Determining the anthropogenic contribution of heavy metal accumulations around a typical industrial town: Xushe, China

Journal of Geochemical Exploration 110 (2011) 92–97

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Journal of Geochemical Exploration j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j g e o ex p

Determining the anthropogenic contribution of heavy metal accumulations around a typical industrial town: Xushe, China Shaohua Wu a,b, Shenglu Zhou a,⁎, Xingong Li b a b

School of Geographic and Oceanographic Sciences of Nanjing University, Nanjing 210093, China Department of Geography, University of Kansas, Lawrence, KS 66045, USA

a r t i c l e

i n f o

Article history: Received 21 September 2010 Accepted 4 April 2011 Available online 9 April 2011 Keywords: Heavy metals Component partition Industrial Anthropogenic Soil pollution China

a b s t r a c t Industrial emissions are the major sources of heavy metal pollutions. This study investigates the anthropogenic contribution of heavy metal accumulation surrounding an industrial town in China, using the component partition method. Results show that Cu, Ni, Pb, and As have higher concentrations compared with their background levels and the industrial town has a significant impact on heavy metal accumulations in its surrounding agricultural soil. Prevailing wind direction may explain the difference of heavy metal concentration in different directions. Anthropogenic components of Cu, Ni, Pb and As, which decrease with distance following exponential functions, account for 11.4%, 6.2%, 18.5% and 7.9% of their total concentrations, respectively. The natural components are modeled as the functions of physico-chemical variables through multiple regressions. The accumulating processes of heavy metals affected by industrial activities could be explained by anthropogenic and natural components, and thus, it could provide basic information for further simulation of heavy metal accumulations. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Heavy metal contamination on the environment is widely studied due to its potential hazards to ecosystems and human health. The contribution of metals to environmental pollution from traffic, agricultural and mining processes as well as industrial emissions had been the main subjects of many studies in recent years (Al-Khashman and Shawabkeh, 2006; Cicchella et al., 2008a, 2008b; Li et al., 2008a; Manta et al., 2002; Rehman et al., 2008; Sekhar et al., 2006; Zhou et al., 2007). Anthropogenic activities release metals into the atmosphere, which subsequently deposit into the soils nearby. Due to its high retention capacity, soil is usually regarded as the sink for different metals discharged into the environment (Chen, 2002). Under the influence of long-term human activities, Heavy metal distributions in soils usually have formed decrease gradients from urban to rural (Biasioli et al., 2007; Cattle et al., 2002).Different mechanisms, such as physical, chemical and biological processes, determine metal retention capacity. The relative impacts of those mechanisms depend on the nature of the soil and the composition of the soil solution (Pagotto et al., 2001). Anthropogenic emissions and retention capacity are two main factors controlling heavy metal accumulations in soils. Heavy metal emissions have been declining in some industrialized countries over the last few decades (Ester et al., 2000; Hjortenkrans et al., 2006; Vink, 2002). In developing countries, however, anthro-

pogenic sources have been increasing with rapid industrialization and urbanization (Govil et al., 2008; Huang et al., 2007; Krishna and Govil, 2008; Li et al., 2008b; Sekhar et al., 2006; Wang et al., 2001; Wu et al., 2007). More and more attentions are now focused on the regions where industrialization is underway. China, known as “the world's factory”, has some agricultural soils near industrial development areas contaminated by many potentially toxic elements (Li et al., 2002; Li et al., 2003; Liu et al., 2006; Ni et al., 2007). At high concentrations, the contamination may result in phytotoxicity and the transfer of heavy metals into human diet from crop uptake (Chen et al., 2008). With the raised awareness of soil contamination in industrial towns, there is the need for more detailed information on where and how the metals accumulate in soil caused by industrial development. However, it remains unknown how to partition anthropogenic contribution from total heavy metal concentrations. This needs to be resolved first in order to model heavy metal dynamics and predict future changes of metal levels in environment systems. This study investigates the accumulation of heavy metals around the industrial town of Xushe, in the Yangtze River Delta, China. Specifically, we use component partition approach to determine the anthropogenic component and natural components from total soil metal concentrations. 2. Materials and methods 2.1. Study area

