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Volume 27 2015
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1
The potential risk assessment for different arsenic species in the aquatic environment
Meng Du, Dongbin Wei, Zhuowei Tan, Aiwu Lin, and Yuguo Du
9
Synthesis of linear low-density polyethylene-g -poly (acrylic acid)-co-starch/organo-montmorillonite
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Maryam Irani, Hanafi Ismail, Zulkifli Ahmad, and Maohong Fan
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Yian Zheng, Jintao Wang, Yongfeng Zhu, and Aiqin Wang
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59
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Changhong Chen, Hongli Wang, Fang Zhang, Qing Yu, and Qiong Wu
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Jianfa Gao, Jake O'Brien, Foon Yin Lai, Alexander L.N. van Nuijs, Jun He, Jochen F. Mueller,
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Shuang Deng, Junping Xie, and Shicheng Long
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Modulation of the DNA repair system and ATR-p53 mediated apoptosis is relevant for tributyltininduced genotoxic effects in human hepatoma G2 cells
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124
Enhanced bio-decolorization of 1-amino-4-bromoanthraquinone-2-sulfonic acid by Sphingomonas
xenophaga with nutrient amendment
Hong Lu, Xiaofan Guan, Jing Wang, Jiti Zhou, Haikun Zhang
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Winter survival of microbial contaminants in soil: An in situ verification
Antonio Bucci, Vincenzo Allocca, Gino Naclerio, Giovanni Capobianco, Fabio Divino,
Francesco Fiorillo, and Fulvio Celico
139
Assessment of potential dermal and inhalation exposure of workers to the insecticide imidacloprid using
whole-body dosimetry in China
Lidong Cao, Bo Chen, Li Zheng, Dongwei Wang, Feng Liu, and Qiliang Huang
CONTENTS
147
Biochemical and microbial soil functioning after application of the insecticide imidacloprid
Mariusz Cycoń and Zofia Piotrowska-Seget
159
Comparison of three-dimensional fluorescence analysis methods for predicting formation of
trihalomethanes and haloacetic acids
Nicolás M. Peleato and Robert C. Andrews
168
The migration and transformation of dissolved organic matter during the freezing processes of water
Shuang Xue, Yang Wen, Xiujuan Hui, Lina Zhang, Zhaohong Zhang, Jie Wang, and Ying Zhang
179
Genomic analyses of metal resistance genes in three plant growth promoting bacteria of legume
plants in Northwest mine tailings, China
Pin Xie, Xiuli Hao, Martin Herzberg, Yantao Luo, Dietrich H. Nies, and Gehong Wei
188
Effect of environmental factors on the complexation of iron and humic acid
Kai Fang, Dongxing Yuan, Lei Zhang, Lifeng Feng, Yaojin Chen, and Yuzhou Wang
197
Resolving the influence of nitrogen abundances on sediment organic matter in macrophyte-dominated
lakes, using fluorescence spectroscopy
Xin Yao, Shengrui Wang, Lixin Jiao, Caihong Yan, and Xiangcan Jin
207
Predicting heavy metals' adsorption edges and adsorption isotherms on MnO2 with the parameters
determined from Langmuir kinetics
Qinghai Hu, Zhongjin Xiao, Xinmei Xiong, Gongming Zhou, and Xiaohong Guan
217
Applying a new method for direct collection, volume quantification and determination of N2 emission
from water
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225
Effects of water management on arsenic and cadmium speciation and accumulation in an upland rice cultivar
Pengjie Hu, Younan Ouyang, Longhua Wu, Libo Shen, Yongming Luo, and Peter Christie
232
Acid-assisted hydrothermal synthesis of nanocrystalline TiO2 from titanate nanotubes: Influence of acids on
the photodegradation of gaseous toluene
Kunyang Chen, Lizhong Zhu, and Kun Yang
241
Air–soil exchange of organochlorine pesticides in a sealed chamber
Bing Yang, Baolu Han, Nandong Xue, Lingli Zhou, and Fasheng Li
251
Effects of elevated CO2 on dynamics of microcystin-producing and non-microcystin-producing strains during
Microcystis blooms
Li Yu, Fanxiang Kong, Xiaoli Shi, Zhen Yang, Min Zhang, and Yang Yu
259
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Yanchen Liu, Chen Wu, Xiaohong Zhou, David Z. Zhu, and Hanchang Shi
266
Steel slag carbonation in a flow-through reactor system: The role of fluid-flux
Eleanor J. Berryman, Anthony E. Williams-Jones, and Artashes A. Migdisov
276
Amine reclaiming technologies in post-combustion carbon dioxide capture
Tielin Wang, Jon Hovland, and Klaus J. Jens
290
Do vehicular emissions dominate the source of C6–C8 aromatics in the megacity Shanghai of eastern China?
Hongli Wang, Qian Wang, Jianmin Chen, Changhong Chen, Cheng Huang, Liping Qiao, Shengrong Lou,
and Jun Lu
298
Insights into metals in individual fine particles from municipal solid waste using synchrotron radiation-based
micro-analytical techniques
Yumin Zhu, Hua Zhang, Liming Shao, and Pinjing He
J O U RN A L OF E N V I RO N M EN TA L S CI EN CE S 27 (2 0 1 5 ) 2 0 7–2 1 6
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Predicting heavy metals' adsorption edges and adsorption
isotherms on MnO2 with the parameters determined from
Langmuir kinetics
Qinghai Hu, Zhongjin Xiao, Xinmei Xiong, Gongming Zhou, Xiaohong Guan⁎
State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University,
Shanghai 200092, China. Email: [email protected]
AR TIC LE I N FO
ABS TR ACT
Article history:
Although surface complexation models have been widely used to describe the adsorption of
Received 26 February 2014
heavy metals, few studies have verified the feasibility of modeling the adsorption kinetics,
Revised 7 May 2014
edge, and isotherm data with one pH-independent parameter. A close inspection of the
Accepted 11 May 2014
derivation process of Langmuir isotherm revealed that the equilibrium constant derived from
Available online 13 November 2014
the Langmuir kinetic model, KS-kinetic, is theoretically equivalent to the adsorption constant
Keywords:
modified Langmuir isotherm model (MLI model) incorporating pH factor were developed. The
Heavy metals
MLK model was employed to simulate the adsorption kinetics of Cu(II), Co(II), Cd(II), Zn(II) and
Adsorption edge
Ni(II) on MnO2 at pH 3.2 or 3.3 to get the values of KS-kinetic. The adsorption edges of heavy
Adsorption isotherm
metals could be modeled with the modified metal partitioning model (MMP model), and the
Adsorption constants
values of KS-Langmuir were obtained. The values of KS-kinetic and KS-Langmuir are very close
in Langmuir isotherm, KS-Langmuir. The modified Langmuir kinetic model (MLK model) and
to each other, validating that the constants obtained by these two methods are basically the
same. The MMP model with KS-kinetic constants could predict the adsorption edges of heavy
metals on MnO2 very well at different adsorbent/adsorbate concentrations. Moreover, the
adsorption isotherms of heavy metals on MnO2 at various pH levels could be predicted
reasonably well by the MLI model with the KS-kinetic constants.
