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Using FLOWFFF and HPSEC to determine trace metalecolloidassociations in wetland runoff

Elisabeth Neubauer, Frank v.d. Kammer*, Thilo Hofmann*

Department of Environmental Geosciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria

a r t i c l e i n f o

Article history:

Received 20 April 2012

Received in revised form

11 February 2013

Accepted 12 February 2013

Available online 26 February 2013

Keywords:

Natural nanoparticles

Arsenic speciation

Peat bog

Flow Field-Flow Fractionation

Size exclusion chromatography

Ultrafiltration

* Corresponding authors. Tel.: þ43 1 4277 533E-mail addresses: frank.kammer@univie.

0043-1354/$ e see front matter ª 2013 Publihttp://dx.doi.org/10.1016/j.watres.2013.02.030

a b s t r a c t

Natural organic matter (NOM) and iron colloids can coexist in surface water. These colloids

might exhibit different affinities to metals and metalloids. Previously it has been shown,

that organic and inorganic colloids in the low nanometer range can be fractionated using

Flow Field-Flow Fractionation analyzes (FlowFFF), but it is not yet understood how the

presence of inorganic colloids influences results obtained by High Performance Size

Exclusion Chromatography (HPSEC). Studies that compare the use of these size-separation

techniques for the analyzes of organic and inorganic colloids and associated elements

are needed in order to interpret results obtained by either of these methods. Therefore,

associations between colloids from a small stream draining a wetland area and a selected

range of elements (Fe, Al, Ti, Pb, Cu, Ni, As, U, and Rare Earth Elements (REE)) have been

investigated. FlowFFF analyzes and HPSEC analyzes were combined with ultrafiltration,

functional group titration and arsenic speciation analysis.

NOM and, in a sample with a pH > 5.2, slightly larger iron organo-mineral colloids, were

present in the <0.2 mm fraction in the surface water. Both exhibited notably different

affinities for trace elements. Cu, Ni, Al, and the REE all showed similar modes (i.e. peak

maxima) and size distributions to the NOM, while Pb and As showed a preferential

association with iron organo-mineral colloids. It was not possible to differentiate between

NOM and iron-organo mineral colloids with HPSEC. The differences in the results regarding

the apparent molecular mass distributions obtained by FlowFFF and HPSEC are discussed.

ª 2013 Published by Elsevier Ltd.

1. Introduction Kammer, 2008; Neubauer et al., 2011) and surface water

In surface water the association of metals andmetalloids with

colloids has classically been studied using filtration and ul-

trafiltration (Hofmann et al., 2003a,b; Pokrovsky et al., 2005;

Vasyukova et al., 2010). However, these techniques are not

capable of distinguishing between different types of colloids

with sizes in the nanometer range, e.g., “soft” organic colloids

like natural organic matter (NOM) and “solid” inorganic col-

loids. In previous studies, it has been shown that NOM and

iron colloids can coexist in soil extracts (Hassellov and v.d.

01; fax: þ43 1 4277 9533ac.at (F. v.d. Kammer), thshed by Elsevier Ltd.

(Benedetti et al., 2003; Dahlqvist et al., 2007; Krachler et al.,

2010; Lyven et al., 2003). These colloids might exhibit

different affinities tometals andmetalloids. High Performance

Size Exclusion Chromatography (HPSEC) and Flow Field-Flow

Fractionation (FlowFFF) are analytical techniques that can be

used to separate dissolved metals and stable metaleligand

complexes fromcolloids on the basis of size ormolecularmass

differences (Hassellov et.al., 2007; Jackson et al., 2005).

When combined with appropriate detection systems, such

as UVeVis spectroscopy (UVeVis), fluorescence spectroscopy

.ilo.hofmann@univie.ac.at (T. Hofmann).

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 92758

(Fl), and inductively-coupled plasma mass spectrometry (ICP-

MS), detailed information on the association of metals and

metalloids with organic and inorganic colloids can be ob-

tained. UVeVis spectroscopy is used for the quantification of

light-absorbing organic substances. Especially in fractionated

samples, errors may occur if the specific absorbance of the

NOM varies with molecular mass, and non-chromophoric

NOM will not be detected. Fluorescence is used for the char-

acterization of fluorescing NOM, which is often attributed to

the smaller, more fulvic-like NOM fraction (Neubauer et al.,

2011) but can originate from various, also non-humic frac-

tions of NOM [Leenheer and Croue (2003)]. ICP-MS is capable of

detecting metals and metalloids at high sensitivity.

HPSEC is traditionally used for the separation of NOM

(Hongve et al., 1996; Pelekani et al., 1999; Zhou et al., 2000) and,

when coupled to UVeVis detection and ICP-MS, it can be used

to explore metal/metalloid-NOM complexation (Bolea et al.,

2006; Jackson et al., 2005; Kaschl et al., 2002; Laborda et al.,

2009; Pelekani et al., 1999). In HPSEC, samples are eluted

over a column containing a porous gel material. Low molec-

ular mass NOM can access more of the internal pore volume

than high molecular mass NOM, which is excluded from such

pores. High molecular mass NOM therefore elutes first, fol-

lowed by the smaller components. HPSEC is applicable to the

separation of NOM with molecular masses ranging from

approximately 200 to 1,000,000 g mol�1 (Jackson et al., 2005),

but the whole range is not accessible with one column. HPSEC

analyzes suffer from artifacts such as intermolecular in-

teractions of the analyte components with the packing ma-

terial of the HPSEC column. This can be related to repulsive

forces, which cause molecules to elute at an apparent mo-

lecular mass higher than the actual molecular mass, or to

charge attraction, which can cause sorption to the column

packing material (Chin and Gschwend, 1991; Jackson et al.,

2005; Pelekani et al., 1999).

FlowFFF is used for the fractionation of organic and inor-

ganic colloids in the low nanometer size range (Hassellov

et al., 2007; Hassellov and v.d. Kammer, 2008), and for the

characterization of metal/metalloidecolloid associations

(Dahlqvist et al., 2004; Dubascoux et al., 2010; Lyven et al., 2003;

Neubauer et al., 2011). It is a separation technique that pro-

vides a continuous separation of colloids without the disad-

vantage of a stationary phase, like the column packing

material. The separation takes place in a thin (0.1e0.75 mm

height) channel under the effect of a flow-generated field

applied perpendicular to the main parabolic flow of a mobile

phase. The field created by this secondary flow drives the

sample components to the accumulation wall, which in

FlowFFF is covered by an ultrafiltration membrane. This

membrane retains those sample molecules and particles that

are larger than the effective pore size of themembrane within

the channel, which then can be separated. The applied sec-

ondary flow is countered by the diffusion of the particles and

molecules back into the channel. Smaller particles have higher

diffusion rates than larger particles and build up diffusional

clouds which extend higher into the channel than those of the

larger particles (Hassellov et al., 2007). The smaller particles

experience on average a higher longitudinal flow rate of the

mobile phase and therefore elute before the larger ones. Using

FlowFFF theory or calibration with suitable standards allows

the conversion of the retention volumes to diffusion co-

efficients, hydrodynamic diameters or molecular masses

(Dubascoux et al., 2010). The final outcome of a FlowFFF sep-

aration is governed by a multitude of factors, such as the ionic

strength and the pH of the mobile phase, particleemembrane

interactions, sample dilution, washing of sample components

and overloading. These parameters need to be well controlled,

taken into account, or acknowledged when interpreting data

(Baalousha et al., 2011; Neubauer et al., 2011).

Ultrafiltration, HPSEC and FlowFFF each have their own

particular analytical merits, such as the superior resolution

within the molecular mass range of NOM in HPSEC analyzes,

the absence of a stationary phase in FlowFFF analyzes, and the

absence of a mobile phase and associated change in the

hydrochemical environment and dilution in ultrafiltration.

Organic and inorganic colloids in the low nanometer size

range can be fractionated using FlowFFF analyzes (Hassellov

and v. d. Kammer, 2008), but it is not yet understood how

the presence of inorganic colloids influences results obtained

by HPSEC analyzes. This potentially results in misinterpreta-

tion of HPSEC data on trace elementecolloid associations.

Studies that compare the use of these size-separation

techniques for the analyzes of organic and inorganic colloids

and associated elements in surfaceewater samples are

needed in order to interpret results obtained by either of these

methods.

