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Kinetics of Heavy Metal Dissociation from Natural Organic Matter: Roles of the Carboxylic and Phenolic Sites Zhenqing Shi, Pei Wang, Lanfang Peng, Zhang Lin, and Zhi Dang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b01809 • Publication Date (Web): 31 Aug 2016 Downloaded from http://pubs.acs.org on August 31, 2016

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Environmental Science & Technology

1

Kinetics of Heavy Metal Dissociation from Natural

2

Organic Matter: Roles of the Carboxylic and

3

Phenolic Sites

4

Zhenqing Shi,* † ‡ Pei Wang,† ‡ Lanfang Peng,† ‡ Zhang Lin,† ‡ and Zhi Dang† ‡

5 6

†

7

Guangzhou, Guangdong 510006, PR China

8

‡

9

Clusters, Ministry of Education, South China University of Technology,

10

11 12

13

School of Environment and Energy, South China University of Technology,

The Key Lab of Pollution Control and Ecosystem Restoration in Industry

Guangzhou, Guangdong 510006, PR China *

Corresponding author: email: [email protected], phone: 86-20-39380503, fax: 86-20-39380508

Total Words: 4896+ 600 + 600 + 600 + 300 = 6996

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ACS Paragon Plus Environment


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Abstract

16

We developed a unifying model for the kinetics of heavy metal dissociation from natural organic

17

matter (NOM) in this study. The kinetics model, integrated with the equilibrium model WHAM

18

7, specifically considered metal ion reactions with various NOM sites formed by the carboxylic

19

and phenolic sites. The association and dissociation rate coefficients for metal reactions with

20

various NOM sites were constrained by WHAM predicted equilibrium distribution coefficients

21

at specific reaction conditions. We developed the relationship for the dissociation rate

22

coefficients among different binding sites for each metal, which was internally constrained by

23

the metal binding constants. The model had only one fitting parameter, the dissociation rate

24

coefficient for the metal complexes formed with two weak carboxylic sites, and all other

25

parameters were derived from WHAM 7. The kinetic data for metal dissociation from NOM

26

were collected from the literatures, and the model was able to reproduce most of relevant data

27

analyzed. The bidentate complexes appeared to be the predominated species controlling metal

28

dissociation under most environmental conditions. The model can help to predict the reactivity

29

and bioavailability of heavy metals under the impact of multiple competing ligands including

30

NOM.

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TOC Art

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35

Introduction

36

Natural organic matter (NOM) is probably the most important ligand in the environment that

37

affects the speciation and reactivity of heavy metals. Extensive work has been done on the

38

equilibrium of heavy metal complexation with NOM and a few predictive models have been

39

developed.1-4 Less progress has been made for predicting the kinetics of metal

40

association/dissociation reactions with NOM.5-10 The release of metal ions from NOM may be

41

slow and the slow dissociation rates of metal-NOM complexes may limit the bioavailability of

42

metal ions in natural environment.11, 12 The quantitative understanding of the kinetics of heavy

43

metal dissociation from various NOM binding sites is essential for accurately predicting the

44

reactivity, bioavailability and transport of heavy metals in the environment.9 NOM contains a

45

variety of heterogeneous sites and the carboxylic and phenolic sites are two of the most

46

important functional groups controlling metal binding.13 Even with the success of the widely

47

used equilibrium models for metal complexation such as WHAM (Windermere Humic Aqueous

48

Model),3, 14, 15 little is known about the distribution of metal ions among various binding sites of

49

NOM, and, especially, its impact on the rates of metal dissociation from NOM.

50

The majority of the experimental studies on the kinetics of heavy metal dissociation from

51

NOM employed a competing ligand exchange (CLE) method, in which a strong metal

52

complexing ligand, either as a dissolved ligand5-7, 16-19 or as a cation exchange resin,20-28 was

53

used to quickly bind metal ions released from metal-NOM complexes. The kinetic data were

54

usually analyzed by assuming few dissociation sites in NOM with varying dissociation rate

55

coefficients responsible for the observed dissociation kinetics.20, 24, 25 One major issue for those

56

analyses is the lack of the mechanistic constraint for the metal distribution among NOM sites and 4


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57

the corresponding rate coefficients. One fundamental assumption used in those analysis is that

58

the re-association of metal ions with NOM was negligible due to fast uptake of metal ions by the

59

competing ligands, but the validity of this assumption was not assessed.29 As a result, large

60

variations of the dissociation rate coefficients were reported in various studies, some of which

61

even cannot be reconciled by the experimental variables or the heterogeneity of NOM sites.30

62

Therefore, a mechanistic-based approach, which considers various NOM binding sites, varying

63

solution chemistry, and the effect of metal re-association reactions with NOM, is desired.

64

We have developed a mechanistic-based model for the kinetics of heavy metal

65

adsorption/desorption reactions with soil organic matter (SOM),31-34 by integrating the

66

equilibrium model WHAM.3, 35 Our previous results demonstrated that the mono-, bi-, and

67

tridentate metal complexes may have different reaction rates.32, 33 The kinetics model, in

68

principle, should be able to account for the effects of solution chemistry and the heterogeneity of

69

NOM sites on metal dissociation kinetics in the CLE reactions. However, the carboxylic and

70

phenolic sites may form various bidentate and tridentate sites with different binding strength.