© 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences.
Published by Elsevier B.V.
⁎ Corresponding author. E-mail: [email protected]
(Xiaohong Guan).
.cn
The behavior, transport, and ultimate fate of heavy metals in aquatic
environment are often controlled by the sorption reactions on the
reactive surfaces of soil minerals (Wen et al., 1998; Serrano et al., 2009).
Moreover, adsorption is found to be a promising technique to remove
heavy metal ions from aqueous solutions because of its low
operational and maintenance costs and high efficiency, especially
for the heavy metal ions at low concentrations (Shi et al., 2005).
Different empirical approaches, in particular the measurements of
adsorption kinetics, adsorption isotherms and adsorption edges, have
been commonly used in the studies of the adsorption behavior of
heavy metals on various adsorbents including minerals. However, it
is troublesome to get these data under various practical conditions.
Thus, accurate description of the sorption process with mathematic
modeling is critical for the assessment of environmental risk of heavy
metals and the development of sound adsorption technologies for
removing heavy metals. Therefore, sorption modeling has received
considerable attention.
Many studies have been performed on surface complexation
models (constant capacitance model, double layer model, triple layer
model, and charge distribution multi-site complexation model) to
describe the adsorption of heavy metals on pure mineral surfaces or
soils and the results are quite promising (Wen et al., 1998; Ponthieu et
al., 2006; Weerasooriya et al., 2007). These above-mentioned models
incorporate the surface electrostatic effects and thus contain many
.ac
Introduction
je
sc
http://dx.doi.org/10.1016/j.jes.2014.05.036
1001-0742/© 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S 27 (2 0 1 5 ) 2 07–2 1 6
The consistency between the Langmuir kinetic rate constant
(equilibrium constant) and the Langmuir isotherm parameter (adsorption constant) can be expected since the Langmuir isotherm
expression is derived based on the Langmuir kinetics (Yiacoumi and
Tien, 1995a, 1995b). Moreover, Wang et al. (2004) incorporated the
variation of surface site density with pH in the Langmuir isotherm
and then developed a modified metal partitioning model (MMP
model) to simulate and predict the adsorption edges of heavy metals
on fly ash very well with only one parameter, KS (adsorption
constant). Therefore, it is feasible to model the adsorption kinetics,
edge, and isotherm data with one pH-independent parameter,
adsorption constant or equilibrium constant, by developing proper
adsorption models. However, to the best of our knowledge, there was
no researcher taking advantage of the consistency between the
equilibrium constant and adsorption constant to predict the adsorption edges and isotherms based on the equilibrium constant derived
from the adsorption kinetics.
Manganese oxides are highly reactive minerals in sedimentary
environments, where they influence the fate and transport of
potentially toxic trace elements and essential micronutrients
(Frierdich and Catalano, 2012). Moreover, manganese dioxide
(MnO2) exhibits higher affinity and larger adsorption capacity for
heavy metals than other metal oxides such as Al2O3, Fe2O3, Fe3O4,
and TiO2 (Tamura et al., 1996, 1997). Therefore, MnO2 was employed
as the adsorbent in this study. The objectives of this article were (1)
to incorporate the pH effects into the Langmuir kinetic model and
employ this modified Langmuir kinetic model (MLK model) to
simulate and calculate the adsorption kinetics of heavy metals on
MnO2; (2) to calculate the adsorption edges and adsorption
isotherms of heavy metals on MnO2 with the equilibrium constants
obtained from the MLK model; and (3) to further verify this practice
by comparing the simulation results and parameters with the MMP
model developed by Wang et al. (2004, 2006).
1. Materials and methods
1.1. Materials
All chemicals were of analytical grade and were purchased
from Shanghai Reagent Station (China). The stock solutions
containing target heavy metals were prepared by dissolving
their corresponding chloride salts into 18.2 MΩ Milli-Q water
and acidified with HCl to pH 2.0.
1.2. Batch equilibrium titration
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Surface site densities and acidity constants of MnO2 were
determined through a batch acidimetric-alkalimetric titration
method. Unless otherwise specified, 0.01 mol/L NaCl was used
as a background electrolyte in all experiments. The titration
procedure consisted of: (1) adding appropriate amount of MnO2
to double distilled deionized water and shake for 12 hr to
disperse the aggregates and hydrate adsorption sites prior to
each experiment; (2) distributing 100 mL of the suspension to a
series of 125-mL conical flasks; (3) adding different amounts of
acid (HCl) and base (NaOH) to the 125-mL flasks to adjust pH to a
range of 2.0–10.0; a control flask without acid or base addition
was included in each batch; (4) capping all flasks and then
shake at 220 oscillation/min using an SHZ-C shaker for 24 hr at
25 ± 1°C; (5) recording the final pH and plot the acid/base
addition volume as a function of pH to obtain an overall
titration curve; (6) filtering the suspensions and titrate the
filtrate (50 mL) back to the final pH of the control; and (7)
sc
empirical parameters, which makes the modeling process very
complex and limits their practical application (Guan et al., 2009).
