Journal of Geochemical Exploration 130 (2013) 15–21
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Journal of Geochemical Exploration
journal homepage: www.elsevier.com/locate/jgeoexp
Environmental geochemical mapping and multivariate geostatistical analysis of
heavy metals in topsoils of a closed steel smelter: Capital Iron & Steel Factory,
Beijing, China
Guo-Li Yuan a, b,⁎, Tian-He Sun a, Peng Han a, Jun Li a
a
b
School of the Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
State key laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, China
a r t i c l e
i n f o
Article history:
Received 24 November 2012
Accepted 28 February 2013
Available online 18 March 2013
Keywords:
Geochemical mapping
Multivariate geostatistical analysis
Heavy metals
Steel smelter
Sources
a b s t r a c t
A high-density survey was conducted in this study to determine the spatial distribution and possible sources
of heavy metals at the former Capital Iron & Steel Factory, Beijing, China. A total of 400 surface soil samples
were collected at a density of 16 samples per km 2, and the concentrations of heavy metals, such as Ni, Cr, V,
As, Cu, Pb, Cd, Zn and Hg, were analyzed. The spatial distribution characteristics of these metals were demonstrated by environmental geochemical mapping. The mean concentrations of Ni, Cr, V and As are close
to the background values. Enrichment factors show that the soil concentrations of Cu, Pb, Cd, Zn and Hg
were higher than the background values, especially Hg. Multivariate geostatistical analysis suggests that
Cu, Pb, Cd and Zn have similar properties and their presence was mainly from steel-smelting activities. However, the Hg contamination was more weakly related to the steel smelting activities, partially due to other
anthropogenic activities, such as the combustion of coal for heating.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
In the last three or four decades, the study of urban soils has emerged
as an important frontier in environmental research (Morel and Heinrich,
2008; Wong et al., 2006). Urban pollution by heavy metals has recently
become a subject of numerous studies because of the serious risk it
poses to the environment and human health (Albanese and Cicchella,
2012; Filippelli et al., 2012; Giaccio et al., 2012; Luo et al., 2011; Yang
et al., 2011).
Urbanization in China has taken place at an unprecedented pace in
the last three decades and will continue at a striking rate over the
next couple of decades. China's urbanization rate increased from
17.9% in 1978 to 45.7% in 2008 (Luo et al., 2012a, 2012b). Given the
rapid industrialization and urbanization, environmental pollution,
including urban soil pollution, has become a very important issue in
China (Chen, 2007; Luo et al., 2012a, 2012b; Wei and Yang, 2010).
Due to the increased demand for housing in China, industrial complexes are being moved from urban areas to the suburbs, and the former
industrial districts are being developed as housing. The people living in
communities built on former industrial sites may be at risk of cancer
and other adverse health effects, even long after the source is removed
(Stephens et al., 2004; Wong et al., 2006). Thus, contamination of
former industrial sites is a critical and pressing environmental issue in
⁎ Corresponding author at: School of the Earth Sciences and Resources, China University
of Geosciences, Beijing 100083, China. Tel.: +86 10 82334657.
E-mail address: yuangl@cugb.edu.cn (G.-L. Yuan).
0375-6742/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.gexplo.2013.02.010
China (Zhang et al., 2005). Soils at former industrial sites are particularly prone to having relatively high concentrations of heavy metals derived from the discharge of a variety of industrial pollutants in the
form of gasses, liquids, and solids (Fakoyade and Onianwa, 2002).
Beijing is the capital of China and one of the largest cities in the
world. Different areas in Beijing have become contaminated with a
wide range of chemicals (Luo et al., 2008). Recently, some papers
have reported heavy metal pollution in Beijing, including soil pollution at former industrial sites (Luo et al., 2008, 2009a, 2009b). However, few of these studies have focused on one specific site or area
located on a former industrial area (Hu et al., 2008; Wang et al.,
2012).
The Capital Iron & Steel Factory was first built in 1919 in Beijing
and further developed after the 1950s. During the 1980s and 1990s,
it became one of the largest factories in China, with approximately
100 000 workers; in fact, it was more of an industrial town than a factory. With Beijing's successful bid to host the 29th Olympic Games in
2008, the most plants were gradually shut down and moved to
sub-cities by 2007. In 2010, the last plant was completely closed.
