Environ Earth Sci DOI 10.1007/s12665-010-0526-2
ORIGINAL ARTICLE
Heavy metal enrichment of soil in Sarcheshmeh copper complex, Kerman, Iran F. Rastmanesh • F. Moore • M. Kharrati Kopaei B. Keshavarzi • M. Behrouz
•
Received: 2 April 2009 / Accepted: 15 March 2010 Ó Springer-Verlag 2010
Abstract Copper smelting and toxic emissions in Sarcheshmeh Copper Complex have resulted in soil pollution especially in the vicinity of the smelting plant. Calculated geoaccumulation index, contamination factor (Cf), and contamination degree (Cdeg) indicate surface soil enrichment in potentially toxic metals (As, Cu, Pb, Zn, Mo, and Cd). The results also indicate that most contaminated areas are located in the prevailing wind directions (N and NE). However, continuous copper smelting can result in extensive pollution in the study area. This is especially alarming for adjacent townships. Since, the sampled sites are also used as grazing land, the soils are likely to become phytotoxic and provide a potential pathway for the toxic elements to enter the food chain. Cf based on distance and direction give more reasonable results; that is, the decrease of contamination degree with distance. This is in agreement with Igeo and also statistical analysis, which show a decreasing trend of metal loadings of soil with distance from the smelter. Statistical analysis reaffirms the polluting role of the smelting plant.
F. Rastmanesh (&) F. Moore B. Keshavarzi Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz 71454, Iran e-mail:
[email protected] M. Kharrati Kopaei Department of the Statistics, College of Sciences, Shiraz University, 71454 Shiraz, Iran M. Behrouz Research and Development Division, Sarcheshmeh Copper Complex, Kerman, Iran
Keywords Sarcheshmeh Copper Complex Soil pollution Geoaccumulation index Contamination factor Iran
Introduction Soil as a part of the terrestrial ecosystem plays a crucial role in elemental cycling. It has important functions as a storage, buffer, filter, and transformation compartment supporting a homeostatic interrelationship between the biotic and abiotic components (Kabata-pendias and Sadurski 2004). However, the most important role of soil is its productivity, which is basic for the survival of human (Kabata-Pendias and Pendias 2001). Metal pollution of soils is as old as man’s ability to smelt and process ores. The metal industry is one of the most important sources of anthropogenic contamination of the soil environment today (Ettler et al. 2007). Mining and smelting of ore-bearing rocks can release large quantities of trace element bearing volatiles and dust particles into the environment, creating a pollution problem (Salomons 1995; Thornton 1996); as metals from atmospheric deposition will in most cases sooner or later interact with the soil (Steinnes 2001). The regions where mining and smelting have been conducted for long times are potential candidates for a wide diffusion of pollutant elements in the environment (Boni et al. 1999). In the vicinity of non-ferrous metal smelters, high concentrations of toxic compounds have been detected in soils and vegetation (e.g. Ettler et al. 2005). Several recent reports have indicated that regardless of the forms of the anthropogenic trace metals, their availability to plants is significantly higher than those of natural origin (Kabata-Pendias and Pendias 2001).
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The dense fumes, which come from smelters of metalbearing ores are rich in metallic pollutants, such as Cd, Cu, Pb, and Zn (Tembo et al. 2006). Once in the air these metallic pollutants eventually precipitate on the ground surface depending on wind flow pattern, and their concentration increases in adjacent areas. Since these metals are non-biodegradable, their pollution is long lasting and would entail pollution remediation strategies in future (Lim et al. 2005; Luo et al. 2005; Meers et al. 2005). The main purpose of this study is assessing the impact of copper smelting on soil pollution using different pollution indices and also to investigate the spatial variation of soil pollution.
of gaseous emissions, 24 h a day, including NOX, SO2, CO2, and metal particulates into the atmosphere, respectively. SO2 gas constitutes 2.6 and 4.8% of the reverb and converter stack emissions, respectively (Ebrahimi and Hakimi 2002). Ambient temperature varies between -20°C in the winter to ?32°C in the summer (Shirashiani 2004). The mean annual rainfall is 440 mm. Prevailing wind directions based on available meteorological data from Sarcheshmeh meteorological station are NE and N (R & D division internal report).
