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Changes in soil organic matter composition and quantity with distance to a nickel smelter — a case study on the Kola Peninsula, NW Russia
Geoderma 127 (2005) 216 – 226 www.elsevier.com/locate/geoderma
Changes in soil organic matter composition and quantity with distance to a nickel smelter — a case study on the Kola Peninsula, NW Russia Ekaterina Viventsova (Ruth)a,*, Jurate Kumpieneb, Lars Gunneriussonc, Allan Holmgrenc a
Lulea˚ University of Technology, Department of Chemical Engineering and Geosciences, Division of Applied Geology, SE-97187 Lulea˚, Sweden b Lulea˚ University of Technology, Department of Civil and Environmental Engineering, Division of Waste Science and Technology, SE-97187 Lulea˚, Sweden c Lulea˚ University of Technology, Department of Chemical and Metallurgical Engineering, Division of Inorganic Chemistry, SE-97187 Lulea˚, Sweden Available online 6 January 2005
Abstract The capacity of soils to absorb contamination depends on a number of factors, such as soil organic matter (SOM) that plays an essential role in adsorption of metal ions, especially in Podzols with their low content of clay minerals. Detailed analysis of SOM can provide information about the potential capacity of a soil to mobilise or immobilise contaminating substances, which in turn can be used to predict potential recovery of the soil ecosystem following heavy metal pollution. The purpose of this study was to learn how an increasing load of heavy metals (Cu and Ni) affects SOM content and structure, and which of the SOM functional groups are responsible for metal retention. The study area is located in the central part of the Kola Peninsula, south of Monchegorsk city and the nickel smelting complex dSeveronickelT. The amount of total carbon in the soil decreased from 86% to 0.6% as the distance from the smelting complex decreased from 34 to 2 km. Functional groups of the SOM had a larger diversity in less polluted soils than in the soils located closer to the smelting complex. Carboxyl groups had a higher intensity of infra red (IR) bands in soils collected at the distance of 34–27 km than those located closer to the smelter. The most disturbed soil at the site closest to the smelter showed almost no presence of COOH groups. We conducted a laboratory experiment using soils from the least polluted sampling site to assess which SOM functional groups may be responsible for metal retention. Experimental contamination of the soil with the Cu/Ni solution resulted in an overall decrease in the absorbance for all studied functional groups within the measured range, except for COOH. This could be attributed both to changes in the structure of the SOM caused by its
* Corresponding author. Tel.: +46 920 492879; fax: +46 920 491697. E-mail addresses: [email protected], [email protected] (E. Viventsova (Ruth)). 0016-7061/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2004.12.010
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reaction with the contaminating substances, and to the leaching of some of the organic compounds from the soil during the experiment. Further studies are needed to better understand which functional groups in the SOM are active in the adsorption processes. D 2004 Elsevier B.V. All rights reserved. Keywords: Soil organic matter; Functional groups; Coppe r; Nickel; Heavy metals; Kola Peninsula; Barents Region
1. Introduction Soil pollution and its remediation is one of the biggest environmental concerns in Northern Europe. There are various models for the assessment and risk evaluation of polluted areas followed by implementation of appropriate remediation practices. One of the aims of an assessment model is to reduce the expenses of remediation practices by delimiting areas with the highest environmental and health risk. In the commonly used models, soil is considered mostly as a transport medium for contaminants and not as an agent that can support ecosystem recovery, which in turn could further reduce the area planned for remediation. Soil degradation caused by various anthropogenic impacts, e.g., contamination by toxic substances, can be more or less pronounced depending on the soil absorbing capacity. The degree to which soil can buffer contamination depends on a number of factors, such as soil organic matter (SOM) quantity and quality, proportion of nutrient elements, biological activity, composition of mineral soil component, and physical properties of the soil. One of the most important soil components that can provide an ecosystem with necessary prerequisites for recovery is soil organic matter. SOM plays an essential role in ecosystem functioning, both indirectly through improving physical properties of the soil and directly through an impact on plant biomass by supplying nutrient elements and by participating in plant metabolism (Nardi et al., 2002; Tan, 1998; Taiz and Zeiger, 1998; Orlov, 1985). Cation exchange positions in the soil are associated in general with mineral particles and SOM. In soils with a predominance of sandy fractions, as in the soils presented in this paper, the cation exchange capacity (CEC) is rather low and is controlled mostly by SOM (Black, 1968; Smith et al., 1993). This means that concentrations of
exchangeable nutrient elements are higher in soils with a larger amount of SOM. SOM also strengthens the soil buffering capacity, providing resistance against severe chemical changes. The ability of SOM to form complexes with metals depends on its high content of functional groups (carboxyl COOH, phenolic-OH, etc.) with COOH playing a predominant role in metal binding (Stevenson, 1994). Depending on soil pH, the functional groups can dissociate their H+ ions, hence contributing to cation exchange reactions, complex formation and/or chelation reactions with metal ions. Bound as chelates, the toxic metals are known to have a significantly lower chemical activity (Tan, 2000). Depletion of SOM, decrease in biomass carbon and nutrient imbalance are among the most important of the processes caused both by mechanical or chemical ecosystem disturbances (Lal and Stewart, 1992a,b). The long-term stability of restored ecosystems after implementation of remediation practices depends on an adequate nutrient pool being maintained without continued fertiliser applications. Since most nutrient elements are related to SOM, the maintenance of the SOM pool will ensure favourable nutrient conditions for ecosystem restoration. Therefore, when planning assessment and remediation practices, it is important to pay special attention to the status of the SOM. The study presented here is a part of a bigger pilot project aimed at testing an integrated methodological approach to the assessment of polluted areas (Ruth et al., 2002; Ruth and Kharytonov, 2003). The purpose of this part of the project is to learn how an increasing load of heavy metals (Cu and Ni) affects SOM content and structure, and which of the SOM functional groups are responsible for metal retention. This knowledge provides one of the steps that have to be taken to enable the soil properties, controlling ecosystem recovery, to be included in an assessment model.
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2. Area description and methods
purposes, three samples were taken at each sampling site and analysed separately.
