Bioresource Technology 160 (2014) 32–42

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Specific chemical interactions between metal ions and biological solids exemplified by sludge particulates C.P. Huang a,⇑, Jianmin Wang b a b

Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716, USA Department of Civil, Architectural & Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA

h i g h l i g h t s  pH plays an universal role on metal uptake by sludge particulates.  Dissolved organic matter can compete with sludge particulates for metal ions at specific pH value.  The surface acidity of sludge particulates is defined in acidity strength and acidity capacity.  Surface complex formation can describe metal ions and sludge particulates well.

a r t i c l e

i n f o

Article history: Available online 22 January 2014 Keywords: Sludge particulates Dissolved organic matter (DOM) Surface acidity Metal ions Surface complex formation

a b s t r a c t The adsorption of metals onto biological surfaces was studied exemplified by municipal sludge particulates of the primary, the secondary, and the tertiary sludge types from four regional wastewater treatment plants. Major factors affecting the extent of metal adsorption including pH, DOM, total biomass, and total metal loading were studied. The acidity–basicity characteristics of the DOM, the metal ions (Lewis acids), and the surface of the sludge particulates make pH the most important parameter in metal adsorption. Change in pH can modify the speciation of the metal ions, the DOM, and the surface acidity of the sludge particulates and subsequently determines the degree of metal distribution between the aqueous phase and the sludge solids. Information on the acidity–basicity characteristics of the DOM and the sludge particulates are used to calculate the stability constant of metal ion–sludge complexes. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The presence of meals in the aquatic environment is a potential hazard to human and the ecosystem. In order to protect human and ecological health, it is necessary to control the flux of metals into the environment. A major approach for controlling metals in the environment is to remove metals from wastewater streams before discharge to the receiving water bodies. Another strategy is achievable by better understanding the fate and transport of metals in the water environment. There have been many studies on the removal of metals from dilute water solutions using various adsorbents. Unconventionally solid materials such as waste residues, natural minerals, and biomass materials can be potential metal adsorbents. Metal adsorbents derived from waste residues such as concrete residues (Weng and Huang, 2001), activated carbon (Corapcioglu and Huang, 1987; Huang and Blankenship, 1984), soil minerals (Elliott et al., 1986a,b), and sludge particulates (Tien and Huang, 1987, 1991; Wang et al., 1999, 2000, 2003a,b, 2006, 2007) ⇑ Corresponding author. Tel.: +1 302 831 8428. E-mail address: [email protected] (C.P. Huang). 0960-8524/$ - see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2014.01.043

have been studied. These materials can be simply or minimally processed from a host of solid waste products and converted to metal adsorbents. Metal adsorbents based on well-cultured biomass materials such as bacteria, algae and fungi have been studied also. Several researchers have studied the adsorption of metals onto fungal biomass (Huang et al., 1990, 1991, 1998; Huang and Huang, 1996; Filipovic-Kovacevic et al., 2000; Al-Hakawati and Banks, 2000; Sag et al., 2001; Sag, 2013; Haytoglu et al., 2001; Akar et al., 2005; Akar and Tunali, 2006; Shroff and Vaidya, 2011; Faghihian and Peyvandi, 2012; Lin et al., 2012; Wang et al., 2012). Davis et al. (2003) reviewed the biochemistry of metal biosorption by brown algae and evaluated the role of cellular structure, storage polysaccharide, cell wall and extracellular polysaccharides on biosorption of metals. Wang and Chen (2009) report that biological materials, especially, bacteria, algae, yeast, and fungi are better metal adsorbents than ion exchange resign due to the presence of a wide variety of functional groups, such as carboxyl, imidazole, sulphyldryl, phosphate, sulfate, thioether, phenol, carbonyl, amide, and hydroxyl moieties on the cell surface. Rodriguez-Triado et al. (2012) studied the adsorption of Cu(II) and Pb(II) onto Bacilus thioparans at different salinity and pH values.

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42

Chakravarty and Banerjee (2012) attributed the removal of Cd(II) by acidophilic bacteria to the surface functional groups of amino, carboxyl, hydroxyl, and phosphate. Most recently, Liu et al. (2013) studied the adsorption of Cu(II) and Pb(II) onto Pseudomonas pseudoalcaligenes biomass. Chojnacka et al. (2005) reported that surface functional groups, namely, carboxyl, phosphate, and hydroxyl or amine was responsible for the removal of heavy metals ions, Cr(III), Cd(II), Cu(II) by blue-green algae, Spirulina sp. Al-Rub et al. (2006) reported the suppression of Cu(II) adsorption by Pb(II) and Zn(II) on green algae, Chlorella vulgaris. Chen et al. (2012) reported that Ca2+ ions can regenerated Cd(II)-laden y microalga Scenedesmus obliquus indicating the electrostatic nature of the metal binding reaction. Due to unique physical and biological characteristics, sludge particulates can be ideal metal adsorbents. Stones (1955) were probably among the first to study the uptake of heavy metals by sludge particulates. Barth et al. (1965a,b) reported percentage removal of 27%, 55%, 15%, and 63% versus 2.4%, 9.0%, 2.5%, and 14% for Cr(VI), Cu(II), Ni(II), and Zn(II) by the secondary and the primary sludge particulates, respectively. Several researchers reported a two-phase metal removal process, a rapid initial phase of great magnitude followed by a slow and less important secondary phase by secondary sludge particulates (Cheng et al., 1975; Neufeld and Hermann, 1975; Kelly et al., 1979; Nelson et al., 1981; Elenbogen et al., 1987). Some researchers investigated the role of extracellular polymers on metal uptake by sludge particulates (Brown and Lester, 1979, 1982a,b; Hunter et al., 1983; Rudd et al., 1982, 1984; Kelly et al., 1979; Corpe, 1974; Saunders and Fick, 1981;Higham et al., 1984; Trevors et al., 1986; Ghosh and Bupp, 1992; Chang et al., 1995; Fukushi et al., 1996). Studies have shown that Hg(II), Pb(II), and Cd(II) are among the most removable metals, whilst Ni(II) is the least removable by sludge particulates (Cheng et al., 1975; Neufeld and Hermann, 1975; Sherrard, 1983; Tien and Huang, 1987, 1991; Barth et al., 1965a,b; Stones, 1955, 1960). Wang et al. (1998a, 1999, 2003a,b, 2006, 2007) reported that pH, dissolved organic matter (DOM), concentration of sludge particulates and type and concentration of metal ions play important role on metal uptake by sludge particulates. Davis et al. (2003) and Sag (2013) even suggested that it is feasible to use sludge particulates as metal adsorbents for the removal of metals from waste streams. Most of these studies only provide qualitative or semi-quantitative account of the adsorption of a limited number of metal ions on sludge particulates in particular and other biomass materials in general under limited experimental conditions. Little quantitative information on how to characterize the physical– chemical properties of sludge particulates and the dissolved chemical matrixes in the wastewater environments and the specific chemical interactions among them is available. Langmuir and/or the Freundlich adsorption isotherms are the most commonly used equation to describe the equilibrium adsorption of metals by biomass adsorbents. As far as quantitative description of the uptake of metals by sludge particulates is concerned, there is a lack of unified methodology. The acidity of dissolved organic matter, derived from the biomass, and the surface acidity and surface properties of biological particulates relevant to metal ion adsorption are important information. This study aimed at developing a methodology for quantifying the DOM, the biological surface, and the interactions between metal ions and sludge particulates from municipal wastewater treatment plants using pH as a master variable. It is expected that results will be useful to the design of strategy for the control of metals in wastewater treatment plants and the possible use of sludge particulates as adsorbents for the removal of metals from industrial waste streams. Furthermore, it is possible that the results of meal adsorption onto sludge particulates can be applied to other biosorption systems as well.

