For. Snow Landsc. Res. 80, 3: 305–322 (2006)
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Comparison of methods for copper free ion activity determination in soil solutions of contaminated and background soils Tatiana Pampura1, Jan E. Groenenberg2* and René P.J.J. Rietra2 1
Institute of Physicochemical and Biological Problems in Soil Science, Pushchino, Russia.
[email protected] 2 Alterra, WUR Wageningen, the Netherlands.
[email protected],
[email protected] * Corresponding author
Abstract In many cases bioavailability and toxicity of cationic metals in soils is determined by the free metal ion (FMI) activity in soil solution. Recently methods were developed that relate biological effects to FMI activity. The use, validation and further development of such approaches require determination of FMI activities in soil. FMI activity determination is, however, prone to methodological and analytical problems. Therefore we tested two different analytical methods: Cu activity measurement with (1) an ion selective electrode (ISE) and (2) a Donnan Membrane Technique (DMT). In addition we used computational methods to predict copper activity: (1) using speciation models WHAM VI and NICA and (2) using transfer functions (TF) which relate metal partitioning to soil properties. Methods were tested on soils contaminated with Cu by a Cu-Ni smelter. Emphasis was given to the organic horizon because of its importance for soil life and because FMI data for this horizon are lacking. Results show good agreement between Cu activities measured with Cu-ISE and DMT for the high Cu concentration range. The use of ISE method was limited to solutions with total Cu concentrations above 10–6 mol⋅L–1, below this concentration the method gave unreliable results. The DMT technique was limited by the detection limit of the ICP-MS used (10–8 mol⋅L–1). Deviation between models (WHAM, NICA, and TF) and measurements are within uncertainties due to different analytical techniques and the spatial variation found in soils. Keywords: copper, free ion activity, bioavailability, Donnan Membrane Technique, metal speciation, critical limit, transfer function
1
Introduction
Speciation of metals in soils is a crucial factor controlling metal mobility and bioavailability. Speciation mostly depends on metal interaction with organic matter in the soil solid phase (SOM) and with dissolved organic matter in soil pore water (DOM). Still it is not completely understood in which forms and under which conditions metals are available for soil micro-organisms, soil fauna, and plants. There is evidence that free metal ion (FMI) activity determines metal toxicity to aquatic organisms (SUNDA and GUILLARD 1976; DI TORO et al. 2001b) and soil micro-organisms (VULKAN et al. 2000), whose predominant exposure media is water or soil solution. Metal uptake by aquatic organisms, plants and invertebrates (LEXMOND and VAN DER VORM 1981; KIEWIET and MA 1991; CAMPBELL 1995; HARE and TESSIER 1996) is also influenced by competition with protons and divalent macro ions such
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as Ca2+ and Mg2+. For plants, evidence for FMI uptake as well as evidence for uptake of other metal species (complexes with chelating agents, inorganic and natural organic ligands) were summarized by MCLAUGHLIN (2001). For soil-grown plants, data are scarce, and other factors like diffusion control may trouble direct relations between metal contents in plants and FMI activity in soil solution (PEIJNENBURG and JAGER 2003). Based on the importance of FMI activity for uptake and toxicity, the free ion activity model (FIAM) was developed (MOREL 1983; CAMPBELL 1995). A further development was to account for competition of protons and cations in the Biotic Ligand Model (BLM) (DI TORO et al. 2001a). However, at present data for parameterisation of these models, especially FMI activities in relation to biological endpoints are lacking. To overcome this data lack LOFTS et al. (2004) and DE VRIES et al. (2006) derived critical limits in terms of FMI activity using data compiled from toxicity experiments. In this case they used transfer functions (TF) to estimate metal activities in solution from the amount of added metal and soil properties. To be able to use, validate and further develop such approaches for risk assessment the FMI activity has to be known. Determination of FMI activity in soil solution is time consuming and prone to methodological and analytical problems. Therefore the evaluation of reliability and practicality of different methods used for metal activity determination is very important. During the last years a number of analytical methods were improved, developed and proposed for metal speciation measurements. The development of methods and achievements in the field of bioavailability studies since 1982 are comprehensively reviewed by BATLEY et al. (2004) and PEIJNENBURG