Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China

Geoderma 192 (2013) 50–58

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Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China Xiaoyu Li a, b,⁎, Lijuan Liu a, Yugang Wang a, Geping Luo a, Xi Chen a, Xiaoliang Yang c, Myrna H.P. Hall c, Ruichao Guo b, Houjun Wang d, Jiehua Cui b, Xingyuan He b,⁎ a

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang, China State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Liaoning, China College of Environmental Science and Forestry, State University of New York, Syracuse, New York, USA d Weihai Ocean Environment Monitoring Center, Shandong, China b c

a r t i c l e

i n f o

Article history: Received 15 May 2011 Received in revised form 1 August 2012 Accepted 19 August 2012 Available online 16 November 2012 Keywords: Heavy metal contamination Spatial pattern Land use Urban soil Old industrial city

a b s t r a c t The purpose of this study is to investigate the current status of heavy metal soil pollution in one of the cradles of industry in China, the Tiexi Industrial District in the city of Shenyang, Ninety-three soil samples were collected from the top 15 cm of the soil layer and were analyzed for heavy metal concentrations of Pb, Cu, Cr, Zn, Mn, Cd, As and Hg. The data reveal a remarkable variation in heavy metal concentration among the sampled soils; the mean values of all the heavy metal concentrations were higher than the background values, and the mean concentrations of Pb, Cu, Cd and As were as high as 5.75, 5.08, 12.12 and 13.02 times their background values, respectively. The results of principal component analysis (PCA) indicate that Pb, Cu, Zn, Cd, As and Hg are closely associated with the first principal component (PC1), which explained 46.7% of the total variance, while Cr and Mn are mainly distributed with the second component (PC2), which explained 22.5% of the total variance. Geostatistical analyses, including the calculation of semivariogram parameters and model fitting, further confirmed the results of the statistical analysis. In the estimated maps of heavy metals, several hotspots of high metal concentrations were identified; Pb and Cu showed a very similar spatial pattern, indicating that they were likely from the same source. There is a clear heavy polluted hotspot of Pb, Cu, Zn, Cd and As in the northeast part of the Tiexi Industrial District because of the Shenyang Smelting Plant, which was a backbone enterprise of China's metallurgical industry. There were also hotspots for other heavy metals in other areas. This is mainly the result of the industrial processing that occurred in the study area. All of these data confirm that Pb, Cu, Zn, Cd and As are a result of anthropogenic activities, especially from industrial processes. For Cr and Mn, the concentration patterns indicate low spatial heterogeneity, with low correlation to other metals, indicating that the concentration of Cr and Mn are mainly caused by natural factors such as soil parent materials. Although the city government of Shenyang has placed a high priority on improving the environment in recent years, it will require a long time to completely eliminate pollution in this area. © 2012 Elsevier B.V. All rights reserved.

1. Introduction There are two main sources of heavy metals in the soil (Li et al., 2009b): (i) natural background, which represents the heavy metal concentration derived from parent rocks; (ii) anthropogenic contamination, including agrochemicals, organic amendments, animal manure, mineral fertilizer, sewage sludge and industrial wastes. In the last several decades, the natural input of several heavy metals to soils due to pedogenesis has been exceeded by the human input, even on global and regional scales (Facchinelli et al., 2001; Nriagu and Pacyna, 1988). The

⁎ Corresponding author at: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang, China. Tel.: +86 991 7823131. E-mail addresses: [email protected] (X. Li), [email protected] (X. He). 0016-7061/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.geoderma.2012.08.011

estimates made by Nriagu and Pacyna (1988) suggest that the two principal sources of heavy metals in soils worldwide are the disposal of ash residues from coal combustion and general waste from commercial products. Urban refuse represents an important source of Cu, Hg, Pb, and Zn, with notable contributions of Cd, Pb, and V also coming via the atmosphere. Millward and Turner (2001) compared the rates of heavy metal emissions in the atmosphere from natural and anthropogenic sources to assess man's impact on the environment. The ratio, referred to as the ‘interference factor,’ is >1 for all of the metals studied (Ca, Cu, Hg, Pb and Zn), with relatively high values of Pb and Ca (27 and 5.3, respectively), suggesting that there is a significant anthropogenic alteration of their natural cycles. The mobilization of heavy metals into the biosphere from human activity has become an important process in the geochemical cycling of these metals. Soils serve as the most important sink for heavy metal contaminants in the terrestrial ecosystem. The urban environmental