Materials and methods Sampling and analysis
Study area Sarcheshmeh Copper Deposit, the largest porphyry copper deposit in Iran, is located 160 km SW of Kerman city in Kerman Province (Fig. 1). The Sarcheshmeh ore body along with a number of other porphyry copper deposits occurs in the so called central Iranian volcanic belt (Waterman and Hamilton 1975; Forster 1978; Shahabpour and Kramers, 1987). Copper mineralization in Sarcheshmeh is associated with a granitoid stock of 12.2 ± 1.2 Ma intruded into a thrusted and folded Early Tertiary volcano-sedimentary series comprising andesitic lavas, tuffs, ignimbrites, and agglomerates (Shahabpour and Kramers 1987). The ore body contains 1,200 million tons of ore with an average grade of 0.69% Cu and approximately 0.03% Mo. Open pit mining has been active for more than 30 years and the concentration, melting, and molding plants are currently operating at full capacity. Reverb and converter stacks of the smelting plant release 136,000 and 163,000 m3/h Fig. 1 Location of the study area showing sampling point locations. Copper city is the main township in the study area
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In the present study, soil samples were collected from a multiple-metal contaminated area. A total of 42 soil samples, 21 topsoils (0–5 cm) and 21 subsoils (15–20 cm) including a control point (S5, Fig. 1), were collected. These depths roughly correspond to A and B soil horizons in the study area. Locations of the sampling points were chosen in the upwind and downwind directions and also near the population centers. In few locations sampling was constrained due to site accessibility. At each sampling point, five subsamples were taken and mixed to obtain a composite specimen. All samples were collected using a stainless steel spatula and kept in PVC sample bags. The samples were not taken close to the transportation roads to avoid contamination from road vehicles. In the laboratory, the soil samples were air-dried at room temperature, sieved through a 2-mm sieve and grounded in an agate mill. The \2 mm fraction of ungrounded soil, was analyzed by hydrometric methods to determine soil texture. Soil pH
Environ Earth Sci
was determined in a 1:2.5 (W:V) soil:dionized water suspension that was stirred for 15 min. The total organic carbon in soil samples was determined by Walkley and Black method (Mc Cleod 1975). Grounded soil samples were analyzed using ICP-OES method for metals (As, Cu, Ag, Pb, Sb, Mo, Cd, and Zn), soil major cations (Fe, Al, and Mn), and also S. The analyses were carried out in an accredited Australian laboratory (Amdel limited labs ISO 9001). Replicate samples were analyzed to assure precision. Calculation of metal enrichment in soil samples One way to look at the anthropogenic enrichment of metals is to compare the abundance of the metals to background (or reference values). A commonly used reference is average shale (Eby 2004). The geoaccumulation index (Muller 1969) is generally used to assess the degree of contamination. The index is calculated as follows: Igeo ¼ log2 ðCn =1:5Bn Þ where Igeo is the geoaccumulation index, log2 is log base 2, Cn is the concentration of element, and Bn is the background or reference concentration, 1.5 is the background matrix correction factor due to lithologic effects. The index includes seven classes in which zero and smaller values fall within the uncontaminated class and those greater than 5 falls into the very seriously polluted class (e.g. Hu et al. 2006). In this study, Igeo was calculated using metal concentrations in control site. Details of the Igeo values for individual elements are presented in Fig. 2. Contamination factor and contamination degree To assess the soil contamination in the study area, contamination factor (Cif, Hakanson 1980; Liu et al. 2005;
Krishna and Govil 2008) was also calculated. This factor is calculated as follows: cif ¼
cio 1 cin
in which Cio - 1 refers to mean concentration of each metal in soil and Cin refers to baseline or background values. Cif is defined according to four categories: Cif \ 1 1 B Cif \ 3 3 B Cif \ 6 6 B Cif
low contamination factor moderate contamination factor considerable contamination factor very high contamination factor
In the present study, Cin was set as the metal concentration in control site (S5). The sum of contamination factors for all elements examined represents the contamination degree (Cdeg) of the environment and four classes are recognized (Hakanson 1980; Loska et al. 2004):
Cdeg ¼
X
Cfi
Cdeg \ 8 8 B Cdeg \16 16 B Cdeg \ 32 32 B Cdeg
low degree of contamination moderate degree of contamination considerable degree of contamination very high degree of contamination
Statistical analysis According to Simeonov et al. (2005), the pollution process is in principle a multivariate one and therefore only multivariate treatment of data is appropriate for site assessment. In order to investigate elemental associations among metals and major elements in soil samples and also between elements and soil parameters, Kendall’s correlation coefficient was calculated using SPSS software version 13. Also, factor analysis (Krumbein and Graybill 1965) was carried out on data. Factor analysis is a technique whereby a complex dataset is simplified by creating one or more new variables or factors each representing a cluster of interrelated variables within the dataset (Davis 1997). Results and discussion Elemental concentrations
Fig. 2 Box-plot of Igeo for topsoil samples
Table 1 shows minimum, maximum, medians, and means of total concentrations of trace elements in soil samples together with the mean concentrations in surface sandy soils (Kabata-Pendias and Pendias 2001). The world means concentrations for uncontaminated soils (Ure and Berrow
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Environ Earth Sci Table 1 Elemental concentrations (ppm) in topsoil (n = 20) and subsoil (n = 20) samples together with world means (Ure and Berrow, 1982) and means in sandy soils (Kabata-Pendias and Pendias, 2001) Top soil Max
Sub soil Min
As
427
Cu
12,100
23.7 64
Mean
Median
Max
121.9
63.9
293
1,905.6
402.5
2,170
Min 2.5
330.7 2.3 115.7
56.8 894
0.8 77
9.9 213
3.4 127.5
7.7 486
0.5 54
Cd
12.8
0.2
3.1
1.7
2.9
0.1
S
55.9
45
Mo Zn Pb
Mean
0.68
332
9
82.6
44.5
79
4
33.3
6,070
152
1,268.3
368.5
1,330
10
294.9
Median 31.3 154 1.4 82.5 0.45 22.5 188
World mean 11.3
Sandy soils 4.4
Control point (top soil) 30.5
25.8
13
78
1.9 59.8
1.3 45
0.9 69
0.6 29.2
0.37 22
0.3 10 296
OC (%)
1.8
0.01
0.64
0.51
1.3
0.01
0.35
0.25
0.84
pH
7.38
4.3
6.6
7.1
7.7
6
7.1
7.3
7.38
1982) are also represented together with some soil physicochemical properties. As it can be seen from Table 1, the mean concentration of measured trace elements in subsoil is less than topsoil. Since some elements are not normally distributed, Mood’s Median Test (Mood et al. 1974) was used to compare the median of measured elements. Comparison of the median of measured elements using Mood’s Median Test shows that the difference between measured elements in topsoil and subsoil is statistically significant, especially for Cu (P = 0.004), Cd (P = 0.004), and Mo (P = 0.023). The results indicate that dry deposition from mining and smelting is the main source of soil contamination (e.g. Tembo et al. 2006), and that metal mobilities are controlled by geochemical parameters. Table 1 also indicates that in comparison with the elemental concentrations for sandy soils, and world mean concentrations, Sarcheshmeh soil samples are enriched in As, Cu, Mo, Zn, Cd, and Pb. This is especially more evident in topsoil samples. It must be noted that control site samples are also enriched in As and Cu. The reason is that the whole Sarcheshmeh area is somehow affected by copper mineralization and hence contain relatively high background values of ore-forming elements especially As and Cu. Moreover, topsoils are enriched in sulfur. According to Klumpp et al. (2003) decreasing sulfur content with soil depth reflects contribution from atmospheric deposition.
To represent spatial variation of Igeo classes a series of maps were produced for each element using ArcGIS software version 9.1 (Fig. 3). Figure 3 obviously reveals that the most contaminated stations are those close to the smelter (9, 10, and 19). The degree of contamination is the most for Cu. Also Fig. 3 reveals reduced contamination with distance from the smelter and also for upwind direction. According to the Muller scale ‘‘uncontaminated’’ pollution of various metals for most upwind stations are found. This indicates that soil pollution is mostly restricted to a few kilometers from the smelter (e.g. Fernandez-Turiel et al. 2001; Klumpp et al. 2003; Tembo et al. 2006). Applying Screen-2 software also showed that the stack’s plume effective radius is below 2.5 km; however, this matter should be thoroughly investigated in a separate study. Considering Fig. 3 may indicate that particulate size carrying metals can be an important factor for induced soil pollution. For instance, soil contamination with Zn is the most adjacent to the smelter, while the rest of the stations are uncontaminated. For some elements, such as Cu and Cd, most of the stations are contaminated with varying degrees, with the highest contamination occurring in the stations adjacent to the smelter. This may indicate that Cu is carried by a broad range of particulate size, and hence are distributed widely. However, further investigation is needed to confirm this assumption.
Geoaccumulation index
Figure 4 and Table 2 illustrate calculated contamination factor (Cf) for measured heavy metals in topsoil and subsoil samples. Contamination factor for topsoil is clearly much more than subsoil indicating the role played by dry deposition. According to Fig. 4 contamination factor for all of metals except Zn and As is greater than 6 which implies very high contamination factor.
Geoaccumulation index is used to assess the degree of topsoil contamination by metals. The Igeo of As, Cu, Mo, Zn, Cd, and Pb are given in Fig. 2. Considering Fig. 2 and the contamination levels the topsoils in adjacent areas to the smelter are very seriously polluted with Cu.
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Contamination factor
Environ Earth Sci Fig. 3 Spatial variation of Igeo classes for measured elements. a Cu; b As; c Zn; d Pb; e Cd; f Mo
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Environ Earth Sci Fig. 4 Results of calculated contamination factors for topsoil and subsoil samples
Table 2 Contamination factor and contamination degree for soil samples Cdeg
Cf
Cu
A
B
C
D
A
B
C
D
24.4
71.9
6.6
2.6
61.1
158.3
21.5
15
As
4.1
7.8
1.8
2.8
Pb
8.2
19.1
5.2
2.4
Mo
11
28.8
3.6
3.1
Cd
10.3
24.5
7.2
2.4
Zn
3.1
6.2
1.8
1.7
A all stations, B adjacent stations, C downwind stations, D upwind stations
The results of Igeo show reducing trend of contamination with distance in upwind directions. However, the Cf results mean that soil samples are seriously contaminated. Thus, it was decided to separate the stations according to distance from the smelter and wind direction. Calculated Cf for 3 groups of stations (adjacent to the smelter, downwind, and upwind directions) together with Cf for all stations is presented in Fig. 5. As it was predicted, Fig. 5 clearly shows that most contamination factors belong to stations adjacent to the smelter and the least for stations situated in the upwind direction. The results are similar to those obtained for Igeo. Results for Cdeg are presented in Table 2. Cdeg was also calculated for all stations, and also for the three defined groups of stations. The results show that when all stations are considered indiscriminately, a high degree of Fig. 5 Results of calculated contamination factors for topsoil samples based on all stations and defined groups
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contamination is indicated, while the stations are divided into three distinct groups a reducing trend of contamination with distance from the smelter is clearly indicated. However, although the most contaminated stations are those close to the smelter, pollution can become a serious problem in all directions in time. This is especially alarming for the nearby residential centers. Statistical analysis Kendall’s correlation coefficient shows that trace elements are significantly correlated. However, this correlation is the least between As and other metals (As/Cu: R = 0.484, P \ 0.01; As/Mo: R = 0.512, P \ 0.01; As/Zn: R = 0.575, P \ 0.01; As/Cd: R = 0.596 P \ 0.01; As/Pb: R = 0.611, P \ 0.0.1). Moreover, significant negative correlation coefficient between metal concentration and distance from the smelter which shows reducing concentration with distance, is also the least for As (R = -0.490, P \ 0.01). This may indicate that As has one or more sources beside the smelter. This is in agreement with spatial variation of Igeo classes for As that showed a probable source in the W-NW of the smelter (Fig. 3). Determination of various sources of As must be carried out in future investigations. Factor loadings, communalities, and variances of the components for the metal concentration in the topsoil samples are given in Table 3. Factor analysis was carried out using principal component analysis. Rotated components were produced using Varimax method with Kaiser
Environ Earth Sci Table 3 Rotated factor analysis of elements in soils Factor 1
Factor 2
Communality
As
0.857
0.819
Cu
0.982
0.978
Mo
0.959
0.931
Zn
0.973
0.984
Cd
0.965
0.956
S
0.941
0.966
Pb
0.965
Fe
0.311
0.864
0.876
Al
-0.174
0.894
0.896
0.856
0.918
Mn pH OC Percentage of total variance
0.961
20.750 0.320 62.5
0.898 0.992 19.5
Extraction method: principal component analysis, Rotation method: Varimax with Kaiser Normalization. Bold numbers indicate correlation is significant at the 0.01 level (2-tailed)
Normalization. The result reveals that more than 82% of total variance is explained by two factors (Table 3). The communalities shown by the variables are high; therefore all elements are well represented by the factors (Table 3). According to calculated factor loading coefficients, the first factor, which explains more than 62% of the total variance, appears to represent an ‘‘anthropogenic factor’’ (Zhang et al. 2008), since the heavy metals and S are strongly associated. Low correlations between first factor and heavy metals, Fe, Mn, and OC, may indicate the weak controlling effect of Fe oxy-hydroxides and OC on metals distribution and mobilities in topsoil. High negative correlation of the first factor and pH probably reflects decreasing metal mobilities with increasing pH. In general, adsorption of metals onto oxide and humic constituents of soil follows the basic trend of metal-like adsorption, which is characterized by increased adsorption with pH (Bradl 2004). pH is a primary variable which determines cation and anion adsorption onto oxide minerals. However, to determine metal associations among various soil phases, sequential extraction analysis (e.g. Tessier et al. 1979) is suggested to be carried out. The second factor in Table 3, which accounts for more than 19% of the total variance, is mainly composed of Fe, Al, and Mn. It is concluded that factor 2 represents the chemical composition of soils (e.g. Cubukcu and Tuysuz 2007).
Conclusions Metals concentration in the soils of the Sarcheshmeh Copper Complex, especially in areas adjacent to the copper smelting plant, is high and exceed a number of soil
guideline values. Although according to this research, heavy metal contamination reduces with distance from the smelter and wind direction, continuous copper smelting can be a matter of concern particularly in residential areas, as the sampled sites are used for livestock grazing and the soils are likely to become phytotoxic and provide a potential pathway for the toxic elements to enter the food chain. Sarcheshmeh region is polluted mostly with base metals (e.g. Pb, Cu, Zn), and the copper smelting plant appears to be the main source of pollution, as seen in other cases in the world (Pope et al. 2005; Cubukcu and Tuysuz 2007). Moreover, comparisons of measured elements median in topsoil and subsoil show severe contamination in topsoil. It seems that deposited trace elements from smelter plant emissions have relatively limited mobility within the soil. The reasons probably include neutral to slightly alkaline soils pH; climatic factors (mostly precipitation), and the short history of copper smelting in the area (about 30 years). Furthermore, according to Kabata-pendias and Pendias (2001), the persistence of contaminants in soil is much longer than in other compartments of the biosphere, and contamination of soils, especially by metals appear to be virtually permanent. Accumulated metals in soils deplete slowly by leaching, plant uptake, erosion, or deflation. The sandy texture, limited buffering capacity of soils, strong soil contamination with potentially toxic metals, such as, As, Cu, Pb, Mo, and Cd, and mountainous topography of the region, facilitate leaching and hence groundwater contamination in time. Thus, monitoring of groundwater and well water contamination is also recommended especially adjacent to the smelting plant. Since, the induced pollution can pose serious threats to public health, further investigations on soil and vegetation pollution is recommended. Finally, calculating contamination factor based on distance from the pollution source and wind direction can provide more reasonable results. Acknowledgments The authors would like to express their gratitude to the research and development division of Sarcheshmeh copper complex for providing the grant for this research. Thanks are also extended to Shiraz university research committee for logistical assistance.
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