Speciation of heavy metals in River Rhine

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 6 3 e3 7 2

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Speciation of heavy metals in River Rhine Flora A. Vega a,b, Liping Weng a,* a b

Department of Soil Quality, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands Departamento de Biologı´a Vegetal y Ciencia del Suelo, Facultad de Biologı´a, Universidad de Vigo, 36310 Vigo, Pontevedra, Spain

article info

abstract

Article history:

Chemical speciation of Zn, Cu, Ni, Cd and Pb in River Rhine was studied by measuring free

Received 28 April 2012

ion concentration and distribution in nanoparticles, and by comparing the measurement

Received in revised form

with speciation modeling. Concentrations of free metal ions were determined in situ using

10 September 2012

Donnan Membrane Technique (DMT). The percentage of free over total (filtered) metal

Accepted 7 October 2012

concentration is 52%, 33%, 2.6%, 0.48% and 0.12% for respectively Zn, Cd, Ni, Pb and Cu, i.e.

Available online 17 October 2012

the degree of metal complexation in the river is the lowest for Zn and the highest for Cu. Metals in 1e300 nm particles were analyzed using Asymmetric Flow Field Flow Fraction-

Keywords:

ation (AsF-FFF), but the overall recovery is quite low. The nano-sized Cu detected is mainly

Donnan membrane technique

associated with DOM of 1e5 nm, whereas Pb and Zn are dominantly associated with

Free metal ion

particles of iron hydroxides and clay of larger size (30e100 nm). Free ion concentrations

Metal complexation

calculated with the speciation modeling are in good agreement with the measurements,

Nanoparticles

except for Pb. Based on the model, DOM-bound is the most important complexed form for

Field flow fractionation

Cu and Cd, whereas formation of (bi)carbonate and EDTA complexes are more important

Speciation modeling

for Ni and Zn. Adsorption of Pb to DOM is probably overestimated by the model, whereas Pb adsorption to iron hydroxides is underestimated. ª 2012 Elsevier Ltd. All rights reserved.

1.

Introduction

In natural waters, heavy metals are associated with a range of inorganic and organic ligands and particles, resulting in a heterogeneous distribution of complexes with a range of stabilities, kinetic labilities and molecular size (Sigg et al., 2006). Metal complexation with ligands and adsorption to colloidal particles strongly influence their reactivity, mobility, bioavailability and toxicity (Campbell et al., 2002). One approach to determine the degree of metal complexation in natural samples is to measure both total and free metal ion concentration. The difference between these two is the amount of metal in the complexed and adsorbed form. Free ions are considered as the chemical species that are directly available to organisms. However, reliable measurement of free ion concentration in the natural environment remains a challenge due to the co-existence of various chemical forms and

the often very low concentrations of the free ions. The Donnan Membrane Technique (DMT) uses a semi-permeable cation exchange membrane to separate the solution to be analyzed (donor) and an acceptor solution to measure free cation concentrations (Temminghoff et al., 2000; Weng et al., 2001). When applied to surface waters, the DMT analysis can be conducted in situ, which avoids problems associated with sampling storage and handling. The DMT has been applied to measure free metal ion concentrations in surface waters in a few studies (Cox et al., 1984; Kalis et al., 2006; Sigg et al., 2006), and promising results have been obtained. For a more detailed understanding of metal distribution over various chemical forms, and to identify the most important ligands/particles that control the degree of metal complexation in natural waters, speciation modeling can be a very useful tool. Attempts to model metal speciation in surface water have been made in certain studies (Baken et al.,

* Corresponding author. Tel.: þ31 317 482332; fax: þ31 317 419000. E-mail address: [email protected] (L. Weng). 0043-1354/$ e see front matter ª 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2012.10.012

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2011; Balistrieri and Blank, 2008; Bryan et al., 2002; Cheng et al., 2005; Guthrie et al., 2005; Lofts and Tipping, 2011; Unsworth et al., 2006; Van Laer et al., 2006). However, speciation calculation using models for environmental systems is by itself a challenging subject of study due to complexity of the system, heterogeneity of organic and inorganic ligands, and lack or uncertainty in model parameters. Alternatively, the nanoparticle fractionation technique of Asymmetric Flow Field Flow Fractionation (AsF-FFF) offers the possibility to directly measure the metal distribution among colloidal particles. AsF-FFF is a chromatographic like technique, in which a continuous fractionation of nanoparticles is obtained due to differences in diffusion coefficient. This technique has been applied successfully to analyze nanoparticles and elements associated with these particles in surface water samples (Lyven et al., 2003; Stolpe et al., 2010). In this work, we combined free ion measurement with in situ DMT, nanoparticle analysis with AsF-FFF and speciation modeling to study heavy metal speciation and complexation in River Rhine. The combined approach provides information regarding both the overall degree of metal complexation and the relative importance of different ligands and particles in controlling metal speciation in the river. The objectives are: (1) to test a modified DMT method in measuring free ion concentrations (heavy metals) in rivers; (2) to obtain current free ion concentrations of heavy metals (Zn, Cu, Ni, Cd, Pb) in River Rhine; (3) to identify the most important ligands/particles that form soluble complexes with these metals.