and JAGER (2003). FMI activities can be directly measured by ion selective electrodes. Great improvement in sensitivity of metal ion concentration measurements (Cu, Cd) with ion selective electrodes was achieved by implementing a calibration procedure based on the use of metal ion buffers as ethylenediamine (AVDEEF et al. 1983), and used to determine free Cu ion concentrations in soil solution (e.g. SAUVE et al. 1995; VULKAN et al. 2000; PAMPURA 2001; GROENENBERG et al. 2003a) and free Cd concentrations in pore waters (CAVALLARO and MCBRIDE 1980; OSTE et al. 2002). However, this method can be applied only when measured samples themselves behave as metal ion buffers with high total metal concentration and excess of a strong complexant such as natural organic matter (BATLEY et al. 2004). Voltammetric methods (differentialpulse polarography [DPP], differential-pulse anodic stripping voltammetry [DPASV]) are specific methods to measure labile metals (FMI and weak complexes), however, there are several factors limiting their application (BATLEY et al. 2004). Diffusive gradients in thin films (DGT) was developed to asses labile species of metals (ZHANG et al. 1998; DAVISON et al. 2000). Gel permeation chromatography allows separating metal species on the basis of molecular weight (GREGSON and ALLOWAY 1984). All these methods have their limitations, and often suffer from interference, disturbance of solution equilibrium, a high detection limit, or measuring not only FMI, but also some (labile) complexed forms. Either the above mentioned techniques are limited to a few metals (ISE) or are more or less operationally defined as they do not measure the FMI activity directly. The Donnan Membrane Technique (DMT), based on the separation of free and complexed metals by a negatively charged ion exchange membrane, allows determination of free ion activity of several metals simultaneously without disturbance of the soil solution equilibrium (TEMMINGHOFF et al. 2000; WENG et al. 2001b; WENG et al. 2002). The method is limited by the detection limit of ICP-MS. To overcome this problem WENG et al. (2005) work on a method to be able to measure lower FMI activities by adding a ligand to the acceptor part of the cell. Besides analytical methods also computational methods can be used to predict FMI activity. To calculate metal activity from measured concentrations of metals, dissolved organic carbon (DOC), pH and concentrations of competing cations speciation models can be used.
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Among many speciation models WHAM/Model VI (further referred to as WHAM) (TIPPING 1998), and NICA (KINNIBURGH et al. 1999) are the most suitable because of their state of the art modelling of metal-organic matter interactions and their extensive parameterisation. Both models were parameterised using experimental data mostly for purified humic and fulvic acids (HA and FA) and were successfully used for a description of metal interaction with organic matter. To apply these models to natural systems such as surface waters and soil pore water assumptions have to be made about the nature of the organic matter present in DOM and its reactivity (i.e. the % of DOM which behaves as FA and HA). Although some applications of these models for natural systems are published, e.g. BRYAN et al. (2002) for Cu in surface waters, VULKAN et al. (2000) for Cu in soil pore water, WENG et al. (2002) for Cd, Cu, Ni, Pb and Zn in soil solution suspension, NOLAN et al. (2003) for Cd, Cu Pb and Zn in soil solution and GE et al. (2005) for Cd and Pb in soil solution, there is still a need for validation against field measurements. Especially validation of these models for organic horizons of forest soils are missing. Another approach is to use transfer functions to predict the FMI concentration or activity in soil solution based on metal concentrations in the soil solid phase and soil characteristics. Such TF have been proposed by several authors (e.g. SAUVE et al. 1998; SAUVE et al. 2000; GROENENBERG et al. 2003b; PAMPURA 2003a, b; TIPPING et al. 2003; ROMKENS et al. 2004). In this study we compare different methods to measure and predict Cu activity in soil solution. For this comparison we used soils of the same soil type sampled along a Cu pollution gradient created by a Cu-Ni smelter. We have selected those techniques which measure directly the free metal in soil solution i.e. DMT and Cu-ISE. Further we test the applicability of speciation models, in this case WHAM, and NICA, to predict Cu activity from total concentrations in solution and the use of transfer functions to predict activities from metal concentrations in soil and soil properties. To illustrate the use of FMI activity determination in risk assessment we compare measured and calculated Cu activities of soils along the pollution gradient with critical limits defined in terms of FMI activity.