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The metal binding constants for the carboxylic and phenolic sites differ significantly and are

72

interrelated through the proton binding constants of both sites.15, 36 Since the reaction rate

73

coefficients are constrained by the equilibrium distribution coefficients during the kinetic

74

reactions,31 the carboxylic and phenolic sites may have different association/dissociation rate

75

coefficients. How various mono-, bi-, and tridentate sites affect the kinetics of metal dissociation

76

from NOM has not been quantitatively studied.

77

In this study, we proposed a unifying kinetics model for heavy metal dissociation from

78

NOM based on metal reactions with various NOM binding sites formed by the carboxylic and

79

phenolic sites, as described by the WHAM 7 model.15 We developed the relationship for the 5



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dissociation rate coefficients among different NOM binding sites, which was internally

81

constrained by the metal binding constants of the NOM sites. We reviewed the kinetic data

82

published during last three decades, and selected and analyzed some most relevant kinetic data.

83

We assessed how the distribution of metals among NOM sites affected the kinetic behavior of

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metals in the environment. We, for the first time, quantitatively elucidated the roles of both

85

carboxylic and phenolic sites on controlling the dissociation rates of metal-NOM complexes, and

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assessed the effect of the metal re-association reaction in the CLE reaction for different heavy

87

metals.

88

Theoretical Background

89

Competing Ligand Exchange Reactions for Metal-NOM Complexes. The dissociation of

90

heavy metal ions (Me) from NOM can be described as,

91

(1)

92

where MeLi is metal ions associated with the ith binding site of NOM, Li is the ith binding site,

93

and kai (s-1) and kdi (s-1) are the association and dissociation rate coefficients for the ith binding

94

site, respectively. When a strong competing ligand, Lc, is introduced, it drives the reaction

95

toward forming the complex with Lc, MeLc, with a formation rate coefficient k (s-1),

96

(2)

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Generally, the experiments were conducted with a large excess of Lc and the back reaction of

98

equation 2 was minimal. The CLE reaction following equations 1 and 2 is known as the

99

disjunctive pathway, in which the competing ligands do not directly react with the metal-NOM

100

complexes. Another reaction pathway, adjunctive pathway, may exist, in which the competing

101

ligands form intermediate complexes with the metal-NOM complexes and the dissociation of the 6


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intermediate complexes is the rate-limiting step.6 The kinetics of the disjunctive pathway

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provides valuable information on the stability of metal-NOM complexes and thus is more

104

environmental relevant, since the adjunctive pathway is highly dependent on the competing

105

ligands selected for the specific experiments.6

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Thermodynamic Equilibrium Constraint of Reaction Rates. The forward and

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backward reaction rates for a certain reaction can be constrained by the equilibrium constant. Shi

108

et al.31 have recognized the importance of the equilibrium constraint on the reaction rates for the

109

heterogeneous environmental ligands when studying the kinetics of metal desorption from soils,

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and since then have developed kinetics models based on the mechanistic-based equilibrium

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model WHAM.32-34 Similar concept has also been applied to some studies on metal kinetic

112

reactions with model humic acids (HA) and fulvic acids (FA)10, 37-39 or freshwater NOM.30 By

113

assuming that the Eigen mechanism applies to the metal-NOM complexation reaction, the

114

association rate coefficients could be computed theoretically based on the stability constant of

115

the outer-sphere complex formation and the rate coefficient for water loss from the inner

116

coordination sphere of the metal ion.30, 39

117

It is still not clear how the variations of the equilibrium binding constants affect the

118

association or dissociation rate coefficients since they are internally coupled together with the

119

equilibrium binding constants. By analyzing the literature data, Town et al. (2012) found that the

120

variations of metal stable constants was reflected by the dissociation rate constants while the

121

association rate constants are not dependent on the metal occupation in humic acids.10, 38

122

Considering the variations of binding strength of various binding sites in NOM and different

123

techniques to study the kinetics of metal dissociation from NOM, more work is desired to

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develop the theoretical basis for constraining the reaction rate coefficients based on the

125

mechanistic description of metal ion reactions with various NOM sites.

126

Integration of WHAM into the Kinetics Model. WHAM is a chemical speciation

127

model developed by Tipping and co-workers,3, 14, 15, 40 which is capable of calculating the

128

equilibrium of metal binding to humic substances (S1 section, Supporting Information). WHAM

129

7,15 the latest version of WHAM, assumes a discrete distribution of binding sites of humic

130

substances, and there are two types of sites, A sites and B sites, that correspond to the carboxylic

131

and phenolic sites, respectively. Metal ions can form multiple mono-, bi- and tridentate

132

complexes with A and/or B sites.

133

In our previous modeling studies on the kinetics of metal adsorption/desorption on

134

SOM,32-34 the adsorption and desorption rate coefficients for the mono-, bi- and tridentate sites

135

were constrained by the WHAM predicted equilibrium distribution coefficients Kpi. Similar

136

concept can be applied to the kinetics of metal association and dissociation reactions with NOM,

137

kai / kdi = K pi(C pi ,pH,I,...) = Cpi / Cion

(3)

138

in which Kpi is the ratio of metal concentrations in the specific site of NOM, Cpi (M), to the ionic

139

metal concentrations in solutions, Cion (M), and is a function of Cpi and the reaction chemistry

140

conditions at the specific reaction time. A detailed description on integrating WHAM into the

141

kinetics model was presented in the Supporting Information (S2 section). Note that equation 3

142

has general applicability to various kinetic reactions and does not require the kinetic reaction to

143

be close to the equilibrium. It indicates that the reaction is tending toward the equilibrium

144

dictated by the local conditions at each reaction time along the reaction path.