33

2. Characterization of DOM and sludge particulates 2.1. Characterization of DOM The chemical nature of DOM in wastewater systems is complex. It is a mixture of degraded biomass detritus and organic compounds intrinsic to the wastewater stream. Quantification of the chemical functionalities of DOM is useful to estimate the degree of metal ion association with DOM and subsequent metal adsorption on sludge particulates. Acidimetric–alkalimetric titration can be employed to characterize the acidity characteristics of DOM in the sludge samples collected from any wastewater treatment plants. This method is based on the assumption that DOM contains two discrete monoprotic weak acids (two-site model), i.e., HLA and HLB:

HLA ¼ Hþ þ LA ; K aA HLB ¼ Hþ þ LB ; K aB

ð1Þ

 where, L A ; LB = negatively charged soluble organic ligands; KaA, KaB = acidity constants for HLA and HLB, respectively. From law of mass balance, KaA and KaB can be expressed as:

K aA ¼

½Hþ ½LA  ½HLA 

K aB ¼

½Hþ ½LB  ½HLB 

ð2Þ

Since the carbonate species present in the wastewater also consume strong acid and base during titration when titration starts from neutral (generally pH is in the range from 6.0 to 8.0), correction for carbonate neutralization capacity is necessary. The cumulative volume of strong acid–base consumed, DV, is shown in the following mass balance equation, i.e.,

    3 aA aB  ½HþK aAþK þ C B ½HþKþK  ½HþK aBþK þ C A ½HþKþK aB aB aA aA 0 0 6   7 7 V0 6 2 2 7 6 1016:6 ½Hþ 0 1016:6 ½Hþ  DV ¼ C  6 C 4 T ½Hþ 2 þ106:3 ½Hþ þ1016:6 ½Hþ 20 þ106:3 ½Hþ 0 þ1016:6 þ 7 5 2

ð3Þ

ð½OH   ½OH 0 Þ  ð½Hþ   ½Hþ 0 Þ

where, V0 = volume of DOM solution (mL); C = concentration of strong acid/base solution (M); CA, CB = concentration of acidic sites  and basic sites, respectively (M); CA = [HLA] + ½L A ; CB = [HLB] + ½LB ; CT = total concentration of carbonate (M); KaA, KaB = acidity constants of HL A and HL B , respectively; [OH]0 , [H+ ]0 = initial hydroxyl and hydrogen ion concentration (M). Using the alkalimetric–acidimetric titration data, a plot of pH versus DV can be made. From the titration curve, CA, CB, KaA, and KaB can be calculated using nonlinear regression by curve fitting of Eq. (3). The total carbonate concentration CT can be determined separately by inorganic carbon measurement (IC) or by nonlinear regression technique. The DOM solution is prepared/preserved by keeping the collected samples in a refrigerator at 4 °C for 3–5 days then the mixed liquor is centrifuged at 20,000g for 10 min to obtain DOM. The COD, inorganic carbon (IC), and total dissolved solid (TDS) in the DOM samples are measured separately before titration. It is important that both sample and carbonate titrations start at the same initial pH value, which usually is in the range of 6–8. In addition, it is recommended that the pH change after each acid/base addition must not exceed 0.3 units in order to obtain reproducible titration data. Table 1 lists the characteristics of DOM together with COD and total dissolved solids concentration from sludge sample collected from four regional municipal wastewater treatment plants, i.e., WI, PH, BA, and DC, respectively. Results indicate that the pKa values of all samples were independent of sample source, type, and date. The average pKaA and pKaB values were 5.3 and 9.5, respectively.

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C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42

Table 1 Summary of alkalimetic–acidimetric characterization of DOM.

a

Sample type

Sourcea

COD (mg/L)

IC (mg/L)

CA (103 M)

pKa (A)

Primary

WI(2) PH(2) BA(2) DC(2)

257.00 365.50 670.00 235.50

30.00 44.50 17.00 21.00

1.96 3.21 6.72 2.27

4.84 4.73 5.11 5.47

Secondary

WI(3) PH(3) BA(3) DC(3)

82.33 85.00 182.00 246.33

52.33 34.00 15.33 26.00

0.59 0.24 2.37 3.22

5.20 5.98 5.90 5.49

Tertiary

DC(3)

121.67

33.67

1.16

5.11

Digested

WI(1) PH(1) BA(1) DC(1)

89.00 86.00 86.00 78.00

60.00 59.00 48.00 65.00

2.43 3.69 1.40 2.59

6.26 6.31 6.11 6.11

CB (103 M)

pKa (B)

R

2.16 2.14 5.34 2.42

9.41 9.47 9.29 9.51

0.9995 0.9989 0.9966 0.9995

2.43 1.28 3.89 4.87

9.37 9.66 9.71 9.56

0.9995 0.9993 0.9980 0.9990

2.57

9.77

0.9979

8.90 10.90 5.68 9.64

9.52 9.56 9.46 9.53

0.9999 0.9999 0.9999 0.9999

Value in parenthesis indicates number of sampling.