Venema et al. (1996) discussed the scope and limitations of such
models and commented that, none of them can simultaneously
describe all available data for a particular extended data set although
most of these models can satisfactorily quantify the adsorption
phenomenon under the specific conditions used in the experiments.
Background electrolyte within the environmentally relevant ionic
strength range of 0.005–0.04 mol/L (in lakes, rivers, groundwaters but
not in sea) has negligible impact on the adsorption of metals over the
pH range studied, as illustrated in Appendix A Fig. S1, suggesting that
metal ions are specifically adsorbed on the MnO2 (Christophi and Axe,
2000). The negligible influences of ionic strength on heavy metal
adsorption on various minerals had also been reported (Christophi
and Axe, 2000; Tamura and Furuichi, 1997). Mahmudov and Huang
(2011) reported that the change of specific chemical energy constituted the
major portion of the change of total free energy of adsorption whereas
electrostatic energy appeared to play a much less significant role on the
adsorption process. It was argued that simpler hypotheses are more likely
to be a correct priori to make sharper, more testable predictions (Jeffery and
Berger, 1992). McBride (1997) pointed out that the adsorption behavior of
mineral surfaces can usually be understood in terms of simple mass action
principles and coordination chemistry without involving electrostatic
effects. Thus, the adsorption models ignoring the electrostatic effects may
be preferred for practical applications.
Serrano et al. (2009) developed a non-electrostatic equilibrium
model with both surface complexation and ion exchange reactions to
describe the sorption of heavy metals on four soils over a wide range
of pH and metal concentration. Pagnanelli and his colleagues
presented two approaches to model the effect of pH on heavy metal
adsorption on biomaterials (Esposito et al., 2002; Pagnanelli et al.,
2003). One introduced qmax as a pH function into the Langmuir
isotherm to obtain the heavy metal biosorption capacities at different
pH and the other was based on the non-competitive mechanism
between heavy metals and H+ protons (Esposito et al., 2002; Pagnanelli
et al., 2003). To describe the binding of heavy metals to humic
substances containing heterogeneous ligands, Benedetti et al. (1995)
developed a Non-Ideal Competitive Adsorption model based on a
Langmuir–Freundlich type affinity distribution function. Wang et al.
(2004, 2006) developed a modified metal partitioning model from the
Langmuir isotherm which could successfully quantify the adsorption
edges of heavy metals on fly ash. Rudzinski and Plazinski (2010) also
presented an equilibrium sorption isotherm equation incorporating
pH effects, which could describe the pH-dependent adsorption of
heavy metals.
However, the above-mentioned studies did not relate the parameters of adsorption kinetics with those of adsorption edges and
isotherms. The kinetics of adsorption is one of the most important
factors in adsorption system design, since the adsorbate residence
time and the reactor dimensions are controlled by the adsorption/
desorption kinetics. Various kinetic models, such as Statistical Rate
Theory approach, pseudo-first order, pseudo-second order, and the
Elovich equations, had been proposed to simulate the adsorption
kinetics (Plazinski et al., 2009). Their theoretical derivations existing in
literature could not be interpreted as general ones but rather as those
related to a specific system or to a limited range of operating
conditions (Plazinski et al., 2009). The common practice is to
determine the adsorption rate constants by simulating the adsorption
kinetics of heavy metals on minerals obtained under various
conditions with a specific adsorption kinetic model. Thus, the
adsorption rate constants calculated with these models are specific
and not inter-related. For instance, the rate constants obtained
by these models are pH-specific. One reason is that none of these
models incorporates pH effect, which strongly affects the adsorption
processes (Wang et al., 2004). Plazinski and his colleague modified the
statistical rate approach, which could describe the pH-dependent
kinetics of metal ion adsorption with one rate constant, but there
were two uncertain parameters (α or β) ranging from 0 to 1 (Plazinski
and Rudzinski, 2009; Plazinski, 2010).
je
208
209
J O U RN A L OF E N V I RO N M EN TA L S CI EN CE S 27 (2 0 1 5 ) 2 0 7–2 1 6
developing the net titration curve by subtracting the acid/base
consumed by the supernatant from the overall titration curve
under the same pH conditions. Carbon dioxide was not purged
since the impact arose from dissolved CO2 was counteracted by
subtracting the acid/base consumed in back titration from the
corresponding forward titration.
1.3. Batch adsorption experiments
The batch adsorption experiments were carried out to investigate
the adsorption kinetics, adsorption edges and adsorption isotherms of heavy metals on MnO2. The first two steps of adsorption
experiments were the same as those of the titration experiments.
The pH of the solute solutions and the MnO2 suspension were
adjusted to the desired level with NaOH and/or HCl solution before
the adsorption experiments. The adsorption reaction was initiated
by dosing pre-determined amount of stock solution of heavy
metals to the MnO2 suspension. Then the flasks were sealed and
shaken at 220 oscillation/min using an SHZ-C shaker (purchased
from Shanghai Jiapeng Co., Ltd) at 25 ± 1°C. To investigate the
adsorption kinetics, the flasks were taken out from the shaker at
different time intervals. However, the adsorption reaction lasted
for 24 hr to determine the adsorption edges and isotherms.
1.4. Chemical analyses
The morphology of MnO2 was observed with a scanning electron
microscope (Philips XL-30 ESEM purchased from Philips-FEI). The
particle size was determined with laser particle analyzer (Better
Size 2000). The X-ray diffraction pattern of MnO2 was collected on
a Bruker D & Advance X-ray powder diffractometer (D8 Advance
X). The specific surface area was determined by the Brunauer–
Emmett–Teller (BET) (Micromeritics ASAP 2020 Surface Area and
Porosity Analyzer) method. ZetaSizer Nano ZS 90 (Malvern
Instruments, Worcestershire, UK) was used to determine the
zeta potential of MnO2. Our preliminary study confirmed that the
same results were obtained using either 0.22 or 0.45 μm
membrane filter made of cellulose acetate. Thus, at the end of
each adsorption test, the suspensions were immediately filtered
through a 0.45 μm membrane filter and the filtrates were
collected, acidified, and analyzed using the ICP-AES (Agilent
720-ES manufactured by Agilent Technologies.) to determine the
concentration of heavy metals. A pHS-3C pH electrode supplied
by INESA Scientific Instrument Co., Ltd, calibrated with buffers at
pH 4.00, 6.86, and 9.18, was used to measure pH.