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Materials and Methods 8


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Experimental Data. The largest volume of CLE kinetic studies were conducted by Chakrabarti

147

and co-workers,20-28 in which metal-NOM solution samples were mixed with 1% Chelex 100

148

resin and the total dissolved metal concentrations were continuously measured to study the

149

kinetics of metal dissociation from NOM. The resin had a wet capacity of 0.61 meq g-1.24

150

Different from those earlier CLE kinetic studies that employed dissolved competing ligands,5-7,

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16-19

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chance of the direct attack of the resin particles to metal-NOM complexes. Kinetic studies also

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suggested that the disjunctive pathway may predominate in the Chelex resin experiments.24

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the Chelex resin particles did not adsorb metal-DOM complexes,20 which minimized the

The data include experiments with either model FA and HA solutions or natural water

155

samples from different origins. The ionic strength of the water samples was in the range of 3 ×

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10-4 – 2.4 × 10-2 M. We excluded a few data sets from experiments with very low ionic strength

157

for the model FA/HA solutions. A description of the literature data was presented in the

158

Supporting Information (S3 section). Based on the concentration of the Chelex resin and its wet

159

capacity, the total binding sites of the Chelex resin were much higher than the total amount of

160

metals in all water samples. All experimental data were digitized from the published literatures

161

using the OriginPro 9 program.

162

Modeling Methods. The kinetics of metal association/dissociation reactions with NOM

163

may be complicated by diffusion process, outer-sphere complexation and inner-sphere

164

complexation.37, 38 Here we mainly focused on the inner-sphere complexation reactions that may

165

occur during the CLE reactions. The total dissolved metal concentrations Cw (M) equal to the

166

sum of the concentrations of metal-NOM complexes CMeLi (M) and the ionic metal (Cion) in the

167

solution that include all metal ions not complexed by NOM,

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∑C

168

Cw =

169

The rates of metal dissociation from the specific NOM binding site and the change of the

170

171

MeLi

+ Cion

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(4)

ionic metal concentrations can be described as, dC MeLi dt

= −kdi C MeLi + kai Cion = −kdi C MeLi + kdi K pi Cion

(5)

172

dCion = dt

173

In WHAM 7, metal ions can form inner-sphere complexes through mono-, bi-, and

∑ kdi C

MeLi

−

∑ kai C

ion

− kCion =

∑ kdi C

MeLi

−

∑ kdi K pi C

ion

− kCion (6)

174

tridentate bindings to FA and HA and outer-sphere complexes through the electrostatic

175

attractions. The monodentate complexes can be divided into two groups, complexed with one of

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the four A sites or the four B sites. WHAM 7 fixed the metal binding constants of all four A

177

sites, KMA, at the same value, and that of all four B sites, KMB, at the same value, respectively.

178

The relationship between KMA and KMB can be described as,15

179

log K MB = log K MA × pK B / pK A

(7)

180

where pKA and pKB are the average pK values, the negative logarithms of the proton dissociation

181

constants, of the A and B sites, respectively. All above binding constants are dimensionless.

182

The bidentate complexes can be formed either through two A sites (denoted as AA) or

183

one A and another B sites (denoted as AB), and each group can be further divided into three sub-

184

groups according to their binding strength, denoted as AA-weak, AA-medium, AA-strong, AB-

185

weak, AB-medium, and AB-strong metal complexes. The metal binding constants of these six

186

groups of bidentate sites can be derived through the monodentate sites,3

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log K AA = 2 log K MA + x∆LK 2 (x = 0, 1, 2)

188

log K AB = log K MA + log K MB + x∆LK 2

(8) (x = 0, 1, 2)

(9)

189

where x = 0, 1, 2 corresponds to the weak, medium and strong bidentate complexes, respectively,

190

andΔLK2 is a “spread factor” for the metal binding constants used in WHAM, which accounts

191

for the tendency of the metal to interact with N and S atoms in ligands.

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All tridentate complexes are formed by two A and one B sites (denoted as AAB) and,

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similar to the bidentate sites, can be divided into three groups, denoted as AAB-weak, AAB-

194

medium, and AAB-strong. The metal binding constants of the tridentate complexes can be

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calculated as,

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log K AAB = 2 log K MA + log K MB + y∆LK 2 (y = 0, 1.5, 3)

(10)

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where y = 0, 1.5, 3 corresponds to the weak, medium and strong tridentate complexes,

198

respectively. Above formations result in total 11 groups of distinct sites for metal complexation

199

reactions. In WHAM 7, with the known values of KMA, pKA, pKB and ΔLK2, the metal binding

200

constants of any specific binding sites can be calculated based on equations 7-10.