In this study we have investigated the association of col-

loids with a selected range of elements (Fe, Al, Ti, Pb, Cu, Ni,

As, U, REE) in a small stream draining an unpolluted wetland

(Tanner Moor) in Upper Austria. This wetland was chosen as a

model system because NOM and iron organo-mineral colloids

have been observed before at different sampling dates (Jirsa

et al., in press).

The metals and metalloids (Fe, Al, Ti, Pb, Cu, Ni, As, U, REE)

were chosen because of their different affinities for NOM and

iron oxides, and due to their use as geochemical tracers. Iron

can be bound to NOM but can also be present as iron mineral.

Copper and nickel have high affinities toward NOM, arsenic

has a high affinity for sorption onto iron minerals, and lead

sorbs and co-precipitates with iron and manganese oxides.

Titanium is most resistant to weathering; aluminum is an

indicator for the presence, but also dissolution of clay min-

erals. Rare earth elements are used as geochemical tracers in

catchment studies for the source identification of water when

normalized to standard materials. Iron, manganese, arsenic

and uranium can be used as redox indicators. In addition,

information on size-dependent association of those elements

with colloids is scarce in literature.

Results from ultrafiltration and functional group titration

were compared with the concentration patterns for the ele-

ments, as resolved from their particle sizes. These patterns

were recorded using two different high-resolution size-sepa-

ration techniques (HPSEC and FlowFFF) and supplemented

with an arsenic speciation analysis. Our objectives were to

elucidate whether it is possible to differentiate between NOM

and iron organo-mineral colloids from surface waters by

means of HPSEC, like it has been shown for FlowFFF, and to

investigate the competition for trace metal binding of NOM

and iron organo-mineral colloids. In addition, the difference

in the results obtained by FlowFFF and HPSEC are discussed.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2759

2. Experimental procedures

2.1. Sampling site and sample collection

Tanner Moor (48�3003100N, 14�5104900 E) is an unpolluted peat

bog in the Muehlviertel, which is in the north-eastern part of

Upper Austria (Krachler et al., 2005). It is one of the largest

highmoors in Austria, covering a total area of 1.2 km2, and is

now a protected conservation area in the Natura 2000 network

of the European Union. The area is mainly forested with

mountain pine (Pinus mugo), common spruce (Picea abies),

silver birch (Betula pendula) and downy birch (Betula pubes-

cens) (Jirsa et al., in press). Two samples were collected in

consecutive years from a small stream draining the peat bog,

under similar meteorological and hydrological conditions in

the late winter, with snow cover still in place (TM1 in January

2009 and TM2 in January 2010). While the TM1 sample was

collected at the beginning of snowmelt, conditions were still

frosty when TM2 was collected. The pH was measured in situ.

Five liters of water were sampled, filled in PE bottles and

transported in cooling boxes to the laboratory. The samples

were vacuum filtered through 0.2 mm cellulose filters (What-

man, Maidstone, UK). The samples were stored dark and cool

(4 �C) until analysis.

2.2. Bulk analysis

The samples were split for digestion, ultrafiltration, fulvic and

humic acid separation, and functional group titration.

An aliquot of the 0.2 mm filtered samples was digested in a

microwave (Microwave 3000, Anton Paar, Graz, Austria) using

4% H2O2 (Merck Suprapur grade) and 20% HNO3 (Merck

Suprapur grade) at 175 �C for 25 min. The digested samples

were diluted with ultrapure water to 100 mL, and stored in

100 mL PE bottles until analysis.

The proportions of cations and organic carbon present in

the operationally defined colloid fraction (>1000 g mol�1 and

<0.2 mm) and in the dissolved fraction (<1000 g mol�1) were

then determined on the basis of ultrafiltration results.

Approximately 38 mL of the 0.2 mm filtered samples were

ultrafiltered using a Millipore Stirred Ultrafiltration Cell

equipped with regenerated cellulose filters (1000 g mol�1

nominal molecular weight cut-off (NMWCO, determined by

the manufacturer, Merck Millipore, Billercia, US) under argon

pressure. The first 2 mL were discarded. 15 mL of the filtrate

were acidified with 0.6 mL 5N HNO3 (Merck Suprapur grade)

and were used for determination of main and trace cations.

Another 15 mL were used for organic carbon measurements.

Blanks using ultrapure water instead of sample were pre-

pared to evaluate leaching of organic carbon, metals and

metalloids from the ultrafiltration membrane. At the end of

the experiment, and before discarding the filter, the integrity

of the membrane was tested with dextrane blue solution

(molecular mass 2,000,000 g mol�1). We used 1000 g mol�1

NMWCO membranes for ultrafiltration to allow for compari-

son with older studies which used the same membrane type

and NMWCO and because “truly dissolved” species are ex-

pected to pass through them, while such that are associated

with larger NOM fractions (Piccolo, 2001), or iron-dominated

colloids are retained (Pokrovsky et al., 2005). We are, how-

ever, aware, that part of the NOM may have masses as low as

several hundred g mol�1 and will therefore pass through the

membrane (Pokrovsky et al., 2005). In addition, manufac-

turers calibrate their membranes usually with proteins.

Direct comparison to the NOM is difficult, not only because

the shape and charge density of the NOM molecules is

different to the calibration molecules, but NOM molecules

strongly change their shape and charge density according to

surrounding conditions like ionic strength and pH (Piccolo,

2001).

Main and trace cations of the ultrafiltered and the digested

samples were determined by ICP-OES (Optima 5300 DL, Perkin

Elmer, Waltham, USA) and ICP-MS (Agilent Technologies

7700�, Waldbronn, Germany, see Table 1). The ion lens set-

tings of the ICP-MS were adjusted before measurement to

maximum signal intensity for elements contained in a tuning

solution purchased from Agilent Technologies Manufacturing

GmbH (Waldbronn, Germany). For all ICP-MS measurements

a collision cell with He as cell gas (4 mL min�1) was used.

The following isotopes were monitored: 28Si, 56Fe, 55Mn, 27Al,59Co, 47Ti, 60Ni, 63Cu, 65Cu, 208Pb, 75As, 89Y, 139La, 140Ce, 232Th

and 238U.

Anions and dissolved organic carbon (DOC)weremeasured

using an ion chromatograph (ICS-1000, Dionex, Vienna,

Austria) and a total organic carbon analyzer (TOC-VCPH, Shi-

madzu, Duisburg, Germany).

In order to separate fulvic and humic acids, the pH of

the samples was adjusted to pH < 1 and the humic acid

precipitates were removed by filtration (0.2 mm). The concen-

trations of fulvic acids and small organic molecules such as

sugars were measured using the TOC-VCPH total organic car-

bon analyzer.

Functional group titration was performed on the 0.2 mm

filtered samples for the determination of available binding

sites following the method described in detail in Hru�ska

et al. (2001). Briefly, water samples (approx. 25 mL) were

passed at constant flow through a proton saturated Dowex-

50 cation exchanger column. During the passage metals

that are not bound to NOM will exchange for protons

and remain in the column while metals bound strongly to

NOM will pass the column in the eluate. Measured DOC

concentration and inorganic anions were then used to

calculate the amount of readily reactive carboxylic groups

per mg carbon for each sample [meq mg�1]. The concentra-

tions of organic acids (RCOO�) and charge densities

were calculated from site densities, DOC and pH using a tri-

protic organic acid model described in detail in Hru�ska et al.

(2001).

2.3. HPSEC-UVeVis-fluorescence-ICP-MS

A liquid chromatography system (Agilent Technologies

1100 Series, Tokyo, Japan) equipped with a micro-vacuum

degasser was used with a 200 � 8 mm Toyopearl HW 55S

size-exclusion column and a 20 � 8 mm pre-column

(Grace Davison Discovery Sciences, Alltech Grom GmbH,

Rottenburg-Hailfingen, Germany). The mobile phase con-

sisted of a 25 mM solution of ammonium carbonate contain-

ing ammonium carbamate (CH6N2O2$CH5NO3, 1:1, analytical

Table 1 e Summary of DOC, fulvic acid concentration (DOC FA), pH, elemental composition of the samples (<0.2 mm and <1000 g molL1), calculated percentage ofassociationwith colloids (%colloid, based on the calculated difference between themeasured concentration<0.2 mmand the concentration<1000 gmolL1), and recoveryas measured by HPSEC and FlowFFF (in %).