1.5. Data analyses
SigmaPlot, a software that can perform nonlinear regression, was
employed to fit the titration curves of MnO2 to determine
its surface density and acidity as well as to fit the metal adsorption
data to determine the adsorption constants of metals.
Wang et al., 2004, 2008). The following Eq. (1) was employed to
simulate the net titration data for determining the surface
site concentration and the corresponding acidity constant, based
on the assumption that multiple types of monoprotic surface sites
were present on the particle surface (Wang et al., 2004):
ΔV SS ¼
X
V 0 STi K Hi
i ¼ 1−n
C
(
1
1
þ
− H þ K Hi Hþ 0 þ K Hi
)
ð1Þ
where, ΔVSS (mL) is the net volume of stock acid/base (negative
value for acid) solution consumed by surface sites; V0 (mL) is the
total volume of MnO2 suspension; STi (mol/g) is the total
concentration of surface site species i; KHi is the acidity constant
of species i; C (mol/L) is the concentration of the acid/base stock
solution; and [H+]0 (mol/L) is the hydrogen ion concentration of the
control unit. Note the total surface site concentration STi =
Γi × SS., here Γi is the surface site density for species i (mol/g)
and SS (g/L) is the solid concentration.
The net volume of stock acid/base solution consumed by
surface sites under a certain pH condition, ΔVSS, an be
calculated using Eq. (2):
ΔV SS ¼ ΔV overall −ΔV water
ð2Þ
where, ΔVoverall (mL) is the total volume of acid or base added
during titration and ΔVwater for a certain pH is determined
based on the ΔVwater–pH curve, and the ΔVwater–pH curve is
determined by a titration experiment using a water sample
that has the same ionic strength as that in MnO2 suspensions.
2.2. Incorporation of pH effect into the Langmuir kinetic model
The adsorption reaction on metal oxide surface, postulating
that there is one type of surface site and two surface reactions
controlling the metal adsorption, can be elucidated by
Eqs. (3)–(4):
SOH⇌SO− þ Hþ
ð3Þ
KH
−
where, SOH and SO are the protonated and deprotonated
MnO2 sites, respectively; KH is the acidity constant.
ka
SO− þ M2þ ⇌ SOMþ
kb
ð4Þ
KS
where, M2+ is the dissolved free divalent heavy metal ion;
SOM+ is metal-adsorbent complex, KS (L/mol) is the equilibrium constant; ka is the adsorption rate constant, and kd is the
desorption rate constant. Moreover, it is assumed that one
free-surface site (SO−) binds one metal ion (Wang et al., 2004).
According to Langmuir kinetic model, the adsorption
kinetics of heavy metals can be expressed as:
−
h
i
d M2þ
dt
h
i
¼ ka M2þ ½SO− −kd SOMþ
ð5Þ
2.1. Titration model
h
i
d M2þ =dt ¼ 0;
The determination of surface site density and acidity is a key
step for adsorption modeling (Guan et al., 2009; Su et al., 2008;
which is referred to KS-kinetic in the following part.
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2. Model development
where, [ ] (mol/L) stands for the aqueous concentration, and t
(sec) is time.
At equilibrium,
ð6Þ
je
sc
.ac
K S ¼ ka =kd
210
J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S 27 (2 0 1 5 ) 2 07–2 1 6
The following mass balance equations are considered:
metal adsorption, the dissolved metal concentration can be
expressed as:
Surface site balance:
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r
2 4M ffi
1
MT
T
MT
1
−
þ
1−
þ
1
þ
−
þ
h
i
ST
αH ST K S
αH ST K S
ST
ST
2þ
M
¼
2
ST
ST ¼ ½SO− þ ½SOH þ SOMþ
ð7Þ
Metal balance:
h
i MT ¼ M2þ þ SOMþ
ð8Þ
where, ST (mol/L) and MT (mol/L) are the total surface site
concentration and the total metal concentration, respectively. It is postulated that the concentration of soluble
metal hydroxide species is negligible (Tamura and
Furuichi, 1997; Pagnanelli et al., 2003).
þ −
H ½SO KH
ð9Þ
Inserting Eqs. (8) and (9) into Eq. (7), one gets:
þ −
h
i
H ½SO þ MT − M2þ
K
H h
i
1
¼
½SO− þ MT − M2þ
αH
ST ¼ ½SO− þ
ð10Þ
where, αH ¼ K þK HHþ , the ratio of deprotonated sites to the total
H ½
surface sites that are not occupied by heavy metals.
Then inserting Eqs. (8) and ((10) into Eq. (5), one gets:
ð11Þ
ð15Þ
If the surface concentration (mol/g) is defined as,
SOMþ
Mass adsorbent
ð16Þ
and
h
i2
h
i
þ αH ka M2þ þ fαH ka ðST −MT Þ þ kd g M2þ ¼ kd M T
ð12Þ
Eq. (12) indicates that the concentration of residual metal
in the solution, [M2+], is a function of t, pH, total surface site
concentration, and initial metal ion concentration. It is
inferred as MLK model that incorporates the effect of pH
(included in the αH term). The values of ka and kd could be
obtained by simulating the kinetics data of heavy metal
adsorption and the details of the procedure are present in
Appendix A Supplementary data. The derivation process of
MLK model revealed that it was only applicable at constant
pH. Since buffer may influence heavy metal adsorption, no
buffer was used in the adsorption tests. Thus, the adsorption
kinetics experiments should be performed at low pH, which
could maintain almost constant without buffer during the
adsorption experiments, to obtain the values of KS-kinetic
with MLK model.
2.3. Adsorption edge and isotherm
A mathematic model describing the adsorption edge of
metal ions on fly ash was developed by Wang et al. (2004).