201 202 203

For any specific site i, the intrinsic association and dissociation rate constants, kac,i (s-1) and kdc,i (s-1), are constrained by the metal binding constants described above, here denoted as Ki,

log K i = log kac ,i − log kdc ,i

(11)

204

We assume that the metal binding constant has an equal effect on both of association or

205

dissociation rate constants, which results in,

206

log kdc ,i − log kdc ,j = 1 / 2(log K j − log K i ) 11


(12)


207 208

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and

log kac ,i − log k ac ,j = 1 / 2(log K i − log K j )

(13)

209

Equations 12 and 13 set up the relationships for the intrinsic association or dissociation rate

210

constants between two specific binding sites. It indicates that sites with larger metal binding

211

constants have larger intrinsic association rate constants and smaller dissociation rate constants.

212

Since NOM particles were usually charged and the free site concentrations changed with

213

reaction time, we used WHAM 7 calculated equilibrium distribution coefficients to constrain the

214

association and dissociation rate coefficients during the kinetic experiments. Consistent with our

215

previous modeling approach,32, 33 during the dissociation process the dissociation rate

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coefficients for each group of sites remained constant and the association rate coefficients, that

217

virtually equal to the product of the intrinsic association rate constants and the free ligand site

218

concentrations, changed with time as Kpi changed with reaction time (equation 3). Therefore, we

219

adapted the relationship described in equations 12 to constrain the dissociation rate coefficients,

220

log kdi − log kdj = 1 / 2(log K j − log K i )

(14)

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As a result of equation 3 with WHAM predicted Kpi, the association rate coefficients accounted

222

for the effects of reaction chemistry and electrostatic interactions during the kinetic reactions.

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As predicted by WHAM 7, the bidentate complexes predominated in most environmental

224

conditions as shown later. Therefore, we chose the kdi value for the bidentate complexes formed

225

with two weak carboxylic sites (AA-weak), kd,AA-W, as the model fitting parameter, and the kdi

226

values for all other complexes can be calculated from kd,AA-W according to equation 14 with all

227

other parameters derived from WHAM 7 (Table S1, Supporting Information). Under the 12


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experimental conditions analyzed in this study, the contribution of the outer-sphere complexes

229

was negligible.

230

For WHAM 7 calculations, the major input parameters include the solution parameters

231

and the FA or HA concentrations. A detailed description of the WHAM 7 input parameters was

232

presented in the Supporting Information (S1 section). Based on the WHAM 7 output, we

233

calculated the equilibrium distribution coefficients Kpi for all 11 groups of mono-, bi-, tridentate

234

metal complexes. In principle, Kpi can be calculated by WHAM 7 at each time. However, it was

235

not practical when multiple heavy metals were present because the exact chemistry conditions at

236

each time were not available during the numerical calculations. To simplify the calculations, we

237

calculated Kpi at the start and the end of the experiments and then calculated Kpi at each time by

238

linear interpolation. For experimental data that demonstrated typical bi-phasic curves, we

239

separated the kinetic curves into two sections and then calculated Kpi at each time for each

240

section.

241

An implicit finite difference numerical method was used to solve the model equations

242

(equations 4-6). Each data set was tabulated in a Microsoft Excel spreadsheet and, for each

243

observation time, the square of the difference between measured and model calculated dissolved

244

metal concentrations was calculated. The sum of the squares for each data set was calculated to

245

obtain the total squared error. The SOLVER program in EXCEL was used to obtain the model

246

fitting parameter, kd,AA-W, by minimize the total squared error for each experiment data set of each

247

metal

248

Results and Discussion

13



249

Metal Binding to Various NOM Sites. The initial distribution of heavy metals among NOM

250

sites is a key information for predicting metal dissociation kinetics. Different from previous

251

studies to obtain this information with model fitting, we employed WHAM 7 to calculate heavy

252

metal binding to various NOM sites for all water samples before the CLE reactions. Here we

253

showed the results in typical conditions of natural water using the contour plots. For all five

254

metals (Cu, Cd, Ni, Pb, and Zn), the bidentate complexes predominated in most conditions

255

(Figure 1, Figures S1-S4, Supporting Information). At low pH the contribution of monodentate

256

may not be negligible, and, at high pH, the tridentate complexes may play an important role.

257

The roles of both carboxylic and phenolic sites on controlling metal binding can be

258

assessed based on the distribution of metals among the six groups of bidentate sites. For Cu, all

259

six sites may bind significant amount of Cu depending on the pH and Cu concentrations in NOM

260

(Figures 1d-1i). Both the AA-medium and AB-medium sites appeared to play the most important

261

roles while the AA-weak sites bound the least amount of Cu. This Cu binding behavior can be

262

attributed to the large values of both KMA and ΔLK2. The importance of both carboxylic and

263

phenolic sites for metal binding was also observed for Cd, Ni, Pb and Zn, with the relative

264

importance of each bidentate site differing among different metals due to the different KMA and

265

ΔLK2 values (Figures S1-S4, Table S1, Supporting Information).