2.1. Study area The study area is located in the central part of the Kola Peninsula, south of Monchegorsk city and of the dSeveronickelT nickel smelting complex, which is a significant source of environmental pollution. Its yearly emissions of sulphur dioxide and heavy metals, mainly copper and nickel, are among the highest in Europe. According to the report of the Environmental Committee of the Murmansk Region (2001) the emissions of sulphur dioxide from industries on the Kola Peninsula in 2000 were about 274,000 t, for copper 1080 t and for nickel 1570 t. In the report of the Arctic Monitoring and Assessment Programme in 2002 (Arctic Pollution, 2002), it was indicated that the dead forest zone around dSeveronickelT extends for up to 15–20 km. The severe conditions in the region provide unique possibilities for studying changes in SOM content and structure caused by continuous sulphur dioxide and metal emissions. They can be regarded as a bworst caseQ situation that clearly exposes altered ecosystem processes. The original (undisturbed) forest type in the area is spruce-pine forest with a predominance of Vaccinium species (Vaccinium vitis-idaea and Vaccinium myrtillus) in the ground layer, which belongs to the northern taiga zone. The predominant soil type in the area is Podzol. The Podzol is characterised by a distinct profile with a ca. 5- to 10-cm-thick organic horizon AO with black–brown raw organic material, a 6- to 10-cm light grey eluvial horizon E, and a 30- to 40-cm red–brown sandy illuvial horizon Bh. Podzol has an acidic reaction that differs in the different horizons; pHH2O in the AO is about 5.5, in E it is 4.5, and in Bh it is 5.0. Soil samples were collected from the organic horizon on the sampling sites 1–6 (depth is indicated in the description of each site below) and from the upper 3 cm from the site 7. Each sample was taken from the area of ca. 3030 cm. The sampling sites were located at distances of 2, 10, 11, 17, 22, 27 and 34 km south of the dSeveronickelT smelting complex, along the predominant wind direction (Fig. 1). Sampling sites were about 5 m in diameter and had relatively homogeneous topography. For statistical
2.1.1. Description of sampling sites Photos of the sampling sites can be seen at http:// barenvir.project.luth.se/Projects/IAPA/. Site #1 — 34 km from dSeveronickelT (Samples 1.1, 1.2, 1.3)
Fig. 1. The study area and sampling sites: Site #1 — 34 km, site #2 — 27 km, site #3 — 22 km, site #4 — 17 km, site #5 — 11 km, site #6 — 10 km and site #7 — 2 km from the dSeveronickelT smelting complex. The inset shows the Kola Peninsula in the north of European Russia.
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Site: Dry pine–birch forest, 7- to 8-year-old needles. Soil: Podzol with 4-cm-thick organic horizon. Plant cover: Pinus sylvestris, Picea abies, Betula, Empetrum nigrum, Vaccinium uliginosum, V. myrtillus, V. vitis-idaea, Ledum polustre, lichens including fruticose type. Site #2 — 27 km from dSeveronickelT (Samples 2.1, 2.2, 2.3) Site: Wet spruce–birch forest, 5- to 6-year-old needles, there are signs of defoliation. Soil: Podzol, 6- to 7-cm-thick organic horizon, tight root system. Plant cover: P. sylvestris, P. abies, Betula, Juniperus communis, V. uliginosum, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, lichens (less than on the previous site). Site #3 — 22 km from dSeveronickelT (Samples 3.1, 3.2, 3.3) Site: Spruce–birch forest, 4- to 5-year-old needles, about 40% of the conifers are defoliated. Soil: Podzol, 2- to 4-cm-thick organic horizon. Plant cover: P. sylvestris, P. abies, Betula, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, patches of lichens. Site #4 — 17 km from dSeveronickelT (Samples 4.1, 4.2, 4.3) Site: Dry spruce–birch forest, 2- to 3-year-old needles, there are dead trees (spruces). Soil: 6- to 10-cm-thick organic horizon. Plant cover: P. sylvestris, P. abies, Betula, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, patches of lichens. Site #5 — 11 km from dSeveronickelT (Samples 5.1, 5.2, 5.3) Site: Dry pine–birch forest with clear defoliation; 1- to 2-year-old needles; plant cover is thin and weak; soil is covered with a dry crust, signs of acidic precipitation on stones. Soil: Organic material is slightly decomposed. Plant cover: P. sylvestris, P. abies, Betula, E. nigrum, L. polustre, some V. vitis-idaea, crustaceous lichens. Site #6 — 10 km from dSeveronickelT (Samples 6.1, 6.2, 6.3) Site: Dry pine–birch forest, most of the pines are dead, those that are alive have 1 year-old needles; birches have bskirtQ form. Soil is covered with a dry crust; there are many remains of large trees on the ground and signs of acidic precipitation on stones. Soil: Organic horizon is 1–2 cm thick, organic material is slightly decomposed; no clear border between A and B horizons; no E (Podzol) horizon. Plant cover: P.