2
Materials and methods
2.1
Soil
Soil samples were collected from Oh and Bhf horizons of Al-Fe Humus Podzols along a pollution gradient of the Monchegorsk Cu-Ni smelter at 7, 20, 28, 100, 200 km south-east of the Monchegorsk Kola Peninsula, Russia. The area in the vicinity of the smelter is subjected to heavy contamination with Cu, Ni and sulphur deposition. After sampling soil samples were transported at field moisture content in a cool box at a temperature about +10 °C. In the laboratory samples were passed through a 3 mm plastic sieve without drying and were kept in the dark at +4 °C during 8 to 12 months. Moisture content (at 40 °C) was determined just before the experiments to calculate the soil: solution ratio needed for the soil column DMT experiment. Pseudo total and “reactive” metal content in soil were determined using Aqua Regia (Standard Operational Procedure ECO/237/03, RIVM, Bilthoven, the Netherlands) and 0.43 mol⋅L–1 HNO3 (HOUBA et al. 1985) extractions respectively.
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Soil-column – Donnan Membrane Technique
We used the soil column – Donnan Membrane Technique (DMT) as developed at Wageningen University (TEMMINGHOFF et al. 2000; WENG et al. 2001b; WENG et al. 2002). The method is based on the assumption of (pseudo) equilibrium between a donor and an acceptor solution in a Donnan cell separated by a cation exchange membrane. The membrane allows fast transport of free cations but hinders the transport of complexed cations and anions. For both the donor and acceptor parts of the cell we used 0.002M Ca(NO3)2 solutions which were circulated using peristaltic pumps (Desaga and Gilson Minipuls 2 with tygon tubes, 2.06 1.D.) with a speed of 2 ml/min. The donor solution is in contact with the soil and circulates continuously from the bottom to the top through the soil column and the donor part of Donnan exchange cell. The acceptor solution circulates through the acceptor part of the Donnan cell. The Donnan cells were designed and made at the department of Soil quality of Wageningen University. The cation exchange membrane which we used (55165 2U, BDH Laboratory Supplies, UK) has polystyrene and divinylbenzene matrix with sulphonic acid groups which are fully deprotonated at pH>2, thickness is 0.15–0.17 mm, CEC = 0.8 mmolc.g–1. Before starting the experiment the Donnan cell with membrane was connected to a pump and washed with 0.1 mol⋅L–1 HNO3 (0.5 L/cell) followed by washing with Milli-Qwater (1 L/cell), 1 mol⋅L–1 CaCl2 solution (0.5 L/cell), Milli-Q water (1 L/cell), and finally with the background solution 0.002 mol⋅L–1 Ca(NO3)2 (2L/cell) at a speed of 30 ml/min. The soil column was filled with soil at field moisture content. We used a soil:solution ratio (air dry weight) of 1:2 for samples from the Bhf horizon. For litter (Oh horizon) we had to increase this ratio to 1:7, by reducing the amount of solid phase, because of the very high water holding capacity of this material (up to 450 g/100g dry soil). We modified the column design introducing a piston to keep litter at the bottom of the column, in order to prevent it from floating at the surface where it hinders permanent pumping of solution though the column. The volume of Ca(NO3)2 solution circulating through the donor (connected to soil column) and acceptor part of cell was 175 and 18 ml correspondingly. The experiment was run for 48 hours as recommended by WENG et al. (2001b) who showed that in most cases this is enough time to reach equilibrium in the soil system and Donnan exchange cell. Donor and acceptor solutions were analyzed for Ca, Mg, Na, K, Fe, Al, Mn, Cr, using ICP-AES (Perkin Elmer OPTIMA-3300DV or Thermo-Optek IRIS), and Cd, Pb, Cu, Ni by ICP-MS (Perkin Elmer, ELAN 6000), DOC concentration was determined using a Shimadzu-TOC analyzer (NPOC method). At equilibrium