.
2014
Kinetic studies of Cd (II) and Pb (II) ions biosorption from aqueous media using untreated and chemically treated biosorbents
.
Water Science & Technology
69
(
11
),
2230
2236
.
Blázquez
G.
Martín-Lara
M. A.
Dionisio-Ruiz
E.
Tenorio
G.
Calero
M.
2011
Evaluation and comparison of the biosorption process of copper ions onto olive stone and pine bark
.
Journal of Industrial and Engineering Chemistry
17
(
5–6
),
824
833
.
Ding
Y.
Jing
D.
Gong
H.
Zhou
L.
Yang
X.
2012
Biosorption of aquatic cadmium(II) by unmodified rice straw
.
Bioresource Technology
114
,
20
25
.
Freundlich
H. M. F.
1906
Über die adsorption in lösungen
.
Zeitschrift für Physikalische Chemie (Leipzig)
57A
,
385
470
.
Goldberg
S.
Tabatabai
M.
Sparks
D.
Al-Amoodi
L.
Dick
W.
2005
Equations and models describing adsorption processes in soils
. In:
Chemical Processes in Soils
, Soil Science Society of America Book Series Number 8 (
Tabatabai
M. A.
Sparks
D. L.
Al-Amoodi
L.
Dick,
W. A.
eds).
Soil Science Society of America
,
Madison, WI
, pp.
489
517
.
Javaid
A.
Bajwa
R.
Shafique
U.
Anwar
J.
2011
Removal of heavy metals by adsorption on Pleurotus ostreatus
.
Biomass and Bioenergy
35
(
5
),
1675
1682
.
Khan
M. A.
Ngabura
M.
Choong
T. S. Y.
Masood
H.
Chuah
L. A.
2012
Biosorption and desorption of nickel on oil cake: batch and column studies
.
Bioresource Technology
103
(
1
),
35
42
.
Kikuchi
T.
Tanaka
S.
2012
Biological removal and recovery of toxic heavy metals in water environment
.
Critical Reviews in Environmental Science and Technology
42
(
10
),
1007
1057
.
Langmuir
I.
1916
The constitution and fundamental properties of solids and liquids. Part I. Solids
.
Journal of the American Chemical Society
38
(
11
),
2221
2295
.
Liu
Y.
Liu
Y.-J.
2008
Biosorption isotherms, kinetics and thermodynamics
.
Separation and Purification Technology
61
(
3
),
229
242
.
Moussous
S.
Selatnia
A.
Merati
A.
Junter
G. A.
2012
Batch cadmium(II) biosorption by an industrial residue of macrofungal biomass (Clitopilus scyphoides)
.
Chemical Engineering Journal
197
,
261
271
.
Muraleedharan
T.
Venkobachar
L. I.
1994
Further insight into the mechanism of biosorption of heavy metals by Ganoderma lucidum
.
Environmental Technology
15
(
11
),
1015
1027
.
Murugesan
A.
Ravikumar
L.
SathyaSelvaBala
V.
SenthilKumar
P.
Vidhyadevi
T.
Kirupha
S. D.
Kalaivani
S.
Krithiga
S.
Sivanesan
S.
2011
Removal of Pb(II), Cu(II) and Cd(II) ions from aqueous solution using polyazomethineamides: equilibrium and kinetic approach
.
Desalination
271
(
1
),
199
208
.
Oguz
E.
2005
Adsorption characteristics and the kinetics of the Cr(VI) on the Thuja orientalis
.
Colloids and Surfaces A: Physicochemical and Engineering Aspects
252
(
2
),
121
128
.
Pahlavanzadeh
H.
Keshtkar
A. R.
Safdari
J.
Abadi
Z.
2010
Biosorption of nickel(II) from aqueous solution by brown algae: equilibrium, dynamic and thermodynamic studies
.
Journal of Hazardous Materials
175
(
1–3
),
304
310
.
Zhu
B.
Fan
T.
Zhang
D.
2008
Adsorption of copper ions from aqueous solution by citric acid modified soybean straw
.
Journal of Hazardous Materials
153
(
1
),
300
308
.

Supplementary data