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sylvestris, P. abies, Betula, V. vitis-idaea, Arctostaphylos uva-ursi, very little crustaceous lichens. Site #7 — 2 km from dSeveronickelT (Samples 7.1, 7.2, 7.3) Site: The site varies very much depending on microtopography and underlying material. There are some signs of a forest fire. Stones are eroded by acidic precipitation. Very sparse plant cover, most of the birches are dead. Soils: In a microelevation, the soil is sandy yellowish-grey without any organic layer, with only partly remaining traces of organic matter. In a microdepression with soil covered with a dry crust, the organic horizon is thick with well-decomposed organic material; E horizon is dark grey. In a second microdepression between stones with thick soil-like material, the organic horizon is ca. 0.5 cm, sometimes thinnerdark crust; no clear borders between A and B horizons. Plant cover: Betula, Salix, V. vitis-idaea, V. uliginosum, grasses, small patches with mosses. 2.2. Laboratory batch experiment A metal solution, containing 50 mg/l (0.79 mM) Cu and 40 mg/l (0.68 mM) Ni, was prepared from CuCl2d 2H2O and Ni(ClO4)2d 6H2O. Air-dried soil samples (samples collected from site #1 — 34 km from the nickel smelter) were added to 0.5l polyethylene bottles and mixed with the metal solution at a liquid to solid ratio (L/S) of 10 l/kg. The bottles were closed and shaken on a rotating lab shaker for 24 h at room temperature. Liquid–solid separation of the eluates was performed by filtration through a 0.45-Am nitrocellulose membrane filter. The pH of the liquid phase was measured. The pH of the blanks, which contained only Cu/Ni solution, was 4.6–4.9. The separated soil was dried at 50 8C for 24 h, ground to pass through a 0.25-mm sieve and analysed for the metal content with an XRF analyser (see Section 2.3). IR analysis was performed in order to evaluate the spectra of soil organic material prior to and after the contamination of the soil with Ni and Cu. 2.3. Analytical methods Total carbon was determined using the total combustion of the soil samples under an O2 pressure
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of 20 bar. SOM composition was studied using Fourier Transform Infra Red (FT-IR) spectroscopy (Perkin Elmer System 2000 FT-IR spectrometer equipped with a triglycine sulphate detector). The samples were prepared according to the KBr-disc method (2.0 mg soil+300 mg KBr) and spectra were recorded in the range of 370 to 4000 cm 1. The relative transmittance peak area of functional groups was calculated using the program SpectrumLite (1.45b Lite, Perkin-Elmer). Soil samples for Cu and Ni analyses were ground to pass through a 0.25-mm sieve. The metal content was determined in the air-dried samples by use of Xray fluorescence spectrometry (Niton 700 XRF multielement analyser with internal calibration and automatic self-calibration when is turned on). The accuracy of the instrument was checked with the NITON standard reference soil containing certified amounts of metals. In order to study interactions between various measured parameters, the cumulative data were analysed by multivariate data analysis using SimcaP9.0 developed by Umetrics AB (Eriksson et al., 1999). Principal Component Analysis (PCA) ( pb0.05) was used. Only principal components with eigenvalues greater than 0.1 were included in the models.
3. Results and analyses 3.1. Alteration of SOM along the distance gradient from the nickel industry Concentrations of Cu and Ni in the soil samples increased with decreasing distance to the smelter, although the highest concentration of Cu was recorded at a distance of 11 km (site #5) (Fig. 2a and b). Variations between replications (samples taken from the same sampling site) were larger on the sites closer to the smelter. This can probably be explained by the openness of the surface in the more polluted area. The less polluted sites had tighter vegetation cover that provides a shield for the soil surface and causes more even distribution of the contaminating agents, while on more polluted sites, with sparse vegetation, the distribution of contaminants over the surface might be influenced by microtopography.
Fig. 2. Box-and-Whisker Plots: Concentrations of Cu (a), Ni (b) and total carbon (c) in the soils at different distances from the dSeveronickelT smelting complex. The size of the box shows how the variability among three replicate samples; the horizontal line represents the median and the cross the average value.
The amount of total carbon in the soil decreased from 86% to 0.6% as the distance from the smelting complex decreased from 34 to 2 km (Fig. 2c). Functional groups of the SOM had a larger diversity in less damaged soils than in the soils located closer to the smelting complex (Fig. 3). Carboxyl (COOH) groups, having a strong peak around 1732 cm 1,
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Fig. 3. IR spectra and total carbon (Ct) measured in the soil samples along the distance gradient from the dSeveronickelT smelting complex. Since results strongly correlated to the distance, to make the figure more legible, we excluded some spectra (site #2 — 27 km, site #3 — 22 km and site #5 — 11 km).
and phenol-OH groups (3375–3431 cm 1) had a higher intensity of IR bands in soils collected at the distance of 34–27 km than those located closer to the industry. The most disturbed soil on the site closest to the smelter (site #7) showed almost no presence of COOH groups. This soil also had the lowest peaks at 2850–2920 cm 1 that characterises aliphatic C–H bonds, and at 1629 cm 1 that is attributed to aromatic CjC bonds. Even though the peaks around 1732 cm 1 were attributed to carboxyl groups, the presence of other carbonyl groups in the same area of the spectrum cannot be excluded. Relationships between all the measured variables were analysed by applying the PCA model (Eriksson et al., 1999). Three components were analysed. Variables Cu-XRF and Ni-XRF were log-transformed for a better fit to the model. The model explained 93% of variance in the data set. The loading scatter plot (Fig. 4a) illustrates the strong negative correlation between metal concentrations
and all functional groups and total carbon. Correlations between pH and phenolic, aliphatic and aromatic groups were rather weak as well as between pH and total carbon. Carboxyl groups showed somewhat higher correlation with pH than the other groups. 3.2. Analysis of SOM prior to and after the experimental contamination with Cu and Ni The experimentally contaminated soil from the batch test was analysed for the content of Cu and Ni. The original sample of the same soil (from site #1) was used as a reference. Concentrations of Cu and Ni in the soils increased significantly after the batch test. The Cu concentration in the reference soil was 66–98 mg/kg, and after exposure to the Cu and Ni solution, it increased to 650–921 mg/kg. The concentration of Ni increased from 138–174 mg/kg in the reference soil to 353–653 mg/kg in the experimentally polluted soil.
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Fig. 4. Multivariate data analyses: (a) PCA loading plot — analysis of the parameters measured in the soils at different distances from the dSeveronickelT smelting complex: concentrations of Cu and Ni (Cu-XRF and Ni-XRF, respectively), pH, total carbon (Ctot), carboxyl groups (COOH), aliphatic C–H groups (C–H), aromatic CjC groups (arom) and phenolic groups (OH). (b) PCA loading plot — analysis of the batch test results (same abbreviations as in a).
IR-spectrum analysis was performed in order to compare the spectra of soil organic material prior to and after the experimental contamination with Cu and Ni (Fig. 5a), and the relative transmittance peak area of functional groups was calculated and compared (Fig. 6). The experimental contamination of soil with metals had an insignificant effect on COOH groups, while the amount of phenolic, aliphatic and aromatic groups decreased compared to the reference soil.
The relationships between concentrations of Cu and Ni, pH and relative transmittance peak areas of SOM functional groups were analysed using the PCA model. The Cu-XRF variable was log-transformed for a better fit to the model. The model explained 95% of the variance in the data set. As illustrated on the PCA loading scatter plot (Fig. 4b), all functional groups, except for COOH, had a strong negative correlation with metal concentrations. Carboxyl groups had weaker correlation with Cu and almost no correlation
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Fig. 5. IR spectra of soil samples from the batch test: (a) IR spectra in the whole range, 370–4000 cm 1; (b) more detailed IR spectra in the range 900–1800 cm 1. 1.1, 1.2, 1.3 — replications of the reference soil; 1.1 c, 1.2 c, 1.3 c — replications of the experimentally contaminated soil.
with Ni. However, carboxyl groups showed much stronger positive correlation with pH, than other functional groups.