2.

Materials and methods

2.1.

The site

The measurement was carried out in October 2010 in River Rhine at the place of Wageningen, the Netherlands. The location is the same as in the DMT measurement of 2003/2004 (Kalis et al., 2006).

2.2.

DMT measurement

The so-called field DMT cells were used to measure free metal ion concentrations in the river. One DMT cell consists of an acceptor chamber separated from the sample or donor solution (the river) by two cation exchange membranes on two sides of the chamber. The volume of the acceptor chamber (Vacc) is 12 mL. The sum of the surface area of the two cation exchange membranes is 19 cm2. Some characteristics of the cation exchange membrane (BDH 55165 2U) used in this work can be found elsewhere (Weng et al., 2011). Before analysis, the membranes were prepared by shaking several times successively with 0.15 M HNO3, ultrapure water, 0.5 M CaCl2 and background solution at the concentration that is going to be used as the acceptor solution in the experiment. All bottles and cells were washed before use with 0.1 M HNO3 and ultrapure water. Based on the major ion composition of the river water, DMT acceptor solutions were prepared from 2.3 mM CaCl2 and 2.0 mM NaCl to obtain a similar ionic strength as in the river water. There were two treatments of the acceptor

solutions: one without NTA and one with 16 mM NTA (nitrilotriacetic acid). NTA was added as a complexing ligand to accumulate metals in the acceptor. pH of the acceptor solution was adjusted to about 8.1 with NaOH and HCl. For each treatment, 6 DMT cells were prepared (in total 12 cells). These cells were attached to a floating life buoy and immersed into the river water at a 30 cm depth. Two cells from each treatment were taken out after respectively 2, 4, and 6 days. In the mean time, 1 L river water was taken as well. When back to the lab, the acceptor solution was sucked out of the cells. The river water (donor) was filtered through 0.45 mm. Then both solutions (acceptor and filtered river water) were acidified with HNO3 to a final concentration of 0.15 M and stored for further analysis. Filtration over 0.45 mm is a commonly used approach to obtain the operational “soluble” fraction. In all samples (filtered river water and DMT acceptor solution), pH was measured with a pH meter. Concentrations of Na, K, Ca, and Mg were measured with ICP-OES (Thermo Scientific, Iris Advantage). Concentrations of Zn, Cu, Ni, Cd, Pb, Al, Fe, S and P were measured with High Resolution ICPMS (Thermo Scientific, Element2). The detection limit of the High Resolution ICPMS is: Zn 0.3 mg L1, Cu 0.02 mg L1, Ni 0.03 mg L1, Cd 0.01 mg L1, Pb 0.04 mg L1. Chloride concentration was measured using a Foss-Tecator Fiastar 5000 (continuous flow system). Concentration of dissolved organic carbon (DOC) was measured with a Segmented Flow TOC Analyzer using high temperature catalytic combustion and infrared detector (Skalar, the Netherlands). UV absorbance at 254 nm and 370 nm was measured with a spectrophotometer. The free ion concentrations in the river were derived from the DMT results in two ways. For those that the Donnan membrane equilibrium has been reached, their free ion concentrations in the acceptor were calculated first from their total concentrations in acceptor. Thereafter their free ion concentrations in the river water were derived using Eq. (1), in which Naþ was used as a reference ion to correct the ionic strength difference between the river water and acceptor: ðMdon Þ ¼ ðMacc Þ



2 ðNadon Þ ðNaacc Þ

(1)

where (M) represents the activity of a metal ion of interest and (Na) is the activity of Naþ. The subscripts “don” and “acc” refer to respectively the donor (river water) and acceptor solution (cell solution). For those that the Donnan membrane equilibrium was not reached, their free ion concentrations in the river were derived based on their transport kinetics (Weng et al., 2005, 2011): Mdon ¼

lm dm Vacc Mtot;acc DM B2m Am tDMT

(2)

where Mdon: free metal ion concentration in the river; Mtot,acc: total metal concentration in the acceptor solution at time tDMT; lm (40) a tortuosity factor that relates the apparent diffusion coefficient in the membrane to the diffusion coefficient of free ions in water (DM ¼ 7  1010 m2 s1); dm (1.6  104 m): thickness of the membrane; Bm (32): the Boltzmann factor; Am (3.8  104 m2): effective surface area of the membrane, which has been estimated as 20% of the total surface area (Weng et al., 2005); Vacc (12  106 m3): volume of the acceptor solution.

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2.3.