⁎ Corresponding author at: School of Geographic and Oceanographic Sciences of Nanjing University, Nanjing 210093, China. E-mail address: [email protected] (S. Zhou). 0375-6742/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gexplo.2011.04.002

Xushe is a typical industrial town located in the Yangtze River Delta in the Jiangsu Province, China (Fig. 1). The town has a total area

S. Wu et al. / Journal of Geochemical Exploration 110 (2011) 92–97

120 E

93

121 E 32 N

Changzhou

Ρ Xushe

5

Wuxi

Ρ

Suzhou

Ρ

Shanghai

Beijing China

East Chiba Sea

Ρ

Xushe Town

Build-up area Farmland River

31 N

Road 30 km

1 km

Soil sampling sites Major polluter sources

Fig. 1. Study area and sampling sites in Xushe, China.

of 73.6 km2 and permanent residents of 50,000 people. Before 1978, agriculture is the primary economy in Xushe. Since the adoption of the open-door policy in China in 1978, individual, township and joint venture enterprises have developed rapidly in Xushe. Now, there are nearly 500 industrial enterprises, and over 9200 on-the-job employees. Xushe has a typical combination of main industrial products (e.g. textile, chemical industry, electrical cable, environmental protection equipments, and electronics). Industrial output accounts for 67% of its gross domestic product in Xushe. The annual mean temperature and precipitation in Xushe is 15.7 °C and 1158 mm, respectively. The prevailing wind direction is east by south and it occupies 19% of the total direction (Sun and Huang, 1993). The area is a low-lying polder, with a monotonous geological setting, surrounded by agricultural land with scattered houses. All of the investigated area is covered by quaternary fluvial-lacustrine sediment. Paddy soil is the dominant soil type in the area (Department of Soil Survey in Yixing, 1988). In agricultural soil, the background values of heavy metals Cd, Cr, Cu, Ni, Pb, Zn, Hg and As are 0.11, 62.94, 21.55, 27.42, 20.79, 63.96, 0.14 and 8.17 mg kg−1, respectively. Those background values are from the Agricultural-environmental Background Research Program in China, obtained by sampling in the nonpollution area in the Taihu watershed in 1985 (Xia et al., 1997). 2.2. Sample collecting and analysis Samples of soil top layer (0–15 cm) were taken at regularly spaced distance from Xushe. In order to discern heavy metal accumulations in different directions, we sampled 45 locations in four cardinal directions every 200 m, about 3 km away from the town. The locations of these samples are shown in Fig. 1. To avoid local variability, five samples within 10 m diameter of each sampling location were collected and mixed into one sample. The minimum weight of a sample has to be 1 kg. All samples were air-dried at room temperature (20–22 °C) and sieved to 2 mm to remove large plant roots and gravel-sized materials before analysis. Particle size distributions were determined by Laser Particle Size Analyzer. Soil chemistry properties were determined by following the standard procedures (Madrid et al., 2002). The pH values were determined in water with a 1:2.5 soil to solution ratio. Percentage of organic matter in the soil was measured by the titration method, which is based on the oxidation of organic matter by K2Cr2O7. Elements Cd, Cu, Ni, Pb, Zn, Li, Ti, V, Fe, Mn, P, Ca, Na, Mg, and K were determined using Inductively Coupled Plasma-Atomic Emission Spectrograph (ICP-AES), after digesting the soil samples with

analytically pure grade nitric (HNO3), perchloric (HClO4), and hydrofluoric (HF) acids. Hg and As were determined by an AF-640 atomic fluorescence analyzer. The quality of the soil samples was assured by the analysis of duplicate samples, blanks and reference materials. To verify the accuracy of the procedures, certified reference materials (GBW-07405) were treated with the same procedure as the samples were. The recovery of concentration (%R) in the certified reference material is included in Table 1. 2.3. Component partition The spatial distribution of heavy metal in the environment is controlled by three factors: 1) natural background concentration that is inherently determined by soil parent material and soil-forming processes; 2) environmental variables, such as pH, redox, organic matter and so on, that influence heavy metal retention processes in soil; and 3) anthropogenic sources that cause the excessive concentration of heavy metal in soil. At a small scale, the gradients of background concentration and environmental variables are so small that they can be assumed uniform. When heavy metal concentrations have significant gradient structures in a small area, it is likely the result of anthropogenic sources (Wu et al., 2009). Based on such an assumption and by combining trend analysis and multivariate analysis, we developed a method to partition the anthropogenic component from the total concentration in soils (Fig. 2) (Wu et al., 2011). The total concentration can be divided into three components: anthropogenic component (AC), natural component (NC), and unexplained residual (ε): TotalðxÞ = AC ðxÞ + NC ðxÞ + ε