4. Discussion and conclusions Analysis of soils along the distance gradient from the nickel smelter showed that the increasing longterm load of heavy metals (Cu and Ni) caused a significant decrease in the SOM content and the amount of all functional groups (Figs. 2c and 3). This is the result of a drastic decrease of biomass input due
to the toxic impact of contaminating substances on soil organisms and vegetation. Furthermore, the sparse vegetation cover on the sites close to the industry causes increased evaporation from the soil surface, containing most of the organic substances, and increased susceptibility to wind erosion. Carboxylic and phenolic functional groups showed a stronger negative correlation with metal concentrations (Fig. 4a) than other functional groups. This can be interpreted as an indication of their engagement in metal binding through reactions associated to deprotonation, and in this way they change their position in the IR spectra. Moreover, since metals can
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Fig. 6. Comparison of IR spectra (relative transmittance peak area) between functional groups of organic matter in the reference soil and the same soil after experimental contamination with Cu/Ni solution: OH, C–H, COOH and arom — functional groups in the reference soil; OH_c, C–H_c, COOH_c and arom_c — functional groups in contaminated soil; 1.1, 1.2, 1.3 — replications of the soil samples.
build soluble complexes with some soil organic substances, such as fulvic acids and substances with low molecular weight (Schnitzer and Skinner, 1962; Stevenson, 1994), the loss of organic matter in general and the reduction of the amount of different functional groups can partly be explained by leaching from the upper soil horizon. This is in accordance with the study by Lukina and Nikonov (1996), who reported that in the polluted soils in the vicinity of the dSeveronickelT smelter, the ratio between humic and fulvic acids was often higher than in unpolluted soils, although the SOM content was much lower in the polluted soils. Although the time factor is essential in the processes related to ecosystem degradation and transformation of soil organic matter, the laboratory experiment using soils from the least polluted sampling site (#1) was expected to reveal which of the SOM functional groups are responsible for metal retention. However, this question was only partly answered by the outcome of the experiment, which gave rise to objects for further study. The experimental contamination of the soil with the Cu/Ni solution resulted in an overall decrease of absorbance for all studied functional groups within the measured range (370 to 4000 cm 1), except for COOH (Fig. 5a). This could be attributed both to changes in the structure of the SOM caused by its reaction with the contaminating substances and to the
leaching of some of the organic compounds from the soil during the experiment. To some extent, this could also be caused by some minor differences in water content in the samples. It was anticipated that the contamination of the soil would result in a decrease of free carboxylic groups, i.e., the transmittance for COOH at 1720F16 cm 1 would strongly decrease as a result of reaction with metal ions (Orlov, 1985; Stevenson, 1994). Besides, new bands for asymmetric and symmetric stretching vibrations of the carboxylate (COO ) groups were expected to appear near 1600 and 1380 cm 1, respectively (Orlov, 1985; Stevenson, 1994). The former was observed in the spectra of the soils collected along the distance gradient from the smelter (Fig. 3), while no significant decrease in the relative transmittance peak area for COOH groups was found in the experimentally contaminated soils (Fig. 6). The shoulder of the band measured at 1714–1732 cm 1, which describes the vibrations of carboxylic functional groups, remained almost the same after the experimental contamination (Fig. 5a), while the bands attributed to deprotonated carboxylic sites (1600 and 1380 cm 1) showed a pronounced decrease in absorbance (Fig. 5b). The presence of high [H+] could displace the equilibria and thereby partially destabilise eventual Cu(II)-carboxylate and Ni(II)carboxylate complexes. However, the band at 1600 cm 1 is also attributed to double bonds in carbon
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chains (CjC), and therefore cannot be unambiguously associated to COO groups. Metal retention proved to be very strong in the batch test. The pH of the Cu/Ni solution before and after the batch test changed significantly, from 4.6–4.9 in the blank to 3.1–3.2 in the eluate. The decrease in pH is the expected result of an ion exchange process, where protons in the active functional groups are displaced by Cu(II) and Ni(II) ions. The lowering of pH was, however, not in accordance with complete ion exchange of the Ni(II) and Cu(II) ions for protons. In that case, the expected pH would have been as low as 2.5–2.6. This could be explained by either incomplete adsorption or by metal ions forming complexes with SOM without ion exchange. This means that less acidic –OH groups, bound as phenols or in aliphatic chains, could be of importance. Lu and Allen (2002) analysed Cu complexation by dissolved organic matter and concluded that Cu has a higher affinity to phenolic sites than to more acidic carboxylic sites, which could explain the unaltered amount of carboxyl groups and decreased amount of free phenolic groups in the experimentally contaminated soil. On the other hand, it is known that low pH tends to destabilise phenolic groups — metal ion complex formation that involves deprotonation of the phenolic groups (Tan, 1998). This might be the case in the analysed system since the pH of the eluate was 3.1–3.2 and the pH of the soil was ca. 3.5. However, several authors reported that in soils with a high organic matter content, such as humic Podzols, some heavy metals, e.g., Cu, can create stable complexes with solid organic substances at a pH of around 3 (Bru¨mmer et al., 1986). Based on the results of our study and analysis of the literature, we conclude that heavy metals that are added to soils through industrial emissions may not only consume free binding sites of the SOM, but also cause loss of total organic matter by suppressing the productivity of vegetation and soil organisms, and therefore decrease the capacity of SOM for further binding over the long term. Further studies are needed to elucidate what types of functional groups in the SOM are active in the adsorption processes. Experiments involving a combination of spectroscopical methods and potentiometry would enhance the capability to achieve a better understanding of those processes.
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Acknowledgements The authors express their gratitude to Maine Ranheimer, research engineer at the Dept. of Chemical and Metallurgical Engineering, Lule3 University of Technology (LTU), for her assistance in the laboratory; to Dr. Walter Ruth, researcher at the Dept. of Human Work Science, LTU, to Frauke Ecke, researcher at the Dept. of Environmental Engineering, LTU, and to Dr. Michael A.D. Ferguson, researcher at the Department of Sustainable Development, Nunavut Wildlife Service, Canada, for their valuable comments. The authors also are very grateful to Ragnar Muller-Wille for language editing.