Wang et al. (1998a) suggested that the first acidic site is composed of organic carbons while inorganic constituents contribute to the second acid site. It is interesting to note that the acidity constants of DOM were close to those of carboxylic (pKa = 4.8–5.0) and amino (pKa  9.0) groups, although it cannot be concluded that these two functional groups are exactly those making up the DOM. 2.2. Specific surface area of sludge particulates Due to highly heterogeneous physical structure of sludge particulates, the specific surface area cannot well defined. For the sake of simplicity, sludge particulates can be visualized as an ensemble of various biological, physical and chemical entities, including decomposers (bacteria), consumers (protozoan), inorganic (clays and oxides) and organic (proteins, sugars, and lipids) particles (Brown and Lester, 1979; Sag, 2013). Tien and Huang (1991) measured the specific surface area of municipal sludge particulates by freeze–drying a municipal sludge in supercritical CO2 to obtain a sponge-like material and then determine the specific surface area of the dried sludge materials using nitrogen-gas adsorption. They reported a specific surface area of sludge particulates of 84.2 m2/g. It was hypothesized that drying in CO2 supercritical fluid can preserve the heterogeneous structure of the sludge particulates. Gas adsorption coupling with BET analysis for the determination of specific surface area of solid adsorbents has been criticized for not able to revealing the true surface area when the material is in water phase. Therefore, determining the specific surface area in wet process is a much appealing option. Wang et al. (1998b) has determined the specific surface area of sludge particulates in situ by the adsorption of new Cocine dye (acid red #18, ICI No. 16255) as suggested by Longmuir (1975). The dye molecule has a flat area of 196 A2 and perpendicular area of 90 A2. The adsorption density was described by the following equation (Wang et al., 1998b):

C¼

Cm K 1 C ð1  K 2 CÞ½1 þ ðK 1  K 2 ÞC

ð4Þ

where C = total multilayer adsorption density (mol/g); Cm = the monolayer adsorption density (mol/g); K1, K2 = the adsorption constant for the first layer and the multilayer adsorption, respectively. The specific surface area, R (m2/g), is calculated from the monolayer adsorption density, Cm (mol/g), Avogadro’s number, NA (molecule/ mol), and molecular surface area of the dye compound, r (m2/molecule), i.e.,

R ¼ Cm N A r

ð5Þ

Table 2 shows the specific surface area of sludge particulates determined by dye adsorption under field conditions. Results show clearly that the specific surface area were 77 ± 11, 119 ± 20, and 108 ± 18 m2/g, for the primary, the secondary and the tertiary sludge particulates. The specific surface area of the secondary and tertiary sludge particulates was slightly greater than that of the primary sludge particulates mainly due to difference in particle size. The result was in agreement with Weng et al. (2001) who reported specific surface area of 100, 103, and 113 m2/g for the primary, secondary and digested sludge particulates, respectively using dye (new Cocine) adsorption. The difference in specific surface area between the secondary and tertiary sludge particulates was insignificant. Dye adsorption technique can provide specific surface area under the field conditions of the sludge particulates. 2.3. Surface acidity of sludge particulates Many have attempted to characterize the surface functional groups of sludge particulates and biomass such as bacterial and fungal using specific surface analysis techniques such as FTIR (Lin et al., 2012). Wang et al. (2003) reported that the surface functional groups of sludge particulates from the Wilmington Wastewater Treatment Plant consisted of AOAH, ANAH, ACAH, AC@O, ACAOA and HAOAH. Wang and Chen (2009) suggested the presence of numeral functional groups on biomass surface, i.e., hydroxyl, carboxyl, amino, ester, sulfhydryl, carbonyl, and phosphate. Lin et al. (2012) detected amino, carboxyl, hydroxyl, and carbonyl groups on the cell wall of mycelia biomass, Streptomycetes zinciresistens. While knowing the nature of the surface functional groups is useful to understanding the contributing factors and mechanisms on metal ion adsorption, it nonetheless provides no quantitative information on the degree of metal ion adsorption on the biological surfaces. The surface of sludge particulates can be treated readily as Bronsted acid/base that undergoes proton transfer rendering the surface positively, neutrally or negatively charged. The surface acidity of hydrous particulates has two major components: the quantity and the strength of Bronsted acid/base (or surface site). Huang and Stumm (1973) were the first to describe the surface acidity of hydrous solids in terms of Bronsted acidity strength (acidity constants) and Bronsted acidity capacity (ST), that is:

SOHþ2 () SOH þ Hþ ; K int a1

ð6aÞ

SOH () SO þ Hþ ; K int a2

ð6bÞ

35

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42 Table 2 Summary of specific surface area and surface energy of sludge particulates by dye adsorption. Sludge type

Sludge sample

Primary

WI PH BA DC

Secondary

Tertiary

R (m2/g)

log K1

log K2

DG1 (kJ/mol)

DG2 (kJ/mol)

80.25 ± 9.60 71.25 ± 14.61 74.50 ± 13.70 81.67 ± 4.16

5.00 ± 0.36 5.20 ± 0.12 5.18 ± 0.46 5.53 ± 0.15

2.83 ± 0.10 2.65 ± 0.06 2.80 ± 0.12 2.73 ± 0.06

28.56 ± 2.03 29.70 ± 0.66 29.56 ± 2.65 31.60 ± 0.87

16.13 ± 0.55 15.14 ± 0.33 15.99 ± 0.66 15.61 ± 0.33

WI PH BA DC

127.00 ± 13.71 128.25 ± 23.07 110.25 ± 24.35 121.67 ± 15.50

5.50 ± 0.16 5.48 ± 0.32 5.15 ± 0.39 5.50 ± 0.20

2.63 ± 0.15 2.68 ± 0.17 2.78 ± 0.17 2.77 ± 0.12

31.41 ± 0.93 31.27 ± 1.83 29.41 ± 2.21 31.41 ± 1.14

14.99 ± 0.86 15.28 ± 0.98 15.85 ± 0.98 15.80 ± 0.66

DC

105.00 ± 18.19

5.33 ± 0.40

2.87 ± 0.06

30.46 ± 2.31

16.37 ± 0.33

 where SOHþ 2 , SOH, and SO stands for the positively, neutrally and negatively charged surface hydroxo groups, respectively. The total surface sites, ST, is the sum of all three hydroxo groups, namely,