AsF-FFF analysis

An AsF-FFF system (Postnova AF2000) was coupled online consecutively with a UV detector and with a High Resolution ICPMS. The AsF-FFF channel, in which the particle separation takes place, is 27.5 cm long and 350 mm thick. At the channel bottom, a 1 kDa PES (polyether sulfone) membrane was enclosed. Due to the low metal concentrations in the sample, a slot pump was used to split the outlet flow (channel flow, FCh) of 1 mL min1 into a waste-flow of 0.5 mL min1 and a concentrated detector flow of 0.5 mL min1 2 mM NaHCO3 solution at pH 8.0 was used as the eluent. The on-line UV detector recorded the UV-spectrum (at 254 and 370 nm) every 2.2 s, whereas the High Resolution ICPMS measured Fe, Al, Si, Ca, Mg, P, Zn, Cu, Ni, Cd and Pb concentration every 11.2 s. Calibration of the High Resolution ICPMS was done at the beginning and at the end of each measurement day to correct for drifting. 10 mL filtered (0.45 mm) Rhine water was injected in 45 min using a cross flow rate of 3 mL min1. This on-channel preconcentration by injecting relatively large volume of sample has been used previously (Lyven et al., 1997, 2003). During the elution step, the cross flow rate (Fx) was 3 mL min1 for the first 20 min, then was decreased in 3 min to 0.1 mL min1, and was maintained at 0.1 mL min1 from 23 to 44 min. At 44 min the cross flow was turned off and the channel was eluted for another 5 min. The use of a stepwise change in cross flow allows for fractionation of <20 nm particles with a high sizeresolution and fractionation of 20e300 nm particles with a low size-resolution, and can shorten the eluting time (Regelink et al., 2011). The hydrodynamic diameter (d) of particles can be calculated from the retention time (tr) (Litzen, 1993): d¼

2kTV0 tr phFx w2 t0

(3)

t0 ¼

  V0 Fx Ln 1 þ Fx FCh

(4)

where d: hydrodynamic diameter (m); k: Boltzmann constant (1.38  1023 J K1); T: absolute temperature (293 K); V0: void volume (1.11  106 m3); t0: void time (s); tr: retention time (s); h: viscosity of the eluent solution (1.00  103 Pa s, water at 20  C); w: channel thickness (350  106 m); Fx: cross flow rate (m3 s1); FCh: channel flow rate (m3 s1). In our experimental setup, Fx was decreased after 20 min from 3 mL min1 to 0.1 mL min1 linearly in 3 min. For simplicity, we used a Fx ¼ 3 mL min1 for tr of 0e22.5 min, whereas Fx was shifted to 0.1 mL min1 from 22.5 min, taking into account the continuation of the size derived upon shifting.

2.4.

Speciation modeling

Speciation modeling was carried out for Zn, Cu, Ni, Cd, and Pb in River Rhine. The average measured data on the filtered (0.45 mm) river water samples were used as model input (total concentration) (Table 1 and 2). The calculations were executed with the computer program ECOSAT (Keizer and Van Riemsdijk, 1994). Formation of inorganic metal complexes 2 ) was calculated using the (OH, Cl, HCO 3 and CO3

365

formation constants in the database of ECOSAT. Complexation with EDTA was calculated using the constants in Table S1. The dissolved organic matter (DOM) concentration was calculated as two times of the measured concentration of DOC (Table 1). We assumed that the metal reactive fraction of DOM behaves as fulvic acids (FA) (Unsworth et al., 2006). A reasonable fit with the data was found when it was assumed that 30% of DOM is FA, and the other 70% is inert in terms of metal complexation. Cation adsorption to FA was simulated using the NICA-Donnan model (Kinniburgh et al., 1999). The generic NICA-Donnan model parameters for FA were used (Milne et al., 2001, 2003) (Table 3), except those for Fe(III) adsorption, which were adjusted in this study (see below). Competition of Ca2þ, Al3þ and Fe3þ to metal adsorption to DOM was taken into account. Solubility of iron hydroxides (Fe(OH)3) in the river was derived using solubility of Fe(OH)3 (aged for 1 week) measured by Liu and Millero (1999, 2002) at 25  C. Following Hiemstra and Van Riemsdijk (2006), the temperature effect was corrected using Eq. (5) for the water temperature of Rhine in November (10  C):  
DH 1 1 log Fe3þ ¼ ð42  39:11Þ  3:0pH þ  2:3R 298 T

(5)

where DH is the enthalpy change of the dissolution reaction of Fe(OH)3; R is the gas constant and T is absolute temperature. The correction factor that Hiemstra and Van Riemsdijk have used corresponds to a DH of 61 kJ mol1, which is close to the value (78 kJ mol1) measured by Liu and Millero. The calculated solubility for 10  C is logKso ¼ 38.48. In the speciation modeling, possible formation of precipitation of Al(OH)3 (logKso ¼ 32.34, database ECOSAT) and Fe(OH)3 (logKso ¼ 38.48, see above) were allowed. Adsorption of metals to colloidal iron hydroxides was calculated using the two sites surface complexation e diffuse double layer model of Dzombak and Morel (1990), with the model parameters therein (Table S2). The amount of iron hydroxides present is calculated in the speciation modeling from the total Fe concentration in the filtered river sample, and the specific surface area was set to 600 m2 g1 (Dzombak and Morel, 1990). Metal adsorption to aluminum hydroxides was ignored due to a relatively low amount of aluminum hydroxides (if present) compared to iron hydroxides and a lack of model parameters.

3.

Results and discussion

3.1.