266

Generally little has been done experimentally on verifying the metal distributions among

267

NOM sites at the environmental relevant conditions, although spectroscopic techniques have

268

been used to study the mechanisms of metal reactions with humic substances.41-43 At high metal

269

concentrations, the formation of bidentate complexes of Pb with both the carboxylic and

270

phenolic sites of humic substances has been verified with the X-ray absorption spectroscopy

271

measurements,43 highlighting the importance of both the carboxylic and phenolic sites. 14


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Analysis of Metal Dissociation Kinetics. The kinetics of heavy metal dissociation from

273

NOM samples, as shown by the percent of metal remaining in solutions (including both metal

274

complexed by NOM and ionic forms of metal), differed significantly depending on the metals,

275

pH, competing cations, and NOM samples (Figure 2). Additional results were shown in the

276

Supporting Information (Figures S5-S7). For natural water samples, generally a gradual slow Cu

277

decrease of dissolved Cu in solutions was observed (Figures 2a). For most of Cd, Ni, Pb, and Zn

278

samples, however, a typical rapid decline of dissolved metals in solutions in the first a few

279

minutes was observed and, after that, a similar gradual slow metal release followed (Figures 2b,

280

2c, 2e, and 2f). For the model FA and HA solutions, it is interesting to observe that the release of

281

Ni was extremely slow under low cation competition conditions (Figure 2d and Figure S6,

282

Supporting Information). The difference of the kinetic behavior has been attributed to the

283

properties of cations such as the ligand field stabilization energy and dehydration rates.24, 38

284

Metal speciation calculations with WHAM 7 for the original water samples showed that

285

the majority of Cu (93% - 99%) was complexed by NOM but, for Cd, Ni, Pb, and Zn, significant

286

amount of metals was present as the ionic forms for some samples. The uptake of metal ions by

287

the Chelex resin was rapid,20 which may explain the observed quick decline of metals in

288

solutions at the beginning of the experiments for some samples (Figures 2b, 2c, 2e, and 2f). After

289

that, the overall rates of slow metal release from NOM were controlled by the combination of

290

both reactions 1 and 2.

291

Generally, the model can describe the kinetics of metal release from various NOM

292

samples in a wide range of experimental conditions (Figure 2). More deviations between the

293

model calculations and data were observed at the beginning of the experiments, which suggests

294

that various ionic metals may have different rates of the uptake by the Chelex resin. All the 15



295

variations of reaction chemistry were handled by WHAM 7, including metal distribution among

296

various NOM sites in the original samples (Figure 1, Figures S1-S4, Supporting Information)

297

and the equilibrium distribution coefficient for each site.

298

The dissociation rate coefficients obtained in this study were presented in Figure 3 for

299

natural water samples and in Figure S8 (Supporting Information) for model FA/HA solutions.

300

For each binding site, the dissociation rate coefficients varied among samples for each metal. For

301

the natural water samples, the dissociation rate coefficients of different binding sites for each

302

metal spread a wide range from very low values ( < 10-5 s-1), mostly for those tridentate and

303

strong bidentate complexes, to large values ( > 10-3 s-1) for those abundant but weak bidentate

304

and monodentate complexes (Figure 3). For the model FA/HA solutions, the kd values of Ni were

305

much smaller when lack of the cation competition (Figure S8, Supporting Information) than

306

those of natural water samples. This highlights the importance of the competing cations on

307

controlling metal dissociation rates. The variations of kd values among sites with varying binding

308

strength are consistent with the concept by Town et al. (2012),38 in which the distribution in

309

metal stability constant is reflected in that of the dissociation rate constant.

310

The Roles of Metal Re-association, Competing Ligands, and NOM Binding Sites.

311

Using the model developed in this study, we evaluated how re-association reactions affected the

312

overall metal release from NOM samples. For Cu, the re-association reaction significantly

313

inhibited the overall Cu release from NOM within a range of kd,AA-W values that may provide

314

reasonable model fits to the experimental data (Figure 4a). When the kd,AA-W value was

315

unrealistically low that cannot provide a reasonable model fit, the re-association reaction was

316

insignificant due to the low re-association rate coefficients (equation 3). Therefore, for Cu the re-

317

association reactions can effectively compete with the binding by the Chelex resin during the 16


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318

CLE reactions. In comparison, the re-association reactions had little impact on Ni release and the

319

variations of kd,AA-W values dominated the overall reaction rates (Figure 4b), which can be

320

explained by both lower values of KMA and kd,AA-W for Ni than that for Cu. Similarly, since both

321

Cd and Zn had low KMA and kd,AA-W values, the re-association reactions were insignificant in most

322

conditions. For Pb, which has a similar KMA to Cu but smaller kd,AA-W values, the impact of the re-

323

association reactions was moderate compared with Cu and Ni (Figure S9, Supporting

324

Information).

325

We further assessed the significance of the formation rate coefficient, k, for metal ion

326

complexation with the Chelex resin. The k values may be affected by the concentrations of the

327

Chelex resin and reaction conditions.28, 29 It was reported that the k values for typical heavy

328

metals were in the order of magnitude of 10-2 s-1 when the concentration of the Chelex 100 resin

329

was 1%,20, 24, 25 but little was known on how k changed with pH and metal concentrations. Our

330

model analysis showed that the overall CLE reaction rates for Cu were sensitive to k values

331

(Figure S10a, Supporting Information). This indicates that both equations 1 and 2 had

332

comparable reactions rates for Cu and affected the overall CLE reaction rates, contrary to the

333

assumption used by previous CLE studies. In comparison, the variations of k values had little

334

impact on the overall CLE reaction rates for Ni (Figure S10b, Supporting Information). This

335

suggests that the dissociation of Ni from NOM (equation 1) was the rate-limiting step for the

336

CLE reactions.