X. Li et al. / Geoderma 192 (2013) 50–58

quality is of vital importance because the majority of people now live in cities. Urban soils are therefore an important indicator of human exposure to heavy metals in the urban terrestrial environment (Nriagu and Pacyna, 1988). Many studies have indicated that urban soils are contaminated by heavy metals and this phenomenon has been attributed mainly to modern industries, traffic and mining activities in urban areas (De Kimpe and Morel, 2000; Gallagher et al., 2008). The abundance of contamination sources in urban systems results in chemical pressures that often manifest as high pollution concentrations or loadings (Wong et al., 2006), which consequently have detrimental impacts on human and ecosystem health (Taylor and Owens, 2009). Heavy metals reaching the soil remain present in the pedosphere for many years; even after removing the pollution sources, increased amounts of heavy metals in urban soils have been reported (Imperato et al., 2003; Pichtel et al., 1997). Some urban soils, in particular those observed on brownfields that were previously used for industrial production, may contain large amounts of mineral pollutants that have accumulated over time. Urban soils can vary widely over short distances, compared to naturally developed and anthropogenically influenced soils. Most urban soils are relatively young, resulting from soil exchange and mixture due to construction activities (Norra et al., 2001). Because urban landscapes are complex in nature and have numerous sources of heavy metals, assessing the extent and degree of metal contamination presents a challenge to researchers (Yesilonis et al., 2008). Urban land use and structure have an influence on soil contamination. The urban area possesses a wide range of different land uses, such as traffic, industry, business, residential uses, gardens and public green spaces, differing in their patterns of human activity and their possible impacts on soil quality (Tiller, 1992). Land use and cover may serve as an indicator of disturbance, site history, management, and the urban environment; these factors result in a mosaic of soil patches (Pouyat et al., 2007). Areas consisting predominantly of man-made soils and influenced by anthropogenically caused emissions may exhibit the highest pollutant values of urban land (Meuser, 2010); for example, many isolated sites with an industrial history show extremely high values. Yet, many potentially influential factors associated with urban land transformation have received limited attention. The relationship between land use and soil pollution needs to be explored further. As the largest developing country in the world, China has achieved rapid economic development, averaging an annual growth rate of 10% in gross domestic product (GDP) over the past two decades. However, this success comes at a cost to the environment (Kan, 2009). Heavy metal pollution in urban soils, urban road dust and agricultural soils has become significant with the rapid industrialization and urbanization of China over the last two decades. Wei and Yang (2010) reviewed studies of heavy metal contamination in several Chinese cities over the past 10 years and found that contamination with Cr, Ni, Cu, Pb, Zn and Cd is widespread in urban soils and in urban road dust. Beijing, the capital of China, is one of the oldest and most densely populated cities in the world; the accumulation of Cu and Pb in Beijing was readily apparent in the soils of urban parks (Chen et al., 2005), and roadside soils had low-level contamination from Cu, Pb and Zn and moderate contamination from Cd (Chen, et al., 2010). In Shanghai, the biggest metropolitan area and the fastest growing Chinese city with a population of more than 19 million, had significant concentrations of Pb, Zn, Cu, Cr, and Cd in urban soils. Heavy metal pollution was even more severe in roadside dust (Shi et al., 2008); the concentrations of Pb, Cr, Cd, As and Hg in urban dusts are 11.3, 2.1, 10.3, 0.997 and 1.7 times the soil background values, respectively (Wang, et al., 2009). In Nanjing, another large city located in eastern China, the total Cu, Zn, Pb and Cr concentrations of urban soils were 84.7 (48.1–139.7), 66.1 (12.2–869.4), 162.6 (57.7–851.6) and 107.3 (36.3–472.6) mg/kg, respectively; the soils were also polluted with Cu, Zn and Cr to some extent and heavily polluted with Pb (Lu et al., 2003). Xi'an, one of the biggest tourist cities in China, has gradually developed into an important industrial city in China beginning in