General water composition

pH of the river water is around 7.7 (Table 1). DOC concentration is 3.1 mg L1. The ratio of UV absorbance at 254 nm to the DOC concentration (SUVA254nm) equals 22 L g1 cm1. In a study of 23 fresh water samples collected from Belgium and western Germany, SUVA ranges from 14 to 46 L g1 cm1 (Baken et al., 2011). The SUVA measured in Rhine is relatively low, indicating a relatively low aromaticity and small average size of its DOM, and thus probably a relatively low metal binding affinity (Baken et al., 2011).

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Table 1 e pH, DOC and major element concentration in River Rhine measured in November 2010 in this study. The data are average of 8 samples, including duplicates taken at 0, 2, 4 and 6 days since the start of the DMT measurement. pH 7.73  0.07

DOC (mg L1)

EDTAa (mg L1)

Ca (mM)

K (mM)

Mg (mM)

Na (mM)

3.1  0.4

5

1.91  0.01

0.12  0.01

0.49  0.01

2.11  0.07

Al (mM)

Fe (mM)

0.45  0.06

0.87  0.33

Cl (mM)

S (mM)

2.27  0.10

0.59  0.04

HCO3b

P (mM) 1.59  0.26

(mM)

2.7

a Concentration of EDTA was based on monitoring data of River Rhine (RIWA, 2011). b Bicarbonate concentration was calculated from the principle of charge neutrality.

The total (after filtration 0.45 mm) heavy metal concentration ranges from less than 0.3 (Cd) to more than 100 (Zn) nM (Table 2). When comparing the metal concentration measured in this study (November 2010) with those measured in February 2004 (Kalis et al., 2006), concentrations for all heavy metals are lower in 2010 (Table 2). The strongest decrease was found for Zn (47%) and Ni (40%), whereas the decrease for Cu, Cd and Pb is about 10%.

3.2.

Free metal ion concentrations

In the DMT analysis, when no NTA was added to the acceptor, concentrations of Cu, Cd and Pb in acceptor are below the detection limit of the High Resolution ICPMS and free ions of these metals cannot be measured with DMT in this treatment. Concentrations of Zn and Ni in acceptor are above the detection limit, and remained relatively stable at 2e6 day of the measurements (data not shown), indicating that the Donnan membrane equilibrium has been reached. Average Zn and Ni concentration in acceptor is respectively 108  14 nM and 2.56  0.51 nM. Free Zn2þ and Ni2þ in the river was derived based on Donnan membrane equilibrium using Eq. (1), which equals respectively 571 nM (52% of total Zn) and 0.51 nM (2.6% of total Ni) (Table 2). With 16 mM NTA added to the acceptor, concentrations of all five metals in the acceptor are above the detection limit of High Resolution ICPMS. Concentrations of Zn and Cd in the acceptor remained relatively stable at day 4 and 6 (Zn 1850  30 nM; Cd 1.52  0.23 nM), indicating equilibrium. Concentration of Ni in the acceptor increased continuously from day 2 to day 6 and the end concentration at day 6 is

122 nM. Equilibrium for Ni was not reached during the measurement. For Cu and Pb, their concentrations in the acceptor are the highest at day 2 (Cu 31.8 nM; Pb 1.40 nM), but decreased during day 4 and 6 and the end concentration in day 6 is 21.9 nM and 0.53 nM for respectively Cu and Pb. The reason for the decrease of Cu and Pb concentration in the acceptor after day 2 is not very clear. One possible explanation can be attributed to oxidation of Fe, as will be explained in Section 3.4. The DMT data of 16 mM NTA treatment was interpreted in two ways: (1) For Zn and Cd, interpretation based on Donnan membrane equilibrium (Eq. (1)) using concentration measured at day 4 and 6. Free Zn2þ concentration thus derived is 34 nM, which is in the same order of magnitude as that derived from the no NTA treatment (Table 2), but somewhat lower. Free Cd2þ concentration derived is 0.083 nM and it accounts for 33% of total Cd in the river (Table 2). (2) For Cu, Ni and Pb, data interpretation was based on their initial transport flux (Eq. (2)) using acceptor concentration measured at day 2. Thus derived free Cu2þ and Pb2þ concentration equals respectively 0.052 nM and 0.0023 nM (respectively 0.12% and 0.48% of their total concentration) (Table 2). Free Ni2þ concentration thus derived is 0.098 nM, which is about 2 times lower than measured in the no NTA treatment at equilibrium (Table 2). Besides other possible uncertainties in the measurement and data interpretation, this relatively low free Ni2þ derived from the flux is possibly also caused by the fact that the transport flux for Ni is not controlled by diffusion through the membrane, but by dissociation of Ni complexes in the river (Weng et al., 2010). Complexes of Ni are known for its slow dissociate rate (Van Leeuwen and Buffle, 2009). In a previous work (Weng et al., 2010), an expression has been derived for the change

Table 2 e Total metal (filtered over 0.45 mm) concentration measured, free metal ion concentration measured and modeled in Rhine of this study. For comparison, results of Kalis et al. (2006) are also given. Zn (M) Results Total (filtered) this Free measured study Free modeled (no EDTA) Free modeled (with EDTA) Free/total measured Free/total modeled (no EDTA) Free/total modeled (with EDTA) Results Total (filtered) Kalis Free measured et al. Free/total measured