337

Although metal ions may distribute among all 11 groups of specific binding sites, only a

338

few of them, mostly weak bidentate sites and monodentate sites, were kinetically labile under

339

specific experimental conditions (Figures 1 and 3). For Cu, it was the weak bidentate sites that

340

were responsible for Cu dissociation observed under those experimental conditions, and the 17



341

tridentate sites and strong AB bidentate sites were virtually inert due to the low kd values and

342

large ka values arising from the large equilibrium distribution coefficients. For Cd, Ni, and Zn,

343

since re-association reactions were minimal in most conditions, the lability of metals in various

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sites can be assessed based on the dissociation rate coefficients. Since both tridentate and strong

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AB bidentate complexes only accounted for a small percent of total metal complexes, most

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metal-NOM complexes formed with the carboxylic or/and phenolic sites could be kinetically

347

labile in typical environmental conditions.

348

Model Assessment and Implications. The accuracy of WHAM 7 predictions is essential

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for the performance of the kinetics model. The ability of WHAM 7 to predict the equilibrium of

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metal partitioning between NOM and water has been extensively studied.44-48 WHAM 7

351

predicted predominated bidentate complex formation in most conditions. It appears the model

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can reproduce the observed metal dissociation kinetics appropriately under a wide range of

353

environmental conditions, supporting the validity of the WHAM 7 based kinetics model. Our

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current model only considered the overall kinetic rates of metal reactions with NOM when the

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formation of outer-sphere complexes was minimal, while in natural environment the reactions

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may involve multiple processes/steps, such as ion diffusion, formation of outer-sphere

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complexes and then inner-sphere complexes, etc.,10 which may be affected by the reaction

358

conditions and the types of cations.

359

The uncertainties of the dissociation rate coefficients obtained in this study were not

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assessed. For Cu, the previously reported kd values in the Chelex resin CLE studies varied in the

361

order of magnitude of 10-4 – 10-5 s-1,24, 25 which Warnken et al.30 have pointed out to be

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unrealistically too low for freshwater samples. Our study has demonstrated that the kd values of

363

Cu may spread to a wide range for different sites but most bidentate sites had kd values larger 18


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Page 19 of 27


364

than 10-4 s-1 (Figure 3). The spread of kd values reflected the varying binding strength of NOM

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sites and was internally constrained by the metal binding constants, which is a fundamental

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improvement from the previous studies that obtained variable kd values purely from the model

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fitting.20-27 For each metal, the variations of kd values among different samples may arise from

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the difference of the chemical properties of NOM samples, which was not characterized in

369

original studies. A accurate determination of the kd values will require further work including

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accurate determination of the k values, the Kpi values during the kinetic reactions, and the

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“active” portion of NOM.48

372

A few chemical speciation models, such as NICA-Nonnan,49 WHAM14 and the

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Stockholm Humic Model4, have shown success for simulating metal equilibrium binding to

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NOM, but less is known about how metal ions distribute to various NOM binding sites, which

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might differ for model predictions among these speciation model.4, 40, 43 It may make little

376

practical difference in term of predicting equilibrium distribution within certain environmental

377

conditions, but may significantly affect the prediction of the kinetic behavior of metal ions since

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binding to various sites may result in different metal dissociation rates. In order to more

379

accurately to predict the dynamic behavior of metals in the environment, mechanistic

380

understanding of metal binding to various NOM sites and its kinetic effect at the molecular level

381

needs to be further studied.

382

Our model has significance in predicting the bioavailability and toxicity of metal ions in

383

the presence of NOM since faster metal dissociation from NOM may result in more metal

384

bioavailability.50 The large variations of kd values among NOM binding sites suggest that the

385

rates of supply of metal ions from NOM sites differ for a few order of magnitude, and the

386

correspondent time scales of the dissociation reactions for the bidentate sites range from a few 19



387

seconds to several days. This information should be carefully considered when predicting the

388

metal bioavailability in natural environment when NOM is present, and our model provides a

389

quantitative tool in this aspect.

390

Acknowledgments

391

We thank Dr. Dominic Di Toro for his advice and Dr. Stephen Lofts for providing the

392

customized version of WHAM 7. Funding was provided by the National Science Foundation of

393

China (Project number: 41573090) and the Thousand Talent Program for Young Outstanding

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Scientists of China.

395

Supporting Information Available

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Supporting information includes (1) description of WHAM and model calculations, (2)

397

integrating WHAM into CLE kinetics model, (3) description of the literature data, (4)

398

calculations of kd values for each site, and (5) additional figures. This material is available free of

399

charge at http://pubs.acs.org.