Assuming that only free metal ions are present in the system
and that only one type of acid site is responsible for the
Γmax ¼
ST
Mass adsorbent
ð17Þ
Thus, the MLI equation can be re-organized to be:
h
i
αH K S Γ max M2þ
h
i
Γ¼
1 þ αH K S M2þ
ð18Þ
3. Results and discussion
3.1. Characterization of MnO2
The MnO2 particles were aggregated with many small
particles (Fig. 1a), resulting in a rough surface and a porous
structure. Its particle size is mainly in micro-size range with
the average size of 20 μm, as shown in Fig. 1b. The BET specific
surface area of MnO2 was determined to be 49.1 m2/g. The
XRD pattern, as illustrated in Fig. 1c, revealed that MnO2
employed in this study was a γ-MnO2 (Adelkhani, 2012). As
demonstrated in Fig. 1d, the point of zero charge (pHpzc) of
MnO2 was 3.1, which fell within the range of point of zero
charge values reported by Zhang et al. (2008). The concentration of soluble Mn(IV) was less than 0.1 mg/L at pH ≥ 4.2, as
.cn
dt
h
i
αH K s ST M2þ
h
i
SOMþ ¼
1 þ αH K S M2þ
Γ¼
Eq. (11) can be further modified to be:
h
i
d M2þ
The MMP model can be obtained by incorporating Eqs. (13)
in (14), which can be used to describe metal partitioning under
different metal loadings and different pH conditions. It was
easy to derive the MMP model for a system that contains
multiple surface sites following the practice of Wang et al.
(2004).
In addition, the modified Langmuir isotherm model (MLI
model) that incorporates the pH effect in metal adsorption
isotherm can be expressed as (Wang et al., 2004):
.ac
dt
h
i
h
i
h
i
¼ ka αH ST þ M2þ −M T Þ M2þ −kd ðMT − M2þ
ð14Þ
MT
sc
−
h
i
d M2þ
h
i
M2þ
je
½SOH ¼
where, KS (L/mol) is the adsorption constant of heavy metal
ions in Langmuir adsorption isotherm, which is referred to
KS-Langmuir in the following part. Then, the metal partitioning or the ratio of metal concentration in the solid phase
to the total metal concentration in the system (R) can be
expressed as:
R ¼ 1−
From the definition of KH, one can get:
ð13Þ
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J O U RN A L OF E N V I RO N M EN TA L S CI EN CE S 27 (2 0 1 5 ) 2 0 7–2 1 6
(Lewis acids). Compared to S1O−, S2O− was expected to have
much greater tendency to coordinate with heavy metals since
the pKH of S2 was much larger than that of S1. Simulating the
adsorption edges of heavy metals with the MMP model for two
types of surface sites, when both S1O− and S2O− were
considered as adsorption sites, revealed that the adsorption
constants of heavy metals on surface site S1 were much
smaller than those on surface site S2 or even negative, as
listed in Appendix A Table S1. Moreover, the simulation
results with MMP model for single type of surface sites (S2O−)
were almost identical to those with MMP model for two types
of surface sites. Therefore, S2O− should be the adsorption sites
accounting for the adsorption of heavy metals and only one
type of surface sites (S2O−) was considered in the following
modeling process.
shown in Appendix A Fig. S2. However, the dissolution of
MnO2 was not negligible at very low pH and the concentration
of Mn(IV) was as high as 11.8 mg/L at pH = 2.0.
3.2. Surface site acidity and speciation
Fig. 2a shows the net titration results for MnO2 at different
concentrations. The site densities and acidity constants of
MnO2 were determined by fitting the titration curves using
Eq. (1). The best curve fitting results were based on the
two-site assumption, as indicated by smooth curves. Thus, it
was hypothesized that the MnO2 surface contains two types of
surface sites, denoted as sites S1 and S2. The densities (Γ) of
acid sites S1 and S2 are 1.6 × 10− 4 ± 0.1 × 10−4 and 1.3 × 10− 4 ±
0.1 × 10−4 mol/g, respectively and their acidity constants (pKH)
are 3.5 ± 0.1 and 7.0 ± 0.2, respectively. Since the values of pKH
for these two sites are all greater than pHpzc, the deprotonated
surface sites are negatively charged. As the surface is
positively charged at pH below 3.1, there must be another
surface site S0 which is positively charged before deprotonation. However, site S0 was not observed since our titration
was limited to pH above 2.0 due to the considerable
dissolution of MnO2 at pH ≤ 2.0. Fig. 2b demonstrates the
speciation of S1 and S2 at various pH levels. Then the
deprotonated form of surface sites S1 and S2, S1O− and S2O−,
which are negatively charged and can act as Lewis base,
should contribute to the adsorption of heavy metal ions
3.3. Modeling the adsorption kinetics of metal ions
Fig. 3 shows the adsorption kinetics of Cu(II), Co(II), Cd(II),
Zn(II), and Ni(II) on MnO2. For all metals, the adsorption was
fast and majority of metals were removed within the first
120 min of contact with the adsorbent. However, the adsorption experiments lasted for 24 hr to ensure that the adsorption equilibrium could be achieved. Moreover, Fig. 3 reveals
that the adsorption of heavy metals increased significantly
with the increase of pH, which may be ascribed to the increase
in the amount of deprotonated surface site (S2) as illustrated
5
a
b
4
3
2
1
500 nm
0
0.1
1.0
10.0
100.0
Size (μm)
c γ-MnO
(021)
(221)
Zeta Potential (mV)
(110)
(121)
Intensity (a.u)
d
10
2
0
-10
-20
-30
40
60
2 Theta (degrees)
80
0
2
4
6
8
10
12
pH
.cn
20
je
sc
.ac
Fig. 1 – (a) Scanning electron microscope image of MnO2; (b) Particle size distribution of MnO2; (c) X-ray diffraction patterns of
γ-MnO2 employed in this study; (d) zeta potential of MnO2 as a function of pH using 0.01 mol/L NaCl as the background electrolyte,
the concentration of MnO2 was
0.1 g/L.