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the 1950s. The urban dust in Xi'an has elevated heavy metal concentrations of Ag, Cr, Cu, Pb, Zn and Hg (Han et al., 2006). Urumqi, the capital of Xinjiang Uygur Autonomous Region in northwest China, with a population of more than 2 million people, had concentrations of Cd, Cr, Cu, Ni, Pb, Mn and Zn that were much higher in urban areas than the background values, and the spatial distribution pattern of Cu, Pb, Cr and Zn was mainly associated with the main roads where high traffic density was identified; Ni and Mn coincided with industrial areas (Wei et al., 2010). Urban soils were also polluted by heavy metals in many other Chinese cities, such as Baoji (Lu et al., 2009) and Guangzhou (Duzgoren-Aydin et al., 2006),. Shenyang, the largest industrial city in Northeast China and the administrative center of Liaoning Province is historically known as an industrial base and well-constructed city, with heavy machinery and manufacturing as the major industries. The growth of the GDP in 2009 was over 14.0% greater than that of the previous year. It is not only a quickly developing city but also an ancient city with more than 2300 years of history. In 2009, the built-up area was 310 km2 , and the urban population was 5.12 million. With rapid economic development, environmental quality has severely deteriorated. Heavy metal pollution is one of the most serious problems. According to the Shenyang Environment Protection Agency, it has been estimated that 66.8 t of metals from the Shenyang smelters were annually deposited in the environment before 2000. The main objectives of this study were the following: (1) to assess the concentration and distribution patterns of heavy metals in urban soil; and (2) to identify the relationship between urban land use and the heavy metal contamination of urban soil. 2. Materials and methods 2.1. Study Area This study was carried out in the Shenyang Tiexi Industrial District (Fig. 1), located in the western part of Shenyang, with a total area of 3900 ha and a population of 0.8 million. It is the main industrial area of Shenyang and is one of the cradles of industry in China. As early as 1905, there were industrial enterprises in this area. After the founding of the People's Republic of China, the Tiexi district became China's largest heavy industrial area and there were more than 300,000 workers and 1200 factories and plants in the 39 km 2 built-up area by the end of 2002 (Li et al., 2009a). In the past 10 years, many large factories and plants have closed or relocated to suburban areas from the industrial zone because of serious environmental pollution. 2.2. Soil sampling and chemical analyses Ninety-three soil samples were collected in the Tiexi Industrial District. The sampling points were randomly distributed in the study area based on a regular grid of 1 × 1 km, and each grid had at least one sampling point (Fig. 1.). The topsoil (0–15 cm) was collected. Each of the soil samples consisted of 3–5 sub-samples, obtained in different directions using a stainless steel hand auger. All of the sample sites were recorded using a hand-held global positioning system (GPS). Related information, such as land use history, vegetation, and soil type were also recorded in detail. The soil samples were collected in September 2007. The samples were oven-dried at 45 °C for 3 days, sieved through a 2-mm plastic sieve to remove large debris, gravel-sized materials, plant roots and other waste materials and stored in closed plastic bags until analysis. Soil was digested with a 5:2:3 mixture of HNO3HClO4\HF (Li et al., 2012). The digested solutions were analyzed via inductively coupled plasma-atomic emission spectroscopy (ICP-AES; Perkin Elmer Optima 3300 DV). All of the soil samples were analyzed for their total concentrations of Pb, Cu, Cr, Zn, Mn,

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X. Li et al. / Geoderma 192 (2013) 50–58

QuickBird Image of Shengyang City

Liaoning Province

Shenyang City

Tiexi Industrial District and sampling points

Fig. 1. Map of the Shenyang Tiexi Industrial District and sampling points.

Cd, As and Hg. Quality control involved the following: 1) analysis of 15 random samples and 6 national standard samples; and 2) a random selection of samples to ensure that the mean deviation was less than 3%. 2.3. Statistical analysis To identify the relationship among heavy metals in urban soils and their possible sources, Pearson's correlation coefficient analysis and principal component analysis (PCA) were performed (Facchinelli et al., 2001) using the commercial statistics software package SPSS version 17.0 for Windows. The correlation coefficient measures the strength of the inter-relationship between two heavy metals. PCA, as a multivariate analytical tool, is used to reduce a set of original variables to extract a small number of latent factors (principal components, PCs) and analyze relationships among heavy metals. 2.4. Geostatistical methods Geostatistics provides a set of statistical tools for incorporating the spatial and temporal coordinates of observations in data processing, and its increasing use in environmental applications testifies to its utility and success (Saito and Goovaerts, 2000). A semivariogram is a basic tool of geostatistics and is the mathematical expression of the square of regional variables z(xi) and z(x + h i, namely the variance of regional variables. Its general form is: 1 X 2 ½zðxi Þ−zðxi þ hÞ 2NðhÞ i¼1

spacing is h; and z(xi) and z(x i + h) are the values when the variable Z is at the xi and xi + h positions, respectively. In this study, the step length was divided by 300-m intervals: 300, 600, 900, 1200, … 4200 m; in total, there were 14 class intervals. Because 4200 m is smaller than half of the maximum distance (8523 m) between the various sampling points, it also coincides with the requirements of the geostatistical analysis. Kriging, as a geostatistical interpolation method, uses the semivariogram to quantify the spatial variability of regionalized variables and provides parameters for spatial interpolation. The maps of spatial distribution of heavy metal concentrations were generated by Kriging interpolation with the support of ArcGIS-Geostatistical Analyst. 2.5. Mapping of land use The land use map of the study area was obtained from highresolution remote sensing images (QuickBird) in 2006 using ArcGIS, Erdas and field survey. Land use in the Shenyang Tiexi Industrial District was classified into 8 types: Industrial land, New residential land (built after 1990), Old residential land (built before 1990), Commercial and public facilities land, Roads, Land under construction, Parks and Public Squares Land, and Railways (Fig. 3). To analyze the impacts of land use on the heavy metal pollution of soils, a superposition calculation was performed using the map of land use types and the maps of the spatial distribution of heavy metal concentrations.

N ðhÞ

γ ðhÞ ¼

where r(h) is the semivariogram; h is the step length, namely the spatial interval of sampling points used for the classification to decrease the individual number of spatial distances of various sampling point assemblages; N(h) is the logarithm of sampling points when the

2.6. Background values of heavy metals The heavy metal background values are from the report “The Background Concentrations of Soil Elements of China” at the provincial level (China National Environmental Monitoring Center, 1990); this was a final report of a national program “Investigations on Background Data for Concentrations of Elements in Soils of Mainland

X. Li et al. / Geoderma 192 (2013) 50–58

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Table 1 Heavy metal concentrations and background values (mg/kg) of urban top soils in the Shenyang Tiexi Industrial District.