Unit TM1 TM2

<0.2 mm <1000 g mol�1 % Colloid FlowFFF R% HPSEC R% <0.2 mm <1000 g mol�1 % Colloid FlowFFF R% HPSEC R%

DOC mmol L�1 2282 1058 54 74d 70d 3086 1022 67 79d 100d

DOC (FA) mmol L�1 1941 2509

pH 5.3 4.8

Site density meq mg�1 4.3 13.4

Charge density meq mg�1 2.72 7.51

Nab mmol L�1 197 176 11 82 73 11

Kb mmol L�1 <26 <26 <26 <26

Cab mmol L�1 147 95 35 81 37.15 54

Mgb mmol L�1 61.3 43.5 29 <41 <41

Sia mmol L�1 304 296 2 145 168 0

Fea mmol L�1 43.8 0.5 99 61 86 67.7 2.76 96 74 47

Mna nmol L�1 24.4 16.8 31 2780 657 76

Ala nmol L�1 2557 343 87 65 6151 884 86 37

Coa nmol L�1 12.5 3.0 76 44 66 25.5 4.96 81 67 83

Tia nmol L�1 59.4 3.9 93 71 93.1 5.31 94 47

Nia nmol L�1 29.6 8.2 72 82 129 23.6 <8.5 81 163

Cua nmol L�1 322 19 94 64 110 315 7.41 98 65 128

Pba nmol L�1 8.93 0.94 89 116 15 16.90 0.66 96 70 76

Asa nmol L�1 27.4 10.5 62 47 84 29.3 6.52 78 43 83

Ya nmol L�1 3.27 0.38 88 102 90 2.96 0.42 86 102 69

Laa nmol L�1 0.42 0.04 91 137 302 0.53 0.04 93 114 205

Cea nmol L�1 0.65 0.07 89 138 327 1.49 0.11 93 112 226

Tha nmol L�1 0.79 0.02 97 67 80 0.53 0.03 94 74 104

Ua nmol L�1 1.03 0.18 83 59 77 0.89 0.17 81 47 60

HCO3� mmol L�1 n.d. 216

Cl�c mmol L�1 109 137

NO32�c mmol L�1 99.2 25.2

PO43�c mmol L�1 e 1.58

SO42�c mmol L�1 7.60 6.56

F�c mmol L�1 7.37 3.68

a ICP-MS.

b ICP-OES.

c IC.

d Recovery from UVeVis signal of the analyte in the separation system, relative to the same sample injected directly into the detector.

water

research

47

(2013)2757e2769

2760

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2761

grade), adjusted to pH 7 with 5 N HNO3 (Merck Suprapur

grade). A flow rate of 0.3 mL min�1 was used. The SEC column

was followed by a variable wavelength ultraviolet/visible

spectrophotometer (Agilent 1100 VWD, UVeVis) tuned to

l ¼ 250 nm, and a fluorescence detector (Agilent 1100 FLD,

excitation l ¼ 250 nm, emission l ¼ 410 nm), for the charac-

terization of NOM. The true UVeVis absorption of organic

substances inmixed samples outweighs the turbidity signal of

particles, and therefore no interference of the UVeVis signal

by particles in the sample was expected (v.d. Kammer et al.,

2005). We are, however, aware that the UVeVis signal may

be biased due to the distribution of chromophores in the NOM

molecules across the molecular mass distribution of the

sample (Her et al., 2002). However, Hassellov et al. (1999) and

Stolpe et al. (2005) showed that the UVeVis signal and the

carbon signal (measured by ICP-MS) show good agreement for

NOM colloids. The UVeVis signal was converted to DOC con-

centrations. The conversion is based on the assumption that

chromophores are distributed evenly over the whole molec-

ular mass range of NOM. The total injected mass of DOC was

first corrected with the recovery of the UVeVis signal. A

response factor was then fitted until the integrated concen-

tration signal (i.e. the mass under the UVeVis peak) yielded

the recovered mass. Finally, the concentration was converted

from mg L�1 to mmol L�1.

An ICP-MS (Agilent Technologies 7700�) was coupled to the

outlet of the fluorescence detector. This set-up allows the

elemental composition to be determined as a function

of colloid size. Before entering the ICP-MS, the effluent passed

through an interface in which rhodium (10 mg L�1) was mixed

in as internal standard. A multi-point calibration was per-

formed prior to sample analysis. Standards were made up

with MilliQ water and acidified with 10% (v/v) 5 N HNO3

(Suprapur). The concentration ranges of the standards were

adjusted to match the concentration of the sample. One

measurement per sample of the mobile phase used in the

HPSEC analysis was used as a “zero concentration” standard

in the calibration. The calibration was carried out via an

autosampler. For the calibration, the same instrument set-

tings were used as for the measurement of the samples. The

intensity signal of the samples (counts per second) was then

recalculated to concentration. Information on limit of detec-

tion and limit of quantification for the ICP-MS coupling is

given in the Supplementary Material.

All sample injections were performedwith an autosampler

(Agilent Technologies 1100 Series), using a 100 mL aliquot.

Recovery runs were conducted by replacing the column and

the precolumn with a poly(methyl methacrylate) tube. As in

the sample runs, the eluent was directed through the UVeVis

and fluorescence detectors, and then to the ICP-MS. The in-

jection volume for the recovery runs was 20 mL. The standard

deviation for duplicate measurements is <5% for the UVeVis

and the fluorescence signal, and <10% for the elements

measured by ICP-MS.

The columnwas calibratedwithmolecularmass standards

of 1100; 3610; 6780; 10,600; 16,800; 32,800; 48,600; and

145,000 g mol�1. The standards were prepared from poly-

styrene sulfonate sodium salt (PSS, Polymer Standards Service

GmbH, Mainz, Germany) at 0.1 g L�1 concentration in the

mobile phase. The injection volumewas 5 mL. The column void

volume was determined using 149,000 g mol�1PSS. The total

permeation volume was determined using acetone.

2.4. FlowFFF-UVeVis-fluorescence-ICP-MS

The FlowFFF used was the Eclipse 3þ Asymmetric-Flow Field-

Flow Fractionation System (Wyatt Technology, Dernbach,

Germany). The samples were analyzed using the setup

described in detail in Neubauer et al. (2011). A 0.75 mm spacer

was used in the channel (tip to tip length 19.5 cm) to keep

required cross-flow rates low and prevent excessive pressure

build-up in the channel as a result of using a membrane with

very low cut-off size. The channel flow rate was 1 mL min�1,

and the cross-flow rate and the focus flow-rate were

1.5 mL min�1 (2 min focus time). In order to improve the re-

covery of very small NOM molecules and become comparable

with the HPSEC method with regard to the smallest deter-

minable molecule sizes, a 300 g mol�1 nominal cut-off poly-

ether sulfone membrane (Postnova Analytics, Landsberg,

Germany) was used as the accumulation wall. Flows were

controlled using an Agilent Technologies 1200 Series quater-

nary pump equipped with a micro-vacuum degasser. The

detection chain consisted of a diode-array ultraviolet/visible

detector (UV-DAD, Agilent Technologies 1200 Series, primary

detection wavelength l ¼ 254 nm), a fluorescence detector

(Agilent Technologies 1200 Series FLD) with an excitation

wavelength of 250 nm and an emission wavelength set to

410 nm, and an ICP-MS (Agilent Technologies 7700�). The

recalculation of the UVeVis signal to concentrations was

performed in the same manner as for the HPSEC analysis. All

injections were performed using an autosampler (Agilent

Technologies 1200 Series, large volume kit); injection volumes

were 500 mL. The recovery runs were conducted by injecting

100 mL of the samples without applying of any cross-flow (in-

jection with elution). We used 15 mM ammonium carbonate

(adjusted to pH 7 with HNO3) as mobile phase. Calibration

with PSSmolecularmass standardswas performed at a higher

ionic strength, as has been described in detail elsewhere

(Neubauer et al., 2011).

The FlowFFF e ICP-MS coupling used the same standardi-

zation procedure as for coupling to the HPSEC system. The

only modification was that the flow was split after the switch

valve, before entering the ICP-MS, reducing the flow rate from

1mLmin�1 to 0.5 mL min�1. Information on limit of detection

and limit of quantification for the ICP-MS coupling is given in

the Supplementary Material. The standard deviation for

duplicate measurements is <2% for the UVeVis and the

fluorescence signal, and <10% for the elements measured by

ICP-MS.