Cu (M) 7

Ni (M) 8

Cd (M) 8

Pb (M) 10

1.09  0.07 (10 ) 5.71  0.87 (108) 3.88 (108) 3.64 (108) 52  8% 36%

4.22  0.43 (10 ) 5.18  0.02 (1011) 3.87 (1011) 3.59 (1011) 0.12  0.00% 0.09%

1.94  0.05 (10 ) 5.10  1.17 (1010) 7.73 (1010) 4.11 (1010) 2.6  0.6% 4.0%

2.55  0.41 (10 ) 8.30  0.64 (1011) 1.09 (1010) 1.02 (1010) 33  3% 43%

4.7  1.1 (1010) 2.27  0.50 (1012) 3.48 (1013) 3.45 (1013) 0.48  0.11% 0.07%

33%

0.09%

2.1%

40%

0.07%

2.04 (107) 8.71 (109) 4.3%

4.57 (108) 1.17 (1011) 0.026%

3.24 (108) 1.55 (109) 4.8%

2.82 (1010) 2.24 (1011) 7.9%

5.25 (1010) 8.91 (1013) 0.17%

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Table 3 e NICA-Donnan model parameters for fulvic acids (FA). Most of the parameters are the generic model parameters for FA (Milne et al., 2001; Milne et al., 2003), except parameters for Fe (III). Fe(III) (adjusted): logK1 and logK2 for Fe(III) were adjusted in this study; Fe(III) (marine): Model parameters derived for marine DOM by Hiemstra and Van Riemsdijk (2006). Electrostatic Donnan b value in Eq. logVD ¼ aþblogI 0.57 Carboxylic site Phenolic site Site density Q

max

1

(mol kg )

Heterogeneity p

5.88

1.86

0.59

0.70

log K1

n1

log K2

n2

H Ca Cu Cd Zn Ni Pb Al Fe(III) (generic) Fe(III) (adjusted) Fe(III) (marine)

2.34 2.17 0.26 0.99 3.84 2.07 1.22 4.11 6.0 4.0 2.70

0.66 0.85 0.53 0.68 0.67 0.65 0.60 0.42 0.25 0.25 0.36

8.60 3.29 8.24 0.73 0.73 2.03 6.87 12.16 36 20 8.30

0.76 0.83 0.36 0.50 0.61 0.53 0.70 0.31 0.19 0.19 0.23

of metal concentrations in the acceptor when dissociation of a metal complex is controlling ion transport over the membrane:  Mtot;acc ¼ kd Mtot;don

DM ka aL

12

Am tDMT VAcc

2010. But for Zn, the measured free fraction in this study is more than 10 times higher than that of Kalis et al. (Table 2). The reason of this difference is not clear. This relatively high free Zn faction found in this study was observed also in a study of 2011, in which both the voltammetry method AGNES (Absence of Gradients and Nernstian Equilibrium Stripping) and DMT were applied to measure free Zn2þ at the same location (Chito et al., 2012), in which AGNES and DMT gave similar results regarding free Zn2þ in the river.

3.3.

Ion specific parameters

(6)

where Mtot,don is the donor concentration of metal complex of concern; ka and kd are the rate constants for respectively the association and dissociation of the complex; aL is the activity of the free ligand in the donor solution. By assuming that all soluble Ni in Rhine is in EDTA complexed form (see below), the apparent dissociation rate constant (kd) of this complex can be obtained by fitting Eq. (6) to the measured data using the free EDTA activity calculated in the speciation modeling (see below). Thus derived kd for Ni complex in the river equals 0.0055 s1. In summary, the most reliable results for free metal ion concentrations were derived for Zn2þ and Ni2þ from no NTA treatment based on Donnan membrane equilibrium, for Cd2þ from 16 mM NTA treatment based on Donnan membrane equilibrium, whereas for Cu2þ and Pb2þ their free ion concentrations were derived based on the initial transport flux in the 16 mM NTA treatment. The ratio of free to total metal increases in the order of Cu < Pb < Ni < Cd < Zn (Table 2). The fractions of free over total Cu, Cd and Pb measured in this study is a few times higher than those measured by Kalis et al. (2006), whereas for Ni is about 2 times lower (Table 2). Considering the low concentration of the free ions, and all the challenges involved in measuring them in the natural surface water (Sigg et al., 2006), 2e3 times variation in the free metal ion concentration is within the measurement uncertainty and we cannot claim that the degree of complexation for these metals in River Rhine has changed over the period of 2004 and