Pb Cu Cr Zn Mn Cd As Hg

Minimum

Maximum

Mean

Median

SD

CV%

1.90 7.60 4.80 25.00 132.00 0.01 7.48 0.06

940.00 430.00 207.00 1140.00 1030.00 9.64 137.68 1.34

116.76 92.45 67.90 234.80 635.88 1.10 22.69 0.39

70.10 71.10 65.10 182.00 657.00 0.54 17.56 0.33

149.37 72.09 25.32 172.41 139.87 1.54 18.13 0.28

127.93 77.97 37.29 73.43 21.99 139.14 79.93 72.31

China” started in 1986. Chen et al. (1991) described the sampling collection and chemical analysis in detail.

3. Results and discussion 3.1. Analysis of concentrations of heavy metals in the soil Table 1 presents the descriptive statistics of the heavy metal concentrations of urban topsoils and background values for Shenyang soils. There was a remarkable change in the content of heavy metals among the sampled soils: the concentrations of Pb, Cu, Cr, Zn, Mn, Cd, As and Hg varied between 1.9 and 940, 7.6 and 430, 4.8 and 207, 25 and 1140, 132 and 1030, 0.0098 and 9.641, 7.48 and 137.68, and 0.0605 and 1.343 mg/kg, respectively, with average concentrations of 116.76, 92.454, 67.895, 234.801, 635.876, 1.1032, 22.686, and 0.3897 mg/kg, respectively. All of the mean values of the heavy metal concentrations were higher than their background values; the mean concentrations of Pb, Cu, Cd and As were as high as 5.75, 5.08, 12.12 and 13.02 times their background values. In fact, all of the metals showed contamination in the 75th percentile, and almost all samples of Cu, Zn, Cd, As and Hg showed above-background levels. This clearly demonstrated the anthropogenic contribution and revealed the significant pollution levels in the area. The distribution of concentrations was skewed by a small number of large values (contamination hotspots). For all metals, the total concentrations showed

Table 2 Correlations between heavy metal concentrations.

Pb Cu Cr Zn Mn Cd As Hg ⁎ ⁎⁎

Pb

Cu

1.000 0.793⁎⁎ 0.363⁎⁎ 0.730⁎⁎ 0.197⁎ 0.835⁎⁎ 0.650⁎⁎ 0.525⁎⁎

1.000 0.528⁎⁎ 0.584⁎⁎ 0.284⁎⁎ 0.622⁎⁎ 0.530⁎⁎ 0.361⁎⁎

Cr

Zn

Mn

Cd

As

Hg

Skewness

Kurtosis

Background value

3.15 2.63 2.18 2.51 −0.64 3.54 4.41 1.44

12.97 8.57 10.23 8.66 2.86 15.75 24.17 2.00

20.30 18.20 54.20 59.80 524.00 0.09 8.20 0.03

a great degree of variability, indicated by the large coefficients of variation (CV) from 21.99% of Mn to 139.14% of Cd. The elevated coefficients of variation reflected the non-homogeneous distribution of concentrations of anthropogenically emitted heavy metals. Large standard deviations were found in all heavy metals except Hg. This also indicated the wide variation of concentrations in urban soils. The extent of this skew was shown by the differences between the mean and the median values. The difference (expressed as a percentage of the median value) was greatest for Cd (105%) and Pd (66.6%) and the least for Cr (4.3%). The results of the K–S test (Pb 0.05) showed that the concentrations of measured metals, except Mn, were not normally distributed, showing positively skewed data. 3.2. Correlation between heavy metals Correlation analyses have been widely applied in environmental studies. They provide an effective way to reveal the relationships between multiple variables and thus have been helpful for understanding the influencing factors as well as the sources of chemical components. Heavy metals in soil usually have complicated relationships among them. The high correlations between heavy metals in soil may reflect that the accumulated concentrations of these heavy metals come from similar pollution sources. The results of the Pearson's correlation coefficients and their significance levels (Pb 0.01) are shown in Table 2. The concentration of Pb showed a high significant positive relationship with Cu (0.793), Cr (0.363), Zn (0.730), Cd (0.835), As (0.650), and Hg (0.525). Additionally, the correlations between Cu and Pb, Cr, Zn, Mn, Cd, As and Hg were significantly positive (Pb 0.01). However, the concentrations of Cr and Mn showed very weak correlations with Cd, As and Hg. This indicates that Cr and Mn were from different sources than Cd, As and Hg. 3.3. Principal component analysis (PCA)

1.000 0.295⁎⁎ 0.475⁎⁎ 0.193 0.119 0.193

1.000 0.322⁎⁎ 0.628⁎⁎ 0.420⁎⁎ 0.512⁎⁎

1.000 0.162 0.071 0.087

1.000 0.546⁎⁎ 0.422⁎⁎

1.000 0.502⁎⁎

1.000

P b 0.05. P b 0.01.