2.5. Determination of arsenic species by IC-ICP-MS

The IC system used consisted of the same components as

used in the HPSEC e UVeVis - fluorescence e ICP-MS anal-

ysis. Separations were performed on a PRP-X100 anion-ex-

change column (250 � 4.1 mm, 10 mm) from Hamilton (Reno,

USA) with a gradient method, using 12.5 mM and 30 mM

ammonium carbonate as the mobile phase following the

procedure described by Leist (2011). The flow rate was

1.2 mL min�1, and the flow was split before entering the ICP-

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 92762

MS, reducing the flow rate to 0.3 mL min�1. The arsenic

compounds used for identification of arsenic species in IC e

ICP-MS in this study included sodium hydrogen arsenate

heptahydrate (98%, Alfa Aesar, Karlsruhe, Germany), sodium-

arsenite (0.05 M, Titripur, Merck), dimethyl arsenic acid tri-

hydrate (for synthesis, Merck), methylarsonic acid mono-

sodium salt (Dr. Ehrensdorfer GmbH, Augsburg, Germany)

and arsenobetaine (purum p.a., �95%; Fluka, St. Louis, US).

These standard compounds were diluted in MilliQ water to a

concentration of approximately 1 mM arsenic. Injection vol-

umes were 10 mL for the arsenic standard compounds, 100 mL

for the samples, and 10 mL for the sample recovery tests. In

this study, we used arsenic speciation as a complementary

analytical technique to cross-check the concentrations of

“dissolved “arsenic that we obtained from ultrafiltration

(passage through a 1000 g mol�1 NMWCO membrane) and

FlowFFF (passage through a 300 g mol�1 NMWCO membrane),

rather than with the purpose of determining the in situ

speciation of the dissolved arsenic components. No efforts

were made to investigate the influence of sample storage on

arsenic speciation.

2.6. Elemental quantification and recoveries

The signal versus retention volume curves, referred to as

fractograms (FlowFFF) and chromatograms (HPSEC), were in-

tegrated using OriginPro 7.5 software (OriginLab Corporation,

Northampton, USA) to determine the peak area. The recovery

percentages (R%) for UVeVis and fluorescence signal (HPSEC

and FlowFFF analysis), and arsenic in the speciation analysis

(IC e ICP-MS), were calculated as follows:

R% ¼ ðA=ðA0$xÞÞ$100%

where A is the peak area of the fractogram or chromatogram

of a sample, A0 is the corresponding area from the recovery

run with no cross-flow or by-pass, and x is the factor for

normalizing the injection volume of the recovery run.

The recoveries for all elements in the FlowFFF

and HPSEC analyzes were calculated by comparing the

recovered mass concentration from the sample runs to the

mass concentration measured in bulk analysis after sample

digestion.

2.7. Calculation of plate number and plate height

Plate number (Na, Nb) and plate height (Ha, Hb, in mm) [Mori

and Barth (1999), Sant and Gale (2006)] were calculated for a

molecular mass standard (10,600 g mol�1) for the FlowFFF

analysis and the HPSEC analysis as follows:

Na ¼ 16

�vR

bB

�2

; Nb ¼ 5:55� vR

FWHM

�2

; Hi ¼ LNi

; i ¼ a;b

Na and Nb are two types of plate numbers which are

commonly used to describe peak broadening (Mori, 1989), with

the difference that the baseline width (bB, mL) is used for the

calculation of Na, while Nb uses the full width at half

maximum (FWHM,mL). vR is the retention volume (mL), and L

(mm) is the length of the HPSEC column or the length of the

channel from focusing point to outlet (FlowFFF).

3. Results and discussion

3.1. General properties of the samples

DOC, pH, and elemental sample composition is summarized

in Table 1. The TM1 and TM2 samples both show elevatedDOC

concentrations (2280 and 3090 mmol L�1, respectively)

compared to average river water concentrations, as would be

expected for a peat bog drainage, and are low in major cations

and anions (see Table 1). The pH was measured as 5.3 and 4.8,

respectively. The dominant cations are sodium, calcium, and

iron with concentrations >40 mmol L�1. Silicon was

>140 mmol L�1. The ultrafiltration results indicate that 46% of

the organic carbon in TM1 and 37% in TM2 had a molecular

mass <1000 g mol�1. Large quantities of organic carbon

passing through the ultrafiltration membrane may be

explained by the high fulvic acid content (>80% of the DOC,

Table 1). Organic carbon and iron have by far the highest

concentrations in the operationally defined colloidal size

fraction between 1000 g mol�1 and 0.2 mm and (Table 1). High

proportions (>89%) of sodium and silicon, pass through the

1000 g mol�1 NMWCO membrane, indicating that they are

present as dissolved species, such as silicic acid. The presence

of silicate minerals can be excluded. Between 35 and 54% of

the calcium in the two samples is present as species larger

than 1000 g mol�1, as is 29% of the magnesium in TM1. Mag-

nesium and calcium are more likely to be associated with a

different colloidal phase, rather than being present as colloids

of magnesium and calcium carbonates, since equilibrium

calculations for TM2 using Visual Minteq showed that calcite

is undersaturated.

3.2. Apparent molecular mass distributions for NOM,obtained by two different methods

The apparent molecular mass distributions for TM1 and TM2

differ considerably depending on the sample itself, and on the

techniques used for their determination (Fig. 1). The differ-

ences between the two techniques are discussed below, fol-

lowed by a detailed discussion of the differences between the

samples.

The apparent molecular masses of NOM obtained by

FlowFFF clearly cover a similar range for both samples ranging

from a few 100 g mol�1 up to approximately 30,000 g mol�1

(Fig. 1, left). Sample TM1, however, exhibits a narrow main

peak at a lowmolecular mass (1800 g mol�1) and a shoulder at

18,000 g mol�1. This shoulder occurs at a similar apparent

molecular mass to the slope down from the peak in TM2. The

apparent molecular mass distribution for TM2 is perfectly

unimodal, with the mode being at 2700 g mol�1. From the

FlowFFF fractograms we can conclude that TM1 has a gener-

ally similar distribution of apparentmolecularmasses of NOM

like TM2, but shows a distinct fall in NOM concentration in the

intermediate molecular weight-region which is accompanied

by a slight increase in the low molecular mass region. The

fluorescence signals are almost identical in both the FlowFFF

and HPSEC analyzes (marked with Fl in Fig. 2 a and b).

The apparentmolecular masses obtained by HPSEC exhibit

a wider range, ranging from 500 g mol�1 to >145,000 g mol�1,

Fig. 1 e FlowFFF fractogram (left) and HPSEC chromatogram (right) for samples TM1 and TM2. Reported signals are NOM

(expressed as DOC concentration in mmol LL1, measured as UVeVis signal at 254 and 250 nm).

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2763

with a maximum peak for TM2 at 4600 g mol�1 and for TM1 at

2900 g mol�1 (Fig. 1, right).

It should be noted that in both techniques singlemolecules

and assemblies and aggregates of single molecules cannot be

distinguished. With regard to the measured apparent molec-

ular masses, the two techniques investigated yield substan-

tially different values. These techniques use fundamentally

different mechanisms for separating colloids. There are

several possible explanations for the discrepancies between

the apparent molecular mass distributions determined by

HPSEC and those determined by FlowFFF: (i) dependence of

retention time on sample load and mobile phase ionic

strength, (ii) charge exclusion between NOM and the packing

material in the HPSEC analysis, (iii) influence of the rod-like

structure of PSS, and (iv) the choice of standards used.

As shown previously by Neubauer et al. (2011), the reten-

tion times for both PSS and NOM are subject to marked vari-

ations, depending on the ionic strength and sample load. For

all conditions tested, the most reliable molecular mass cali-

brations were obtained at elevated ionic strengths in the

carrier solution and low injected PSS mass.

The same effects with regards to sample load and ionic

strength have also been previously observed for HPSEC:

retention volumes increase with increasing volume/mass of

sample injection and with increasing ionic strength. For PSS,

the dependence on concentration is considered to be due to

the decrease in hydrodynamic volume of the polymer mole-

cule in solution (Mori and Barth, 1999). The effect of ionic

strength on the retention volume for PSS has been ascribed to

a combination of ion exclusion, hydrophobic interaction, and

size exclusion (Mori, 1989). To avoid the ionic strength effect

and the bias created by injecting a too high sample load, we

used even lower injected masses for PSS than suggested in

previous studies (Pelekani et al., 1999) and a lower ionic

strength than recommended in optimization studies (Hongve

et al., 1996). Charge exclusion effects between NOM and the

packing material also need to be taken into account, and may

differ between standard and samplemolecules, as in FlowFFF.