367

AsF-FFF results

The recovery of elements in the AsF-FFF analysis is calculated from the accumulative amount measured from the start (0 min) to the end (49 min) of the measurement to their amount injected. The recovery is in general rather low for all elements (Table 4). The low recovery can be caused by (1) loss of true dissolved species smaller than the cutoff of the membrane (1 kDa), which are leached out with the cross flow; (2) loss of particles that are too large to be eluted; (3) loss of particles that are attached strongly to the membrane and therefore not eluted. The AsF-FFF chromatograms can be found in Fig. 1. There are in general two major peaks considering all elements. The first peak has a maximum at about 2 min (1e5 nm), which is relatively high in UV absorbance, but low in Fe, Al and Si content, indicating a peak of DOM. The UV254nm absorbance in this peak accounts for about only 4% of the total UV254nm absorbance of the sample injected (Table 4). From our experience in using AsF-FFF to analyze purified humic and fulvic acid, leaching through the membrane is expected as the main reason for the low recovery for DOM. This suggestion is in line with the relatively low SUVA (see Section 3.1) of DOM in the river sample. However, quantifying leaching of DOC through the membrane is rather difficult because of DOC released from the membrane material. Nano-sized Cu measured is mainly associated with this DOM peak (Fig. 1), indicating the importance of DOM in complexing Cu. The low recovery for Cu (Table 4) can be explained by the similar level of recovery of DOM. A large proportion of Cu could have been lost together with small DOM particles in the cross flow. The second peak is broader, has a maximum around 28 min (30e100 nm) (Fig. 1), and is rich in Fe, Al and Si (Fig. 1), which indicates presence of a mixture of iron hydroxides and some fine clay minerals. The relatively large amount of P in this peak also supports the presence of oxides and clay, which have high affinity for phosphate adsorption. The dominance of a narrower DOM peak and a broader mineral peak is typical for FFF chromatograms of natural water samples (Lyven et al., 2003; Stolpe et al., 2010). About 1/3 of Zn particles measured is associated with the DOM peak, as probably DOM bound Zn. The other 2/3 of Zn particles appear in the second peak. Zn in this second peak can be Zn adsorbed to minerals and/or Zn containing precipitates. Pb has the highest recovery among the five heavy metals studied (Table 4), probably due to its strong association with the larger mineral particles, as shown in the chromatograms (Fig. 1). Significant association between Pb and iron hydroxides in the river samples has been observed previously in FFF analysis (Lyven et al., 2003; Stolpe et al., 2010). For elements that are mainly in the mineral forms in

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Table 4 e Recovery of elements in the AsF-FFF analysis. The recovery was calculated as the ratio between the accumulative amount measured in the AsF-FFF eluent (0e49 min) to amount injected. Element Recovery (%)

UV254

UV370

Ca

Mg

Fe

Al

P

Zn

Cu

Ni

Cd

Pb

9

31

0.01

0.01

16

5

10

6

5

2

5

19

the second peak (Fe, Si, Pb and Zn), there is a strong concentration increase after 44 min (Fig. 1) when the cross flow was turned off. This indicates presence of particles larger than 300 nm that contain these elements, which nevertheless have

passed the 0.45 mm filter. Although the AsF-FFF analysis gives indications of the main particles to which metals bind, cautions should be taken while interpreting the AsF-FFF results due to a low recovery. Combination with other

Fig. 1 e AsF-FFF chromatograms. 10 mL Rhine water filtered over 0.45 mm was injected. Cross flow (Fx): 0e20 min: 3 mL minL1; 20e23 min: decreased linearly to 0.1 mL minL1; 23e44 min: 0.1 mL minL1; 44e49 min: no cross flow. The size of the particles corresponding to a retention time was calculated using Eqs. (3) and (4).

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approaches such as speciation modeling can lead to a more reliable conclusion.

3.4.

About Fe e in relation to DMT

Iron plays important roles in interpreting DMT results and in the speciation modeling, and needs further consideration in several aspects. Firstly, 0.36 mM and 7.68 mM of Fe were measured in the acceptor of DMT in respectively the no NTA and 16 mM NTA treatment. This leads to the question: in which form(s) was Fe transported from the river water to the acceptor solution? Using the solubility product of iron hydroxides of logKso ¼ 38.48 (see Section 2.4), concentration of the dominant monomeric Fe(III) in the river ( FeðOHÞþ 2 and FeðOHÞ03 ) are both in the order of 0.1 nM. According to our previous estimation (Weng et al., 2011), up to 500 times of accumulation in the acceptor can be achieved within 6 days. However, Fe concentration in acceptor is in the order of 1000e10,000 times of that of monomeric Fe(III) in the river. Therefore it looks unlikely that Fe was transported over the DMT membrane in the form of Fe(III). More likely, Fe was transported mainly as Fe2þ. The range of Fe(II) found in river samples are between 36 and 340 nM, accounting in general for a few percent of the total Fe concentration in the river (Lofts et al., 2008). Considering this, Fe transported over the membrane is mainly in the form of Fe(II) because of a much higher soluble concentration of Fe(II) compared to Fe(III). This concentration level of Fe(II) can explain the relatively fast Fe transport in the DMT. The second question regarding iron is: which chemical form does iron take in the acceptor of DMT? Answer to this question affects free Zn2þ and Cd2þ concentration derived from the DMT results in the 16 mM NTA treatment based on the Donnan membrane equilibrium (Section 3.2). In the data processing, it has been assumed that all Fe in the acceptor is Fe(III) and not forming precipitates. This is because the alternative choice implies a much weaker competition of iron on metal complexation with NTA, and therefore a much lower free Zn2þ and Cd2þ concentration derived. Complexation of Fe(II) with NTA (logK ¼ 8.8) (Callander and Barford, 1983) is much weaker compared to Fe(III) (Table S1). We mentioned earlier in this section that concentrations of Cu and Pb in the acceptor of the 16 mM NTA treatment increased at day 2 but decreased afterwards. A possible explanation can be attributed to Fe(II) oxidation. Iron enters the acceptor mainly as Fe(II). Contrary to Fe(III), Fe(II) binds weakly to NTA. The competition of Fe with Cu and Pb for NTA complexation is therefore low in the beginning, and a relatively large amount of Cu and Pb was quickly accumulated in the acceptor. With the oxidation of Fe(II) to Fe(III) in the acceptor, strong competition of Fe(III) leads to dissociation of NTA-Cu and NTA-Pb complexes, therefore a decrease of Cu and Pb concentration in the acceptor took place. This effect is stronger for the relatively strongly complexed Cu and Pb, and not so obvious for other metals.