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Changes in soil organic matter composition and quantity with distance to a nickel smelter — a case study on the Kola Peninsula, NW Russia Ekaterina Viventsova (Ruth)a,*, Jurate Kumpieneb, Lars Gunneriussonc, Allan Holmgrenc a
Lulea˚ University of Technology, Department of Chemical Engineering and Geosciences, Division of Applied Geology, SE-97187 Lulea˚, Sweden b Lulea˚ University of Technology, Department of Civil and Environmental Engineering, Division of Waste Science and Technology, SE-97187 Lulea˚, Sweden c Lulea˚ University of Technology, Department of Chemical and Metallurgical Engineering, Division of Inorganic Chemistry, SE-97187 Lulea˚, Sweden Available online 6 January 2005
Abstract The capacity of soils to absorb contamination depends on a number of factors, such as soil organic matter (SOM) that plays an essential role in adsorption of metal ions, especially in Podzols with their low content of clay minerals. Detailed analysis of SOM can provide information about the potential capacity of a soil to mobilise or immobilise contaminating substances, which in turn can be used to predict potential recovery of the soil ecosystem following heavy metal pollution. The purpose of this study was to learn how an increasing load of heavy metals (Cu and Ni) affects SOM content and structure, and which of the SOM functional groups are responsible for metal retention. The study area is located in the central part of the Kola Peninsula, south of Monchegorsk city and the nickel smelting complex dSeveronickelT. The amount of total carbon in the soil decreased from 86% to 0.6% as the distance from the smelting complex decreased from 34 to 2 km. Functional groups of the SOM had a larger diversity in less polluted soils than in the soils located closer to the smelting complex. Carboxyl groups had a higher intensity of infra red (IR) bands in soils collected at the distance of 34–27 km than those located closer to the smelter. The most disturbed soil at the site closest to the smelter showed almost no presence of COOH groups. We conducted a laboratory experiment using soils from the least polluted sampling site to assess which SOM functional groups may be responsible for metal retention. Experimental contamination of the soil with the Cu/Ni solution resulted in an overall decrease in the absorbance for all studied functional groups within the measured range, except for COOH. This could be attributed both to changes in the structure of the SOM caused by its
* Corresponding author. Tel.: +46 920 492879; fax: +46 920 491697. E-mail addresses: [email protected], [email protected] (E. Viventsova (Ruth)). 0016-7061/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2004.12.010
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reaction with the contaminating substances, and to the leaching of some of the organic compounds from the soil during the experiment. Further studies are needed to better understand which functional groups in the SOM are active in the adsorption processes. D 2004 Elsevier B.V. All rights reserved. Keywords: Soil organic matter; Functional groups; Coppe r; Nickel; Heavy metals; Kola Peninsula; Barents Region
1. Introduction Soil pollution and its remediation is one of the biggest environmental concerns in Northern Europe. There are various models for the assessment and risk evaluation of polluted areas followed by implementation of appropriate remediation practices. One of the aims of an assessment model is to reduce the expenses of remediation practices by delimiting areas with the highest environmental and health risk. In the commonly used models, soil is considered mostly as a transport medium for contaminants and not as an agent that can support ecosystem recovery, which in turn could further reduce the area planned for remediation. Soil degradation caused by various anthropogenic impacts, e.g., contamination by toxic substances, can be more or less pronounced depending on the soil absorbing capacity. The degree to which soil can buffer contamination depends on a number of factors, such as soil organic matter (SOM) quantity and quality, proportion of nutrient elements, biological activity, composition of mineral soil component, and physical properties of the soil. One of the most important soil components that can provide an ecosystem with necessary prerequisites for recovery is soil organic matter. SOM plays an essential role in ecosystem functioning, both indirectly through improving physical properties of the soil and directly through an impact on plant biomass by supplying nutrient elements and by participating in plant metabolism (Nardi et al., 2002; Tan, 1998; Taiz and Zeiger, 1998; Orlov, 1985). Cation exchange positions in the soil are associated in general with mineral particles and SOM. In soils with a predominance of sandy fractions, as in the soils presented in this paper, the cation exchange capacity (CEC) is rather low and is controlled mostly by SOM (Black, 1968; Smith et al., 1993). This means that concentrations of
exchangeable nutrient elements are higher in soils with a larger amount of SOM. SOM also strengthens the soil buffering capacity, providing resistance against severe chemical changes. The ability of SOM to form complexes with metals depends on its high content of functional groups (carboxyl COOH, phenolic-OH, etc.) with COOH playing a predominant role in metal binding (Stevenson, 1994). Depending on soil pH, the functional groups can dissociate their H+ ions, hence contributing to cation exchange reactions, complex formation and/or chelation reactions with metal ions. Bound as chelates, the toxic metals are known to have a significantly lower chemical activity (Tan, 2000). Depletion of SOM, decrease in biomass carbon and nutrient imbalance are among the most important of the processes caused both by mechanical or chemical ecosystem disturbances (Lal and Stewart, 1992a,b). The long-term stability of restored ecosystems after implementation of remediation practices depends on an adequate nutrient pool being maintained without continued fertiliser applications. Since most nutrient elements are related to SOM, the maintenance of the SOM pool will ensure favourable nutrient conditions for ecosystem restoration. Therefore, when planning assessment and remediation practices, it is important to pay special attention to the status of the SOM. The study presented here is a part of a bigger pilot project aimed at testing an integrated methodological approach to the assessment of polluted areas (Ruth et al., 2002; Ruth and Kharytonov, 2003). The purpose of this part of the project is to learn how an increasing load of heavy metals (Cu and Ni) affects SOM content and structure, and which of the SOM functional groups are responsible for metal retention. This knowledge provides one of the steps that have to be taken to enable the soil properties, controlling ecosystem recovery, to be included in an assessment model.
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2. Area description and methods
purposes, three samples were taken at each sampling site and analysed separately.