The stationary phase of the HPSEC column is composed of

modified silica and possesses residual negatively charged

sites at mobile phase pH values above the surface pHpzc

between 2 and 4 (Chin and Gschwend, 1991). As a result, large

polyelectrolytes such as humic substances may be prevented

by electrostatic repulsion from diffusing into the pores of the

stationary phase (Chin and Gschwend, 1991), leading to

decreased retention volumes. Furthermore, in HPSEC the

fractal dimensions of an analyte have a greater impact on its

passage through the porous packing material than in FlowFFF

analysis. The rod-like structure of PSSmay, therefore, result in

elution volumes that are smaller than expected. Thismight, at

least to some extent, explain the higher apparent molecular

masses obtained by HPSEC.

The determination of apparent molecular mass distribu-

tions on the basis of calibrations using molecular mass stan-

dards that are not completely identical with the sample

molecules always carries a risk of systematic errors. If two

methods that both depend on different physical and chemical

properties of standards and the sample are then compared,

then deviations in the results obtained are likely to be com-

mon. Since the character of the sample NOM does not allow

direct determination of its molecular mass after separation

using, e.g., static light scattering photometers, it is likely that

the effectsmentioned abovemay bias our apparent molecular

mass determinations by FlowFFF andHPSEC. Bolea et al. (2006)

and Jackson et al. (2005) explained deviations in the apparent

molecular mass distributions of NOM that was measured by

FlowFFF analyzes and HPSEC analyzes, by interaction with

equipment surfaces, charge repulsion and changes of colloid

conformation upon dilution in the respective mobile phase

solution.

Comparisons of chromatographic characteristics such as

plate number and plate height (Mori and Barth, 1999; Sant and

Gale, 2006) for a molecular mass standard (10.6 kgmol�1) have

shown that the peak broadening is much less pronounced

with HPSEC which yielded more than three times larger plate

numbers (Na 75, Nb 146) than achieved by FlowFFF (Na 26, Nb

45). The plate height for the 10.6 kg mol�1 standard was lower

for HPSEC analysis (Ha 2.7 mm, Hb 1.4 mm) than for FlowFFF

analysis (Ha 7 mm, Hb 4 mm). The plate height derived from

the FlowFFF analysis is consistent with plate heights reported

by Giddings et al. (1984), when extrapolating their data to our

run conditions (approximately 0.3 cm s�1 flow velocity).

Fig. 2 e FlowFFF-ICP-MS fractograms (Fig. 2a) and HPSEC e ICP-MS chromatograms (Fig. 2b) for samples TM1 and TM2.

Reported signals are NOM (expressed as DOC concentration in mmol LL1, measured as UVeVis signal at 254 and 250 nm),

fluorescence (Fl, in FU), and selected elements (nmol LL1).

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 92764

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2765

3.3. The apparent molecular mass distributions of ironwhich is bound to NOM and present as iron organo-mineralcolloids

FlowFFF analysis shows that NOM is the only identifiable

colloid type in TM2 (Fig. 2a). The apparent molecular mass

distribution for NOM extends from a few hundred g mol�1 up

to approximately 30,000 gmol�1 in FlowFFF analysis, and from

a few hundred g mol�1 to >145,000 g mol�1 in HPSEC analysis

(Fig. 2a and b). The iron signal derived from FlowFFF analysis

and HPSEC analysis is parallel to the UVeVis signal. This is

indicative of iron which is associated with NOM. The iron

occupation of binding sites of the DOC was calculated by

comparing the peak area of the iron signal (60 meqmL�1) to the

peak area of the functional group distribution (104 meq mL�1)

from the FlowFFF analysis. This indicates that iron occupies

up to 60% of the binding sites of the NOM (HPSEC and FlowFFF

data for TM2 in Supplementary Material, Figure S1).

Sample TM1 has a more complex NOM and iron distribu-

tion than TM2. As mentioned above, in the FlowFFF fracto-

gram, NOM shows a single peak at 1800 gmol�1 and a shoulder

at a molecular mass of 18,000 g mol�1 (Fig. 2a), while iron has

two distinct peaks. Part of the ironco-elutes with the NOM,

consistent with iron complexation. Iron accounts only for

approximately 30% of the total complexation capacity of the

NOM in this domain. The amount of iron present in the peak at

the higher apparent molecular mass (18,000 g mol�1), howev-

er, exceeds the available binding sites in the NOM at this peak

(Supplementary Material, Figure S1). Since probe-sonication

(0.75 kJ) of TM1 did not significantly change the iron and the

UVeVis patterns, the higher molecular mass iron organo-

mineral colloids are considered to be rigid, rather than

occurring as loose aggregates of iron containing NOM colloids.

We explain this pattern as being due to NOM partly com-

plexing iron, and the higher molecular mass iron organo-

mineral colloids that consist of NOM and mainly iron min-

erals, possibly iron oxides.

The data shows that different colloid types can be exported

from the wetland over time. There are several factors that

might influence colloid composition. On the one hand, during

changing hydrodynamic conditions, e.g., variation of

groundwater levels, NOM and elements could be mobilized

from different soil/peat layers. Freezing and thawing possibly

affects the quantity and quality of the mobilized NOM. The

complexation capacity of NOM is important for the speciation

of iron.NOM forms stable complexes with iron, and the

complexation by NOM may hinder the hydrolysis of iron and

the precipitation of iron hydroxides (Vilge-Ritter et al., 1999).

Additionally, pH is 0.5 units higher in sample TM1 (TM1:

pH 5.3, TM2: pH 4.8) and iron oxides are less soluble at higher

pH. We cannot answer yet which of these parameters is

the driving factor for the differences in colloidal composition.

More detailed studies with time series of samples and

the use of techniques suitable for iron speciation such as X-

ray absorption spectroscopy are required for a mechanistic

understanding.

Despite the fact that HPSEC results show less peak-

broadening than those from FlowFFF, these two colloid types

cannot be clearly separated (Fig. 2b). Approximately 30% of the

iron elutes in the column void volume (>145,000 gmol�1). This

is most likely because the iron organo-mineral colloids are too

large to enter the pores of the column, or due to charge

repulsion effects.

The rest of the iron is associated with the NOM at high

apparent molecular mass (>10,000 g mol�1). The mode of the

NOM is at 2900 g mol�1. The greater shear forces in HPSEC

possibly disrupt the iron organo-mineral colloids. Some of the

larger particles may also be lost in the column (the recovery

for iron in the HPSEC analysis was 86%).

Iron colloids have previously been observed with FlowFFF

in the Kalix River (Dahlqvist et al., 2007) and in theDjelso creek

(Lyven et al., 2003), both of which are in Sweden. Iron colloids

were found in the Kalix River during baseflow conditions

before the spring flood [Dahlqvist et al. (2007)], but were not

present during or after the spring flood. In contrast to our

results, no iron was associated with the NOM in the Kalix

River, when iron colloids were present. In the Djelso creek,

some of the iron was associated with the NOM.

3.4. Binding of elements in the presence of NOM-dominated phases (TM2)

In the FlowFFF analysis of sample TM2 (Fig. 2a) the NOM, as

represented by the UVeVis absorption signal, covers a wider

range of apparent molecular masses, while only the low mo-

lecularmass NOMexhibits fluorescence. This observation is in

accordance with previous studies (Dahlqvist et al., 2004;

Hassellov, 2005; Stolpe and Hassellov, 2007), who found that

the fluorescence signal is limited to the low molecular mass

NOM, while UVeVis absorption covered a broader apparent

molecular mass distribution in NOM. It has been argued that

the low molecular mass and the fluorescent properties are

typical of fulvic compounds (Stolpe and Hassellov, 2007 and

references therein). TheNOMin thehigher apparentmolecular

mass regions could behumic substances (Stolpe andHassellov,

2007). The associationwith iron could quench the fluorescence

signal of the NOM (Seitz, 1981). Element concentrations show

patterns that are essentially parallel the UVeVis absorption

signal. It is noticeable, however, that the modes of iron, lead,

arsenic, yttrium and the REEs are shifted toward higher

retention volumes in the highermolecularmass fraction of the

NOM, indicating an increased abundance of these elements

within this fraction. This observation from FlowFFF analysis

wasconfirmedbyHPSECanalysis (Fig. 2b): thepeakmaxima for

iron, lead, yttrium, REEs, and some of the titanium are shifted

toward higher apparent molecular masses, compared to the

NOM (UVeVis) peak. Nickel and copper have the same peak

pattern as the NOM, indicating an even distribution over all

molecularmass fractions (Fig. 2b). Kaschl et al. (2002) andBolea

et al. (2006) also showed a preferred association of iron with

highmolecularmassNOM, in contrast to divalent cations such

as copper, zinc, nickel and manganese. It has been shown

(Geckeis and Rabung, 2008) that the higher charge of trivalent

cations induces a stronger NOM agglomeration by linking

together several organic molecules. We have not, however,

been able to distinguish iron-induced aggregation of smaller

molecules to largermolecularmass clusters from iron-binding

to single molecules of larger apparent molecular mass.