the free metal ion activities in both donor and acceptor solutions are equal. This makes it possible to determine the FMI activity of metal in soil solution by measuring the metal concentration in the acceptor part of the cell (in which complexation is negligible) and multiplying the concentration by an activity coefficient γ, accounting for the electrostatic interaction of ions in solution. The activity coefficient depends on the ionic strength of the solution, and can be calculated using the Davies equation. The free ion activity of Cu (aCu don) in the donor part of the cell (soil column) was calculated from the activity of Cu in acceptor (aCu acc) corrected by the ratio of Na activities in donor and acceptor using Equation 1 (TEMMINGHOFF et al. 2000) to consider the effect of a difference in ionic strength between acceptor and donor parts of the cell. Free ion activity of Cu in acceptor and Na in donor and acceptor were calculated from total concentrations of all major cations and anions taking into account inorganic complexation using WHAM.
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(1)
2.3
Copper activity measurements with Cu-ion selective electrode
After 48 hours of running the experiment the pH and FMI activity of Cu in the DMT soil column was determined directly in the solution in the soil column using a multi channel ion-pH meter (ORION RESEARCH EA 940) and a set of two electrodes – pH electrode (combination semimicro, research-grade electrode U-05711-11, Orion) and a Cu-selective electrode (combination solid state glass electrode U 27502-15, Cole Parmer). Copper electrode potentials were recorded when drift had dropped below 0.1 mV min–1 or after a maximum equilibration time of 15 minutes. Before and after use Cu-electrode detectors (Cu-sulfide) were polished with special polishing strip followed by vigorous rinsing with distilled water. The electrode was rinsed by distilled water after every measurement. Between measurements electrodes were stored according to their instruction manuals. To be able to measure low metal activities we used the procedure of electrode calibration with Cu-ethylenediamine buffers as suggested by AVDEEF et al. (1983). An aqueous solution containing 10–3 mol⋅L–1 Cu(NO3)2, 15.10–3 mol⋅L–1 ethylenediamine in 0.1 mol⋅L–1 NaNO3 was titrated with Na(OH) (Merck, Titrisol, 1.09959) to regulate Cu activity in the range pCu 3.5–10. Titration was carried out in the dark at the constant flow of inert gas (nitrogen) through the solution. Copper activity was calculated using log K0 values from Table 1.
Table 1. Values for log K0 used to calculate Cu speciation in the En-Cu titrations (LINDSAY 1979; AVDEEF et al. 1983) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
K10 K20 K30 K40 K50 K60 K70 K80 K90 K100
= [CuEn2+] γ2+/[En0][Cu2+] γ2+ = [CuEn22+] γ2+/[En0][CuEn2+] γ2+ = [Cu2+][S2–] (γ2+)2 = [H+][HS–] (γ+)2/ [H2S0] = [H+][S2–] (γ2+)/ [HS–] = [EnH+] g+/[H+][En0] γ+ = [EnH22+] γ2+/[H+][EnH+] (γ+)2 = [CuOH+][H+](γ+)2+ / [Cu2+] γ2+ = [Cu(OH)20][H+]2(γ+)2+ / [Cu2+] γ2+ = [H+][OH–](γ+)2
log K10 log K20 log K30 log K40 log K50 log K60 log K70 log K80 log K90 log K100
= = = = = = = = = =
10.44 8.93 –36.1 –7.02 –12.9 9.94 6.96 – 7.70 –13.78 –14
In the range of pCu 3–19 the cupric ion selective electrode demonstrated linear response and nearly theoretical (96.7%) Nernstian slope, as shown by the relation between the measured electrode potential E (mV) and the copper activity according to:
E = 27.95 log aCu +332.2 (R2 = 0.999)
(2)
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On the basis of the titration data, four buffered standard solutions with Cu activities covering the range of expected values were prepared on the days of measurements. Calibration was performed both before and after the measurements in the DMT columns in order to correct for drift.