2.1. Study area The study area is located in the central part of the Kola Peninsula, south of Monchegorsk city and of the dSeveronickelT nickel smelting complex, which is a significant source of environmental pollution. Its yearly emissions of sulphur dioxide and heavy metals, mainly copper and nickel, are among the highest in Europe. According to the report of the Environmental Committee of the Murmansk Region (2001) the emissions of sulphur dioxide from industries on the Kola Peninsula in 2000 were about 274,000 t, for copper 1080 t and for nickel 1570 t. In the report of the Arctic Monitoring and Assessment Programme in 2002 (Arctic Pollution, 2002), it was indicated that the dead forest zone around dSeveronickelT extends for up to 15–20 km. The severe conditions in the region provide unique possibilities for studying changes in SOM content and structure caused by continuous sulphur dioxide and metal emissions. They can be regarded as a bworst caseQ situation that clearly exposes altered ecosystem processes. The original (undisturbed) forest type in the area is spruce-pine forest with a predominance of Vaccinium species (Vaccinium vitis-idaea and Vaccinium myrtillus) in the ground layer, which belongs to the northern taiga zone. The predominant soil type in the area is Podzol. The Podzol is characterised by a distinct profile with a ca. 5- to 10-cm-thick organic horizon AO with black–brown raw organic material, a 6- to 10-cm light grey eluvial horizon E, and a 30- to 40-cm red–brown sandy illuvial horizon Bh. Podzol has an acidic reaction that differs in the different horizons; pHH2O in the AO is about 5.5, in E it is 4.5, and in Bh it is 5.0. Soil samples were collected from the organic horizon on the sampling sites 1–6 (depth is indicated in the description of each site below) and from the upper 3 cm from the site 7. Each sample was taken from the area of ca. 3030 cm. The sampling sites were located at distances of 2, 10, 11, 17, 22, 27 and 34 km south of the dSeveronickelT smelting complex, along the predominant wind direction (Fig. 1). Sampling sites were about 5 m in diameter and had relatively homogeneous topography. For statistical
2.1.1. Description of sampling sites Photos of the sampling sites can be seen at http:// barenvir.project.luth.se/Projects/IAPA/. Site #1 — 34 km from dSeveronickelT (Samples 1.1, 1.2, 1.3)
Fig. 1. The study area and sampling sites: Site #1 — 34 km, site #2 — 27 km, site #3 — 22 km, site #4 — 17 km, site #5 — 11 km, site #6 — 10 km and site #7 — 2 km from the dSeveronickelT smelting complex. The inset shows the Kola Peninsula in the north of European Russia.
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Site: Dry pine–birch forest, 7- to 8-year-old needles. Soil: Podzol with 4-cm-thick organic horizon. Plant cover: Pinus sylvestris, Picea abies, Betula, Empetrum nigrum, Vaccinium uliginosum, V. myrtillus, V. vitis-idaea, Ledum polustre, lichens including fruticose type. Site #2 — 27 km from dSeveronickelT (Samples 2.1, 2.2, 2.3) Site: Wet spruce–birch forest, 5- to 6-year-old needles, there are signs of defoliation. Soil: Podzol, 6- to 7-cm-thick organic horizon, tight root system. Plant cover: P. sylvestris, P. abies, Betula, Juniperus communis, V. uliginosum, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, lichens (less than on the previous site). Site #3 — 22 km from dSeveronickelT (Samples 3.1, 3.2, 3.3) Site: Spruce–birch forest, 4- to 5-year-old needles, about 40% of the conifers are defoliated. Soil: Podzol, 2- to 4-cm-thick organic horizon. Plant cover: P. sylvestris, P. abies, Betula, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, patches of lichens. Site #4 — 17 km from dSeveronickelT (Samples 4.1, 4.2, 4.3) Site: Dry spruce–birch forest, 2- to 3-year-old needles, there are dead trees (spruces). Soil: 6- to 10-cm-thick organic horizon. Plant cover: P. sylvestris, P. abies, Betula, V. myrtillus, V. vitis-idaea, E. nigrum, grasses, patches of lichens. Site #5 — 11 km from dSeveronickelT (Samples 5.1, 5.2, 5.3) Site: Dry pine–birch forest with clear defoliation; 1- to 2-year-old needles; plant cover is thin and weak; soil is covered with a dry crust, signs of acidic precipitation on stones. Soil: Organic material is slightly decomposed. Plant cover: P. sylvestris, P. abies, Betula, E. nigrum, L. polustre, some V. vitis-idaea, crustaceous lichens. Site #6 — 10 km from dSeveronickelT (Samples 6.1, 6.2, 6.3) Site: Dry pine–birch forest, most of the pines are dead, those that are alive have 1 year-old needles; birches have bskirtQ form. Soil is covered with a dry crust; there are many remains of large trees on the ground and signs of acidic precipitation on stones. Soil: Organic horizon is 1–2 cm thick, organic material is slightly decomposed; no clear border between A and B horizons; no E (Podzol) horizon. Plant cover: P.
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sylvestris, P. abies, Betula, V. vitis-idaea, Arctostaphylos uva-ursi, very little crustaceous lichens. Site #7 — 2 km from dSeveronickelT (Samples 7.1, 7.2, 7.3) Site: The site varies very much depending on microtopography and underlying material. There are some signs of a forest fire. Stones are eroded by acidic precipitation. Very sparse plant cover, most of the birches are dead. Soils: In a microelevation, the soil is sandy yellowish-grey without any organic layer, with only partly remaining traces of organic matter. In a microdepression with soil covered with a dry crust, the organic horizon is thick with well-decomposed organic material; E horizon is dark grey. In a second microdepression between stones with thick soil-like material, the organic horizon is ca. 0.5 cm, sometimes thinnerdark crust; no clear borders between A and B horizons. Plant cover: Betula, Salix, V. vitis-idaea, V. uliginosum, grasses, small patches with mosses. 2.2. Laboratory batch experiment A metal solution, containing 50 mg/l (0.79 mM) Cu and 40 mg/l (0.68 mM) Ni, was prepared from CuCl2d 2H2O and Ni(ClO4)2d 6H2O. Air-dried soil samples (samples collected from site #1 — 34 km from the nickel smelter) were added to 0.5l polyethylene bottles and mixed with the metal solution at a liquid to solid ratio (L/S) of 10 l/kg. The bottles were closed and shaken on a rotating lab shaker for 24 h at room temperature. Liquid–solid separation of the eluates was performed by filtration through a 0.45-Am nitrocellulose membrane filter. The pH of the liquid phase was measured. The pH of the blanks, which contained only Cu/Ni solution, was 4.6–4.9. The separated soil was dried at 50 8C for 24 h, ground to pass through a 0.25-mm sieve and analysed for the metal content with an XRF analyser (see Section 2.3). IR analysis was performed in order to evaluate the spectra of soil organic material prior to and after the contamination of the soil with Ni and Cu. 2.3. Analytical methods Total carbon was determined using the total combustion of the soil samples under an O2 pressure
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of 20 bar. SOM composition was studied using Fourier Transform Infra Red (FT-IR) spectroscopy (Perkin Elmer System 2000 FT-IR spectrometer equipped with a triglycine sulphate detector). The samples were prepared according to the KBr-disc method (2.0 mg soil+300 mg KBr) and spectra were recorded in the range of 370 to 4000 cm 1. The relative transmittance peak area of functional groups was calculated using the program SpectrumLite (1.45b Lite, Perkin-Elmer). Soil samples for Cu and Ni analyses were ground to pass through a 0.25-mm sieve. The metal content was determined in the air-dried samples by use of Xray fluorescence spectrometry (Niton 700 XRF multielement analyser with internal calibration and automatic self-calibration when is turned on). The accuracy of the instrument was checked with the NITON standard reference soil containing certified amounts of metals. In order to study interactions between various measured parameters, the cumulative data were analysed by multivariate data analysis using SimcaP9.0 developed by Umetrics AB (Eriksson et al., 1999). Principal Component Analysis (PCA) ( pb0.05) was used. Only principal components with eigenvalues greater than 0.1 were included in the models.