Our results indicate that 67% of the NOM (measured as

organic carbon) in TM2 is retained by the ultrafiltration

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 92766

membrane (1000 g mol�1 NMWCO, Table 1). The recovery of

the UVeVis signal in the FlowFFF analysis is higher (79%),

which can be explained by the lower mass weight cut-off

(300 g mol�1) in this analysis (Table 1). Some lack of corre-

spondence could also be attributable to the using UVeVis as a

proxy for DOC concentration in FlowFFF analyzes. However,

the recovery of many metals and metalloids (e.g., iron,

aluminum, cobalt, copper, lead, arsenic, thorium, uranium) in

the FlowFFF analysis is generally lower than the colloid-

associated concentrations calculated from ultrafiltration

data. Recovery losses of elements can occur in FlowFFF anal-

ysis when the metal and metalloid species are smaller than

the NMWCO of themembrane, e.g., if they are present as ionic

species or bound to very small organic molecules. Although

metal/metalloidehumate complexes are more stable at the

pH of the mobile phase in the FlowFFF measurements (pH 7)

than at the pH of the surface-water samples (Tipping, 2002),

metals and metalloids can still desorb from the NOM. This

may be due to re-equilibration with the mobile carrier phase

during the analysis, where new sorption equilibriummight be

established between the NOM and the metals and metalloids.

Desorption of metals and metalloids from NOM in the metal-

free mobile carrier phase might also explain the relatively

poor recovery of metals compared to colloid-associated con-

centrations determined by ultrafiltration. In order to get a

rough estimation on the error introduced by the addition of

NH4þ to the sample, chemical equilibrium modeling with Vi-

sual Minteq was performed. The parameters which were used

in the chemical equilibrium model are described in the

Supplementary Material. The addition of 15 mM NH4þ to the

sample results in changes <10% with respect to the distribu-

tion of elements between the “truly dissolved”, the “NOM

bound” and the “iron hydroxide-bound” fraction. This is

within the standard deviation of duplicate measurements

(10%). Variations in the range of 15% are indicated for the REEs.

Specifically, about 15% more yttrium, lanthanum and cerium

should be “dissolved” (or forming complexes with inorganic

ligands) with the addition of NH4þ.The higher pH of the mobile

phase (pH 7 versus pH around 5 in the samples) could also

cause adsorption of REEs on iron hydroxides. However, these

are estimations from equilibrium modeling, and the kinetics

of these reactions is not known.

The recoveries of lanthanum and cerium were >200% in

HPSEC analysis. The REE concentrations of the samples were

low, and therefore only qualitative and no quantitative in-

formation be derived from this data.

In HPSEC analysis part of the arsenic elutes together with

the low molecular mass NOM (Fig. 2b, fluorescence signal).

Arsenic speciation analysis results from IC e ICP-MS show

that these fractions are not bound colloids; instead, 42% of the

total arsenic in TM2 is present as dissolved arsenic species (as

compared to 22% of the arsenic passed through the

1000 g mol�1 NMWCO ultrafiltration membranes). The rela-

tively high amount of NOM-bound arsenic (>40%) is in

contrast to results from Bolea et al. (2006), who found that

more than 90% of the arsenic was present as dissolved species

or bound tomaterial<1000 gmol�1 in the humic substances of

a compost leachate. They also found no trace of arsenic in

their FlowFFF and HPSEC analyzes and therefore assumed

that arsenic was mainly present as ionic oxyanions.

According to the speciation analysis of the dissolved

arsenic species, As(V) is predominant (90%) while minor

quantities (10%) are present as arsenobetaine (Supplementary

Material, Figures S2 and S3). The elution times for dimethyl

arsenic and As(V) were tested using HPSEC and found to

match the arsenic peaks at low apparent molecular masses.

Methylated arsenic species such as monomethyl arsenic and

dimethyl arsenic have previously been reported in soils

(Pongratz, 1998), but there are only a few reports of arsen-

obetaine being present in terrestrial environments (Geiszinger

et al., 2002; Huang and Matzner, 2006).

The comparison of arsenic data from ultrafiltration, HPSEC

analysis and FlowFFF analysis allows the following conclu-

sions: In HPSEC analysis, dissolved arsenic species co-eluted

with NOM. This prevents the distinction between colloid-

associated and dissolved trace elements. The fraction of

arsenic passing an ultrafilter (1000 g mol�1) was lower (22%)

than the dissolved arsenic fraction determined by IC-ICP-MS

(42%). This indicates that dissolved arsenic species might be

retained by the ultrafiltration membrane. According to IC-ICP-

MS analysis 60% of the arsenic was associated with colloids.

The colloid-associated arsenic fraction determined by FlowFFF

was underestimated (43%). This is related to sorption of colloids

and associated elements to themembrane, and desorption and

permeation of dissolved arsenic through the membrane.

We want to stress that arsenic speciation was used here as

a complementary analytical technique to cross-check the

concentrations of “dissolved “arsenic that we obtained from

ultrafiltration (passage through a 1000 g mol�1 NMWCO

membrane) and FlowFFF (passage through a 300 g mol�1

NMWCO membrane), rather than with the purpose of deter-

mining the in situ speciation of the dissolved arsenic compo-

nents. Sample storage might have changed the in situ

speciation of arsenic.

Uranium interacts strongly with the NOM, and ultrafiltra-

tion shows that approximately 80% of the uranium in sample

TM2 is associated with the NOM.

3.5. Binding of elements in the presence of NOM and ironorgano-mineral colloids (TM1)

The sample TM1 exhibits a distinctively different distribution

pattern for iron and some of the trace elements from sample

TM2. Fig. 3 shows schematic FlowFFF fractograms for samples

TM2 and TM1, illustrating the different distributions for both

of these samples. The NOM, iron and copper have parallel

elution patterns in sample TM2, whereas in TM1 iron is partly

(approximately 30%) associated with the NOM, but the ma-

jority elutes at higher retention volumes (Fig. 3). Copper elutes

together with the NOM in sample TM1, indicating that the

NOM and the iron organo-mineral colloids in TM1 have

significantly different affinities for trace elements.

Most of the arsenic is associated with the iron organo-

mineral colloids (Fig. 2a and b). However, approximately 20%

of the arsenic co-elutes with the NOM despite the strong

tendency for arsenic to bind to iron minerals (Sharma et al.,

2010). A possible interaction mode is the binding of arsenic

to NOM via an iron bridging ternary complex (Mikutta and

Kretzschmar, 2011; Ritter et al., 2006).The IC - ICP-MS anal-

ysis revealed that 32% of the arsenic is present as dissolved

Fig. 3 e Schematic FlowFFF e ICP-MS fractograms for samples TM2 and TM1, showing NOM, iron and copper.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2767

arsenic species, with As(V) and As(III) predominating while

minor quantities are present as arsenobetaine and dimethyl

arsenic (Supplementary Material, Figure S2).

The interaction of inorganic arsenic with NOM and iron-

minerals has already been intensively investigated (e.g., Liu

and Cai, 2010; Liu et al., 2011; Redman et al., 2002), but sorp-

tion and partitioning of methylated and non-methylated

organic arsenic species onto NOM and iron minerals is as

yet only poorly understood.

From FlowFFF analysis it can be seen that some of the lead

(approximately 30%) is associated with NOM, and approxi-

mately70% isassociatedwith the ironorgano-mineral colloids.

Copper, Nickel, aluminum, yttrium, lanthanum and

cerium have a similar mode and size distribution to the NOM,

with a main peak at a low apparent molecular mass

(1800 g mol�1) and a shoulder at a higher apparent molecular

mass (18,000 g mol�1, Fig. 2a). In contrast to our results, it has

been shown that the light REEs, to which lanthanum and

cerium belong, associate more with iron colloids than with

NOM (Andersson et al., 2006). The higher affinity of lead for the

iron organo-mineral colloids, compared to that of copper,

aluminum, and rare earth elements, is in agreement with the

results of previous FlowFFF studies (Lyven et al., 2003).