In multivariate statistical analysis, PCA can be used to identify the sources of contamination (Facchinelli et al., 2001). The results of the PCA for metal concentrations in urban soils are tabulated in Table 3. According to the results of the initial eigenvalues, two principal components were considered, which account for over 69% of the total variance. The communalities shown by the variables, considering two

Table 3 Total variance of explained and component matrixes. Component

1 2 3 4 5 6 7 8

Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

4.190 1.350 0.689 0.618 0.522 0.305 0.230 0.096

52.370 16.880 8.613 7.726 6.522 3.814 2.877 1.198

52.370 69.251 77.863 85.590 92.111 95.925 98.802 100.000

4.190 1.350

52.370 16.880

52.370 69.251

3.735 1.805

46.693 22.558

46.693 69.251

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Table 4 Matrix of the principal component analysis loadings of heavy metals.

PC1 PC2

Pb

Cu

Cr

Zn

Mn

Cd

As

Hg

0.936 −0.134

0.844 0.142

0.49 0.701

0.807 0.006

0.362 0.751

0.821 −0.222

0.705 −0.368

0.638 −0.269

factors only, vary from 63.8% for Hg to 93.6% for Pb. All of the elements are consequently well represented by these two principal components. The initial component matrix (Table 4) indicated that Pb, Cu, Zn, Cd, As and Hg are closely associated with the first principal component (PC1), which explained 46.7% of the total variance, while Cr and Mn are mainly distributed with the second component (PC2), which explained 22.5% of the total variance. However, not all heavy metals could be distributed on one component; for example, Pb was mainly associated with PC1, and partially with PC2. This suggested that all of the metals might be controlled by more factors. The metals in the PC1 mainly come from anthropogenic sources, such as industrial production and traffic activities. Cr and Mn in PC2 are strongly correlated and clearly separate from the other heavy metals regarding their correlation coefficient analysis and PCA. This separation between them and other heavy metals may suggest that Cr and Mn mainly came from non-anthropogenic sources, indicating that they originated from local natural sources. 3.4. Spatial structure of heavy metals Semivariogram calculation was conducted, and the experimental semivariogram of soil heavy metal concentrations could be fitted with a Gaussian model for Pb, Cu and As, a spherical model for Cr and Zn, and an exponential model for Mn, Cd and Hg. The theoretical variation function and experimental variation function exhibited a better fit. The parameters are presented in Table 4, including a nugget value (C0), sill (C0 + C), ratio of nugget to sill (C0 + C), range, coefficients of determination (R), residual sum of squares (RSS) and the values of the F test. The values of R were significant at the 0.01 level by F test, which showed that the semivariogram models reflected the spatial structural characteristics of soil heavy metals. Geostatistical theory recognizes that the variable Z used to describe the landscape heterogeneity can be divided into two components, an autocorrelation component (SHA) and a stochastic component (SHR). SHA and SHR can be quantified through the analysis of variation functions. The spatial heterogeneity of SHA caused by the spatial autocorrelation component falls in the range a, defined by the variation function r(h), and corresponds to the medium scale. However, the spatial heterogeneity SHR caused by the stochastic component occurs on a small scale; it may be smaller than the sum of variations of a resolution scale and can be expressed by the nugget variance (Co). In addition, the relative contribution of SHA and SHR to the total spatial heterogeneity SH(Z) is negatively correlated. Both the sill (CO + C) and nugget (CO) can be used to describe spatial heterogeneity (Cambardella et al., 1994). The sill (CO + C)

expresses the attributes of the system, or the maximum variation of the regional variables; the higher the sill, the larger the degree of total spatial heterogeneity. However, the sill (CO + C) is not effective when used to make the comparison of different regional variables because of the larger influence of the sill itself and the measurement unit. The nugget (CO) expresses the spatial heterogeneity of the stochastic component SHR. Large nugget variance shows that there is an assignable process of small scale; it cannot be used to compare the difference of the stochastic components of different variables. However, using the ratio of nugget and sill (CO/CO + C) to reflect the total spatial heterogeneity in the nugget variance is very useful. A low ratio indicates a strong spatial autocorrelation, whereas a high ratio shows that the spatial heterogeneity SHR caused by the stochastic component plays a major role; if the ratio is approximately 1, the studied variable has a constant variation on the whole. The data in Table 5 indicated that the stochastic variation of SHR was below 300 m at a small scale of Pb, Cr, Zn, Cd and Hg and was higher than the structural variation of SHA caused by the spatial autocorrelation component of 300–4200 m medium scale; this shows that the industrial production and point pollution were the main sources of these metals, especially Hg, which was almost completely dominated by stochastic variation. For Cu, Mn and As, the stochastic variation equaled the spatial structural variation; they made exactly the same contribution to the variation of Cu, Mn and As, indicating that the variation of Cu, Mn and As were under the control of geological and anthropic factors at the same time.