ST ¼ fSOHþ2 g þ fSOHg þ fSO g

ð7Þ

Since all biomass materials have low pHzpc, it is technically difficult to determine the first intrinsic acidity constant in aqueous solution. Therefore, for the purpose of simplicity, the surface acidity of sludge particulates and most biomass as well with low pHzpc, can be expressed as:

SOH () SOþ þ Hþ ; K int H

ð8Þ

From law of mass action, the acidity constant, KH, is expressed as:

K int H ¼

fHþ gfSO g fSOHg

ð9Þ

where {H+} is the surface proton concentration and is related to its bulk phase concentration [H+] by the Boltzmann distribution equation, i.e.,

  F W0 fHþ g ¼ ½Hþ  exp  RT

ð10Þ

where F = Faraday constant, 96,490 (C/mol), W0 = surface potential (V), R = molar gas constant (8.311 J/mol K), and T = absolute temperature (K). Eq. (9) is related to surface concentration of proton, which is can be calculated from bulk pH and Eq. (10). In principle, the surface potential can be calculated from the diffuse double layer theory using the acidimetric/alkalimetric titration technique (Huang and Stumm, 1973). Another approach is to calculate surface potential from zeta potential measurements, i.e.,

    zeW0 zeW1 ¼ ln tanh þ jxf ln tanh 4kT 4kT

ð11Þ

where W1 = zeta potential; xf = thickness of the shearing plane of the electrical double layer; k = Boltzmann constant; j = reciprocal thickness of the electrical double layer which is calculated from the ionic strength, I, i.e.,

j¼

2x103 F 2 I ee0 RT

!12 ð12Þ

where e = dielectric constant of water (78.5); e0 = permittivity in vacuum (8.854  1012 C/V-m); I = ionic strength (M). For a 1:1 electrolyte at room temperature, j = 3.29  109 I0.5 1 (m ). The surface potential can be estimated from Eq. (12) at given ionic strength and pH values. The macroscopic surface acidity constant, KH, is related to the intrinsic constant, K int H ; by the following equation:

pK int H

¼ pK H þ 0:05915W0

ð13Þ

Note that in the pH range of interest, the surface potential of biological surface remains relatively constant. The surface potential of most hydrous materials, including biomass, is in the range of 30–50 mV, which implies that the correction of microscopic pKH for surface potential is 0.002–0.03 units according to Eq. (13). Therefore it is reasonable to equate KH to K int H for all biological surfaces such as sludge particulates. The surface acidity of municipal sludge particulates can be readily determined by acidimetric–alkalimetric titration of a given amount of sludge particulates in the presence of inert salts such as NaNO3 or NaClO4 NaClO4, according to the following procedures. (1) Filter the sludge sample through a No. 10 sieve to remove grit and debris. (2) Determine the suspended solids (SS) concentration of the mixed liquor. (3) Transfer the same volume of the mixed liquor (50 or 100 mL) to each of a series of 125-mL plastic bottles. (4) Adjust the pH of the samples to a desired range (from 4 to 8) with 1.0 M HClO4 or NaOH. Record the volume of acid/base added. Keep one unit without adding acid or base as control. (5) Shake the mixtures constantly at 180 excursions per minute using a mechanical shaker for 4 h. (6) Transfer 20 mL of the suspensions to centrifugation tubes and centrifuge at 15,000 rpm (20,000g) for 10 min to separate solids from the suspension. (7) Collect the supernatant and determine the COD concentration. (8) Measure and record the final pH using the suspensions remaining in the bottles. Experimentally the net strong base (or acid) consumed by surface sites is equal to the increase (or decrease) of the deprotonated surface sites, that is:

DV SS C ¼ V 0 DfSO g

ð14Þ

where, DVSS = the net volume of acid/base consumed by sludge particulates (mL), negative value in acidimetric titration; C = the concentration of the titrating strong acid/base solution (M); V0 = the total volume of the sample titrated (mL); D{SO} = the concentration change of the deprotonated surface sites after titration(M). The concentration change of the deprotonated surface sites, D{SO}, is related to the total surface site concentration, the acidity constant, and the pH and can be expressed as the following:

DfSO g ¼ ST K H



1 1  ½H  þ K H ½Hþ 0 þ K H þ

 ð15Þ

where: ST = the total surface site concentration (M); [H+]0 = the equilibrium proton concentration of blank sample (M), i.e., without addition of strong acid or base. Eq. (15) gives the change in {SO}

36

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42

concentration before and after titration. Combining Eqs. (14) and (15), one has:

DV SS ¼

  V 0 ST K H 1 1  C ½Hþ  þ K H ½Hþ 0 þ K H

ð16Þ

The total site concentration (ST) and the acidity constant (KH) can be determined using the experimental data sets (DVSS, pH) and nonlinear regression technique. Since the sludge sample (mixed liquor) contains solid particulates, DOM, and carbonate species, which can all consume strong acid or base during titration, the overall strong acid/base consumption must be corrected for DOM and carbonated species. Fig. 1 shows typical titration curve of sludge particulates exemplified by the secondary sludge of

Wilmington Wastewater Treatment, Wilmington, DE. The amount of titrants, HClO4 and NaOH, consumed was corrected for those by the carbonate and other soluble ligands present in the sludge samples. Table 3 lists the surface acidity of sludge particulates in terms of macroscopic acidity constant, KH, and total surface site, ST. Chojnacka et al. (2005) reported the presence of three acidity constants on the surface of algal biomass at 2.59 ± 0.43, 7.24 ± 0.44 and 11.22 ± 0.39, respectively for pKa1, pKa2 and pKa3. As mentioned above, the uniformity in acidity constant and zeta potential among all sludge particulates studied imply that all sludge particulates have the same surface functional groups. Wang et al. (2000) determined the surface functional groups of sludge particulates together with the DOM from sludge samples collected from the Wilmington Wastewater Treatment Plant using FTIR spectroscopy