The FlowFFF and HPSEC analyzes show that uranium is

only associated with the NOM and that, as with sample TM2,

approximately 80% of the uranium is retained by the

1000 g mol�1 NMWCO membrane in ultrafiltration. Almost all

of the iron is associated with colloids (Table 1).

As in sample TM2, the percentages of metals in TM1 that

are associated with colloids were underestimated by FlowFFF

analysis compared to ultrafiltration, except for the REEs,

which had recoveries >100%. The quantification of the REEs

was, however, hindered by their low concentrations

(<2 nmol L�1). The quantitative association of metals and

metalloids with NOM and iron organo-mineral colloids cannot

be deduced by FlowFFF analysis alone, but requires additional

measurements such as those obtained by ultrafiltration.

4. Environmental implications

The results demonstrate that NOM and very small

sized iron organo-mineral colloids can be exported from

wetlands. The different nature of the colloids being exported

at different situations cannot be distinguished with filtration

or ultrafiltration. Analysis with FlowFFF e UVeVis e fluores-

cence e ICP-MS provides both information on the apparent

molecular mass of these colloid types as well as information

of the association of metals and metalloids. NOM and iron

organo-mineral colloids showed different affinities for trace

elements. Copper, nickel, aluminum, yttrium, lanthanum,

cerium and uranium are associated with the NOM. Lead and

arsenic exhibited a higher affinity for the iron organo-mineral

colloids.

The inconsistencies observed for the fractionation of NOM

and iron by the various techniques points to the limitations of

invasive size fractionation techniques. With HPSEC e UVeVis

e fluorescencee ICP-MS it was not possible to resolve the iron

organo-mineral colloids, in contrast to FlowFFF analysis. The

resolution with respect to peak broadening was better in

HPSEC compared to FlowFFF analysis. However, HPSEC e

UVeVis e fluorescence e ICP-MS is applicable, with some

limitations, for the analysis of NOM and associated trace ele-

ments. The quantification of colloid-associated arsenic is

however impeded by the co-elution of dissolved arsenic spe-

cies with NOM.

Ultrafiltration and arsenic speciation analysis are useful

for the determination of dissolved metals and metalloids.

During the analysis with FlowFFF and HPSEC there is the

possibility that metals and metalloids desorb from the NOM

when re-equilibrating with the mobile phase and establish-

ment of a new sorption equilibrium may occur. We conclude

that the combination of FlowFFF analysis with ultrafiltration

and element-specific speciation techniques such as IC-ICP-MS

is advantageous for 1) the detection of NOM and iron organo-

mineral colloids and 2) the quantification of dissolved and

colloid-associated trace elements.

NOM and the iron organo-mineral colloids are expected to

exhibit different geochemical behavior when subjected to

changing hydrochemical environments, i.e. varying ionic

strength, pH, and redox conditions. These conditionsmight be

found at the transition from, e.g., a fluvial system to an estu-

arine system, and finally to a marine system, and are likely to

impact on trace metal transport. NOM is thought to be more

mobile in aquatic systems than iron organo mineral colloids

that are subject to aggregation and sedimentation (Stolpe and

Hassellov, 2007). This could result in the selective removal of

adsorbed/complexed metals and metalloids from the water

column. Iron organo-mineral colloids can easily be overlooked

if high-resolution size-separation techniques such as FlowFFF

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 92768

coupled to ICP-MS are not available. Mineralogical identifica-

tion of the iron organo-mineral colloids and determination of

the associated metal speciation, availability and dynamics,

however, remains still a challenge for the future.

Acknowledgment

The authors thank Dr. Stephan Kohler who performed the

functional group titration at the Department of Aquatic Sci-

ences and Assessment (Swedish University of Agriculture

Sciences, Uppsala, Sweden) and Wolfgang Obermaier for the

assistance with ICP-OES and ICP-MS measurements.

Appendix A. Supplementary data

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.watres.2013.02.030.

r e f e r e n c e s

Andersson, K., Dahlqvist, R., Turner, D., Stolpe, B., Larsson, T.,Ingri, J., Andersson, P., 2006. Colloidal rare earth elements in aboreal river: changing sources and distributions during thespring flood. Geochimica et Cosmochimica Acta 70 (13),3261e3274.

Baalousha, M., Stolpe, B., Lead, J.R., 2011. Flow field-flowfractionation for the analysis and characterization of naturalcolloids and manufactured nanoparticles in environmentalsystems: a critical review. Journal of Chromatography A 1218(27), 4078e4103.

Benedetti, M.F., Ranville, J.F., Allard, T., Bednar, A.J., Menguy, N.,2003. The iron status in colloidal matter from the Rio Negro,Brasil. Colloids and Surfaces A: Physicochemical andEngineering Aspects 217 (1e3), 1e9.

Bolea, E., Gorriz, M.P., Bouby, M., Laborda, F., Castillo, J.R.,Geckeis, H., 2006. Multielement characterization ofmetal-humic substances complexation by size exclusionchromatography, asymmetrical flow field-flow fractionation,ultrafiltration and inductively coupled plasma-massspectrometry detection: a comparative approach. Journal ofChromatography A 1129 (2), 236e246.

Chin,Y.-P., Gschwend, P.M., 1991.Theabundance,distribution, andconfigurationofporewaterorganic colloids in recent sediments.Geochimica et Cosmochimica Acta 55 (5), 1309e1317.

Dahlqvist, R., Benedetti, M.F., Andersson, K., Turner, D.,Larsson, T., Stolpe, B., Ingri, J., 2004. Association of calciumwith colloidal particles and speciation of calcium in the Kalixand Amazon rivers. Geochimica et Cosmochimica Acta 68 (20),4059e4075.

Dahlqvist, R., Andersson, K., Ingri, J., Larsson, T., Stolpe, B.,Turner, D., 2007. Temporal variations of colloidal carrierphases and associated trace elements in a boreal river.Geochimica et Cosmochimica Acta 71 (22), 5339e5354.

Dubascoux, S., Le Hecho, I., Hassellov, M., v.d. Kammer, F.,Gautier, M.P., Lespes, G., 2010. Field-flow fractionation andinductively coupled plasma mass spectrometer coupling:history, development and applications. Journal of AnalyticalAtomic Spectrometry 25 (5), 613e623.

Geckeis, H., Rabung, T., 2008. Actinide geochemistry: from themolecular level to the real system. Journal of ContaminantHydrology 102 (3e4), 187e195.

Geiszinger, A., Goessler, W., Kosmus, W., 2002. Organoarseniccompounds in plants and soil on top of an ore vein. AppliedOrganometallic Chemistry 16 (5), 245e249.

Giddings, J.C., Schure, M.R., Myers, M.N., Velez, G.R., 1984. Endeffects in Field-Flow Fractionation channels: theory andmeans for reducing incremental zone broadening. AnalyticalChemistry 56 (12), 2099e2104.

Hassellov, M., Lyven, B., Haraldsson, C., Sirinawin, W., 1999.Determination of continuous size and trace elementdistribution of colloidal material in natural water by on-linecoupling of flow field-flow fractionation with ICPMS.Analytical Chemistry 71, 3497e3502.

Hassellov, M., 2005. Relative molar mass distributions ofchromophoric colloidal organic matter in coastal seawaterdeterminedbyFlowField-FlowFractionationwithUVabsorbanceand fluorescence detection. Marine Chemistry 94, 111e123.

Hassellov, M., v.d. Kammer, F., Beckett, R., 2007. Characterisationof aquatic colloids and macromolecules by field-flowfractionation. In: Wilkinson, K.J., Lead, J.R. (Eds.),Environmental Colloids and Particles: Behaviour, Separation,and Characterisation. John Wiley & Sons Ltd, Chichester.

Hassellov, M., v.d. Kammer, F., 2008. Iron oxides as geochemicalnanovectors for metal transport in soileriver systems.Elements 4, 401e406.

Her, N., Amy, G., Foss, D., Cho, J., Yoon, Y., Kosenka, P., 2002.Optimization of method for detecting and characterizing NOMby HPLC-size exclusion chromatography with UV and on-lineDOC detection. Environmental Science and Technology 36,1069e1076.