3.5.

About Fe e in relation to speciation modeling

For the speciation modeling, accurately accounting for iron competition with metals in their adsorption to DOM is crucial.

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Due to a lack of adsorption data, the generic NICA-Donnan model parameters for Fe(III) (Table 3) are estimations based on the hydrolysis constants (Milne et al., 2001, 2003), therefore involve large uncertainties. In Fig. 2, the upmost line is the Fe:DOM ratio calculated with the NICA-Donnan model using generic model parameters (Table 3) for a background solution of 2 mM CaCl2 with Fe3þ activity controlled by the solubility of Fe(OH)3 (logKso ¼ 38.48). In this figure, the model calculations are compared to iron loading on DOM in surface water samples (symbols, Fig. 2) measured using dialysis by Lofts et al. (2008). The comparison shows that the model calculation using the generic parameters overestimated Fe:DOM ratio even when assuming 30% of DOM behaves similarly as FA, whereas the other 70% DOM is inert. Similarly, when modeling Fe(III) complexation to DOM in marine waters, Hiemstra and Van Riemsdijk have found that the generic NICA-Donnan model parameters are over predicting Fe(III) adsorption (Hiemstra and Van Riemsdijk, 2006). For this reason, Hiemstra and Van Riemsdijk have reoptimized the NICA-Donnan parameters (Table 3) to get a good fit with the marine data. However, adopting these model parameters for Fe(III) derived by Hiemstra and Van Riemsdijk in our calculation (the lowest line, Fig. 2) led to 1e2 orders of magnitude underestimation of the Fe:DOM ratio

Fig. 2 e NICA-Donnan modeling of Fe(III) adsorption to DOM in surface water. 2 mM CaCl2 background. Fe3D activity controlled by Fe(OH)3 with logKso [ L38.48. Model parameters can be found in Table 3. Three NICA-Donnan model calculations were carried out with different model parameters for Fe(III): (1) Generic 30%: generic model parameters. 30% of DOM is FA, the rest is inert; (2) Adjusted 30%: adjusted model parameters for Fe(III) derived in this study. 30% of DOM is FA, the rest is inert; (3) Marine DOM: model parameters derived by Hiemstra and Van Riemsdijk (2006) for sea water. 100% of DOM is FA; In the figure, we compared the modeling results to the data of Lofts et al. (2008): (1) Sample not dialyzed: surface water samples filtered (0.7 mm); (2) Sample dialyzed: surface water samples dialyzed. Further, the model calculation using WHAM/Model IV presented in the paper of Lofts et al. (2008) is also included (WHAM, dotted line).

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around the neutral pH compared to the data of Lofts et al. It is not clear why apparently Fe loading on DOM in marine water is much lower than that in the surface water. It can be caused by differences in the chemical properties of DOM in sea water and in fresh water. It is also possible that the dialyzing method used in the measurement of Lofts et al. (2008) is less efficient in excluding iron hydroxide particles than the method used for sea water samples, and therefore there are still very small iron hydroxide particles included in the dialyzed surface water samples. However, even in fact small iron hydroxide particles are involved in the Fe:DOM ratio derived, considering this part of iron hydroxides as Fe(III) adsorbed to DOM can still be necessary. This is because the interaction between iron hydroxides and DOM may have a similar competition effect as Fe(III) on metal adsorption. Whereas the n1 and n2 for Fe(III) in the generic NICADonnan model parameters were kept constant, the two logK values for Fe(III) binding to FA were re-optimized (Table 3) (solid line, Fig. 2) to get a better fit with the data of Lofts et al. This NICA-Donnan parameter set is probably more suitable than the two other parameter sets for Fe(III) adsorption to natural organic matter of terrestrial origin (soil, river, lake). These newly derived model parameters will be used in the speciation modeling below.

3.6.