(a) 9

Water Carbonate Ligand SS Total

8

pH

7 6

SS=1.80 g/L

5 4 3 2 -0 .4

-0 .3

-0 .2

-0 .1

0

0 .1

0 .2

0 .3

0 .4

Volume of 1 N acid/base added (mL)

(b) 12

12

10

10

BA/P/8/96

8

pH

pH

8

BA/S/8/96

6

6

4

4

2

-0.4

-0.2

0.0

0.2

2 -0.4

0.4

-0.2

1 N base added (mL)

0.0

0.2

0.4

1 N base added (mL)

12

12

10

10

BA/P/4/96

BA/S/4/96 8

pH

pH

8

6

6

4

4

2 -0.4

-0.3

-0.2

-0.1

0.0

0.1

1 N base added (mL)

0.2

0.3

0.4

2 -0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

1 N base added (mL)

Fig. 1. Acidimetric–alkalimetric titration of sludge particulates. (a) Titration of total sludge samples, carbonate fractions, ligand DOM fraction and suspended solid, (b) typical net titration curves based on which the acidity constant of the sludge articulates were calculated.

37

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42 Table 3 Summary of the surface acidity of sludge particulates.

a

Sludge type

Sludge samplea

ST (m-mol/g)

ST (l-mol/m2)

pKH

Ionic strength (M)

Primary

WI(2) PHL(2) BA(2) DC(2)

2.88 3.02 2.49 2.16

35.83 42.39 33.42 49.98

6.11 6.14 6.1 5.88

7.50E04 1.11E03 4.25E04 5.25E04

Secondary

WI(6) PHL(4) BA(3) DC(3)

1.49 2.78 1.59 1.81

11.75 21.68 14.42 14.85

6.23 6.61 5.99 6.27

1.31E03 8.50E04 3.83E04 6.50E04

Tertiary

DC(4)

1.69

16.07

5.79

8.42E04

Value in parenthesis indicates number of sampling.

and reported that all sludge particulates had the same surface functional groups and that only the carboxylic groups and amino groups are weak Bronsted acids. It is also interesting to note that the acidity constants of these two functional groups are close to those of the DOM derived from the sludge. The acidity constant of an amino functional group is greater than 9.0 (e.g., glycine, pKa = 9.78) (CRC, 1988) and that of the carboxyl group is less than 8.0.

Zeta potential (mV)

40

2.4. Surface charge As mentioned above, the Bronsted acid–base nature of the surface functional groups on sludge particulates (and many other biomass surfaces) enables the surface to be negatively, neutrally or even positively charged via proton transfer reaction. That is, proton is a potential determining species for the sludge particulates. The zeta potential was measured as a function of pH in various electrolytes such as NaClO4 and NaNO3 for the primary, secondary, and tertiary sludge particulates collected from the Wilmington, Philadelphia, Baltimore, and Washington DC wastewater treatment plants using a Laser Zee zetameter (model 500, Pen Ken, Inc.). The procedures are as follows: (1) Filter the sludge sample through a No. 40 sieve to remove grit and debris. (2) Prepare solutions with suitable solid concentration at several ionic strengths, for example, 1  10–3, 5  10–3, 1  102, and 2.5  10–2 M of NaClO4 or (NaNO3 and LiNO3). (3) Measure the zeta potential with the zetameter as a function of pH from 2.0 to 9.0. Adjust solution pH with HClO4 (1 M), (HNO3 if NaNO3 is used) or NaOH (1 M) (Li(OH) if LiNO3 is used). (4) Plot zeta potential value vs. pH at different ionic strengths. The pHZPC is obtained at the pH of zero zeta potential. The ionic strength of field samples can be determined from total dissolved solids by the following equation (Fair et al., 1968):

I ¼ 2:5x105 ½TDS

ð17Þ

where [TDS] = the concentration of total dissolved solids (mg/L); I = ionic strength (M). A field study for the TDS of wastewater and sludge samples collected from different plants at different times showed a value in the range of 300–700 mg/L, which is relatively constant and independent of the sample sources and sampling time. Based on Eq. (17), the ionic strength for all field samples was 0.01 ± 0.003 M. Therefore, the pHzpc of sludge particulates studied was taken at I = 102 M. Fig. 2 shows typical zeta potential as a function of pH for the primary and secondary sludge particulates at various levels of ionic strength. Table 4 lists the pHZPC values of various sludge particulates at various ionic strengths. Results indicate that all sludge particulates, despite of the sludge type, location,

I=0.001 M I=0.005 M I=0.01 M I=0.025 M

20

0

-20

-40 1

2

3

4

5

6

7

8

9

pH Fig. 2. Typical zeta potential measurements as a function of pH.

Table 4 Summary of zeta potential of sludge particulates. Sludge source WI PH BA DC WI PH BA DC WI WI PH BA DC WI PH BA DC WI PH BA DC

pHzpc Primary

Secondary

2.9 3.2 3.1 2.3 ND 4.6 3.0 3.2 3.2 2.4 2.6 2.6 2.6 3.2 3.2 2.9 2.8 3.0 3.2 3.1 3.2

3.0 3.6 3.0 2.4 3.8 4.7 2.9 3.8 3.6 2.5 2.4 2.6 2.9 3.1 3.2 3.0 3.3 3.0 3.0 3.2 3.2

Ionic strength (10–3 M)

Electrolyte

25 25 25 10 25 25 10 25 25 10 10 10 10 10 10 10 10 10 10 10 10

NaClO4 NaClO4 NaClO4 NaClO4 KNO3 KNO3 LiNO3 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4 NaClO4

Tertiary

2.7

3.6

2.6

3.2

3.4

and the sampling time, have the same pHzpc value at the same ionic strength. The pHzpc value for all sludge particulates, at the field ionic strength, was 2.9 ± 0.3. Generally, biological materials as well as other organic materials have very low pHzpc. Huang and Huang (1996) reported a pHzpc of 4.0, the intrinsic stability constant is practically equal to the microscopic constant.