With the rapid expansion of urban areas in recent years, the sites of
closed factories changed from suburban to urban areas. The former
smelter sites are being planned as commercial and residential areas.
It is well known that smelting activities lead to enormous environmental and soil contamination, especially by heavy metals (Cappuyns
et al., 2006; Juillot et al., 2011; Zhang et al., 2011). It is believed that
the soil in the area of the Capital Iron & Steel Factory is polluted by
heavy metals due to smelting and other industrial activities over the
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G.-L. Yuan et al. / Journal of Geochemical Exploration 130 (2013) 15–21
past 100 years. So far, few reports have focused on the distribution of
heavy metals in the soils of this area.
The present study focuses on obtaining geochemical maps that
reveal the distribution and degree of pollution in factory area soils
related to heavy metals. High-density geochemical mapping is
performed to reveal the detailed spatial distribution of heavy metals
at different functional plants.
In addition to understanding the spatial distribution of heavy
metals in the soil, the determination or discrimination of anthropogenic and geogenic components is an important and significant task
(Acosta et al., 2011; Cicchella et al., 2005; Guillén et al., 2011; Li and
Feng, 2012; Lu et al., 2012; Mrvić et al., 2011; Wu et al., 2011). Most
reports have focused on the anthropogenic influence of industrial
areas on the surface environment and agricultural soils around or surrounding industrial sites (Albanese et al., 2010; Khorasanipour and
Aftabi, 2011; Li and Feng, 2010; Pelfrêne et al., 2011; Stafilov et al.,
2010; Wu et al., 2011). Because former industrial sites such as Capital
Iron & Steel Factory are being planned as areas for commercial and
residential properties, it is essential to determine the origin of
heavy metals for remediation and regulation (Luo et al., 2009a,
2009b, 2012a, 2012b). In this study, multivariate and geostatistical
analysis approaches were used to determine the anthropogenic and
natural components of total heavy metal concentrations in the soil.
samples were ground in an agate grinder, sieved through a
0.15-mm sieve, and stored in brown bottles at 4 °C for chemical
analysis.
2.2. Analytic methods and quality control
Chemical analyses were carried out by the National Research Center for Geoanalysis (China) according to the national standard of soil
environmental quality standards (GB 15618-95). Cr, Fe, Ni, V, Pb, Cu,
and Zn were analyzed using X-ray fluorescence spectrometry
(RS-1818, HORNGJAAN). Cd was analyzed using a graphite atomic absorption spectrophotometer (AA6810 SONGPU). Hg and As were analyzed using an atomic fluorescence spectrophotometer (XGY-1011A).
Standard reference materials (GSS-1 and GSS-4) obtained from the
Center of National Standard Reference Material of China were analyzed as part of quality assurance and quality control (QA/QC) procedures. Good agreement was achieved between the data obtained
from the present work and the certified values, with recoveries between 92 and 108%. Analysis of the samples, including soil samples
and blanks, was set up in triplicate, and the standard deviation was
within 5%. Additionally, 8% blind duplicates were analyzed to check
the quality of analysis.
2.3. Statistical analysis and geochemical mapping
2. Materials and method
2.1. Study area and sampling
The former Capital Iron & Steel Factory is located in the west of
Beijing city. The study area is approximately 20 km 2 and mainly includes four parts, the Yongding River, former plants, the residential
area of workers and an undeveloped hill area in the north of study
area (Fig. 1). The plant area includes a blast furnace and smelting,
steel-casting, pyrogenation, and steel-rolling plants.
During the summer of 2010, 400 soil samples taken from the top
layer (0–20 cm) were collected using a 0.25 × 0.25 km grid in study
area (Fig. 1). To minimize sampling errors, each sample was composed of five sub-samples weighting approximately 1.0 kg, taken
with 2-m-wide area, using a stainless steel drill, and stored in
polyethylene bags, following internationally adopted methods
(Salminen et al., 1998). To ensure sampling quality, the ratio of duplicate sampling was 8%.
The soil samples were air-dried, mixed thoroughly and passed
through a 0.2-mm sieve. Portions (approximately 50 g) of soil
Multivariate statistical techniques, such as Factor Analysis (FA),
Principal Component Analysis (PCA), Cluster Analysis (CA), and
geostatistical analysis are powerful tools for segregating sources contributing to observed pollution (Li and Feng, 2012; Lu et al., 2012).
PCA was interpreted in accordance with the hypothetical source of
heavy metals (lithogenic, anthropogenic or mixed). Varimax rotation
was applied because orthogonal rotation minimizes the number of
variables with a high loading on each component and facilitates the
interpretation of results. Correlation matrix (CM) was used to identify
the relationship between heavy metal contents and soil properties.
Pearson's product moment correlation coefficient was used for normal populations whereas Spearman's rank correlation coefficient
was used for non-normal populations. CA was applied to identify different geochemical groups, clustering the samples with a similar
heavy metal content. CA was undertaken according to the
Ward-algorithmic method. Results are shown in a dendrogram
where steps in the hierarchical clustering solution and values of the
distances between clusters (squared Euclidean distance) are represented (Micó et al., 2006).
Fig. 1. Location of the sampling sites.
G.-L. Yuan et al. / Journal of Geochemical Exploration 130 (2013) 15–21
In this case, the result of the KMO and Bartlett test (0.836) suggested that PCA was suitable for analysis of the data set. Prior to the
Pearson correlation analysis and Cluster Analysis, a non-parametric
Kolmogorov–Smirnov test was used to evaluate the data set distribution. The data were log transformed, and the distribution was
non-normal (Acosta et al., 2011). The Single Linkage method was
used to plot dendrograms. All statistical analyses were processed
using SPSS 19.0 software. MapGis 6.7 software was used for mapping
by Kriging interpolation. Box-plot Intervals (5%, 25%, 75%, 95%, 100%)
are used to map the distributions of the metals.
3. Results and discussion
3.1. Heavy metal concentrations in the soils
The descriptive statistics of the heavy metal concentrations are
summarized in Table 1 after eliminating some abnormal concentrations. The background values of heavy metals in the Beijing region
are also listed for reference and to calculate the enrichment factor
(EF). Herein, EF is the ratio of the mean value of elements in topsoils
to background values. As observed, the EF values of some elements
such as Ni, As, V and Cr, are between 0.95 and 1.08. For other elements, such as Cu, Pb, Cd, Zn and Hg, the EF values are higher than
1.3 and even 2.56 for Hg. The lower coefficient of variation (CV%) of
Ni, As, V and Cr also suggests that the distribution of these elements
in topsoils is relatively homogenous in the area (Luo et al., 2012a,
2012b; Zhao et al., 2010). However, the CV% of Cu, Pb, Cd and Hg is
higher, even 87.5% for Cd and 99.2% for Hg, which suggests that
their distribution is not as homogenous as that of Ni, As, V and Cr
(Wang and Lu, 2011).
Histograms and box-plots are also very useful for the rough estimation of element distribution (Mrvić et al., 2011), as shown in
Fig. 2. The natural concentrations of Ni, As and V are normally distributed in the topsoils; Cr and Cu have an approximately normal distribution; and Pb, Cd, Zn and Hg have a non-normal distribution. In
nature, the distribution of element concentrations is usually normal
if there are no other source inputs (Zhao et al., 2010). Such analysis
results are consistent with the EF values in Table 1.
Parameters such as the 25th percentile, median, 75th percentile
and whiskers are shown in the box-plots. In the cases of Ni, As, V
and Cr, the median is almost in the middle of the percentile box. Additionally, there are few outliers and almost no extreme outliers. In
the cases of Cu, Pb, Cd, Zn and Hg, the concentration median is asymmetric with some extreme outliers, which indicates highly polluted
spots of these metals in the topsoils.
3.2. Spatial distribution of heavy metals in the soils
The distribution maps of the heavy metals are shown in Fig. 3, and
the segment division was according to the concentration of heavy
Table 1
Statistical summary of heavy metals in topsoil (n, 400).
Enrichment factor Beijing background
Fe
Ni
As
V
Cr
Cu
Pb
Cd
Zn
Hg
–
1.02
1.02
0.95
1.08
1.32
1.31
1.76
1.57
2.56
–
24.7
7.70
79.2
60.8
18.7
23.7
0.119
57.5
0.590
a
Min
Max
26.0
153
13.9
37.2
3.50
12.15
57.3
97.9
41.8
100.0
12.2
51.5
17.4
82.8
0.024
1.213
37.1
287.5
0.015
0.927
Mean
CV(%)
50.2
25.1
7.85
74.9
65.9
24.7
31.1
0.210
90.5
0.151
32.3
16.3
19.5
9.7
12.8
27.6
38.4
87.5
49.8
99.2
Concentration: mg/kg for Ni, As, V, Cr, Cu, Pb, Cd, Zn and Hg; g/kg for Fe.
Enrichment factor (EF) calculated as mean/background.
a
Chen et al., 2004; Wu et al., 2010.
17
metals in histogram intervals of 5%, 25%, 75%, 95% and 100%. As introduced, the study area includes four parts, the Yongding River, the former plant area, the residential area of workers and the undeveloped
hill area in the north of study area (Fig. 1). High Ni values
(>27.1 mg/kg) mainly appeared in the hill area in the northern part
of the study area and part of the river area. In the former plant area,
the Ni values are sparsely distributed, with a small area of high concentration. The distribution of three other heavy metals, such as As,
V and Cr, showed characteristics similar to that of Ni. High values of
Cu are mainly distributed in the former plant area close to residential
areas. The distributions of Pb, Cd and Zn are similar, and there are two
obvious high-concentration areas: the former plant area and the residential area, from the center to the east of the study area. High Cu
concentration values are mainly distributed in the joint plant and residential areas. The Hg distribution showed no apparent regularity,
and the high-value areas are distributed randomly in the river, residential and former plant areas. Previous studies have reported Cu,
Pb, Cd and Zn in topsoil near steel plants (Omanayi et al., 2011;
Yang et al., 2012). However, there are relatively few reports on Ni, V
and As in the soils near metal plants (Adamo et al., 2002; Albanese
et al., 2010). The high value distribution area suggests that Cu, Pb,
Cd and Zn in the soils were more directly influenced by the industrial
activities of steel plants and by human activity.
3.3. PCA for heavy metals in the soils
PCA was used to identify the origin of heavy metals in the soil, and
the percentages of variance for each of them are presented in Table 2.
The results of the factor loaded with the quartimax rotation as well as
the eigenvalues and communalities indicate that three components
explain 80.18% of the total variance.
F1 explained 33.67% of the total variance and loadings on Ni
(0.930), As (0.807), V (0.860), and Cr (0.727). As shown in Table 1,
the mean concentration of these four elements is close to the background values, which suggests that they mainly come from weathered soil rather than anthropologic or industrial activities.
F2 explained 33.188% of the total variance and loadings on Cu
(0.601), Pb (0.834), Cd (0.886), and Zn (0.908). The PCA results are
confirmed by the enrichment factors in Table 1, and the EF values of
these four elements are higher than 1.3. It is suggested that these
four elements were affected by anthropologic or industrial activities
in addition to the original content from weathered soil. These four
elements show a close relationship with Fe. The Pearson correlation
coefficients of these elements are also higher than 0.5 under
p b 0.01, such as Cu (0.580), Pb (0.512), Cd (0.601), and Zn (0.598).
Such a result suggests that, at least partially, these elements in the
topsoil are related to the iron-smelting activities. Through PCA, this
group of elements (Cu, Pb, Cd and Zn) is clearly distinguished from
the group of Ni, As, V and Cr. The former contents in the topsoil are
affected by anthropologic influences besides the natural component,
and the latter is close to the natural background values in this area.
F3 explained 13.327% of the total variance and loadings on Hg
(0.923), accounting for approximately half the variability of F3. The
enrichment factor of Hg is higher than the other eight elements at
2.569. However, the Pearson correlation coefficient with Fe is only
0.138. At the same time, the high value of Hg is randomly distributed
in the study area (Fig. 3). Such results suggest that the Hg contamination at the topsoil is not limited to the influence of iron smelting
activities. Luo et al. (2009a) reported that the Hg contamination in
the soil of Beijing city was serious, due in part to the combustion of
coal and leakage from industrial processes. Other studies have
reported that Hg contaminants in urban soils mainly resulted from
the combustion of coal for heating in the north of China (Shi et al.,
2010). In Beijing, the winter heating was dependent on the combustion of coal before the 2008 Olympic Games. Thus, the sources of Hg
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G.-L. Yuan et al. / Journal of Geochemical Exploration 130 (2013) 15–21
Fig. 2. Histogram and box-plot of heavy metals in the topsoil.
G.-L. Yuan et al. / Journal of Geochemical Exploration 130 (2013) 15–21
19
Fig. 3. Spatial distribution of heavy metals in the topsoil.
in the topsoil are distinguished from those of Cu, Pb, Cd and Zn in the
study area.
The spatial distribution of factors is shown in Fig. 4. F1, including
Ni, As, V and Cr, showed high values in the undeveloped hill area,
where the metals mainly originated from the soil materials. The
high values of F2, including Cu, Pb, Cd and Zn, were distributed in
area of the plants, where steel-smelting dominated anthropogenic
or industrial activities. High values of F3 were sparsely or randomly
distributed in the study area, which suggests that the Hg contaminants were not solely from the anthropogenic or industrial activities
of the steel-smelter.
Table 2
Varimax-rotated factor (three-factor) model for soil samples.
Factors
F1
F2
F3
Elements
Ni,As
V,Cr
Cu,Pb
Cd,Zn
Hg
Ni
As
V
Cr
Cu
Pb
Cd
Zn
Hg
Variance%
Cumulative %
0.930
0.807
0.860
0.727
0.466
0.084
0.165
0.149
−0.011
33.667
80.182
0.176
0.026
0.144
0.382
0.601
0.834
0.886
0.908
0.188
33.188
0.074
0.239
−0.146
0.004
0.479
0.381
0.066
0.110
0.923
13.327
Extraction method: principal component analysis.
3.4. Cluster analysis
In previous reports, cluster analysis was used to classify the heavy
metals and help identify the sources, whether anthropogenic or natural (Lee et al., 2006; Li and Feng, 2012; Wang and Sun, 2009). Herein,
cluster analysis was performed to check the results of the PCA for
heavy metals. In this case, the Pearson cluster method was used.
Before clustering, the distribution of heavy metals was tested, and
log transformation of data was performed if the concentration of
one element was not normally distributed. To avoid the influence of
outliers in the concentrations, the clustering analysis was performed
according to the nearest neighbor element method. In detail, the variables in similar properties were classified as a category. Then, the two
categories with the highest similar degree were merged to form a
new category. The merging process was repeated until all individuals
were classified as a category. According to this method, 9 heavy
metals were classified and merged into three distinct clusters with a
standard distance of 15 (Fig. 5). The first cluster included Zn, Cd, Pb
and Cu; the second cluster included Ni, As, V and Cr; and the third included Hg. This result is consistent with that of the PCA discussed in
Section 3.3. In this case, Fe was also used for cluster analysis.
According to Fig. 5, Fe shows a higher degree of relationship with
the first cluster, including Zn, Cd, Pb and Cu, than the other heavy
metals. Such a result is also consistent with the discussion in
Section 3.3, that is, the sources of the Zn, Cd, Pb and Cu contaminants
were most likely associated with Fe, which mainly originated from
steel-smelting activities. The cluster analysis result also verifies that
the properties of Hg in the topsoil were different from those of the elements in the second cluster. In other words, the source of Hg in the
soil was different from that of Zn, Cd, Pb and Cu.
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Fig. 5. Hierarchical clustering of heavy metals in the topsoil.
characteristics of Cu, Pb, Cd and Zn are also similar. The Pearson correlation analysis between these elements and Fe indicates that these anthropogenic contaminants may have originated from iron-smelting
activities. On the other hand, the Hg contaminant was due to in part
to the combustion of coal and leakage from industrial processes. This
study demonstrates that the combination of environmental mapping
and multivariate geostatistical analysis can be an appropriate tool to
characterize spatial distribution of heavy metals and to determine
their sources.
Acknowledgments
This research was financially supported by the Fundamental
Research Funds for the Central Universities (2010ZY24, 2011YXL017,
2011YYL136) and the Specialized Research Fund for the Doctoral
Program of Higher Education (20090022120001).
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Fig. 4. Spatial distribution of the factor score (F1, F2 and F3) obtained by factorial analysis (n, 400).
4. Conclusion
The results of this study show that although iron-smelting activities may lead enormous heavy metal contamination, the concentrations of Ni, As, V and Cr in the topsoil of the studied factory are
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