3. Results and analyses 3.1. Alteration of SOM along the distance gradient from the nickel industry Concentrations of Cu and Ni in the soil samples increased with decreasing distance to the smelter, although the highest concentration of Cu was recorded at a distance of 11 km (site #5) (Fig. 2a and b). Variations between replications (samples taken from the same sampling site) were larger on the sites closer to the smelter. This can probably be explained by the openness of the surface in the more polluted area. The less polluted sites had tighter vegetation cover that provides a shield for the soil surface and causes more even distribution of the contaminating agents, while on more polluted sites, with sparse vegetation, the distribution of contaminants over the surface might be influenced by microtopography.
Fig. 2. Box-and-Whisker Plots: Concentrations of Cu (a), Ni (b) and total carbon (c) in the soils at different distances from the dSeveronickelT smelting complex. The size of the box shows how the variability among three replicate samples; the horizontal line represents the median and the cross the average value.
The amount of total carbon in the soil decreased from 86% to 0.6% as the distance from the smelting complex decreased from 34 to 2 km (Fig. 2c). Functional groups of the SOM had a larger diversity in less damaged soils than in the soils located closer to the smelting complex (Fig. 3). Carboxyl (COOH) groups, having a strong peak around 1732 cm 1,
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Fig. 3. IR spectra and total carbon (Ct) measured in the soil samples along the distance gradient from the dSeveronickelT smelting complex. Since results strongly correlated to the distance, to make the figure more legible, we excluded some spectra (site #2 — 27 km, site #3 — 22 km and site #5 — 11 km).
and phenol-OH groups (3375–3431 cm 1) had a higher intensity of IR bands in soils collected at the distance of 34–27 km than those located closer to the industry. The most disturbed soil on the site closest to the smelter (site #7) showed almost no presence of COOH groups. This soil also had the lowest peaks at 2850–2920 cm 1 that characterises aliphatic C–H bonds, and at 1629 cm 1 that is attributed to aromatic CjC bonds. Even though the peaks around 1732 cm 1 were attributed to carboxyl groups, the presence of other carbonyl groups in the same area of the spectrum cannot be excluded. Relationships between all the measured variables were analysed by applying the PCA model (Eriksson et al., 1999). Three components were analysed. Variables Cu-XRF and Ni-XRF were log-transformed for a better fit to the model. The model explained 93% of variance in the data set. The loading scatter plot (Fig. 4a) illustrates the strong negative correlation between metal concentrations
and all functional groups and total carbon. Correlations between pH and phenolic, aliphatic and aromatic groups were rather weak as well as between pH and total carbon. Carboxyl groups showed somewhat higher correlation with pH than the other groups. 3.2. Analysis of SOM prior to and after the experimental contamination with Cu and Ni The experimentally contaminated soil from the batch test was analysed for the content of Cu and Ni. The original sample of the same soil (from site #1) was used as a reference. Concentrations of Cu and Ni in the soils increased significantly after the batch test. The Cu concentration in the reference soil was 66–98 mg/kg, and after exposure to the Cu and Ni solution, it increased to 650–921 mg/kg. The concentration of Ni increased from 138–174 mg/kg in the reference soil to 353–653 mg/kg in the experimentally polluted soil.
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Fig. 4. Multivariate data analyses: (a) PCA loading plot — analysis of the parameters measured in the soils at different distances from the dSeveronickelT smelting complex: concentrations of Cu and Ni (Cu-XRF and Ni-XRF, respectively), pH, total carbon (Ctot), carboxyl groups (COOH), aliphatic C–H groups (C–H), aromatic CjC groups (arom) and phenolic groups (OH). (b) PCA loading plot — analysis of the batch test results (same abbreviations as in a).
IR-spectrum analysis was performed in order to compare the spectra of soil organic material prior to and after the experimental contamination with Cu and Ni (Fig. 5a), and the relative transmittance peak area of functional groups was calculated and compared (Fig. 6). The experimental contamination of soil with metals had an insignificant effect on COOH groups, while the amount of phenolic, aliphatic and aromatic groups decreased compared to the reference soil.
The relationships between concentrations of Cu and Ni, pH and relative transmittance peak areas of SOM functional groups were analysed using the PCA model. The Cu-XRF variable was log-transformed for a better fit to the model. The model explained 95% of the variance in the data set. As illustrated on the PCA loading scatter plot (Fig. 4b), all functional groups, except for COOH, had a strong negative correlation with metal concentrations. Carboxyl groups had weaker correlation with Cu and almost no correlation
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Fig. 5. IR spectra of soil samples from the batch test: (a) IR spectra in the whole range, 370–4000 cm 1; (b) more detailed IR spectra in the range 900–1800 cm 1. 1.1, 1.2, 1.3 — replications of the reference soil; 1.1 c, 1.2 c, 1.3 c — replications of the experimentally contaminated soil.
with Ni. However, carboxyl groups showed much stronger positive correlation with pH, than other functional groups.