Hofmann, T., Baumann, T., Bundschuh, T., v.d. Kammer, F.v.d.,Leis, A., Schmitt, D., Schafer, T., Thieme, J., Totsche, K.U.,Zanker, H., 2003a. Aquatic colloids: Definition and relevance e

a review. Aquatische Kolloide I: Eine Ubersichsarbeit zurDefinition, zu Systemen und zur Relevanz 8 (4), 203e213.

Hofmann, T., Baumann, T., Bundschuh, T., v.d. Kammer, F.v.d.,Leis, A., Schmitt, D., Schafer, T., Thieme, J., Totsche, K.U.,Zanker, H., 2003b. Aquatic colloids: sampling andcharacterization e a review. Aquatische Kolloide II: EineUbersichtsarbeit zur Probennahme, Probenaufbereitung undCharakterisierung 8 (4), 213e223.

Hongve, D., Baann, J., Becher, G., Lømo, S., 1996. Characterizationof humic substances by means of high-performance sizeexclusion chromatography. Environment International 22 (5),489e494.

Hru�ska, J., Laudon, H., Johnson, C.E., Kohler, S., Bishop, K., 2001.Acid/base character of organic acids in a boreal stream duringsnowmelt. Water Resources Research 37 (4), 1043e1056.

Huang, J.H., Matzner, E., 2006. Dynamics of organic and inorganicarsenic in the solution phase of an acidic fen in Germany.Geochimica et Cosmochimica Acta 70 (8), 2023e2033.

Jackson, B.P., Ranville, J.F., Bertsch, P.M., Sowder, A.G., 2005.Characterization of colloidal and humic-bound Ni and U in the“dissolved” fraction of contaminated sediment extracts.Environmental Science and Technology 39 (8), 2478e2485.

Jirsa, F., Neubauer, E., Kittinger, R., Hofmann, T., Krachler, R., v.d.Kammer, F. and Keppler, B.K. Natural organic matter and ironexport from the Tanner Moor, Austria Limnologica, in press,http://dx.doi.org/10.1016/j.limno.2012.09.006.

Kaschl, A., Romheld, V., Chen, Y., 2002. Trace metal distributionin soluble organic matter from municipal solid waste compostdetermined by size-exclusion chromatography.Environmental Toxicology and Chemistry 21 (9), 1775e1782.

Krachler, R., Jirsa, F., Ayromlou, S., 2005. Factors influencing thedissolved iron input by river water to the open ocean.Biogeosciences 2 (4), 311e315.

Krachler, R., Krachler, R.F., v.d. Kammer, F., Suphandag, A.,Jirsa, F., Ayromlou, S., Hofmann, T., Keppler, B.K., 2010.Relevance of peat-draining rivers for the riverine input of

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 7 5 7e2 7 6 9 2769

dissolved iron into the ocean. Science of the TotalEnvironment 408 (11), 2402e2408.

Laborda, F., Ruiz-Beguerıa, S., Bolea, E., Castillo, J.R., 2009.Functional speciation of metal-dissolved organic mattercomplexes by size exclusion chromatography coupled toinductively coupled plasma mass spectrometry anddeconvolution analysis. Spectrochimica Acta e Part B AtomicSpectroscopy 64 (5), 392e398.

Leenheer, J.A., Croue, J.P., 2003. Characterizing aquatic dissolvedorganic matter. Environmental Science & Technology 37,18Ae26A.

Leist, M., 2011. Speciation of Arsenic By LC-ICP-MS. AgilentTechnologies.

Liu, G., Cai, Y., 2010. Complexation of arsenite with dissolvedorganic matter: conditional distribution coefficients andapparent stability constants. Chemosphere 81 (7), 890e896.

Liu, G., Fernandez, A., Cai, Y., 2011. Complexation of arsenite withhumic acid in the presence of ferric iron. EnvironmentalScience and Technology 45 (8), 3210e3216.

Lyven, B., Hassellov, M., Turner, D.R., Haraldsson, C.,Andersson, K., 2003. Competition between iron- and carbon-based colloidal carriers for trace metals in a freshwaterassessed using flow field-flow fractionation coupled to ICPMS.Geochimica et Cosmochimica Acta 67 (20), 3791e3802.

Mikutta, C., Kretzschmar, R., 2011. Spectroscopic evidence forternary complex formation between arsenate and ferric ironcomplexes of humic substances. Environmental Science andTechnology 45 (22), 9550e9557.

Mori, S., 1989. Secondary effects in aqueous size exclusionchromatography of sodium poly(styrene sulfonate)compounds. Analytical Chemistry 61 (6), 530e534.

Mori, S., Barth, H.G., 1999. Size Exclusion Chromatography.Springer, Berlin Heidelberg.

Neubauer, E., v.d. Kammer, F., Hofmann, T., 2011. Influence ofcarrier solution ionic strength and injected sample load onretention and recovery of natural nanoparticles using FlowField-Flow Fractionation. Journal of Chromatography A 1218(38), 6763e6773.

Pelekani, C., Newcombe, G., Snoeyink, V.L., Hepplewhite, C.,Assemi, S., Beckett, R., 1999. Characterization of naturalorganic matter using high performance size exclusionchromatography. Environmental Science and Technology 33(16), 2807e2813.

Piccolo, A., 2001. The supramolecular structure of humicsubstances. Soil Sciences 166, 810e832.

Pokrovsky, O.S., Dupre, B., Schott, J., 2005. Fe-Al-organic colloidscontrol of trace elements in peat soil solutions: results ofultrafiltration and dialysis. Aquatic Geochemistry 11 (3),241e278.

Pongratz, R., 1998. Arsenic speciation in environmental samplesof contaminated soil. Science of the Total Environment 224(1e3), 133e141.

Redman, A.D., Macalady, D.L., Ahmann, D., 2002. Naturalorganic matter affects Arsenic speciation and sorption ontohematite. Environmental Science and Technology 36 (13),2889e2896.

Ritter, K., Aiken, G.R., Ranville, J.F., Bauer, M., Macalady, D.L.,2006. Evidence for the aquatic binding of arsenate by naturalorganic matter�suspended Fe(III). Environmental Science andTechnology 40 (17), 5380e5387.

Sant, H.J., Gale, B.K., 2006. Geometric scaling effects oninstrumental plate height in field flow fractionation. Journal ofChromatography A 1104 (1e2), 282e290.

Seitz, W.R., 1981. Fluorescence methods for studying speciationof pollutants in water: fluorescence quenching yieldsinformation on the binding of metal ions to humicsubstances. Fluorescence polarization may be used to studythe conformation of humics and the binding of organicpollutants to them. TrAC Trends in Analytical Chemistry 1,79e83.

Sharma, P., Ofner, J., Kappler, A., 2010. Formation of binary andternary colloids and dissolved complexes of organic matter, Feand As. Environmental Science and Technology 44 (12),4479e4485.

Stolpe, B., Hassellov, M., Andersson, K., Turner, D.R., 2005. Highresolution ICPMS as an on-line detector for flow field-flowfractionation; multi-element determination of colloidal sizedistributions in a natural water sample. Analytica ChimicaActa 535, 109e121.

Stolpe, B., Hassellov, M., 2007. Changes in size distribution offresh water nanoscale colloidal matter and associatedelements on mixing with seawater. Geochimica etCosmochimica Acta 71, 3292e3301.

Tipping, E., 2002. Cation Binding by Humic Substances.Cambridge University Press, Camebridge.

v.d. Kammer, F., Baborowski, M., Friese, K., 2005. Application of ahigh-performance liquid chromatography fluorescencedetector as a nephelometric turbidity detector following Field-Flow Fractionation to analyse size distributions ofenvironmental colloids. Journal of Chromatography A 1100 (1),81e89.

Vasyukova, E.V., Pokrovsky, O.S., Viers, J., Oliva, P., Dupre, B.,Martin, F., Candaudap, F., 2010. Trace elements in organic-and iron-rich surficial fluids of the boreal zone: assessingcolloidal forms via dialysis and ultrafiltration. Geochimica etCosmochimica Acta 74 (2), 449e468.

Vilge-Ritter, A., Rose, J., Masion, A., Bottero, J.Y., Laine, J.M.,1999. Chemistry and structure of aggregates formed with Fe-salts and natural organic matter. Colloids and Surfaces A:Physicochemical and Engineering Aspects 147, 297e308.

Zhou, Q., Cabaniss, S.E., Maurice, P.A., 2000. Considerations in theuse of high-pressure size exclusion chromatography (HPSEC)for determining molecular weights of aquatic humicsubstances. Water Research 34 (14), 3505e3514.