3.5. Spatial distribution of heavy metals The spatial distribution of metal concentrations is a useful aid to assess the possible sources of enrichment and to identify hotspots with high metal concentrations. The estimated maps of Pb, Cu, Cr, Zn, Mn, Cd, As and Hg are presented in Fig. 2; several hotspots of high metal concentration were identified by the geochemical maps. In these metals, Pb and Cu showed a very similar spatial pattern, with contamination hotspots located simultaneously in the north and east of the study area, indicating that they were from the same sources. This provided a refinement and reconfirmation of the results of the statistical analysis, in which strong associations were found between these two metals. In urban areas, many isolated sites with an industrial history indicate extremely high values; these are termed hotspots. There were obvious hotspots for Pb, Cu, Zn, Cd and As in the northeast part of the Shenyang Tiexi Industrial District. The highest concentration of Pb, Cu, Zn, Cd and As all appeared at this location, up to 940, 430, 1140, 9.641 and 137.68 mg/kg, respectively. These concentrations are a result of Shenyang Smelting Plant being located in this area for 64 years, until it was closed down by the government because of heavy pollution in 2000. Started in 1936, the Shenyang Smelting Plant was one of the backbone enterprises of China's metallurgical industry. In the 1980s, it was 69th out of the 500 largest state-owned

Table 5 Parameters and F-test of fitted semivariogram models for heavy metals in urban soils.

Pb Cu Cr Zn Mn Cd As Hg ⁎⁎

Theoretical model

Nugget (C0)

Sill (C0 + C)

C/(C0 + C)

Range

R2

RSS

F-test

Gaussian model Gaussian model Spherical model Spherical model Exponential model Exponential model Gaussian model Exponential model

3540 2848 122 7660 11470 1.011 194.6 0.001

25,920 5697 569.9 31,100 22,950 3.1 389.3 0.07

0.863 0.500 0.786 0.754 0.500 0.677 0.500 0.981

1484 1588 815 1083 3660 5157 2527 360

0.677 0.638 0.339 0.426 0.311 0.503 0.273 0.147

3.15E+ 08 5.25E+ 06 1.71E+ 05 4.43E+ 08 1.57E+ 08 3.83 1.15E+ 05 0.0011

190.73⁎⁎ 160.38⁎⁎ 46.67⁎⁎ 67.54⁎⁎ 41.08⁎⁎ 92.10⁎⁎ 34.17⁎⁎ 15.68⁎⁎

Significance at α = 0.01 level of F test.

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Fig. 2. Estimated Ordinary Kriging concentration maps for Pb, Cu, Cr, Zn, Mn, Cd, As and Hg (mg/kg).

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enterprises of the country. Its gold, silver, copper, lead and zinc products were some of the biggest industries in China, in terms of quantity. According to the local environmental protection department, every year the plant discharged 66.8 t of heavy metals into the air, polluting one-fourth of the total area of Shenyang. This suggested that Pb, Cu, Zn, Cd and As may come from the same source to a certain extent and that industrial activities contributed greatly to the urban soil contamination of the region. Although the Shenyang city government has prioritized the improvement of its environment in recent years, a long time is still required to completely eliminate pollution in this area. There was another pollution hotspot at the northwest corner of the study area. This area saw the most pollution of Cr and Hg, and the second most pollution for Cu, Pb, Zn, and As. Many industrial enterprises, such as metal casting, nonferrous metals, the chemical industry and leather-making, were located in this area. There were also some other heavy metal hotspots. This pollution was mainly the result of industrial processing in the study area. The difference between urban areas influenced by atmospheric deposition and traffic, and the small-scale sites influenced by industrial factors, is evident. The spatial distribution of Zn and Hg were much more heterogeneous than the other metals. This may be the result of different industrial processes. For Cr and Mn, the contamination hotspots were not clearly evident, in contrast to other metals. Their concentration patterns also showed low spatial heterogeneity. This suggested that the concentration of Cr and Mn may not come from point pollution, such as industrial activities, and that natural factors such as the soil parent materials were also an important heavy metal source. It was found from the concentration patterns of 8 heavy metals studied in the Shenyang Tiexi Industrial District that anthropogenic factors played an important role in the heavy metal concentrations of soil, but natural factors were not insignificant, and the effects of these two factors varied. 3.6. Effects of land use on urban soil contamination The land use pattern is presented in Fig. 3a. The total area of the Shenyang Tiexi Industrial District in is 3900 ha. Almost all of the northern Tiexi Industrial District is occupied by industrial enterprises, and the southern part is residential. The industrial land and the residential land (both built before and after 1990) were the two most dominant land use types and occupied 26.55% and 27.29% of the total area, respectively (Fig. 3b). The land under construction in the northern part was from industrial plants and factories that closed or moved out of the city. There are numerous ways in which the land that was used for industrial purposes may have become contaminated. In most industrial manufacturing processes, the delivery, storage and handling of raw materials, the manufacturing process itself, and the disposal of wastes are the three main stages that may result in contamination (Syms, 2004). Contamination from industrial activities may also be dispersed by air and water, resulting in contaminants being spread over a much wider area than the manufacturing site itself. Not only were most of the contaminated hotspots found in the industrial area of the Tiexi Industrial District, but much of the residential area was also heavily polluted by metals such as Pd, Cu, Zn, Cd and Hg. The Tiexi Industrial District has a well-developed road system as shown in Fig. 3(a), and the land area for roads was up to 16% of the total area. Because of the transportation of raw materials and industrial products, busy traffic is an important characteristic of industrial areas. Traffic pollution was also an important source of heavy metals in the study area. There were three large parks and a north–south green belt in the study area. The concentrations of heavy metals in this area were not very high, partly because the park was located in the middle of the western edge and the green belt was built after 2000; the heavy metals