4. Discussion and conclusions Analysis of soils along the distance gradient from the nickel smelter showed that the increasing longterm load of heavy metals (Cu and Ni) caused a significant decrease in the SOM content and the amount of all functional groups (Figs. 2c and 3). This is the result of a drastic decrease of biomass input due
to the toxic impact of contaminating substances on soil organisms and vegetation. Furthermore, the sparse vegetation cover on the sites close to the industry causes increased evaporation from the soil surface, containing most of the organic substances, and increased susceptibility to wind erosion. Carboxylic and phenolic functional groups showed a stronger negative correlation with metal concentrations (Fig. 4a) than other functional groups. This can be interpreted as an indication of their engagement in metal binding through reactions associated to deprotonation, and in this way they change their position in the IR spectra. Moreover, since metals can
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Fig. 6. Comparison of IR spectra (relative transmittance peak area) between functional groups of organic matter in the reference soil and the same soil after experimental contamination with Cu/Ni solution: OH, C–H, COOH and arom — functional groups in the reference soil; OH_c, C–H_c, COOH_c and arom_c — functional groups in contaminated soil; 1.1, 1.2, 1.3 — replications of the soil samples.
build soluble complexes with some soil organic substances, such as fulvic acids and substances with low molecular weight (Schnitzer and Skinner, 1962; Stevenson, 1994), the loss of organic matter in general and the reduction of the amount of different functional groups can partly be explained by leaching from the upper soil horizon. This is in accordance with the study by Lukina and Nikonov (1996), who reported that in the polluted soils in the vicinity of the dSeveronickelT smelter, the ratio between humic and fulvic acids was often higher than in unpolluted soils, although the SOM content was much lower in the polluted soils. Although the time factor is essential in the processes related to ecosystem degradation and transformation of soil organic matter, the laboratory experiment using soils from the least polluted sampling site (#1) was expected to reveal which of the SOM functional groups are responsible for metal retention. However, this question was only partly answered by the outcome of the experiment, which gave rise to objects for further study. The experimental contamination of the soil with the Cu/Ni solution resulted in an overall decrease of absorbance for all studied functional groups within the measured range (370 to 4000 cm 1), except for COOH (Fig. 5a). This could be attributed both to changes in the structure of the SOM caused by its reaction with the contaminating substances and to the
leaching of some of the organic compounds from the soil during the experiment. To some extent, this could also be caused by some minor differences in water content in the samples. It was anticipated that the contamination of the soil would result in a decrease of free carboxylic groups, i.e., the transmittance for COOH at 1720F16 cm 1 would strongly decrease as a result of reaction with metal ions (Orlov, 1985; Stevenson, 1994). Besides, new bands for asymmetric and symmetric stretching vibrations of the carboxylate (COO ) groups were expected to appear near 1600 and 1380 cm 1, respectively (Orlov, 1985; Stevenson, 1994). The former was observed in the spectra of the soils collected along the distance gradient from the smelter (Fig. 3), while no significant decrease in the relative transmittance peak area for COOH groups was found in the experimentally contaminated soils (Fig. 6). The shoulder of the band measured at 1714–1732 cm 1, which describes the vibrations of carboxylic functional groups, remained almost the same after the experimental contamination (Fig. 5a), while the bands attributed to deprotonated carboxylic sites (1600 and 1380 cm 1) showed a pronounced decrease in absorbance (Fig. 5b). The presence of high [H+] could displace the equilibria and thereby partially destabilise eventual Cu(II)-carboxylate and Ni(II)carboxylate complexes. However, the band at 1600 cm 1 is also attributed to double bonds in carbon
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chains (CjC), and therefore cannot be unambiguously associated to COO groups. Metal retention proved to be very strong in the batch test. The pH of the Cu/Ni solution before and after the batch test changed significantly, from 4.6–4.9 in the blank to 3.1–3.2 in the eluate. The decrease in pH is the expected result of an ion exchange process, where protons in the active functional groups are displaced by Cu(II) and Ni(II) ions. The lowering of pH was, however, not in accordance with complete ion exchange of the Ni(II) and Cu(II) ions for protons. In that case, the expected pH would have been as low as 2.5–2.6. This could be explained by either incomplete adsorption or by metal ions forming complexes with SOM without ion exchange. This means that less acidic –OH groups, bound as phenols or in aliphatic chains, could be of importance. Lu and Allen (2002) analysed Cu complexation by dissolved organic matter and concluded that Cu has a higher affinity to phenolic sites than to more acidic carboxylic sites, which could explain the unaltered amount of carboxyl groups and decreased amount of free phenolic groups in the experimentally contaminated soil. On the other hand, it is known that low pH tends to destabilise phenolic groups — metal ion complex formation that involves deprotonation of the phenolic groups (Tan, 1998). This might be the case in the analysed system since the pH of the eluate was 3.1–3.2 and the pH of the soil was ca. 3.5. However, several authors reported that in soils with a high organic matter content, such as humic Podzols, some heavy metals, e.g., Cu, can create stable complexes with solid organic substances at a pH of around 3 (Bru¨mmer et al., 1986). Based on the results of our study and analysis of the literature, we conclude that heavy metals that are added to soils through industrial emissions may not only consume free binding sites of the SOM, but also cause loss of total organic matter by suppressing the productivity of vegetation and soil organisms, and therefore decrease the capacity of SOM for further binding over the long term. Further studies are needed to elucidate what types of functional groups in the SOM are active in the adsorption processes. Experiments involving a combination of spectroscopical methods and potentiometry would enhance the capability to achieve a better understanding of those processes.
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Acknowledgements The authors express their gratitude to Maine Ranheimer, research engineer at the Dept. of Chemical and Metallurgical Engineering, Lule3 University of Technology (LTU), for her assistance in the laboratory; to Dr. Walter Ruth, researcher at the Dept. of Human Work Science, LTU, to Frauke Ecke, researcher at the Dept. of Environmental Engineering, LTU, and to Dr. Michael A.D. Ferguson, researcher at the Department of Sustainable Development, Nunavut Wildlife Service, Canada, for their valuable comments. The authors also are very grateful to Ragnar Muller-Wille for language editing.
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