400

20


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19. Rate, A. W.; McLaren, R. G.; Swift, R. S., Evaluation of log-normal distribution firstorder kinetic model for copper(II)-humic acid complex dissociation. Environ. Sci. Technol. 1992, 26, (12), 2477-2483. 20. Chakrabarti, C. L.; Lu, Y.; Gregoire, D. C.; Back, M. H.; Schroeder, W. H., Kinetic studies of metal speciation using Chelex cation exchange resin: application to cadmium, copper, and lead speciation in river water and snow. Environ. Sci. Technol. 1994, 28, (11), 1957-1967. 21. Lu, Y.; Chakrabarti, C. L.; Back, M. H.; Grégoire, D. C.; Schroeder, W. H., Kinetic studies of aluminum and zinc speciation in river water and snow. Anal. Chim. Acta 1994, 293, (1), 95-108. 22. Mandal, R.; Salam, M. S. A.; Murimboh, J.; Hassan, N. M.; Chakrabarti, C. L.; Back, M. H.; Gregoire, D. C., Competition of Ca(II) and Mg(II) with Ni(II) for binding by a wellcharacterized fulvic acid in model solutions. Environ. Sci. Technol. 2000, 34, (11), 2201-2208. 23. Mandal, R.; Hassan, N. M.; Murimboh, J.; Chakrabarti, C. L.; Back, M. H., Chemical speciation and toxicity of nickel species in natural waters from the Sudbury area (Canada). Environ. Sci. Technol. 2002, 36, (7), 1477-1484. 24. Sekaly, A. L. R.; Murimboh, J.; Hassan, N. M.; Mandal, R.; Ben Younes, M. E.; Chakrabarti, C. L.; Back, M. H.; Gregoire, D. C., Kinetic speciation of Co(II), Ni(II), Cu(II), and Zn(II) in model solutions and freshwaters: Lability and the d electron configuration. Environ. Sci. Technol. 2003, 37, (1), 68-74. 25. Fasfous, II; Yapici, T.; Murimboh, J.; Hassan, I. M.; Chakrabarti, C. L.; Back, M. H.; Lean, D. R. S.; Gregoire, D. C., Kinetics of trace metal competition in the freshwater environment: Some fundamental characteristics. Environ. Sci. Technol. 2004, 38, (19), 49794986. 26. Hassan, N.; Murimboh, J. D.; Chakrabarti, C. L., Kinetic speciation of Ni(II) in model solutions and freshwaters: Competition of Al(III) and Fe(III). Water Air Soil Pollut. 2008, 193, (1-4), 131-146. 27. Guthrie, J. W.; Mandal, R.; Salam, M. S. A.; Hassan, N. M.; Murimboh, J.; Chakrabarti, C. L.; Back, M. H.; Gregoire, D. C., Kinetic studies of nickel speciation in model solutions of a well-characterized humic acid using the competing ligand exchange method. Anal. Chim. Acta 2003, 480, (1), 157-169. 28. Yapici, T.; Fasfous, I. I.; Zhao, J.; Chakrabarti, C. L., Effects of various competing ligands on the kinetics of trace metal complexes of Laurentian Fulvic Acid in model solutions and natural waters. Anal. Chim. Acta 2009, 636, (1), 6-12. 29. Jones, A. M.; Pham, A. N.; Collins, R. N.; Waite, T. D., Dissociation kinetics of Fe(III)– and Al(III)–natural organic matter complexes at pH 6.0 and 8.0 and 25 °C. Geochim. Cosmochim. Acta 2009, 73, (10), 2875-2887. 30. Warnken, K. W.; Davison, W.; Zhang, H.; Galceran, J.; Puy, J., In situ measurements of metal complex exchange kinetics in freshwater. Environ. Sci. Technol. 2007, 41, (9), 3179-3185. 31. Shi, Z.; Di Toro, D. M.; Allen, H. E.; Ponizovsky, A. A., Modeling kinetics of Cu and Zn release from soils. Environ. Sci. Technol. 2005, 39, 4562-4568. 32. Shi, Z.; Di Toro, D. M.; Allen, H. E.; Sparks, D. L., A WHAM-based kinetics model for Zn adsorption and desorption to soils. Environ. Sci. Technol. 2008, 42 5630-5636. 33. Shi, Z.; Di Toro, D. M.; Allen, H. E.; Sparks, D. L., A general model for kinetics of heavy metal adsorption and desorption on soils. Environ. Sci. Technol. 2013, 47, (8), 3761. 34. Shi, Z.; Peltier, E.; Sparks, D. L., Kinetics of Ni sorption in soils: roles of soil organic matter and Ni precipitation. Environ. Sci. Technol. 2012, 46, 2212-2219. 22