212
J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S 27 (2 0 1 5 ) 2 07–2 1 6
1.6
Surface site density (×10-4 mol/g)
a
10
pH
8
6
Modeling
5 g/L
10 g/L
50 g/L
4
2
-0.15
-0.10
-0.05 0.00
0.05
0.10
0.1 mol/L base added (mL L/g)
b
1.4
1.2
S1OH
1.0
S2OH
0.8
0.6
S1 O -
0.4
S2O-
0.2
0.0
2
0.15
4
6
pH
8
10
Fig. 2 – (a) Net titration results for MnO2 of three concentrations (ΔVSS/SS as a function of pH); (b) surface speciation of MnO2 at
different pH levels. S1, S2: surface sites.
(dotted lines) coincide with the experimental data reasonably
well, indicating that the adsorption kinetics of heavy metal on
MnO2 at various pH could be described with a single set of
parameters. However, this model could not predict the
adsorption kinetics of heavy metal at higher pH so well as it
did at lower pH, which should be ascribed to the variation of
pH (±0.3) when the adsorption kinetics was performed in the
pH range 4.5–5.1. In addition, this MLK model can be employed
to predict the adsorption kinetics carried out at different
concentrations of metals and adsorbent, since they are also
incorporated in this model. Appendix A Fig. S4 shows the
Ni(II) adsorption kinetics on MnO2 in the first 120 min of
adsorption at different pH levels.
in Fig. 2b. The adsorption kinetics of heavy metals on MnO2
fitted very well to the pseudo second order kinetic model with
a high correlation coefficient (R2 > 0.99), as illustrated in
Appendix A Fig. S3, indicating that the adsorption of heavy
metals on MnO2 was reaction-controlled rather than mass
transfer-controlled (Özer et al., 2004).
The MLK model was used to simulate the kinetics of heavy
metal adsorption on MnO2 in 120 min at pH 3.2 or 3.3,
which was very low and kept almost constant during the
adsorption tests, to obtain the adsorption rate constant (ka ),
the desorption rate constant (kd ) and then the corresponding
equilibrium constant (KS-kinetic). The determined ka , kd , and
KS values were summarized in Table 1. As shown in Fig. 3, the
MLK model could simulate the kinetics of heavy metal
adsorption on MnO2 at pH 3.2 or 3.3 quite well. Because the
MLK model incorporates the effect of pH, the MLK model
could be applied to predict the adsorption kinetics of heavy
metals at other pH levels with the parameters (ka and kd)
listed in Table 1. Fig. 3 demonstrates that the calculations
3.4. Predicting the metal adsorption edges
The adsorption edges of Cu(II), Co(II), Cd(II), Zn(II), and Ni(II) ions
on MnO2 were determined in the pH range of 2.0–7.0 and shown
in Fig. 4. The adsorption of heavy metals on MnO2 increased
Cu(II), pH = 4.5 ± 0.3
80
Cu(II), pH = 3.2
60
80
30
25
15
0
40
0
04080120
40
04080120
20
Co(II), pH = 4.5 ± 0.3
0
0
100
Removal efficiency (%)
50
Cd(II), pH = 5.1 ± 0.3
Co(II), pH = 3.3
Cd(II), pH = 3.3
04080120
0
40
80
200 400 600 800 1000 1200 1400
Time (min)
20
20
60
10
0
40
0
04080120
20
Zn(ll), pH = 5.1 ± 0.3
Zn(ll), pH = 3.3
0
0
200 400 600 800 1000 1200 1400 0
Time (min)
04080120
Ni(ll), pH = 5.1 ± 0.3
Ni(ll), pH = 3.3
200 400 600 800 1000 1200 1400
Time (min)
.cn
Removal efficiency (%)
100
je
sc
.ac
Fig. 3 – Adsorption kinetics of heavy metals. Experimental condition: adsorbent dose, 5 g/L; initial metal concentrations (mmol/L),
Cu(II) 0.104, Co(II) 0.126, Cd(II) 0.113, Zn(II) 0.125, Ni(II) 0.110. Symbols are experimental data; solid lines are simulation results with
MLK model; dashed lines are adsorption kinetics predicted with the parameters got from MLK model. The insets show the first
120 min of the kinetics data at low pH for each metal ion.
213
J O U RN A L OF E N V I RO N M EN TA L S CI EN CE S 27 (2 0 1 5 ) 2 0 7–2 1 6
Removal efficiency (%)
100
Cu(II)
Co(II)
Zn(II)
Ni(II)
Cd(II)
80
60
40
20
Removal efficiency (%)
0
100
2
3
4
5
6
Equilibrium pH
7
80
60
Modeling
40
Predicting
20
0
2
3
4
5
6
Equilibrium pH
7
2
3
4
5
Equilibrium pH
6
7
Fig. 4 – Adsorption edges of heavy metals. Experimental conditions: adsorbent dose, 5 g/L; initial metal concentrations (mmol/L):
Cu(II) 0.104, Co(II) 0.126, Cd(II) 0.113, Zn(II) 0.125, Ni(II) 0.110. Solid lines are simulation results with MMP model; dashed lines are
predicted with the corresponding adsorption constants got from the MLK model.
MnO2, 1.0 g/L
MnO2, 5.0 g/L
100
80
60
40
20
0
8
7
Equ 6
5
ilib
rium
pH 4
0
)
0.12 ol/L
0.04 (mm
0.06 i(II)
0.08 of N
0.10 ation
3 0.12 entr
c
on
lc
tia
i
n
I
ency (%)
Removal effici
ency (%)
Removal effici
100
80
60
40
20
0
8
7
Equ 6
ilib 5
rium
pH
4
0
)
0.1 ol/L
0.2 ) (mm
0.3 i(II
0.4 n of N
0.5 ratio
3 0.6 cent
n
o
lc
tia
Ini
Fig. 5 – Adsorption edges of Ni(II) determined at different adsorbate/adsorbent concentrations. Solid lines are the prediction
results at different initial metal concentrations with the corresponding adsorption constants got from the MLK model. The
surfaces, plotted by the MMP model with the equilibrium constant (KS-kinetic), describe the Ni(II) adsorption edges on MnO2 at
different metal/adsorbent concentrations.