In the presence of DOM, metal ion can also become associated with dissolved organic matter (ligand):

ð19Þ

ð25eÞ

As indicated above, pH will play an important role on metal uptake by sludge particulates since both the distribution of surface functional groups (or surface charge) of sludge particulates and the DOM are pH dependent. Furthermore, metal ion and proton are competing for both DOM and sludge particulate surface sites. Batch experiments were conducted using primary, secondary, and tertiary sludge particulates collected from the four regional wastewater treatment, identified as WI, PH, BA, and DC, respectively. Batch equilibrium metal uptake experiments were performed under various pH and SS conditions. The heavy metal uptake by anaerobically digested sludge particulates from the above treatment facilities and aerobically digested sludge particulates from another wastewater treatment plant was also studied. The following metals were selected for investigation: Cu(II), Ni(II), Co(II), Zn(II), Cd(II), Pb(II), Cr(III), Hg(II), and Ag(I). Metal was determined by flame, graphite furnace, or cold vapor atomic absorption spectrophotometry (Perkin–Elmer model 5000) according to procedures described in Standard Methods for the Analysis of Water and Wastewater (1990). Equilibrium metal uptake experiments were conducted according to the following procedures:

where q is conversion factor or concentration of total solid (g/L) and

C will have unit of mol/g. If C is in unit of mol/cm2, q will have unit of cm2/L. By substituting C into the above equations, the R value has the following expression:

 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi11 aa K L ðMD LT Þ1þ ½aa K L ðMD LT Þ12 þ4aa K L MD M 1 þ ð a K Þ D H S 2aa K L C B C pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R¼B A @1 þ 2 a K ðM L Þ1þ ½aa K L ðMD LT Þ1 þ4aa K L MD ðaH K S qCm Þ a L D T 2aa K L 0

ð25Þ Eq. (25) can be further simplified dependent on the total metal loading, MT, total surface sites, ST, and total DOM, LT, in the system.

R¼

a H K S Cm q ðlow DOMÞ aH K S Cm q þ aH K S MD þ 1

ð25aÞ

R¼

aH K S Cm q ðlow DOM and low metal loadingÞ aH K S Cm q þ 1

ð25bÞ

R¼

aH K S Cm q aH K S Cm q þ aa K L LT þ 1

ðsignificant DOM effect and low metal loadingÞ

R¼

ðaH K S Cm qÞ ðaH K S Cm qÞ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2

½aa K L ðM D LT Þ1þ

½aa K L ðMD LT Þ1þ

ðhigh q; low DOMÞ

ð25cÞ

½aa K L ðMD LT Þ1 þ4aa K L MD 2a K

a L pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2

½aa K L ðM D LT Þ1 þ4aa K L M D 2aa K L

þ MD ð25dÞ

(1) Sludge samples were collected and maintained in an icepacked cooler while being transported to the laboratory, and then filtered through a No. 10 sieve to remove grit and debris. (2) Ten mL of mixed liquor was taken for the determination of SS concentration (triplicate measurements). (3) To each of a series of 125-mL plastic bottles, 100 mL of the mixed liquor was added. (4) The mixtures were then adjusted to various pH values (from 3 to 10) with 1.0 M HClO4 and/or 1.0 M NaOH. (5) An appropriate volume of concentrated stock metal solution was then added to the mixtures to achieve a pre-selected initial metal concentration. (6) The mixtures were then placed on a mechanical shaker and constantly mixed for 4 h at 180 excursions per minute. (7) At the end of shaking, 30 mL of the suspension was sampled and centrifuged at 15,000 rpm (20,000g) for 10 min to separate the solids from the suspension. The supernatant was then collected for the measurement of residue metal and COD concentration. (8) The final pH value of the remaining suspensions was determine. Fig. 3a shows the typical metal uptake as affected by pH. Results clearly indicated that pH plays an important role on metal adsorption onto sludge particulates. In the low pH region, the amount of metal uptake increased with pH to reach a plateau then the adsorption capacity remained constant for most metals except

39

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42

(a) 100

80

80

60 Cu(II) Ni(II) Co(II) Cd(II) Zn(II) Pb(II) Cr(III)

40 20

Metal load = 60 µmol/g-SS

0 2

4

6

8

Cu(II) uptake (%)

Metal uptake (%)

(a) 100

60 40

10.8 µmol/g-SS 19.0 µmol/g-SS 66.3 µmol/g-SS 183.9 µmol/g-SS 368.6 µmol/g-SS 708.6 µmol/g-SS

20 0 2

10

4

pH

8

10

pH

(b)

(b) 100

800 Cu(II); C = 200 µM

700

0

80

Ni(II) uptake (%)

Co(II); C0 = 200 µM

600

COD (mg/L)

6

Co(II); C = 1000 µM 0

500

Zn(II); C = 200 µM

400

Pb(II); C = 200 µM

300

Pb(II); C0 = 1000 µM

0

0

200

60 8.8 µmol/g-SS 20.0 µmol/g-SS 66.0 µmol/g-SS 229.4 µmol/g-SS 418.2 µmol/g-SS 804.7 µmol/g-SS

40 20

100 0

0 2

4

6

8

2

10

4

6

8

10

8

10

pH

pH

(c)

Fig. 3. Typical meal adsorption as a function of pH. (a) Percent metal removal, (b) amount of DOM released (measured as COD).

800

4.2. Effect of DOM As mentioned above, organic matter dissolution is significantly affected by pH. The effect of DOM on metal uptake was exemplified by Cu(II) and Ni(II) adsorption onto secondary sludge. Fig. 4a and b show the uptake of Cu(II) and Ni(II), respectively, at various surface metal loadings (in lmol/g SS). It is interesting to note that in the low pH region, the DOM dissolution was small and the percentage of metal adsorption increased with decrease in surface loading. The greater percentage of metal ions was adsorbed the smaller the surface loading. However, in the high pH region, when the DOM dissolution increased, the fraction of metal ion uptake increased with increase in surface loading. High surface loading represents high total metal concentration in the system, which favors both

COD (mg/L)

700

Co(II), Ni(II), and Cu(II), with Cu(II) being the most affected and follow the decreasing order: Cr(III) > Pb(II) > Cu(II) > Zn(II) > Cd(II)  Ni(II) > Co(II). Obviously, the effect of pH on metal adsorption is related to the acid–base nature of the sludge particulates and the DOM released into the solution as pH changed. DOM released into the solution can participate in complex formation with specific metal ions. Moreover, in the high pH region, metal ion hydrolysis will generate metal hydroxo species that may have different affinity toward the sludge percolate surface. It must note that metal uptake occurred via surface adsorption, rather than surface precipitation as the parking area per metal ion was smaller than a monolayer. Fig. 3b shows the amount of DOM solubilized from the sludge particulates in the presence of metal ions as affected by pH. Results indicated that the amount of DOM (measured in terms of COD) increased drastically at pH > 8 and that the dissolution of DOM was independent on the type of metal ions preset.