ð1Þ

The sum of the anthropogenic and natural components represents the predictable part of the total accumulation for a given metal in soils. This method is used to determine the anthropogenic contribution of heavy metal accumulations in the industrial town. 3. Results and discussion 3.1. Heavy metal concentrations and physico-chemical properties of soil Basic statistics of the soil samples is shown in Table 1. Cu concentration ranges from 20.31 to 30.42 mg kg−1, with a mean of 25.31 mg kg−1, which is 17.4% more than its background level. Ni fluctuates between 23.26 and 38.81 mg kg−1, with a mean value of

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Table 1 Basic statistics of heavy metals and physical–chemical properties of the soil samples. R is the recovery of concentration in the certified reference material.

Cd (mg kg−1) Cr (mg kg−1) Cu (mg kg−1) Ni (mg kg−1) Pb (mg kg−1) Zn (mg kg−1) Hg (mg kg−1) As (mg kg−1) Li (mg kg−1) Ti (mg kg−1) V (mg kg−1) Mn (mg kg−1) P (mg kg−1) Fe (%) Mg (%) Ca (%) Na (%) K (%) pH SOM (%) Clay (%) Silt (%) Sand (%)

Minimum

Maximum

Mean

SD

R (%)

0.09 43.34 20.31 23.26 35.56 48.90 0.09 7.13 22.29 3281.00 57.93 263.00 376.30 1.97 0.33 0.46 0.83 1.20 5.78 2.15 20.04 31.41 14.72

0.15 70.75 30.42 38.81 53.66 97.43 0.37 12.12 36.95 4763.00 93.59 520.90 1037.20 3.51 0.67 1.08 1.07 1.69 8.15 3.64 45.60 44.03 36.55

0.11 59.44 25.31 29.27 44.47 64.76 0.16 8.89 28.55 4137.42 75.50 346.46 608.21 2.71 0.50 0.65 0.98 1.40 6.60 3.00 38.49 36.25 25.26

0.01 6.78 2.93 3.10 4.05 10.00 0.07 0.93 3.42 343.70 9.62 66.85 159.2 0.32 0.13 0.17 0.06 0.12 0.47 0.36 5.12 5.37 5.41

90% 92% 110% 98% 103% 108% 86% 97% 102% 93% 101% 91% 89% 103% 96% 103% 107% 111% – – – – –

29.27 mg kg−1, which is 6.8% greater than its background level. Pb concentration in soil ranges from 35.56 to 53.66 mg kg−1, with a mean value of 44.47 mg kg−1, which is twice that of its background level. As varies from7.17 to 12.12 mg kg−1, with a mean concentration that is 8.8% higher than its background value. The mean concentrations of Cd, Cr, Hg and Zn are similar to their background values. From the concentrations of Cu, Ni, Pb and As, it can be inferred that the industrial town has a significant effect on heavy metal accumulation in agricultural soil. Soil physico-chemical variables, such as pH, organic matters, grain size, and the concentrations of P, Ca, Mg, Na and K were examined. The pH values, which range from 4.94 to 6.91, suggest weak acidic conditions for all the soil samples. The particle of an average grain size is composed of 25.26% sand, 38.49% clay and 36.25% silt, which represents a clay loam texture. Mean concentrations of P, Mg, Ca, Na and K are 608.2 mg kg−1, 0.50%, 0.65%, 0.98%, 1.4%, respectively, which are similar to the concentrations found in other agricultural soils far away from Xushe. Li, Ti, V, Fe and Mn were also analyzed to reveal geochemical characters of the investigated area. The fact that the concentrations of those metals are close to their background values in the study area suggests that these elements are not significantly affected by industrial activities.