(a)

(b) 1200 1000

Area (ha)

56

800 600 400 200 0

1

2

3

4

5

6

7

8

Land use type Fig. 3. Maps of land use patterns (a) and the areas of different land use types (b) in the Shenyang Tiexi Industrial District (1, Industrial Land; 2, New Residential Land (built after 1990); 3, Old Residential Land (built before 1990); 4, Commercial and Public Facilities Land; 5, Road; 6, Land under Construction; 7, Park and Public Squares Land; 8, Railway).

did not accumulate in the new soils for as long as in the industrial land. To some extent, the parks and green belts could have reduced the spread and expansion of heavy metals from the source to other areas. To quantify the effects of land use on urban soil contamination, the estimated concentration map of heavy metals was overlaid with an urban land use map; then, the distribution of different concentration levels of metals in different land use types were calculated as showed in Table 6. This overlay showed that 86.70% of the Tiexi Industrial District had concentrations of Pb higher than 50 mg/kg, 91.51% of the area had concentrations of Cu higher than 60 mg/kg, 70% of the area had concentrations of Zn higher than 200 mg/kg, 91.5% of the area had concentrations of Cd higher than 0.5 mg/kg, and 77.57% of the area had concentrations of Hg higher than 0.3 mg/kg. These data indicate that much of the urban area was heavily contaminated by Pb, Cu, Zn, Cd and Hg. Regarding land use contamination, industrial land was the most contaminated followed by construction lands and then by roads. For example, of the land area with a concentration of Pb greater than 250 mg/kg, 46.06% of the land was industrial, 31.99% was land under construction, and 11.55% was roads. The high concentration

X. Li et al. / Geoderma 192 (2013) 50–58

57

Table 6 Relationship between the distribution area of different concentration levels of metals in soil and land use types. Concentrations of metals (mg/kg) Pb

Cu

Cr

Zn

Mn

Cd

As

Hg

b20 20–50 50–100 100–150 150–250 250–450 >450 b20 20–40 40–60 60–80 80–100 100–150 >150 b50 50–55 55–60 60–65 65–70 70–90 >90 b100 100–200 200–300 300–400 400–500 500–600 >600 b500 500–550 550–600 600–650 650–700 700–800 >800 b0.1 0.1–0.5 0.5–1.0 1.0–1.5 1.5–2.0 2.0–4.0 >4.0 b15 15–20 20–25 25–30 30–40 40–50 >50 b0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 >0.7

Area of land use types (ha) 1

2

3

4

5

6

7

8

Total

4.71 26.03 211.41 318.03 294.58 122.79 57.24 0.00 0.00 7.53 81.07 287.03 508.47 150.69 0.00 2.12 41.03 250.35 289.59 426.75 24.96 0.20 102.76 606.97 244.60 56.31 16.58 7.38 0.00 38.90 201.51 426.14 269.78 98.47 0.00 0.00 15.71 329.87 383.63 130.98 110.76 63.84 0.00 267.94 342.85 121.80 178.55 118.83 4.83 5.24 187.88 362.90 232.57 119.36 43.40 83.44

8.22 64.32 162.52 121.18 53.41 17.6 4.45 0.00 0.00 46.79 95.44 142.23 137.58 9.66 0.00 4.52 25.32 175.30 202.25 24.31 0.00 3.63 157.87 183.72 74.89 10.15 1.22 0.20 5.81 20.12 52.75 119.46 166.82 66.74 0.00 0.00 48.12 187.80 116.06 21.27 53.83 4.61 1.38 173.56 135.81 76.02 28.83 16.09 0.00 15.95 69.73 196.41 107.55 39.92 2.12 0.00

9.94 140.76 304.97 136.97 36.39 1.86 1.04 0.00 0.00 115.67 249.38 174.89 89.31 2.67 0.00 2.86 46.78 303.19 258.32 20.61 0.16 1.09 283.70 277.49 66.60 1.98 0.10 0.96 9.95 87.82 103.57 133.78 217.36 79.45 0.00 0.00 93.93 358.80 118.92 51.67 7.56 1.04 0.32 357.32 148.13 51.02 74.06 1.08 0.00 45.35 146.35 284.76 115.29 36.16 3.83 0.20