600 500 400 300 200 100 0

2

4

6

pH Fig. 4. Metal adsorption at various suspended concentrations (q) using pH as master variable. (a) Cu(II), (b) Ni(II), (c) the amount of DOM released (as COD) during metal adsorption.

metal adsorption and complex formation with DOM. Results indicated that the complexed metal species had weak affinity toward the sludge particulate surface. Fig. 4c shows the dissolution of DOM in the system, which can be expressed by an empirical equation in terms of soluble COD (mg/L) as the following:

½COD ¼ 1:23  102 þ

2:58  107 4:7  1010 þ 10pH

ð27Þ

4.3. Stability of surface complexes The stability constant of surface complex can be calculated based on experimental data of metal uptake, DOM dissolved and surface acidity using Eq. (25). As discussed above, the significance of the DOM effect on heavy metal uptake is dependent on the metal type. Only Cu(II) and Ni(II) uptake was affected by DOM (at high

40

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42

(a) 100

80

80

Cr(III)

60

Pb(II) Cu(II)

40

Zn(II) Cd(II) Ni(II)

20

2

3

4

5

6

60 40 20

7

8

4.5

5

5.5

6

6.5

7

7.5

pH

(b)

(b)

100

Metal uptake (%)

100

Metal uptake (%)

SS=2.59 g/L

0

9

pH

80 60

Cu(II) Ni(II) Co(II) Cd(II) Zn(II) Pb(II) Cr(III)

40 20

Cu(II); 17.1µM Ni(II); 2.2 µM Co(II); 1.1µM Pb(II); 6.7µM Cd(II); 1.5 µM Cr(III); 12.8 µM Hg(II); 19.9µM Zn(II); 40.7 µM

Co(II)

SS=3.11 g/L 0

Metal uptake (%)

Metal uptake (%)

(a) 100

SS=2.56 g/L

80 60 Cu(II); 25.1 µM Ni(II); 25.2 µM Co(II); 24.6 µM Cd(II); 23.5 µM Zn(II); 119.0 µM Pb(II); 7.0 µM Cr(III); 9.5 µM

40 20

SS=2.91 g/L

0

0 4

4.5

5

5.5

6

6.5

7

7.5

4

8

4.5

5

5.5

6.5

7

7.5

8

pH

pH Fig. 5. Modeling metal adsorption on the BA secondary sludge in (a) single and (b) mixed-metal systems using Eq. (25d) (lines represent curve fitting results).

(c)

Metal uptake (%)

100 80 60

Cu(II); 15.0 µM Ni(II); 23.3 µM Co(II); 24.0 µM Cd(II); 22.2 µM Zn(II); 88.7 µM Pb(II); 5.9 µM Cr(III); 8.8 µM

40 SS=2.10 g/L

20 0 5

5.5

6

6.5

7

7.5

pH

(d) 100

Metal uptake (%)

DOM concentrations) significantly. For all other metals, the DOM effect was insignificant even at high DOM concentrations. At neutral and low pH (pH < 8), the DOM effect on Cu(II) and Ni(II) uptake was also insignificant. Therefore, it can be concluded that, at pH < 8, the DOM effect on metal uptake (for all metals) can be neglected; consequently Eq. (25d) can be used. It should be also noted that, the field pH condition is usually below 8, thus Eq. (27) is appropriate for field applications. Fig. 5a shows the curve fitting results for metal uptake by the secondary sludge collected from the BA site in single metal system. Table 5 shows the stability constant of metal-sludge complexes in single-metal systems exemplified by the BA sludge paticulates. Fig. 5b illustrates the metal uptake in the presence of mixed meals by the BA secondary sludge particulates. The same uptake order was found as for the single metal system. Fig. 6 shows results of curve fitting on the uptake of various metals by the secondary (a, b), tertiary (c) and primary (d) sludge collected from the regional municipal wastewater treatment plants. Table 6 summarizes the stability constants of metal-sludge complexes in mixed-metal systems. Metal type is the most important factor determining the magnitude of the stability constant. The sludge type, location, and sampling time are relatively unimportant. The generalized stability constant reflect the following metal uptake order: Hg(II) > Ag(I) > Pb(II) > Cu(II) > Cr(III) > Zn(II)  Cd(II) > Ni(II) > Co(II). It is interesting to note that a unique relationship exists between the stability constant, KS, and the hydrolysis constant of the metal ion (Mn+ + H2O = MOH(n  1)+ + H+; K 1 ). Apparently, the stronger the Lewis acidity of a metal ion (i.e., low pK 1 ), the greater is its stability constant (KS). The relatively independence of the general chemical characteristics of wastewater, the acid–base behaviors of DOM and sludge particulates on the source of wastewater with respect to sampling time and location, enable the application of results reported above

6

80 60 Cu(II); 23.0 µM Ni(II); 37.0 µM Co(II); 21.1 µM Cd(II); 20.2 µM Zn(II); 91.6 µM Pb(II); 5.9 µM Cr(III); 10.0 µM

40 SS=2.85 g/L

20 0 5.5

6

6.5

7

7.5

8

pH Fig. 6. Modeling metal adsorption on secondary sludge samples collected from (a) BA, (b) PH, (c) DC and (d) WI sites in mixed-metal system and low metal-sludge loading conditions.

at a broad scale. With the following additional information such as pH, temperature, wastewater flow rate (e.g., Mgal/d), volume of the

41

C.P. Huang, J. Wang / Bioresource Technology 160 (2014) 32–42 Table 5 Stability constants in single-metal system exemplified by the secondary sludge collected from the BA plant. Metals

Cr(III)

Pb(II)

Cu(II)

Zn(II)

Cd(II)