Table 1 – Comparison of the equilibrium constants determined from the modified Langmuir kinetic model (MLK model)
(logKS-kinetic) with those from the the modified metal partitioning model (MMP model) (logKS-Langmuir) (confidence level:
α < 0.05).
kd (× 10−3)
3.21
2.55
1.42
1.13
0.64
1.82
6.68
7.26
3.76
5.16
±
±
±
±
±
0.96
0.52
0.45
0.18
0.21
±
±
±
±
±
0.71
1.50
2.48
0.65
1.88
Eq. (14)
R
logKS-kinetic
((mol/L)− 1)
logKS-Langmuir
((mol/L)− 1)
0.937
0.980
0.951
0.974
0.929
7.25
6.58
6.29
6.48
6.09
7.35
6.54
6.39
6.34
6.17
±
±
±
±
±
0.04
0.05
0.03
0.06
0.02
R
logK1 a
0.995
0.992
0.997
0.980
0.999
6.00
4.80
3.92
5.04
4.14
sc
Stability constant of MOH+.
je
a
ka (× 104)
Eq. (6)
.cn
Cu(II)
Co(II)
Cd(II)
Zn(II)
Ni(II)
Eq. (12)
.ac
Element
J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S 27 (2 0 1 5 ) 2 07–2 1 6
0.10
0.08
0.06
0.04
0.02
0
6.0
5.5
5.0
4.5
pH 4.0
14
12
l/L)
mo
8 10 II) (m
i(
6
of N
4
3.5
on
i
t
2
a
r
3.0 0
ent
onc
al c
i
t
i
In
Fig. 6 – Experimental data and predictive curves for the
adsorption isotherms of Ni(II) at different pH (3.3 ± 0.1 and
4.3 ± 0.1). Experimental conditions: adsorbent dose, 5 g/L;
initial Ni(II) concentration 0.110 mmol/L. Solid lines are
predicted results with adsorption constants got from the
MLK model. The surface, plotted by the MLI model with the
equilibrium constant (KS-kinetic), describes the Ni(II)
adsorption isotherms on MnO2 at different pH.
demonstrated by the surface plot in Fig. 6. The predicted
adsorption isotherms matched the experimental results
reasonably well over the entire concentration range. This
prediction approach is also applicable to other divalent heavy
metal ions and to predict the adsorption edges and isotherms
of these metals over a wide range of pH and broad metal
loading conditions.
3.6. Linear correlation
.cn
The divalent heavy metal ions also form hydroxo complexes in
homogeneous solutions (hydrolysis) with different reactivities,
which can be evaluated by the reported stability constants of the
hydroxo complexes. The adsorption constants of heavy metals
on MnO2 were compared with the reactivities of these heavy
metals to form hydroxo complexes, as illustrated in Appendix A
Fig. S5a. There is a good correlation between equilibrium
constants and the stability of MOH+ (Stumm and Morgan, 1996).
Similar phenomenon was observed for heavy metal adsorption
on both MnO2 and Fe2O3 (Tamura and Furuichi, 1997). The
similar tendency of heavy metal ions for adsorption and
hydrolysis indicates similarities in the two reactions, which
can be regarded as the interaction between Lewis acid and Lewis
base. The deprotonated surface hydroxyl sites and hydroxide
ions are Lewis base and they interact with heavy metal ions,
which are Lewis acid, via their oxygen atoms. Thus, the heavy
metal ions with higher reactivity to form hydroxo complexes
have higher adsorption affinities for MnO2. Consequently, the
adsorption constants of other heavy metal ions on MnO2 can be
estimated based on the correlation shown in Appendix Fig. S5a.
Moreover, the correlation between the equilibrium constants
determined from the MLK model (KS-kinetic) and the adsorption
constants obtained from the MMP model (KS-Langmuir) is
demonstrated in Appendix A Fig. S5b. There is a sound linear
.ac
Eq. (18) is a MLI model which incorporates the pH factor,
included in the αH term, in the Langmuir isotherm. According
to this MLI model, the variation of the adsorption capacity of
one adsorbent with pH is not due to the change of adsorption
constant with pH but to the change of effective adsorption site
density with pH. Thus the MLI model can be applied to
simulate or predict the adsorption isotherms over a wide pH
range with only one adsorption constant. The adsorption
isotherms of Ni(II) on MnO2 at pH 3.3–6.0 were predicted by
the MLI model with KS-kinetic as the adsorption constant, as
0.12
sc
3.5. Predicting the adsorption isotherms
0.14
je
with increasing pH. As pH increased, the net surface charge of
MnO2 became more negative, resulting in an increase in cation
adsorption and the “S” shaped adsorption edge was observed.
These adsorption edge results are consistent with what other
researchers have reported (Christophi and Axe, 2000).
In neutral and low pH ranges, adsorption of the free metal ion
is the only mechanism for metal adsorption because the
formation of metal-hydroxide complexes is not significant
(Wang et al., 2004). Tamura and Furuichi (1997) confirmed that
if metal adsorption onto metal oxides took into account hydroxo
complexes (MOH+), it made this adsorption path difficult to
accept since the adsorption affinity of those species would be too
high to be believable. Moreover, Tamura and Furuichi (1997)
suggested that the adsorption of divalent heavy metal ion be
through the interaction between the positive charge of the heavy
metal ions and the negative charge of deprotonated hydroxyl
sites –O−. Thus, only the interaction of free metal ions with the
deprotonated form of surface site S2, i.e., S2O−, was considered in
this study. The MMP model developed by Wang et al. (2004)
(Eq. (14)) was employed to simulate the adsorption edges and the
results are presented in Fig. 4. The KS-Langmuir values of
different heavy metals, listed in Table 1, revealed that the
adsorption affinities of heavy metal ions toward MnO2 surface
followed the order of Cu(II) > Co(II) > Cd(II) ≈ Zn(II) > Ni(II).
Different sequences of affinity of heavy metals for various
adsorbents had been reported (Christophi and Axe, 2000), while
Cu(II) always had greater affinity than the other four heavy
metals investigated in this study.
As discussed above, the equilibrium constants (KS-kinetic)
got from the adsorption kinetics of heavy metals are theoretically identical to the adsorption constants in Langmuir
isotherm. Therefore, this MMP model was employed to predict
the adsorption edges of heavy metal ions on MnO2 with the
equilibrium constants (KS-kinetic) determined from Eq. (6)
(Fig. 4). With the equilibrium constants (KS-kinetic) determined
from MLK model, MMP model could predict the adsorption
edges superbly.