2.4
Speciation modelling
We calculated chemical speciation using models WHAM (version 6.0.10) and NICA. WHAM is based on a set of different discrete binding sites for which protons, metal ions (Al3+, Fe3+, Cu2+, Ca2+ etc.) and their first hydrolysis products (AlOH2+, FeOH2+, CuOH+, CaOH+ etc.) compete. Binding of metal ions and their first hydrolysis products is allowed as bidentate and tridentate binding. Intrinsic equilibrium constants for proton and metal binding are modified by electrostatic terms that take into account the attractive or repulsive interactions between ions and the charged macromolecule. WHAM is parameterized for fulvic and humic acids based on an extensive set of experimental data (TIPPING 2002). NICA describes the competitive binding of protons and metal cations to organic matter, taking into account ion specific nonideality and electrostatic effects. Binding site heterogeneity is accounted for by using a continuous distribution for two different groups of binding sites. The NICA model is parameterized using an extensive data set for proton and metal binding to humic and fulvic acids (MILNE et al. 2001, 2003). Measured concentrations of the metal ions (Cd, Cu, Ni, Zn, Pb), pH and major cations (Ca, Mg, Na, K, Al and Fe) in the donor solution were directly used as input to the models. The concentration of NO3– was set equal to the concentration of the background electrolyte of 0.002 mol⋅L–1, concentrations of PO43– and SO42– were set equal to total P and S as measured with ICP-AES. The concentration of Cl– was estimated from the anion deficit. We assumed that dissolved organic matter (DOM) can be modelled as 65 % active fulvic acid, which is an average value found by optimizing the model for binding of Cu to natural DOM in surface waters (BRYAN et al. 2002) and was shown to be reasonable estimate for the binding of several metals including Cu to DOM in soil solutions (WENG et al. 2002). DOM was calculated by multiplying measured DOC with a factor 2 assuming a 50 % carbon content of DOM.
2.5
Prediction of Cu activities using transfer functions
Several publications present multivariate linear regression equations predicting free ion concentration or activity in soil solution from main soil characteristics (e.g. soil organic matter content [SOM] and pH) and total or reactive metal content in soil (SAUVE et al. 1997; SAUVE et al. 2000; GROENENBERG et al. 2003b; PAMPURA 2003b; TIPPING et al. 2003; LOFTS et al. 2004). Here we compare the copper activities predicted using two transfer functions with our measured activities. Transfer function 1 (TF1) was derived by LOFTS et al. (2004) using several data sets from Canada, the Netherlands, and the United Kingdom (SAUVE et al. 1997; WENG et al. 2001a; WENG et al. 2002; TIPPING et al. 2003) to relate measured (several methods) or WHAM calculated free metal activities to metal contents in soils.
log aCu = 2.20 – 0.63·log(%SOM) – 1.26·pH + 0.93·log QCu R2 0.90, se(Y) 0.61
(3)
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where: QCu aCu % SOM pH
= = = =
Cu concentration (mol⋅kg–1) free Cu ion activity (mol⋅L–1) soil organic matter (%) solution pH
Unfortunately the methods of determination of metal contents in soil and determination of activities in solution vary amongst the data sets used which makes the resulting dataset inconsistent. In order to overcome such inconsistency (GROENENBERG et al. manuscript in prep.) derived TF2 based on two datasets (TIPPING et al. 2003; ROMKENS et al. 2004) in which for both data sets Cu activities were calculated using WHAM, and the reactive metal content was extracted with 0.43 mol⋅L–1 HNO3. This transfer function relates the Freundlich adsorption constant (Kf) to soil properties according to:
log(Kf) = –1.41 + 0.93 log(%SOM) + 1.05 pH
(4)
R2 = 0.92, se(Y) = 0.48 The copper activity can be calculated with:
log(aCu ) =
log(QCu ) − log( K f ) n
(5)
Kf = Freundlich constant n = Freundlich coefficient, n = 1 in the case of Cu
2.6
Statistical analysis of data
We quantified the performance of speciation models and transfer functions to predict measured activities with statistical measures. The Mean Absolute Error (MAE) is the average of the absolute difference between predicted and measured values. n
MAE = ∑
pi − oi n
i =1
with pi the value of the prediction and oi the measured value. The coefficient of residual mass (CRM) gives the tendency of the model to overestimate (CRM is positive) or underestimate (CRM is negative) measured values and is calculated as: n
CRM =
∑ (p i =1
i
− oi )
n
∑o i =1
i
For both measures the optimal value is zero. We also used these measures to quantify the differences between the two analytical techniques used (ISE and DMT).