Ni(II)

Co(II)

Log KS

5.26 ± 0.05

5.00 ± 0.06

4.58 ± 0.07

3.50 ± 0.20

3.41 ± 0.11

2.93 ± 0.10

2.80 ± 0.08

Table 6 Summary of stability constants in mixed-metal systems. Sludge type

Sludge samplea

Cu(II)

Ni(II)

Co(II)

Cd(II)

Zn(II)

Pb(II)

Cr(III)

Hg(II)

Ag(I)

Primary

WI(2) PHL(3) BA(2) DC(3)

5.64 ± 0.04 5.71 ± 0.34 5.01 ± 0.20 6.29 ± 0.80

2.97 ± 0.14 3.37 ± 0.50 2.96 ± 0.25 2.83 ± 0.13

2.65 ± 0.06 2.94 ± 0.22 2.75 ± 0.17 2.60 ± 0.22

4.72 ± 0.78 5.51 ± 0.23 4.87 ± 0.35 5.16 ± 1.10

5.11 ± 0.14 4.00 ± 0.46 4.26 ± 0.86 5.03 ± 0.31

5.37 ± 0.33 5.89 ± 0.29 4.75 ± 0.21 5.68 ± 0.61

5.32 ± 0.06 4.94 ± 0.33 5.01 ± 0.07 5.22 ± 0.83

6.09 ± 0.00 5.77 ± 0.11 4.72 ± 0.00 6.24 ± 0.91

5.32 ± 0.00 5.14 ± 0.00 5.34 ± 0.00 6.02 ± 0.00

Secondary

WI(4) PHL(4) BA(3) DC(5)

5.55 ± 0.48 5.53 ± 0.39 5.53 ± 0.30 5.31 ± 0.47

3.09 ± 0.13 3.43 ± 0.15 3.43 ± 0.07 3.05 ± 0.10

2.67 ± 0.13 3.31 ± 0.09 3.31 ± 0.05 3.00 ± 0.14

4.27 ± 0.66 3.97 ± 0.13 3.97 ± 0.38 3.96 ± 0.26

4.51 ± 0.15 4.32 ± 0.83 4.32 ± 0.37 5.00 ± 0.66

5.42 ± 0.44 6.22 ± 0.69 6.22 ± 0.20 6.21 ± 0.35

5.04 ± 0.24 6.32 ± 0.27 6.32 ± 0.40 6.26 ± 0.35

6.05 ± 0.43 5.68 ± 0.00 5.68 ± 0.00 7.53 ± 0.97

6.13 ± 0.00 6.57 ± 0.00 6.57 ± 0.00 6.39 ± 0.00

Tertiary

DC(3)

5.40 ± 0.33

3.24 ± 0.11

3.14 ± 0.08

3.96 ± 0.31

5.02 ± 1.12

6.40 ± 1.12

6.00 ± 0.61

5.69 ± 0.00

7.35 ± 0.00

5.70 ± 0.60

3.20 ± 0.40

2.90 ± 0.30

4.50 ± 0.80

4.70 ± 0.70

5.90 ± 0.70

5.60 ± 0.70

6.30 ± 0.90

6.10 ± 0.80

General value a

Value in parenthesis indicates number of sampling.

primary or secondary sedimentation basin (e.g., Mgal), influent suspended solids concentration (e.g., mg/L), and effluent suspended solids concentration (e.g., mg/L), the steady-state distribution of metals between soluble and fixed (solid) phases in municipal wastewater treatment plants can be estimated readily. 5. Conclusion There was insignificant difference in the acidity of DOM and the surface acidity of sludge particulates among the four different regional wastewater treatment plants. The acidity of DOM was treated as a mixture of two distinct acidity constants, pKaA and pKaB, respectively and the surface acidity, defined in terms of acidity (KH) and acidity capacity (ST), of sludge particulates was treated as a weak monoprotic acid. Results can be used to calculate the distribution of metals in wastewater treatment systems. Moreover, the stability constant can be used to predict the equilibrium metal removal capacity of specific sludge particulates. References Akar, T., Tunali, S., 2006. Biosorption characteristics of Aspergillus flavus biomass for removal of Pb(II) andCu(II) ions from aqueous solution. Bioresour. Technol. 97, 1780–1787. Akar, T., Tunali, S., Kiran, I., 2005. Botrytis cinerea as a new fungal biosorbent for removal of Pb(II) from aqueous solutions. Biochem. Eng. J. 25, 227–235. Al-Hakawati, M.S., Banks, C.J., 2000. Copper removal by polymer immobilized Rhizopus oryzae. Water Sci. Technol. 42 (7), 345–352. Al-Rub, F.A., El-Naas, M.H., Ashour, I., Al-Marzouqi, M., 2006. Biosorption of copper on Chlorella vulgaris from single, binary and ternary aqueous solutions. Process Biochem. 41, 457–464. Barth, E.F., Ettinger, M.B., Salotto, B.V., McDermott, G.N., 1965a. Summary report on the effects of heavy metal on the biological treatment process. J. Water Pollut. Control Fed. 37, 86–96. Barth, E.F., English, J.N., Salotto, B.V., Jackson, B.N., Ettinger, M.B., 1965b. Field survey of four municipal wastewater treatment plants receiving metallic wastes. J. Water Pollut. Control Fed. 37, 1101–1121. Brown, M.J., Lester, J.N., 1979. Metal removal in activated sludge: the role of bacterial extracellular polymers. Water Res. 13, 817–837. Brown, M.J., Lester, J.N., 1982a. Role of the bacterial extracellular polymers in metal uptake in pure bacterial culture and activated sludge – I: effects of metal concentration. Water Res. 16, 1539–1548. Brown, M.J., Lester, J.N., 1982b. Role of the bacterial extracellular polymers in metal uptake in pure bacterial culture and activated sludge – II: effects of mean cell retention time. Water Res. 16, 1549–1560. Chakravarty, R., Banerjee, P.C., 2012. Mechanism of cadmium binding on the cell wall of an acidophilic bacterium. Bioresour. Technol. 108, 176–183. Chang, D., Fukushi, K., Ghosh, S., 1995. Stimulation of activated sludge cultures for enhanced heavy metal removal. Water Environ. Res. 67, 822–827.

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