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35. Tipping, E., WHAM – A chemical equilibrium model and computer code for waters, sediment, and soils incorporating a discrete site/electrostatic model of ion-binding by humic substances. Comput. Geosci. 1994, 20, 973-1023. 36. Carbonaro, R. F.; Di Toro, D. M., Linear free energy relationships for metal-ligand complexation: Monodentate binding to negatively-charged oxygen donor atoms. Geochim. Cosmochim. Acta 2007, 71, (16), 3958-3968. 37. Buffle, J.; Zhang, Z.; Startchev, K., Metal flux and dynamic speciation at (bio)interfaces. Part I: critical evaluation and compilation of physicochemical parameters for complexes with simple ligands and fulvic/humic substances. Environ. Sci. Technol. 2007, 41, (22), 7609-7620. 38. Town, R. M.; Duval, J. F. L.; Buffle, J.; van Leeuwen, H. P., Chemodynamics of metal complexation by natural soft colloids: Cu(II) binding by humic acid. J. Phys. Chem. A 2012, 116, (25), 6489-6496. 39. Shafaei Arvajeh, M. R.; Lehto, N.; Garmo, O. A.; Zhang, H., Kinetic studies of Ni organic complexes using diffusive gradients in thin films (DGT) with double binding layers and a dynamic numerical model. Environ. Sci. Technol. 2013, 47, (1), 463-470. 40. Tipping, E.; Hurley, M. A., A unifying model of cation binding by humic substances. Geochim. Cosmochim. Acta 1992, 56, (10), 3627-3641. 41. van Schaik, J. W. J.; Persson, I.; Kleja, D. B.; Gustafsson, J. P., EXAFS study on the reactions between iron and fulvic acid in acid aqueous solutions. Environ. Sci. Technol. 2008, 42, (7), 2367-2373. 42. Xia, K.; Bleam, W.; Helmke, P. A., Studies of the nature of Cu2+ and Pb2+ binding sites in soil humic substances using X-ray absorption spectroscopy. Geochim. Cosmochim. Acta 1997, 61, (11), 2211-2221. 43. Xiong, J.; Koopal, L. K.; Tan, W.; Fang, L.; Wang, M.; Zhao, W.; Liu, F.; Zhang, J.; Weng, L., Lead binding to soil fulvic and humic acids: NICA-Donnan modeling and XAFS spectroscopy. Environ. Sci. Technol. 2013, 47, (20), 11634-11642. 44. Almas, A. R.; Lofts, S.; Mulder, J.; Tipping, E., Solubility of major cations and Cu, Zn and Cd in soil extracts of some contaminated agricultural soils near a zinc smelter in Norway: modelling with a multisurface extension of WHAM. Eur. J. Soil Sci. 2007, 58, 1074-1086. 45. Shi, Z.; Allen, H. E.; Di Toro, D. M.; S., L.; Lofts, S., Predicting cadmium adsorption on soils using WHAM VI. Chemosphere 2007, 69, 605-612. 46. Tipping, E., Modelling the interactions of Hg(II) and methylmercury with humic substances using WHAM/Model VI. Appl. Geochem. 2007, 22, 1624-1635. 47. Tipping, E.; Rieuwerts, J.; Pan, G.; Ashmore, M. R.; Lofts, S.; Hill, M. T. R.; Farago, M. E.; Thornton, I., The solid-solution partitioning of heavy metals (Cu, Zn, Cd, Pb) in upland soils of England and Wales. Environ. Pollut. 2003, 125, 213-225. 48. Lofts, S.; Tipping, E., Assessing WHAM/Model VII against field measurements of free metal ion concentrations: model performance and the role of uncertainty in parameters and inputs. Environ. Chem. 2011, 8, (5), 501-516. 49. Kinniburgh, D. G.; van Riemsdijk, W. H.; Koopal, L. K.; Borkovec, M.; Benedetti, M. F.; Avena, M. J., Ion binding to natural organic matter: competition, heterogeneity, stoichiometry and thermodynamic consistency. Colloids Surf. 1999, 151, (1-2), 147. 50. Iwai, H.; Fukushima, M.; Motomura, T.; Kato, T.; Kosugi, C., Effect of iron complexes with seawater extractable organic matter on oogenesis in gametophytes of a brown macroalga (Saccharina japonica). J. Appl. Phycol. 2015, 27, (4), 1583-1591. 23



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537 538 539 540 541

Figure 1. Distribution of Cu on FA binding sites predicted by WHAM 7. (a) Monodentate complexes, (b) Bidentate complexes, (c) Tridentate complexes, and (d)-(i) Various bidentate complexes formed by A and B sites. Refer to the text for the definition of each bidentate site. The background electrolyte was set as 0.01 M NaNO3 and 0.001 M Ca(NO3)2.

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Figure 2. Kinetics of (a) Cu, (b) Cd, (c) and (d) Ni, (e) Pb, and (f) Zn dissociation from NOM in natural water or model FA samples. The experimental data are from Chakrabarti et al. (1994),20 Mandal et al. (2000),22 Mandal et al. (2002),23 Sekaly et al. (2003),24 Fasfous et al. (2004),25 and Yacipi et al. (2008).28 The major experimental variables were shown in the legend. For clarity, only 50% of data points were shown in plots a, b, e and f.

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549 550 551 552 553

Figure 3. Metal dissociation rate coefficients of various NOM binding sites, kd (s-1), for five heavy metals in the natural water samples. (a) Monodentate complexes, (b) Bidentate complexes, (c) Tridentate complexes, and (d)-(i) Various bidentate complexes formed by A and B sites. Refer to the text for the definition of each bidentate site.

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556 557 558 559 560 561 562

Figure 4. Model simulations of the kinetics of Cu and Ni dissociation from NOM samples: (a) model simulations for Cu with varying kd,AA-W (k = 0.033 s-1); (b) model simulations for Ni with varying kd,AA-W (k = 0.04 s-1). Symbols are experimental data. Solid lines are model calculations and dashed lines are model calculations without considering the re-association reactions. The experimental data and conditions for calculations are from Sekaly et al. (2003)24 for Cu and from Mandal et al. (2002) 23 for Ni. 27


Kinetics of Heavy Metal Dissociation from Natural ... - ACS Publications

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