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Comparison of copper activities with critical limits
LOFTS et al. (2004) and DE VRIES et al. (2006) derived critical limit functions for the metals Cd, Cu, Pb and Zn which defines the critical limit in terms of a FMI activity depending on pH. This pH dependence is a lumped factor to include the dependence of metal toxicity on competition by other cations. For the derivation of this critical limit function they used existing No Observed Effect Concentrations (NOEC) for plants, invertebrates, microbial processes, and fungi in soils. From the NOEC data published as added metal concentrations, the pH and organic matter content of the soil and an empirical relation between metal activity, soil metal content and organic matter (TF1 as above) they derived the critical limit function for the protection of 95 % of the species (HC5) according to:
log [Cu]free, crit = –1.21· pHss –2.57
(6)
We used Equation 6 to calculate critical limits for Cu along pollution gradient using soil column solution pH observed in our DMT experiments and compared measured and predicted copper activities with critical limits to see if these limits were exceeded.
3
Results and discussion
3.1
Soil solid phase
Soil organic matter content, pseudo-total (further we call it total) and reactive Cu content, are presented in Table 2. Total and reactive metal content are much higher in the upper organic horizon (Oh) than in the mineral horizon (Bhf), because of the high affinity of Cu for organic matter. In both organic and mineral horizons reactive and total Cu content decreased with increasing distance from the smelter (Table 2). The gradient of Cu content in B horizons shows that despite the strong accumulation of copper in the organic layer part of Cu migrated downward in the soil profile. The degree of soil contamination is very high, with total copper concentrations up to 2010 mg.kg–1 in O horizon at a distance of 7 km from the smelter. Even at 200 km Cu concentration was 193–370 mg.kg–1, which is quite high for the ‘background’ soil.
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Table 2. Selected soil characteristics. a,b,c,d,-replicates. N
Horizon Distance
Code
Soil LOI
Cu Aqua regia
km 7 7 7 7 7 20 20 28 28 100 200 200 200 200 7 7 20 20 28 100 200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Bhf Bhf Bhf Bhf Bhf Bhf Bhf
3.2
Soil column solution
k-9a k-9b k-7a k-7b k-7c k-16 k-17 K-26 k-27 k-34a k-43a k-43b k-43c k-43d k-13 k-15 k-23 k-22 k-31 k-40 k-49
log% 1.79 1.79 1.79 1.79 1.79 1.79 1.79 1.79 1.79 1.79 1.85 1.85 1.85 1.85 0.87 0.87 0.92 0.87 0.92 0.41 0.54
Cu 0.43M HNO3
mg kg–1 1434 1434. 2011 2011 2011 455 207 851 447 193 372 372 372 372 135 74.9 26.4 25.8 17.3 9.76 5.34
1724 1724 1782 1782 1782 373 163 568 442 128 252 252 252 252 89.0 75.0 3.17 6.62 3.90 1.73 0.56
Because measurements with DMT are time consuming and a limited amount of Donnan cells could be used simultaneously we were not able to analyze many replicates for all samples. Therefore we evaluated the reproducibility of the method only for samples from the Oh horizon in the vicinity of the smelter and for the background soil (Table 3). The following standard deviations (SD) were found:
