Ecotoxicology and Environmental Safety 107 (2014) 140–147
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Multivariate geostatistical analyses of heavy metals in soils: Spatial multi-scale variations in Wulian, Eastern China Jianshu Lv a,d,n, Yang Liu b, Zulu Zhang c, Bin Dai a a The Key Laboratory of Coast and Island Development of Ministry of Education, School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China b State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China c College of Population, Resource and Environment, Shandong Normal University, Jinan 250014, China d Geological Survey of Canada (Atlantic), Bedford Institute of Oceanography, Dartmouth, Canada B2Y 4A2
art ic l e i nf o
a b s t r a c t
Article history: Received 5 February 2014 Received in revised form 15 May 2014 Accepted 16 May 2014
The objective of this study was to examine spatial multi-scale variability of six heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in relation to environmental factors in Wulian, Eastern China. Factorial kriging analysis (FKA) was applied to a data set consisting of 432 topsoils. We found that most of the heavy metal contents in soils did not exceed the guideline values of Environmental Quality Standard for Soils (EQSS) in China. Through linear model of coregionalization (LMC) fitting, spatial variation in six heavy metals could be grouped into one nugget effect, and two sphere structures with ranges of 6 km (local scale) and 14 km (regional scale). Spatial correlations among six heavy metals depended on local or regional scales. The high correlations between Cr, Ni and among Cd, Cu, Pb, Zn were found regardless of the spatial scale, while correlations of Cr and Ni with other four metals decreased with increasing spatial scale. Spatial variation of Cr and Ni was related to parent material at both local and regional scales, and was derived from natural sources. Mining activity was observed to affect the spatial variation of Cd, Cu, Pb and Zn at local scale, while parent material dominated spatial variation of those metals at regional scale. However, agricultural practices and human activity in urban area did not alter spatial variation of heavy metals in soils. It could be concluded that human influence on heavy metals variation was noted on local scale, and parent material had greater influence on spatial variation of heavy metals at both local and regional scales. & 2014 Elsevier Inc. All rights reserved.
Keywords: Heavy metals Multi-scale Spatial variability Source Factorial kriging analysis
1. Introduction A significant amount of pollutants from different sources enter into the soil every year, thus soil serves as geochemical sink for various pollutants. Heavy metals are of prime importance for the soil environment quality due to their cumulative toxicity effect (Alloway, 1995). Heavy metals could be transferred from soils to atmosphere, hydrosphere and biomass, and they could affect human health through water supply, food intake, direct ingestion and dermal contact (Kabata-Pendias and Pendias, 2001; Sharma et al., 2007). Natural concentrations of heavy metals depend on their contents in parent materials (Alloway, 1995; Lv et al., 2013). Soil physical–chemical properties are also associated with the n Corresponding author at: The Key Laboratory of Coast and Island Development of Ministry of Education, School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China. E-mail address: [email protected] (J. Lv).
http://dx.doi.org/10.1016/j.ecoenv.2014.05.019 0147-6513/& 2014 Elsevier Inc. All rights reserved.
retention of heavy metals in soils. As suggested by some researchers (Mico et al., 2006; Sun et al., 2013), soils with higher clay percentages and SOM contents tend to have higher heavy metals contents. On the other hand, anthropic activities profoundly affect the contents of heavy metals in soils, and the amount of anthropogenic pollutants released often exceeds the contributions from natural sources (Chen et al., 2005). Therefore, it is necessary to identify that if the concentrations of heavy metals in soils may be explained by natural factors or by anthropic inputs. Natural factors and human activities have scale-dependent influences on spatial variability of heavy metals in soils, because of their different functional ranges (Lv et al., 2013; Nanos and Rodríguez Martín, 2012). The geostatistical methods could facilitate to model and display spatial structures and variation characteristics of heavy metals in a complex soil environment. Among various geostatistical methods, factorial kriging analysis (FKA), as a multivariate geostatistical method, combining principle component analysis (PCA) with geostatistics, seems to be particularly attractive for the multi-scale study (Nanos et al., 2005). FKA can
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examine the spatial interrelations among various variables at multiple scales, based on which the grouping results derived from PCA can indicate the sources of the spatial variation of corresponding variables (Castrignano et al., 2000; Sollitto et al., 2010). Furthermore, the total variation of various variables can be split into multiple spatial components related to different spatial scales (Alary and Demougeot-Renard, 2010). In China, most of regions have been experiencing rapid urbanization and industrialization since 1980s. Recently, numerous investigations of heavy metal in soils have been carried out (Cai et al., 2012; Chen et al., 2005; Sun et al., 2013; Zhong et al., 2011), which mainly focused on the regions with high levels of industrialization or the developed agriculture. There are also many regions with low levels of development existed in China, including some ecological demonstration zones, western areas, and nature reserves. These areas tend to have good environmental quality due to lacking of the industry and agriculture. Since 2000, China government has implemented several national development strategies on the regions with low levels of development, such as Western development and the Development of Yellow River Delta. There are intense conflicts between economic development and environmental protection in these areas. However, few works were conducted on the characteristics of heavy metals on the regions of this type, where economic development strengthens gradually over the last decade. There was also limited information available on the spatial variability of heavy metals in soils and their relationships with environmental factors. Therefore, the knowledge about spatial multi-scale variation of heavy metals and their sources is important, because it can serve a reference for environmental protection and sustainable development in similar areas of China. Wulian is a typical ecological demonstration zone in Eastern China, where the forest and grassland account for about 50 percent of the total area, and sustain a good environmental quality. However, the indusial development rapidly appears over the last ten years, and poses a huge threat to soil environment. Our study addressed spatial multi-scale variations of six key heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) and their sources in Wulian, Eastern China.
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(MgO, Fe2O3 and CaCO3). Particle size distribution in soils was analyzed using a laser grain-size analyzer. Soil pH was analyzed by a pH meter in a 1:2.5 soil–water suspension, and CEC was measured by the ammonium acetate method. Soil organic matter (SOM) contents were analyzed using oil bath-K2CrO7 titration. Fe2O3 and MgO were investigated by X-ray fluorescence spectrometry (XRF). CaCO3 contents were determined by a manometric measurement of the CO2 released after addition of hydrochloric acid (HCl). HSO4–HNO3–HF was used to digest the samples for analyzing Cd, Cr, Cu, Ni, Pb and Zn, and Cr, Cu, Ni, Pb and Zn concentrations were determined by a flame atomic absorption spectrophotometer, and Cd contents were determined by a graphite furnace atomic absorption spectrophotometer (Lu, 2000). The limits of detection were 0.01, 0.4, 0.3, 0.3, 0.1 and 0.5 mg kg 1 for Cd, Cr, Cu, Ni, Pb and Zn, respectively. The national registered standard reference material (GSS-1) obtained from the Center of National Standard Reference Material of China was included in chemical analyses. Recoveries were 91–102 percent for Cd, 89–104 percent for Cr, 97–106 percent for Cu, 96–102 percent for Ni, 94–101 percent for Pb, 98–108 percent for Zn, respectively. 2.3. Statistical and geostatistical analysis At first, descriptive statistics, including maximum, minimum, mean, median, standard deviation (SD), variation coefficient (CV), skewness and kurtosis, were conducted on the soil physical–chemical properties and heavy metals contents. In order to eliminate the skewness and to facilitate the LMC fitting, Gaussian anamorphosis was used to standardize heavy metals contents to Gaussianshaped variables with zero mean and unit variance (Wackernagel, 2003). Factorial kriging analysis (FKA) was carried out on the standardized variables with normal distribution. The theory of FKA has been described in several publications (Castrignano et al., 2000; Wackernagel, 2003), and here the basic steps were briefly presented. Linear model of co-regionalization (LMC), developed by Journel and Huijbregts (1978), was used to model the experimental direct and cross-variograms. All the direct and cross-variograms, modeled as sums of variograms γ uij ðhÞ at each scale u, can be defined as linear combination of elementary functions gu ðhÞ. The LMC can be expressed in matrix terms as Ns
Γ ðhÞ ¼ ½γ ij ðhÞ ¼ ∑ Bu gu ðhÞ u¼1
where Γ ðhÞ is the variogram matrix of order m m, Bu called the coregionalization matrix, which describes the relationships between the variables at the given spatial scale u, and Ns represents the number of the spatial structures. Each original variable can be expressed as the sum of Ns spatial components with each one related to the variogram γ uij ðhÞ, and the estimation of spatial components is performed by ordinary cokriging. In the cokriged estimation of this study, the 30 m mesh grid and the neighborhood of 50,000 m-radius with an optimum number of ten samples were applied. The structure correlation coefficient rij among variables i and j at the uth spatial qffiffiffiffiffiffiffiffiffiffiffiffiffi u u u scale can be calculated as r ij ¼ bij = bii U bjj (Dobermann et al., 1997; Liu et al., 2013), u
2. Methods and materials 2.1. The study area and sampling Wulian covers an area of 1400 km2 and is located in southeastern part of Shandong province, Eastern China (Fig. 1). Elevation ranges from 18 to 706 m above sea level, and the mountainous area accounts about 80 percent of the total area. Parent material is composed of granite and gneiss in eastern part, glutenite and pyroclastic rocks in western part, as well as some small patches of basic rocks around the total area (Song, 2002). The study area has a temperate continental climate, with mean annual temperature of 12.6 1C and the mean annual precipitation of 767 mm. Forest and grassland account for 43.6 percent and 8.1 percent of the total study area, which is much higher than the average levels of China. Currently, the preliminary industry formed with the increasing development over the last decade, and gold mining and quarrying for constructional materials are the main industries in study area. In this study, the systematic grid sampling design (2 km 2 km size) was applied to select 432 sites in topsoils of Wulian. At every sampling site, four to six subsamples in topsoils within a 100 m radius were taken from upper 20 cm soils. The subsamples were mixed thoroughly to obtain a composite sample (1 kg). All the collected samples were stored in polyethylene bags for transportation and storage. The locations of sampling sites are indicated in Fig. 1.
2.2. Soil chemical analysis In the laboratory, the collected samples were air-dried, and analyzed for soil physical–chemical properties and six heavy metals. In this study, soil basic physical–chemical properties were determined, including soil pH, particle size, cation exchange capacity (CEC), soil organic matter (SOM), and three minerals
where bij refers to the variable at position (i, j) in the coregionalization matrix for the uth structure. Principle component analysis (PCA) on each coregionalization matrix Bu would provide a set of transformation coefficients, which were subsequently used in a cokriging system to produce regionalized factors (RF), and the circles of correlations were used to exhibit the correlation coefficients between regionalized factors and the original variables at each scale. Descriptive statistics were calculated using SPSS 16.0 (SPSS Inc.), while Gaussian anamorphosis and FKA were performed by ISATIS Package (Geovariances Inc.).
3. Results and discussion 3.1. Soil properties and heavy metals contents The descriptive statistics of soil properties and heavy metals contents are indicated in Table 1. Soils basic properties determined in the present study were in agreement with the soils of Eastern Shandong province, characterized by acidic properties, low fertility and rough texture (Zhang, 1986). The soils in study area had the mean value of 6.26 with the range from 4.63 to 8.13, and acidic properties could facilitate the mobility of heavy metals in soils. These could be ascribed to the igneous background containing the high percentage of SiO2, and silicic acid can be produced on a weathering process (Alloway, 1995; Zhang, 1986). Guo et al. (2010) found that significant acidification occurred in the main Chinese crop-production areas from 1980s to 2000s because of high-N fertilizer inputs. However, the soil pH in Wulian varied little from
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Fig. 1. The study area and the locations of sampling sites.
6.27 for 1980s to 6.26 for this investigation, since there is no developed agriculture in this area, which is not consistent with the nearby Rizhao city (Donggang and Lanshan), where the mean pH is decreased by 0.28 since 1980s (Lv et al., 2013). The SOM in study area had a mean value of 1.32 percent with the range 0.61–4.57 percent. Loveland and Webb (2003) suggested that the soils with SOM below 1.7 percent cannot maintained the crops; thus the levels of SOM in study area was low. The granulometric fraction was rough with the highest percentage of sand (63.2 percent); the soil textures can be classified as sandy clay loam, loam and sandy loam. In addition, CaCO3, CEC, Fe2O3 and MgO presented relatively low levels with narrow range. The average contents of Cd, Cr, Cu, Ni, Pb and Zn were 0.13, 55.6, 22.2, 23.6, 32.1 and 73.3 mg kg 1, respectively. The CVs varied from 0.30 for Zn to 0.81 for Cu, and some outliers may exist for Cu, Pb and Cd distribution. The values distributions of six metals were all positively skewed with range 1.75–11.4. The mean contents of all six metals were lower than the corresponding limit in Environmental Quality Standard for Soils (EQSS) in China affecting safety of agricultural production and human health (State Environmental Protection Administration of China, 1997). Approximately, only 3.70 percent of samples for Cd, 1.16 percent for Cu, 2.78 percent for Cu, 1.62 percent for Ni, 0.23 percent for Pb, and 0.23 percent for Zn exceeded the limits in EQSS. Therefore, there was insignificant pollution of heavy metals in soils of study area. However, there were significant human inputs existed, because the mean contents of Cd, Cu, Pb and Zn were significantly higher than corresponding background values of eastern Shandong province (Dai et al., 2011). In particular, there were some samples with more than ten times the background values for Cu and Pb. The mean contents of Cr and Ni were close to their background values, suggesting the possible natural sources of these metals. The studies of Cai et al. (2012) and Sun et al. (2013) also found the insignificant human inputs of Cr and Ni in soils. 3.2. Linear model of co-regionalization fitting Through a preliminary visual inspection of the experimental direct and cross variograms (Fig. S1), the variations of three scales were presented. The first structure was a nugget effect, and was obviously observed for Cd and Cu. The nugget effect represents the
field variation occurring within the sampling scale and the measurement error; thus it is not commonly considered for further study (Rodríguez et al., 2008; Sollitto et al., 2010). Local scale (short structure, 6 km) can be distinguished for Cd, Cu, Ni and Zn, while regional scale (long structure, 14 km) was significantly exhibited for Cr, Cu, Ni and Zn. In agreement with the observed structures, the LMC was fitted using three spatial structures: a nugget effect, a spherical model with a range of 6 km and another spherical model with a range of 14 km. Pearson and structure correlation coefficients of six heavy metals in soils are summarized in Table S1. There was highly positive structure correlation between Cr and Ni with 0.973 for local scale and 0.926 for regional scale, reflecting its probably common characteristics of spatial variation in both spatial scales. These results were consistent with the works of Rodríguez et al. (2008) in Ebro river basin, Nanos and Rodríguez Martín (2012) in Duero river basin, Sollitto et al. (2010) in Zagreb and Lv et al. (2013) in Rizhao, where Cr and Ni were also strongly positively correlated in all spatial structures. On the other hand, highly positive correlation coefficients were found among Cd, Cu, Pb and Zn at local and regional scales. The high correlation among Cd, Cu, Pb, Zn and between Cr and Ni at both short and long scales was consistent with the results of classic Pearson correlation coefficients (Table S1). Cd, Cu, Zn, and Pb exhibited decreasing correlation with Cr and Ni with increasing scale, which is not in agreement with the adjacent Rizhao city (Lv et al., 2013). Moreover, there were increasing correlation coefficients among Cd, Cu, Pb and Zn with increasing spatial scale. Therefore, it can be seen that the correlation between six metals depended on the local scale and regional scale.
3.3. Multi-scale spatial variability The correlations between the first two regionalized factors and various metals are displayed in the circles of correlations (Fig. 2). At local scale, regionalized factor 1 (RF1) explained 64.9 percent of the short structure variability, and had highest loadings in Cd, Cu, Pb and Zn as well as partially Cr and Ni, while RF2 amounting on 21.3 percent variance of short structure indicated highly negative loadings in Cr and Ni. The results of PCA at regional scale indicated
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Table 1 The descriptive statistics of soil properties and heavy metals.
SOM pH CEC CaCO3 Fe2O3 MgO Clay Silt Sand Cd Cr Cu Ni Pb Zn
Unit
Minimum
Maximum
Mean
Median
SD
CV
Skewness
Kurtosis
BV
Guideline values of EQSS
Percent pH Unit cmol kg 1 Percent Percent Percent Percent Percent Percent mg kg 1 mg kg 1 mg kg 1 mg kg 1 mg kg 1 mg kg 1
0.61 4.63 7.62 0.80 2.57 0.48 2.60 6.13 38.8 0.06 12.2 6.20 7.40 17.4 37.5
4.57 8.13 3.39 6.68 7.48 2.69 22.2 43.2 92.1 0.48 183 320 81.7 362 224
1.32 6.26 14.0 2.44 4.44 1.28 12.8 24.0 63.2 0.13 55.6 22.2 23.6 32.1 73.3
1.25 6.09 13.6 2.25 4.38 1.22 12.0 22.1 62.4 0.12 51.8 18.9 21.1 27.9 70.0
0.42 0.88 1.74 0.98 0.96 0.44 2.11 5.56 11.8 0.06 25.0 17.9 10.0 22.6 22.0
0.32 0.14 0.12 0.40 0.22 0.34 0.17 0.23 0.18 0.46 0.43 0.81 0.42 0.70 0.30
1.94 0.38 0.65 1.08 0.42 0.74 0.73 1.32 0.98 2.88 1.76 11.4 1.75 9.04 2.14
8.80 1.00 0.91 1.28 0.16 0.43 0.65 0.87 0.52 11.2 4.50 180 4.68 114 8.11
– – – – – – – – – 0.108 56.2 19.6 23.5 25.4 56.1
– – – – – – – – – 0.3 150 50 40 250 200
BV represents the background values of eastern Shandong province (Dai et al., 2011). EQSS represents Environmental Quality Standard for Soils in China (GB15618-1995). “–”: not available
Fig. 2. Circles of correlations between the first two factors and various elements.
that RF1 had heavily positive loads of Cd, Cu, Pb and Zn in RF1, while RF2 had highly positive loads of Cr and Ni. At both local and regional scales, Cr and Ni with strongly positive correlation, were classified into the same group, and could be attributed to the natural geochemical sources. It is well known that anthropic inputs of Cr and Ni in fertilizers, limestone and manure are lower than background contents in soils (Facchinelli et al., 2001). Cr and Ni contents in soils are commonly controlled by their contents in bedrocks, and ultrabasic and basic rocks presented higher contents of Cr and Ni than other parent materials (Alloway, 1995; Facchinelli et al., 2001). These were confirmed by the cokriged maps at both two scales, and spatial components of Cr and Ni showed no association with the locations of urban area, mining site and stone yards (Figs. 1 and 3). At local scale, the hotspots with highest values of Cr and Ni seemed to be random, which may be associated with the small patches of basic rocks. On the other hand, the hotspots of Cr and Ni at longer scale were located in the soils in northwestern part originated from glutenite (Fig. 4), since they contain high percentages of basic rocks such as basalt and peridotite (Song, 2002). Cr and Ni showed positive correlation with soil granulometric fraction and minerals at local scale, while they were related to pH and MgO at regional scale (Table 2). Commonly, lithogenic metals are correlated with soil properties, while the anthropic metals do not exhibit any correlation with soil properties (Cai et al., 2012; Franco-Uria et al., 2009; Rodríguez Martín et al., 2006). The studies of Rodríguez et al. (2008) and Lv et al. (2013) based on PCA also grouped Cr and Ni into the same factors at all scales, and determined their natural sources. Therefore, there was no significant human input of Cr and Ni in the study area.
At local scale, Cd, Cu, Pb and Zn with moderate correlation with Cr and Ni had highly positive loads in RF1, which suggested that parent material contributed to spatial variation of Cd, Cu, Pb and Zn. The contents of these metals also indicated the bedrock influence (Table 1). Positive correlations were found between spatial components of these metals and clay, silt, Fe2O3, MgO, whereas negative correlations were also exhibited with the percentage of sand (Table 2). Nanos and Rodríguez Martín (2012) found that the Cu, Zn, Pb and Cd associated with Cr and Ni had high loads in the same factor at small scale, and regarded this association of these metals as a lithogenic source. In the present study, human inputs also affected the spatial variation of Cd, Cu, Pb and Zn at local scale. The association of these metals in PCA could commonly reflect human influence on heavy metals distribution (Cai et al., 2012; Franco-Uria et al., 2009; Mico et al., 2006). The hotspots with highest values of spatial variation of Cd, Cu, Pb and Zn at local scale were easily linked to the soils of gold mine and stone yards in northwestern and southern parts (Figs. 1 and 3). Mining activity is an abundant source of heavy metals contamination in soils (Boularbah et al., 2006; Rey et al., 2013). A multi-metal mine including Au, Fe, Cd, Pb, Zn and Cu is distributed in northwestern study area (Fig. 1), and it has been mined mainly for Au. Meanwhile, Wulian is the most important base of quarrying for constructional materials in Shandong province, and stone yards are mainly located in southern part of study area. In particular, both gold mines and stones in this area are mined using surface mining. The dust arising from a mining process could be a direct source of heavy metals in soils around mining sites (Castillo et al., 2013). Following mineral processing, mine tailings are commonly disposed onto surrounding soils; heavy metals in the tailings may
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Fig. 3. Cokriged maps of spatial components at local scale. (a) Cd, (b) Cr, (c) Cu, (d) Ni, (e) Pb and (f) Zn.
release to soils through runoff and soil erosion (Castillo et al., 2013; Concas et al., 2006). These activities relatively had a small influencing range, but could produce a significant impact on metals contents in the soil environment. Agricultural practice and urban human activity exhibited insignificant affects on the spatial short-range variation of Cu, Pb, Cd and Zn in Wulian. The study area is mostly covered by mountainous area, and the soils lack the fertility (Table 1). Farmland accounts less than 40 percent of the total area, and there is only one harvest per year for corn with low fertilizers application rate. Moreover, due to high altitude and steep slope, the study area has a small urban region with small population and few industries.
The urban and agricultural areas were mainly distributed in the northern part of study area, in which relatively low values of shortrange components were observed (Fig. 3). Therefore, agricultural practices and urban human activity did not present a visual effect on the heavy metals contents at local scale. The results in the present study are not consistent with the finding of Rizhao city (Lv et al., 2013) and Ebro river basin (Rodríguez et al., 2008), where the intensive industry and agricultural practice have profound influences on the heavy metals contents at local scale. At regional scale, Cd, Cu, Pb and Zn were classified into the same regionalized factor with little correlation with Cr and Ni, and these group metals were controlled by parent material. The
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Fig. 4. Cokriged maps of spatial components at regional scale. (a) Cd, (b) Cr, (c) Cu, (d) Ni, (Pb, (f) Zn.
relatively strong correlations could be found in general between spatial components of these metals and soil properties (Table 2), indicating the lithogenic source of Cd, Cu, Pb and Zn at regional scale. The spatial components of Cd, Cu, Pb and Zn coincided with the evidence of the parent material. The highest values of Cd, Cu, Pb and Zn were located in the soils originating from pyroclastic rock, while lower values were presented on the soils from gneiss, glutenite and granite (Fig. 4). In the geological survey on Wulian, Song (2002) found that the Cd, Cu, Pb and Zn contents in pyroclastic rock were significantly higher than the other parent materials. Davies (1997) showed Cd, Pb, Cu and Zn all had high loadings on the first component and found that these metals were primarily ascribed to the bedrock influence. Rodríguez et al. (2008) showed that Cd, Cu, Pb and Zn had highest loading in regionalized
factor 1 at regional scale and were controlled by the lithologic source.
4. Conclusion This study conducted the investigation of six heavy metals in soils in a typical ecological demonstration zone of Eastern China (Wulian), and examined the sources of spatial multi-scale variation of heavy metals. The mean contents of all six metals were within the limit affecting safety of agricultural production and human health in China, only some samples exhibited clear polluted symptom, suggesting an insignificant contamination of heavy metals in soils. The mean contents of Cd, Cu, Pb and Zn were
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Table 2 Correlation coefficients between spatial components and soil physical–chemical properties. Clay Local scale (6 km) Cd 0.311 Cr 0.224 Cu 0.341 Ni 0.198 Pb 0.379 Zn 0.264 Regional scale (14 km) Cd 0.439 Cr 0.142 Cu 0.342 Ni 0.120 Pb 0.403 Zn 0.533
Silt
Sand
SOM
pH
CEC
Fe2O3
MgO
0.330 0.253 0.386 0.262 0.315 0.176
0.366 0.273 0.354 0.334 0.372 0.405
0.066 0.062 0.077 0.060 0.071 0.070
0.010 0.109 0.031 0.095 0.060 0.022
0.082 0.009 0.054 0.020 0.028 0.053
0.261 0.243 0.277 0.295 0.263 0.301
0.326 0.370 0.332 0.404 0.299 0.355
0.237 0.219 0.240 0.245 0.233 0.258
0.291 0.053 0.297 0.031 0.374 0.205
0.497 0.010 0.405 0.020 0.241 0.451
0.315 0.084 0.257 0.120 0.212 0.241
0.297 0.485 0.477 0.363 0.234 0.027
0.261 0.227 0.193 0.204 0.093 0.112
0.591 0.162 0.560 0.097 0.131 0.426
0.306 0.206 0.349 0.206 0.022 0.168
0.297 0.266 0.044 0.056 0.251 0.250
higher than the background values of eastern Shandong province, which could be attributed to human inputs. Parent material was identified as the main factor influencing spatial variability of six metals at both local and regional scales. Human activity was only seen at local scale. Quarrying and mining contributed to the raised values of Cd, Cu, Pb and Zn in soils, while agricultural practices and human inputs from urban area exhibited no visible effect on spatial variation of those metals. Based on the investigation of heavy metals, it can be concluded that natural variation of heavy metals can be observed at every spatial scale, and human inputs over last ten years had altered the spatial natural variations of Cd, Cu, Pb and Zn at local scale. This study will provide a reference for effectively targeting policies to protect the soils environment and to regulate economic structures.
Acknowledgments This work was jointly supported by China State-Sponsored Postgraduate Study Aboard Program (No. 201306190053), National Natural Science Foundation of China (Nos. 41101079 and 41206092), and Natural Science Foundation of Shandong Province (No.Y2008E13). We are also grateful to the reviewers of this paper for their constructive comments.
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CaCO3
Chen, T.B., Zheng, Y.M., Lei, M., Huang, Z.C., Wu, H.T., Chen, H., Fan, K.K., Yu, K., Wu, X., Tian, Q.Z., 2005. Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China. Chemosphere 60, 542–551. Concas, A., Ardau, C., Cristini, A., Zuddas, P., Cao, G., 2006. Mobility of heavy metals from tailings to stream waters in a mining activity contaminated site. Chemosphere 63, 244–253. Dai, J.R., Pang, X.G., Yu, C., Wang, C.L., Wang, Z.H., Hu, X.P., 2011. Geochemical baselines and background values and element enrichment characteristics in soils in eastern Shandong Province. Geochimica 40, 577–587. Davies, B.E., 1997. Heavy metal contaminated soils in an old industrial area of Wales, Great Britain: source identification through statistical data interpretation. Water Air Soil Pollut. 94, 85–98. Dobermann, A., Goovaerts, P., Neue, H.U., 1997. Scale-dependent correlations among soil properties in two tropical lowland rice fields. Soil Sci. Soc. Am. J. 61, 1483–1496. Facchinelli, A., Sacchi, E., Mallen, L., 2001. Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environ. Pollut. 114, 313–324. Franco-Uria, A., Lopez-Mateo, C., Roca, E., Fernandez-Marcos, M.L., 2009. Source identification of heavy metals in pastureland by multivariate analysis in NW Spain. J. Hazard. Mater. 165, 1008–1015. Guo, J.H., Liu, X.J., Zhang, Y., Shen, J.L., Han, W.X., Zhang, W.F., Christie, P., Goulding, K.W.T., Vitousek, P.M., Zhang, F.S., 2010. Significant acidification in major chinese croplands. Science 327, 1008–1010. Journel, A., Huijbregts, C., 1978. Mining Geostatistics. Academic, SanDiego. Kabata-Pendias, A., Pendias, H., 2001. Trace Elements in Soils and Plants. CSC Press, London. Liu, Y., Lv, J.S., Zhang, B., Bi, J., 2013. Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, Eastern China. Sci. Total Environ. 450, 108–119. Loveland, P., Webb, J., 2003. Is there a critical level of organic matter in the agricultural soils of temperate regions: a review. Soil Tillage Res. 70, 1–18. Lu, R.K., 2000. Analysis Method of Soil and Agricultural Chemistry. China Agricultural Science & Technology Press, Beijing. Lv, J., Liu, Y., Zhang, Z., Dai, J., 2013. Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. J. Hazard. Mater. 261, 387–397. Mico, C., Recatala, L., Peris, A., Sanchez, J., 2006. Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis. Chemosphere 65, 863–872. Nanos, N., Pardo, F., Nager, J.A., Pardos, J.A., Gil, L., 2005. Using multivariate factorial kriging for multiscale ordination: a case study. Can. J. For. Res. 35, 2860–2874. Nanos, N., Rodríguez Martín, J.A., 2012. Multiscale analysis of heavy metal contents in soils: spatial variability in the Duero river basin (Spain). Geoderma 189, 554–562. Rey, J., Martinez, J., Hidalgo, M.C., Rojas, D., 2013. Heavy metal pollution in the Quaternary Garza basin: a multidisciplinary study of the environmental risks posed by mining (Linares, southern Spain). Catena 110, 234–242. Rodríguez, J.A., Nanos, N., Grau, J.M., Gil, L., Lopez-Arias, M., 2008. Multiscale analysis of heavy metal contents in Spanish agricultural topsoils. Chemosphere 70, 1085–1096. Rodríguez Martín, J.A., Arias, M.L., Corbi, J.M.G., 2006. Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations. Environ. Pollut. 144, 1001–1012. Sharma, R.K., Agrawal, M., Marshall, F., 2007. Heavy metal contamination of soil and vegetables in suburban areas of Varanasi, India. Ecotoxicol. Environ. Saf. 66, 258–266. Sollitto, D., Romic, M., Castrignano, A., Romic, D., Bakic, H., 2010. Assessing heavy metal contamination in soils of the Zagreb region (Northwest Croatia) using multivariate geostatistics. Catena 80, 182–194. Song, M.C., 2002. Report on Regional Geological Surveys in Rizhao. Shandong Institute of Geological Survey, Jinan, China.
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State Environmental Protection Administration of China, 1997. Environmental Quality Standard for Soils (GB15618-1995). Standards Press of China, Beijing. Sun, C.Y., Liu, J.S., Wang, Y., Sun, L.Q., Yu, H.W., 2013. Multivariate and geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, Northeast China. Chemosphere 92, 517–523. Wackernagel, H., 2003. Multivariate geostatistics. Springer Verlag, Berlin.
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Multivariate geostatistical analyses of heavy metals in soils: Spatial multi-scale variations in Wulian, Eastern China Jianshu Lv a,d,n, Yang Liu b, Zulu Zhang c, Bin Dai a a The Key Laboratory of Coast and Island Development of Ministry of Education, School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China b State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China c College of Population, Resource and Environment, Shandong Normal University, Jinan 250014, China d Geological Survey of Canada (Atlantic), Bedford Institute of Oceanography, Dartmouth, Canada B2Y 4A2
art ic l e i nf o
a b s t r a c t
Article history: Received 5 February 2014 Received in revised form 15 May 2014 Accepted 16 May 2014
The objective of this study was to examine spatial multi-scale variability of six heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in relation to environmental factors in Wulian, Eastern China. Factorial kriging analysis (FKA) was applied to a data set consisting of 432 topsoils. We found that most of the heavy metal contents in soils did not exceed the guideline values of Environmental Quality Standard for Soils (EQSS) in China. Through linear model of coregionalization (LMC) fitting, spatial variation in six heavy metals could be grouped into one nugget effect, and two sphere structures with ranges of 6 km (local scale) and 14 km (regional scale). Spatial correlations among six heavy metals depended on local or regional scales. The high correlations between Cr, Ni and among Cd, Cu, Pb, Zn were found regardless of the spatial scale, while correlations of Cr and Ni with other four metals decreased with increasing spatial scale. Spatial variation of Cr and Ni was related to parent material at both local and regional scales, and was derived from natural sources. Mining activity was observed to affect the spatial variation of Cd, Cu, Pb and Zn at local scale, while parent material dominated spatial variation of those metals at regional scale. However, agricultural practices and human activity in urban area did not alter spatial variation of heavy metals in soils. It could be concluded that human influence on heavy metals variation was noted on local scale, and parent material had greater influence on spatial variation of heavy metals at both local and regional scales. & 2014 Elsevier Inc. All rights reserved.
Keywords: Heavy metals Multi-scale Spatial variability Source Factorial kriging analysis
1. Introduction A significant amount of pollutants from different sources enter into the soil every year, thus soil serves as geochemical sink for various pollutants. Heavy metals are of prime importance for the soil environment quality due to their cumulative toxicity effect (Alloway, 1995). Heavy metals could be transferred from soils to atmosphere, hydrosphere and biomass, and they could affect human health through water supply, food intake, direct ingestion and dermal contact (Kabata-Pendias and Pendias, 2001; Sharma et al., 2007). Natural concentrations of heavy metals depend on their contents in parent materials (Alloway, 1995; Lv et al., 2013). Soil physical–chemical properties are also associated with the n Corresponding author at: The Key Laboratory of Coast and Island Development of Ministry of Education, School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China. E-mail address: [email protected] (J. Lv).
http://dx.doi.org/10.1016/j.ecoenv.2014.05.019 0147-6513/& 2014 Elsevier Inc. All rights reserved.
retention of heavy metals in soils. As suggested by some researchers (Mico et al., 2006; Sun et al., 2013), soils with higher clay percentages and SOM contents tend to have higher heavy metals contents. On the other hand, anthropic activities profoundly affect the contents of heavy metals in soils, and the amount of anthropogenic pollutants released often exceeds the contributions from natural sources (Chen et al., 2005). Therefore, it is necessary to identify that if the concentrations of heavy metals in soils may be explained by natural factors or by anthropic inputs. Natural factors and human activities have scale-dependent influences on spatial variability of heavy metals in soils, because of their different functional ranges (Lv et al., 2013; Nanos and Rodríguez Martín, 2012). The geostatistical methods could facilitate to model and display spatial structures and variation characteristics of heavy metals in a complex soil environment. Among various geostatistical methods, factorial kriging analysis (FKA), as a multivariate geostatistical method, combining principle component analysis (PCA) with geostatistics, seems to be particularly attractive for the multi-scale study (Nanos et al., 2005). FKA can
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examine the spatial interrelations among various variables at multiple scales, based on which the grouping results derived from PCA can indicate the sources of the spatial variation of corresponding variables (Castrignano et al., 2000; Sollitto et al., 2010). Furthermore, the total variation of various variables can be split into multiple spatial components related to different spatial scales (Alary and Demougeot-Renard, 2010). In China, most of regions have been experiencing rapid urbanization and industrialization since 1980s. Recently, numerous investigations of heavy metal in soils have been carried out (Cai et al., 2012; Chen et al., 2005; Sun et al., 2013; Zhong et al., 2011), which mainly focused on the regions with high levels of industrialization or the developed agriculture. There are also many regions with low levels of development existed in China, including some ecological demonstration zones, western areas, and nature reserves. These areas tend to have good environmental quality due to lacking of the industry and agriculture. Since 2000, China government has implemented several national development strategies on the regions with low levels of development, such as Western development and the Development of Yellow River Delta. There are intense conflicts between economic development and environmental protection in these areas. However, few works were conducted on the characteristics of heavy metals on the regions of this type, where economic development strengthens gradually over the last decade. There was also limited information available on the spatial variability of heavy metals in soils and their relationships with environmental factors. Therefore, the knowledge about spatial multi-scale variation of heavy metals and their sources is important, because it can serve a reference for environmental protection and sustainable development in similar areas of China. Wulian is a typical ecological demonstration zone in Eastern China, where the forest and grassland account for about 50 percent of the total area, and sustain a good environmental quality. However, the indusial development rapidly appears over the last ten years, and poses a huge threat to soil environment. Our study addressed spatial multi-scale variations of six key heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) and their sources in Wulian, Eastern China.
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(MgO, Fe2O3 and CaCO3). Particle size distribution in soils was analyzed using a laser grain-size analyzer. Soil pH was analyzed by a pH meter in a 1:2.5 soil–water suspension, and CEC was measured by the ammonium acetate method. Soil organic matter (SOM) contents were analyzed using oil bath-K2CrO7 titration. Fe2O3 and MgO were investigated by X-ray fluorescence spectrometry (XRF). CaCO3 contents were determined by a manometric measurement of the CO2 released after addition of hydrochloric acid (HCl). HSO4–HNO3–HF was used to digest the samples for analyzing Cd, Cr, Cu, Ni, Pb and Zn, and Cr, Cu, Ni, Pb and Zn concentrations were determined by a flame atomic absorption spectrophotometer, and Cd contents were determined by a graphite furnace atomic absorption spectrophotometer (Lu, 2000). The limits of detection were 0.01, 0.4, 0.3, 0.3, 0.1 and 0.5 mg kg 1 for Cd, Cr, Cu, Ni, Pb and Zn, respectively. The national registered standard reference material (GSS-1) obtained from the Center of National Standard Reference Material of China was included in chemical analyses. Recoveries were 91–102 percent for Cd, 89–104 percent for Cr, 97–106 percent for Cu, 96–102 percent for Ni, 94–101 percent for Pb, 98–108 percent for Zn, respectively. 2.3. Statistical and geostatistical analysis At first, descriptive statistics, including maximum, minimum, mean, median, standard deviation (SD), variation coefficient (CV), skewness and kurtosis, were conducted on the soil physical–chemical properties and heavy metals contents. In order to eliminate the skewness and to facilitate the LMC fitting, Gaussian anamorphosis was used to standardize heavy metals contents to Gaussianshaped variables with zero mean and unit variance (Wackernagel, 2003). Factorial kriging analysis (FKA) was carried out on the standardized variables with normal distribution. The theory of FKA has been described in several publications (Castrignano et al., 2000; Wackernagel, 2003), and here the basic steps were briefly presented. Linear model of co-regionalization (LMC), developed by Journel and Huijbregts (1978), was used to model the experimental direct and cross-variograms. All the direct and cross-variograms, modeled as sums of variograms γ uij ðhÞ at each scale u, can be defined as linear combination of elementary functions gu ðhÞ. The LMC can be expressed in matrix terms as Ns
Γ ðhÞ ¼ ½γ ij ðhÞ ¼ ∑ Bu gu ðhÞ u¼1
where Γ ðhÞ is the variogram matrix of order m m, Bu called the coregionalization matrix, which describes the relationships between the variables at the given spatial scale u, and Ns represents the number of the spatial structures. Each original variable can be expressed as the sum of Ns spatial components with each one related to the variogram γ uij ðhÞ, and the estimation of spatial components is performed by ordinary cokriging. In the cokriged estimation of this study, the 30 m mesh grid and the neighborhood of 50,000 m-radius with an optimum number of ten samples were applied. The structure correlation coefficient rij among variables i and j at the uth spatial qffiffiffiffiffiffiffiffiffiffiffiffiffi u u u scale can be calculated as r ij ¼ bij = bii U bjj (Dobermann et al., 1997; Liu et al., 2013), u
2. Methods and materials 2.1. The study area and sampling Wulian covers an area of 1400 km2 and is located in southeastern part of Shandong province, Eastern China (Fig. 1). Elevation ranges from 18 to 706 m above sea level, and the mountainous area accounts about 80 percent of the total area. Parent material is composed of granite and gneiss in eastern part, glutenite and pyroclastic rocks in western part, as well as some small patches of basic rocks around the total area (Song, 2002). The study area has a temperate continental climate, with mean annual temperature of 12.6 1C and the mean annual precipitation of 767 mm. Forest and grassland account for 43.6 percent and 8.1 percent of the total study area, which is much higher than the average levels of China. Currently, the preliminary industry formed with the increasing development over the last decade, and gold mining and quarrying for constructional materials are the main industries in study area. In this study, the systematic grid sampling design (2 km 2 km size) was applied to select 432 sites in topsoils of Wulian. At every sampling site, four to six subsamples in topsoils within a 100 m radius were taken from upper 20 cm soils. The subsamples were mixed thoroughly to obtain a composite sample (1 kg). All the collected samples were stored in polyethylene bags for transportation and storage. The locations of sampling sites are indicated in Fig. 1.
2.2. Soil chemical analysis In the laboratory, the collected samples were air-dried, and analyzed for soil physical–chemical properties and six heavy metals. In this study, soil basic physical–chemical properties were determined, including soil pH, particle size, cation exchange capacity (CEC), soil organic matter (SOM), and three minerals
where bij refers to the variable at position (i, j) in the coregionalization matrix for the uth structure. Principle component analysis (PCA) on each coregionalization matrix Bu would provide a set of transformation coefficients, which were subsequently used in a cokriging system to produce regionalized factors (RF), and the circles of correlations were used to exhibit the correlation coefficients between regionalized factors and the original variables at each scale. Descriptive statistics were calculated using SPSS 16.0 (SPSS Inc.), while Gaussian anamorphosis and FKA were performed by ISATIS Package (Geovariances Inc.).
3. Results and discussion 3.1. Soil properties and heavy metals contents The descriptive statistics of soil properties and heavy metals contents are indicated in Table 1. Soils basic properties determined in the present study were in agreement with the soils of Eastern Shandong province, characterized by acidic properties, low fertility and rough texture (Zhang, 1986). The soils in study area had the mean value of 6.26 with the range from 4.63 to 8.13, and acidic properties could facilitate the mobility of heavy metals in soils. These could be ascribed to the igneous background containing the high percentage of SiO2, and silicic acid can be produced on a weathering process (Alloway, 1995; Zhang, 1986). Guo et al. (2010) found that significant acidification occurred in the main Chinese crop-production areas from 1980s to 2000s because of high-N fertilizer inputs. However, the soil pH in Wulian varied little from
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Fig. 1. The study area and the locations of sampling sites.
6.27 for 1980s to 6.26 for this investigation, since there is no developed agriculture in this area, which is not consistent with the nearby Rizhao city (Donggang and Lanshan), where the mean pH is decreased by 0.28 since 1980s (Lv et al., 2013). The SOM in study area had a mean value of 1.32 percent with the range 0.61–4.57 percent. Loveland and Webb (2003) suggested that the soils with SOM below 1.7 percent cannot maintained the crops; thus the levels of SOM in study area was low. The granulometric fraction was rough with the highest percentage of sand (63.2 percent); the soil textures can be classified as sandy clay loam, loam and sandy loam. In addition, CaCO3, CEC, Fe2O3 and MgO presented relatively low levels with narrow range. The average contents of Cd, Cr, Cu, Ni, Pb and Zn were 0.13, 55.6, 22.2, 23.6, 32.1 and 73.3 mg kg 1, respectively. The CVs varied from 0.30 for Zn to 0.81 for Cu, and some outliers may exist for Cu, Pb and Cd distribution. The values distributions of six metals were all positively skewed with range 1.75–11.4. The mean contents of all six metals were lower than the corresponding limit in Environmental Quality Standard for Soils (EQSS) in China affecting safety of agricultural production and human health (State Environmental Protection Administration of China, 1997). Approximately, only 3.70 percent of samples for Cd, 1.16 percent for Cu, 2.78 percent for Cu, 1.62 percent for Ni, 0.23 percent for Pb, and 0.23 percent for Zn exceeded the limits in EQSS. Therefore, there was insignificant pollution of heavy metals in soils of study area. However, there were significant human inputs existed, because the mean contents of Cd, Cu, Pb and Zn were significantly higher than corresponding background values of eastern Shandong province (Dai et al., 2011). In particular, there were some samples with more than ten times the background values for Cu and Pb. The mean contents of Cr and Ni were close to their background values, suggesting the possible natural sources of these metals. The studies of Cai et al. (2012) and Sun et al. (2013) also found the insignificant human inputs of Cr and Ni in soils. 3.2. Linear model of co-regionalization fitting Through a preliminary visual inspection of the experimental direct and cross variograms (Fig. S1), the variations of three scales were presented. The first structure was a nugget effect, and was obviously observed for Cd and Cu. The nugget effect represents the
field variation occurring within the sampling scale and the measurement error; thus it is not commonly considered for further study (Rodríguez et al., 2008; Sollitto et al., 2010). Local scale (short structure, 6 km) can be distinguished for Cd, Cu, Ni and Zn, while regional scale (long structure, 14 km) was significantly exhibited for Cr, Cu, Ni and Zn. In agreement with the observed structures, the LMC was fitted using three spatial structures: a nugget effect, a spherical model with a range of 6 km and another spherical model with a range of 14 km. Pearson and structure correlation coefficients of six heavy metals in soils are summarized in Table S1. There was highly positive structure correlation between Cr and Ni with 0.973 for local scale and 0.926 for regional scale, reflecting its probably common characteristics of spatial variation in both spatial scales. These results were consistent with the works of Rodríguez et al. (2008) in Ebro river basin, Nanos and Rodríguez Martín (2012) in Duero river basin, Sollitto et al. (2010) in Zagreb and Lv et al. (2013) in Rizhao, where Cr and Ni were also strongly positively correlated in all spatial structures. On the other hand, highly positive correlation coefficients were found among Cd, Cu, Pb and Zn at local and regional scales. The high correlation among Cd, Cu, Pb, Zn and between Cr and Ni at both short and long scales was consistent with the results of classic Pearson correlation coefficients (Table S1). Cd, Cu, Zn, and Pb exhibited decreasing correlation with Cr and Ni with increasing scale, which is not in agreement with the adjacent Rizhao city (Lv et al., 2013). Moreover, there were increasing correlation coefficients among Cd, Cu, Pb and Zn with increasing spatial scale. Therefore, it can be seen that the correlation between six metals depended on the local scale and regional scale.
3.3. Multi-scale spatial variability The correlations between the first two regionalized factors and various metals are displayed in the circles of correlations (Fig. 2). At local scale, regionalized factor 1 (RF1) explained 64.9 percent of the short structure variability, and had highest loadings in Cd, Cu, Pb and Zn as well as partially Cr and Ni, while RF2 amounting on 21.3 percent variance of short structure indicated highly negative loadings in Cr and Ni. The results of PCA at regional scale indicated
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Table 1 The descriptive statistics of soil properties and heavy metals.
SOM pH CEC CaCO3 Fe2O3 MgO Clay Silt Sand Cd Cr Cu Ni Pb Zn
Unit
Minimum
Maximum
Mean
Median
SD
CV
Skewness
Kurtosis
BV
Guideline values of EQSS
Percent pH Unit cmol kg 1 Percent Percent Percent Percent Percent Percent mg kg 1 mg kg 1 mg kg 1 mg kg 1 mg kg 1 mg kg 1
0.61 4.63 7.62 0.80 2.57 0.48 2.60 6.13 38.8 0.06 12.2 6.20 7.40 17.4 37.5
4.57 8.13 3.39 6.68 7.48 2.69 22.2 43.2 92.1 0.48 183 320 81.7 362 224
1.32 6.26 14.0 2.44 4.44 1.28 12.8 24.0 63.2 0.13 55.6 22.2 23.6 32.1 73.3
1.25 6.09 13.6 2.25 4.38 1.22 12.0 22.1 62.4 0.12 51.8 18.9 21.1 27.9 70.0
0.42 0.88 1.74 0.98 0.96 0.44 2.11 5.56 11.8 0.06 25.0 17.9 10.0 22.6 22.0
0.32 0.14 0.12 0.40 0.22 0.34 0.17 0.23 0.18 0.46 0.43 0.81 0.42 0.70 0.30
1.94 0.38 0.65 1.08 0.42 0.74 0.73 1.32 0.98 2.88 1.76 11.4 1.75 9.04 2.14
8.80 1.00 0.91 1.28 0.16 0.43 0.65 0.87 0.52 11.2 4.50 180 4.68 114 8.11
– – – – – – – – – 0.108 56.2 19.6 23.5 25.4 56.1
– – – – – – – – – 0.3 150 50 40 250 200
BV represents the background values of eastern Shandong province (Dai et al., 2011). EQSS represents Environmental Quality Standard for Soils in China (GB15618-1995). “–”: not available
Fig. 2. Circles of correlations between the first two factors and various elements.
that RF1 had heavily positive loads of Cd, Cu, Pb and Zn in RF1, while RF2 had highly positive loads of Cr and Ni. At both local and regional scales, Cr and Ni with strongly positive correlation, were classified into the same group, and could be attributed to the natural geochemical sources. It is well known that anthropic inputs of Cr and Ni in fertilizers, limestone and manure are lower than background contents in soils (Facchinelli et al., 2001). Cr and Ni contents in soils are commonly controlled by their contents in bedrocks, and ultrabasic and basic rocks presented higher contents of Cr and Ni than other parent materials (Alloway, 1995; Facchinelli et al., 2001). These were confirmed by the cokriged maps at both two scales, and spatial components of Cr and Ni showed no association with the locations of urban area, mining site and stone yards (Figs. 1 and 3). At local scale, the hotspots with highest values of Cr and Ni seemed to be random, which may be associated with the small patches of basic rocks. On the other hand, the hotspots of Cr and Ni at longer scale were located in the soils in northwestern part originated from glutenite (Fig. 4), since they contain high percentages of basic rocks such as basalt and peridotite (Song, 2002). Cr and Ni showed positive correlation with soil granulometric fraction and minerals at local scale, while they were related to pH and MgO at regional scale (Table 2). Commonly, lithogenic metals are correlated with soil properties, while the anthropic metals do not exhibit any correlation with soil properties (Cai et al., 2012; Franco-Uria et al., 2009; Rodríguez Martín et al., 2006). The studies of Rodríguez et al. (2008) and Lv et al. (2013) based on PCA also grouped Cr and Ni into the same factors at all scales, and determined their natural sources. Therefore, there was no significant human input of Cr and Ni in the study area.
At local scale, Cd, Cu, Pb and Zn with moderate correlation with Cr and Ni had highly positive loads in RF1, which suggested that parent material contributed to spatial variation of Cd, Cu, Pb and Zn. The contents of these metals also indicated the bedrock influence (Table 1). Positive correlations were found between spatial components of these metals and clay, silt, Fe2O3, MgO, whereas negative correlations were also exhibited with the percentage of sand (Table 2). Nanos and Rodríguez Martín (2012) found that the Cu, Zn, Pb and Cd associated with Cr and Ni had high loads in the same factor at small scale, and regarded this association of these metals as a lithogenic source. In the present study, human inputs also affected the spatial variation of Cd, Cu, Pb and Zn at local scale. The association of these metals in PCA could commonly reflect human influence on heavy metals distribution (Cai et al., 2012; Franco-Uria et al., 2009; Mico et al., 2006). The hotspots with highest values of spatial variation of Cd, Cu, Pb and Zn at local scale were easily linked to the soils of gold mine and stone yards in northwestern and southern parts (Figs. 1 and 3). Mining activity is an abundant source of heavy metals contamination in soils (Boularbah et al., 2006; Rey et al., 2013). A multi-metal mine including Au, Fe, Cd, Pb, Zn and Cu is distributed in northwestern study area (Fig. 1), and it has been mined mainly for Au. Meanwhile, Wulian is the most important base of quarrying for constructional materials in Shandong province, and stone yards are mainly located in southern part of study area. In particular, both gold mines and stones in this area are mined using surface mining. The dust arising from a mining process could be a direct source of heavy metals in soils around mining sites (Castillo et al., 2013). Following mineral processing, mine tailings are commonly disposed onto surrounding soils; heavy metals in the tailings may
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Fig. 3. Cokriged maps of spatial components at local scale. (a) Cd, (b) Cr, (c) Cu, (d) Ni, (e) Pb and (f) Zn.
release to soils through runoff and soil erosion (Castillo et al., 2013; Concas et al., 2006). These activities relatively had a small influencing range, but could produce a significant impact on metals contents in the soil environment. Agricultural practice and urban human activity exhibited insignificant affects on the spatial short-range variation of Cu, Pb, Cd and Zn in Wulian. The study area is mostly covered by mountainous area, and the soils lack the fertility (Table 1). Farmland accounts less than 40 percent of the total area, and there is only one harvest per year for corn with low fertilizers application rate. Moreover, due to high altitude and steep slope, the study area has a small urban region with small population and few industries.
The urban and agricultural areas were mainly distributed in the northern part of study area, in which relatively low values of shortrange components were observed (Fig. 3). Therefore, agricultural practices and urban human activity did not present a visual effect on the heavy metals contents at local scale. The results in the present study are not consistent with the finding of Rizhao city (Lv et al., 2013) and Ebro river basin (Rodríguez et al., 2008), where the intensive industry and agricultural practice have profound influences on the heavy metals contents at local scale. At regional scale, Cd, Cu, Pb and Zn were classified into the same regionalized factor with little correlation with Cr and Ni, and these group metals were controlled by parent material. The
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Fig. 4. Cokriged maps of spatial components at regional scale. (a) Cd, (b) Cr, (c) Cu, (d) Ni, (Pb, (f) Zn.
relatively strong correlations could be found in general between spatial components of these metals and soil properties (Table 2), indicating the lithogenic source of Cd, Cu, Pb and Zn at regional scale. The spatial components of Cd, Cu, Pb and Zn coincided with the evidence of the parent material. The highest values of Cd, Cu, Pb and Zn were located in the soils originating from pyroclastic rock, while lower values were presented on the soils from gneiss, glutenite and granite (Fig. 4). In the geological survey on Wulian, Song (2002) found that the Cd, Cu, Pb and Zn contents in pyroclastic rock were significantly higher than the other parent materials. Davies (1997) showed Cd, Pb, Cu and Zn all had high loadings on the first component and found that these metals were primarily ascribed to the bedrock influence. Rodríguez et al. (2008) showed that Cd, Cu, Pb and Zn had highest loading in regionalized
factor 1 at regional scale and were controlled by the lithologic source.
4. Conclusion This study conducted the investigation of six heavy metals in soils in a typical ecological demonstration zone of Eastern China (Wulian), and examined the sources of spatial multi-scale variation of heavy metals. The mean contents of all six metals were within the limit affecting safety of agricultural production and human health in China, only some samples exhibited clear polluted symptom, suggesting an insignificant contamination of heavy metals in soils. The mean contents of Cd, Cu, Pb and Zn were
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Table 2 Correlation coefficients between spatial components and soil physical–chemical properties. Clay Local scale (6 km) Cd 0.311 Cr 0.224 Cu 0.341 Ni 0.198 Pb 0.379 Zn 0.264 Regional scale (14 km) Cd 0.439 Cr 0.142 Cu 0.342 Ni 0.120 Pb 0.403 Zn 0.533
Silt
Sand
SOM
pH
CEC
Fe2O3
MgO
0.330 0.253 0.386 0.262 0.315 0.176
0.366 0.273 0.354 0.334 0.372 0.405
0.066 0.062 0.077 0.060 0.071 0.070
0.010 0.109 0.031 0.095 0.060 0.022
0.082 0.009 0.054 0.020 0.028 0.053
0.261 0.243 0.277 0.295 0.263 0.301
0.326 0.370 0.332 0.404 0.299 0.355
0.237 0.219 0.240 0.245 0.233 0.258
0.291 0.053 0.297 0.031 0.374 0.205
0.497 0.010 0.405 0.020 0.241 0.451
0.315 0.084 0.257 0.120 0.212 0.241
0.297 0.485 0.477 0.363 0.234 0.027
0.261 0.227 0.193 0.204 0.093 0.112
0.591 0.162 0.560 0.097 0.131 0.426
0.306 0.206 0.349 0.206 0.022 0.168
0.297 0.266 0.044 0.056 0.251 0.250
higher than the background values of eastern Shandong province, which could be attributed to human inputs. Parent material was identified as the main factor influencing spatial variability of six metals at both local and regional scales. Human activity was only seen at local scale. Quarrying and mining contributed to the raised values of Cd, Cu, Pb and Zn in soils, while agricultural practices and human inputs from urban area exhibited no visible effect on spatial variation of those metals. Based on the investigation of heavy metals, it can be concluded that natural variation of heavy metals can be observed at every spatial scale, and human inputs over last ten years had altered the spatial natural variations of Cd, Cu, Pb and Zn at local scale. This study will provide a reference for effectively targeting policies to protect the soils environment and to regulate economic structures.
Acknowledgments This work was jointly supported by China State-Sponsored Postgraduate Study Aboard Program (No. 201306190053), National Natural Science Foundation of China (Nos. 41101079 and 41206092), and Natural Science Foundation of Shandong Province (No.Y2008E13). We are also grateful to the reviewers of this paper for their constructive comments.
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