The MMP model can also be used to predict the adsorption
edges of heavy metals at different heavy metal concentrations
and sorbent concentrations. Thus, the adsorption edges of
Ni(II) on MnO2 (1.0, 5.0 g/L) over a wide Ni(II) concentration
range were predicted by the MMP model with KS-kinetic, as
shown by the surface plots in Fig. 5. The adsorption edges of
Ni(II) on MnO2 at different initial Ni(II) concentrations (0.058,
0.110, 0.225, 0.550 mmol/L) were determined experimentally
and are present in Fig. 5. There is a quite good agreement
between the experimental data and the model predictions.
(mmol/L.g)
Ni(II) adsorbed
214
215
J O U RN A L OF E N V I RO N M EN TA L S CI EN CE S 27 (2 0 1 5 ) 2 0 7–2 1 6
This work was supported by the National Natural Science
Foundation (No. 21277095), and the Tongji University Open
Funding for Materials Characterization (No. 2013080). The authors
also appreciate the constructive suggestions from Dr. Li Wang.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
http://dx.doi.org/10.1016/j.jes.2014.05.036.
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216
Editorial Board of Journal of Environmental Sciences
Editor-in-Chief
X. Chris Le
University of Alberta, Canada
Associate Editors-in-Chief
Jiuhui Qu
Shu Tao
Nigel Bell
Po-Keung Wong
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, China
Peking University, China
Imperial College London, UK
The Chinese University of Hong Kong, Hong Kong, China
Editorial Board
Aquatic environment
Baoyu Gao
Shandong University, China
Maohong Fan
University of Wyoming, USA
Chihpin Huang
National Chiao Tung University
Taiwan, China
Ng Wun Jern
Nanyang Environment &
Water Research Institute, Singapore
Clark C. K. Liu
University of Hawaii at Manoa, USA
Hokyong Shon
University of Technology, Sydney, Australia
Zijian Wang
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Zhiwu Wang
The Ohio State University, USA
Yuxiang Wang
Queen’s University, Canada
Min Yang
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Zhifeng Yang
Beijing Normal University, China
Han-Qing Yu
University of Science & Technology of China,
China
Terrestrial environment
Christopher Anderson
Massey University, New Zealand
Zucong Cai
Nanjing Normal University, China
Xinbin Feng
Institute of Geochemistry,
Chinese Academy of Sciences, China
Hongqing Hu
Huazhong Agricultural University, China
Kin-Che Lam
The Chinese University of Hong Kong
Hong Kong, China
Erwin Klumpp
Research Centre Juelich, Agrosphere Institute
Germany
Peijun Li
Institute of Applied Ecology,
Chinese Academy of Sciences, China
Michael Schloter
German Research Center for Environmental Health
Germany
Xuejun Wang
Peking University, China
Lizhong Zhu
Zhejiang University, China
Atmospheric environment
Jianmin Chen
Fudan University, China
Abdelwahid Mellouki
Centre National de la Recherche Scientifique
France
Yujing Mu
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Min Shao
Peking University, China
James Jay Schauer
University of Wisconsin-Madison, USA
Yuesi Wang
Institute of Atmospheric Physics,
Chinese Academy of Sciences, China
Xin Yang
University of Cambridge, UK
Environmental biology
Yong Cai
Florida International University, USA
Henner Hollert
RWTH Aachen University, Germany
Jae-Seong Lee
Sungkyunkwan University, South Korea
Christopher Rensing
University of Copenhagen, Denmark
Bojan Sedmak
National Institute of Biology, Slovenia
Lirong Song
Institute of Hydrobiology,
Chinese Academy of Sciences, China
Chunxia Wang
National Natural Science Foundation of China
Gehong Wei
Northwest A & F University, China
Daqiang Yin
Tongji University, China
Zhongtang Yu
The Ohio State University, USA
Environmental toxicology and health
Jingwen Chen
Dalian University of Technology, China
Jianying Hu
Peking University, China
Guibin Jiang
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Sijin Liu
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Tsuyoshi Nakanishi
Gifu Pharmaceutical University, Japan
Willie Peijnenburg
University of Leiden, The Netherlands
Bingsheng Zhou
Institute of Hydrobiology,
Chinese Academy of Sciences, China
Environmental catalysis and materials
Hong He
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Junhua Li
Tsinghua University, China
Wenfeng Shangguan
Shanghai Jiao Tong University, China
Ralph T. Yang
University of Michigan, USA
Environmental analysis and method
Zongwei Cai
Hong Kong Baptist University,
Hong Kong, China
Jiping Chen
Dalian Institute of Chemical Physics,
Chinese Academy of Sciences, China
Minghui Zheng
Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, China
Municipal solid waste and green chemistry
Pinjing He
Tongji University, China
Editorial office staff
Managing editor
Editors
English editor
Qingcai Feng
Zixuan Wang
Suqin Liu
Catherine Rice (USA)
Kuo Liu
Zhengang Mao
Copyright© Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights reserved.
JOURNAL OF ENVIRONMENTAL SCIENCES
环境科学学报(英文版)
www.jesc.ac.cn
Aims and scope
Journal of Environmental Sciences is an international academic journal supervised by Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. The journal publishes original, peer-reviewed innovative research and
valuable findings in environmental sciences. The types of articles published are research article, critical review, rapid
communications, and special issues.
The scope of the journal embraces the treatment processes for natural groundwater, municipal, agricultural and industrial
water and wastewaters; physical and chemical methods for limitation of pollutants emission into the atmospheric environment; chemical and biological and phytoremediation of contaminated soil; fate and transport of pollutants in environments;
toxicological effects of terrorist chemical release on the natural environment and human health; development of environmental catalysts and materials.
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Journal of Environmental Sciences (Established in 1989)
Volume 27 2015
Supervised by
Chinese Academy of Sciences
Sponsored by
Research Center for Eco-Environmental
Sciences, Chinese Academy of Sciences
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X. Chris Le
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