Speciation modeling

Chemical speciation of heavy metals in River Rhine was calculated using the measured element composition (Table 1), as described in Section 2.4. The speciation modeling considers formation of inorganic complexes and adsorption to DOM and iron hydroxides. Two separate calculations were carried out: one without EDTA, and one with 5 mg L1 EDTA (Table 1). Free metal ion concentrations calculated in both ways are given in Table 2. For all metals except Ni, adding EDTA to the calculation has a relatively small effect (<10%) on their free ion concentration calculated (Table 2). But for Ni, EDTA has a strong influence and the free Ni2þ calculated with EDTA is 47% lower than without (Table 2). Based on the calculation with EDTA, the ratios between free to total metal calculated agree reasonably well with those measured, except for Pb (Table 2). According to the model, DOM is the most important ligand forming complexes with Cu, Pb and Cd (Fig. 3). Zn complexation with DOM is very weak. The major complexed Zn forms are Zn carbonate and hydrolyzed Zn. Ni is mainly distributed between its carbonate and EDTA complexes, whereas complexation with DOM is less significant. Due to the low recovery of the AsF-FFF analysis, comparison between the modeling and AsF-FFF results is not straightforward. However, some comparison can still be made. The conclusion of the speciation modeling is in line with the AsF-FFF results for Cu and Ni (Figs. 1 and 3). Both approaches indicate a small contribution of oxides-bound fraction to Cu and Ni associated with particles. For Zn, the AsF-FFF results show that the oxides-associated Zn is more important than the DOM-Zn (Fig. 1), which is also in line with the model (Fig. 3). For Pb and Cd, the model predicted a much larger importance of adsorption to DOM than to iron hydroxides, whereas the opposite was found in the AsF-FFF analysis. This apparent contradiction can be caused by a lower recovery

Fig. 3 e Distribution of heavy metals in River Rhine over various chemical forms calculated with the speciation model (including EDTA). The distribution is presented in this figure as percentage of each species over the total (filtered) concentration.

of DOM compared to oxides in the AsF-FFF analysis. In addition, the model may have overestimated Pb adsorption to DOM, which can be seen from the underestimated free Pb2þ concentration (Table 2). In speciation modeling for metals in surface water and soil solution, a common approach is to use humic acids (HA) and/or fulvic acids (FA) to represent the metal complexation behavior of DOM. The difference between the overall apparent reactivity of DOM and HA/FA is then corrected by considering a certain fraction of DOM as HA/FA (Kalis et al., 2006; Lofts and Tipping, 2000; Unsworth et al., 2006). However, it is often difficult to find a common value of the HA/FA fraction that can give satisfactory modeling results for all metals. While the model gives reasonable predictions for the weakly DOM bound metals (Cd, Zn and Ni), the free ion concentrations of the strongly DOM bound metals (Cu and Pb) are often underestimated, or the other way around (Meylan et al., 2004; Unsworth et al., 2006). In the study of Kalis et al. of Rhine in 2004, it was found that a reasonable agreement between the model and the measurement can be found for Cu, Pb, Cd and Zn by assuming 50% of DOM is HA and 50% is inert. However, the measured free Ni2þ is lower than that modeled, and they suspected at that moment that a certain Ni-binding agent is missing in the model. We show in this work complexation of Ni with synthetic ligand like EDTA is more important than complexation with DOM in River Rhine, which is in line with the results of some other studies (Baken et al., 2011). Omitting the presence of EDTA will thus lead to underestimation of complexation, and overestimation of free ion concentration of Ni.

4.

Conclusions

In this work, we combined free ion measurement with DMT, nanoparticle measurement with AsF-FFF, and speciation modeling to get more accurate quantitative information regarding the free ion concentration, the degree of metal

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 6 3 e3 7 2

complexation and major ligands/particles that form metal complexes in River Rhine. Free metal ion is considered as the major chemical species that can be directly taken up by organisms. The DMT approach was improved by using two treatments simultaneously, one without ligand and one with synthetic ligand in the acceptor. Compared to the single treatment DMT, this DMT approach can provide a more reliable measurement for metals of both above (Zn, Ni) and below (Cd, Cu, Pb) the detection limit of ICPMS. By taking into account complexation with DOM and EDTA, adsorption to iron hydroxides and formation of soluble inorganic species (CO3, Cl, OH), speciation of Zn, Cu, Cd and Ni in River Rhine is reasonably described with the model. According to the model, Cu and to a less extent Cd is mainly bound to DOM, and Ni mainly to EDTA, whereas Zn is mainly in the hydrolyzed and carbonate forms. The NICA-Donnan model parameters for Fe(III) adsorption to fulvic acids were re-optimized in this work to better describe iron loading on DOM and competition with metals for adsorption in surface water. The oftenencountered problem in speciation modeling for surface water in finding a common value of the HA/FA fraction in DOM can be at least partly solved by including EDTA present in the water. The results of AsF-FFF coupled with UV and ICPMS analysis are mostly in agreement with the speciation modeling. However, a low recovery hampers straightforward comparison. Identification of major ligands and particles for heavy metals is helpful in understanding the important factors that control the total soluble concentration of the metal ions. Change of concentration of the corresponding ligands/particles in surface water will influence strongly the concentration and speciation of these metals. This study contributes to the development of both analytical and modeling approaches to better estimate both the free ion and metal complexes in surface waters.

Acknowledgment The authors would like to thank Inge Regelink for her help in the AsF-FFF analysis. Flora A. Vega received a “Ramo´n y Cajal” research grant (MICINN-University of Vigo) and a fellowship from Xunta de Galicia and FSE (Fondo Social Europeo).

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2012.10.012.

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