1.75 67.94 142.77 85.37 31.36 8.84 1.63 0.00 0.00 48.57 120.79 76.42 85.47 8.42 0.00 1.33 13.46 115.41 185.44 24.03 0.00 1.01 172.86 128.72 34.14 1.30 0.00 1.63 1.92 10.21 38.55 74.89 165.65 48.46 0.00 0.00 75.13 154.00 66.99 21.32 20.19 2.06 0.00 126.61 97.55 54.31 49.93 11.27 0.00 16.90 76.99 139.06 77.56 18.03 8.05 3.08

4.18 102.93 209.84 170.27 87.11 28.20 16.96 0.00 0.00 63.17 157.34 203.84 159.52 35.62 0.00 0.36 29.50 251.64 277.80 59.09 1.11 2.64 218.82 285.07 89.99 15.80 3.98 3.19 7.74 61.89 84.47 146.87 235.34 82.96 0.24 0.00 69.05 265.61 156.22 62.24 48.95 17.43 0.00 250.44 175.74 73.05 87.95 31.55 0.77 28.68 137.41 233.28 138.58 62.06 12.16 7.32

4.45 47.31 94.24 139.58 75.98 86.67 38.36 0.00 0.00 32.94 58.89 137.84 154.12 102.80 0.00 0.33 25.72 198.95 150.98 84.86 25.76 0.76 95.27 203.99 136.99 44.27 2.32 3.01 5.15 58.63 88.76 151.04 159.37 23.64 0.00 0.00 17.87 126.23 133.85 80.74 88.06 39.85 0.98 121.13 136.33 78.26 91.04 58.63 0.23 5.21 61.47 151.98 111.26 118.88 14.48 23.32

0.00 16.15 117.45 85.47 52.86 3.16 0.00 0.00 0.00 12.22 36.86 132.82 91.81 1.38 0.00 1.62 6.84 132.00 89.28 42.23 3.12 0.73 104.01 126.90 26.28 8.77 4.91 3.49 9.68 77.42 37.42 57.31 58.68 34.29 0.28 0.00 8.54 99.98 123.96 36.02 6.59 0.00 0.00 123.19 123.67 18.34 4.66 5.23 0.00 1.97 53.48 115.57 79.56 13.91 3.62 6.97

9.78 9.81 30.08 14.70 12.56 2.06 0.00 0.00 0.00 4.04 15.76 36.53 20.92 1.76 0.00 0.86 2.57 14.35 27.99 30.41 2.82 0.00 26.48 37.16 14.49 0.59 0.28 0.00 0.11 3.68 8.19 24.14 16.36 26.53 0.00 0.00 2.93 34.02 35.72 6.34 0.00 0.00 4.45 28.66 27.74 9.03 5.20 3.93 0.00 3.40 18.53 32.43 13.99 2.85 2.10 5.71

43.03 475.25 1273.28 1071.57 644.25 271.18 119.68 0.00 0.00 330.93 815.54 1191.60 1247.20 313.00 0.00 14.00 191.21 1441.19 1481.65 712.27 57.93 10.06 1161.78 1850.01 687.98 139.18 29.40 19.86 40.36 358.66 615.21 1133.63 1289.35 460.54 0.52 0.00 331.29 1556.29 1135.34 410.58 335.94 128.83 7.13 1448.86 1187.81 481.83 520.21 246.60 5.83 122.7 751.84 1516.39 876.36 411.17 89.76 130.04

(1, Industrial Land; 2, New Residential Land (built after 1990); 3, Old Residential Land (built before 1990); 4, Commercial and Public Facilities Land; 5, Road; 6, Land under Construction; 7, Park and Public Squares Land; 8, Railway.)

distribution of Cu, Zn, Cd, As and Hg also followed this trend. Although many heavily polluting factories and plants had closed or moved out of the urban areas nearly 10 years ago, historic pollution is still a serious issue in old industrial areas. It is a difficult process to remediate contaminated urban soil. At the same time, the contribution of urban traffic to urban soil contamination is underestimated. 4. Conclusion This study investigated heavy metal soil concentrations of the Tiexi Industrial District, east of Shenyang in China. The results showed

high values of Pb, Cu, Zn, Cd and Hg and revealed the impact of industrial and anthropogenic activities on heavy metal accumulation in the soil of the study area. There are many hotspots contaminated with Pb, Cu, Zn, Cd and Hg caused by nearly a hundred years of intensive uncontrolled discharge of effluents, resulting from the abundance of industries, such as smelting plants. The distributions of the Pb and Cu concentrations showed a very similar spatial pattern. Mn, Cr and As soil contamination levels are relatively low. The Kriging maps of metal concentration indicated that almost all of the areas in the Tiexi Industrial District were polluted by heavy metals and that the high concentrations of Pb, Cu, Zn, Cd and Hg were not only distributed in industrial areas, but were

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