Fig. 2. Component partitions of heavy metal concentrations.

3.2. Heavy metal accumulation in different directions Directional differences in heavy metal concentration are shown in Fig. 3. The concentrations of Cr, Cu, Ni, Pb and Zn, in the west and south directions are higher than those in the north and east directions, though no visual direction difference for Cd and As is found. Duncan's method was used to test the statistical significance of the means. The results show that the concentrations of four heavy metals, Cr, Cu, Ni and Zn, in the west and south directions are significantly different from those in the north and east directions. The concentrations of Hg in south and west directions are also significantly different, whereas no significant difference was found for Pb, Cd and As in four directions. Heavy metal distributions around pollution sources are affected by the prevailing wind direction (Al-Shayeb and Seaward, 2001). The concentration of heavy metals in leeward area is, as expected, higher than that in windward areas. Our results are in agreement with previous research results and conclusions (Al-Shayeb and Seaward, 2001; Žibret and Šajn, 2008). 3.3. Anthropogenic component In order to determine the contribution of anthropogenic contribution to the total concentrations of heavy metals in the agricultural soils surrounding the industrial town, sample in the east direction was used as an example to partition the total concentration. Metal Cu, Ni, Pb and As were selected for trend analysis because they were found at high concentrations. Different trend functions were developed for Cu, Ni, Pb and As. The coefficients of determination (R2) show that first order exponential equations were the most appropriate functions to fit the trend of heavy concentrations and distance. The exponential equations have the following general form: −βx

y = y0 + αe

ð2Þ

where y0, α and β are coefficients and x is the distance from the industrial town. The equation has two parts: y0 and αe− βx. y0 is a constant which, in theory, should be close to the background value of a given element, while αe− βx is the trend which represents the anthropogenic component of a given element. The α coefficient represents the theoretical maximum value of the anthropogenic component. A higher α value represents a greater accumulation of the element as a result of industrial activities. The β coefficient indicates the rate of decreasing concentration. Element concentrations with higher β values decrease more rapidly than those with smaller value with increasing distance (Žibret and Šajn, 2008). Table 2 and Fig. 4 show the fitted trend lines for Cu, Ni, Pb and As. Overall, the values of R2 for the four elements (N70%) show a good fit, indicating that the distributions of heavy metals do follow an exponential function. The y0 values of Cu, Ni and As are very close to their background values. However, y0 of Pb is much higher than its background value (20.79 mg kg−1), indicating that there may be other sources that affect the accumulation of Pb (e.g. road traffic). The AC values at each sample point can be calculated by the equations (anthropogenic component = αe− βx) when the distance (x) is given. Mean values of the anthropogenic component for Cu, Ni, Pb and As are 2.01, 1.17, 8.19 and 0.55 mg kg−1, and account for 11.4%, 6.2%, 18.5% and 7.9% of their total concentrations, respectively. The proportion of the anthropogenic components of Pb and Cu are more than 10%, indicating that Pb and Cu concentrations in soils around the industrial town have been evidently influenced by industrial activities. The range of industrial influence can be defined as the distance where the influence of pollutant drops to a level that we can no longer distinguish between the anthropogenic input and natural presence of the element (Žibret and Šajn, 2008). There are two different situations. When a trend line crosses the background value, the distance between

S. Wu et al. / Journal of Geochemical Exploration 110 (2011) 92–97

North

South

1.1

West

a a

75 60

East

a a

a

bb

b

a a aa

45

bb

30

a a

aa bb

15

Concentration (mg kg-1)

Concentration (mg kg-1)

90

95

0.9

North

East

South

West

a

aa a

0.8 0.6 0.5

0.2

a

ab

0.3

aa a a

b

b

0.0

0

Cr

Cu

Ni

Pb

Zn

Cd

Hg

As

Fig. 3. Heavy metal concentrations in different directions. Data are expressed as mean + SE. Unit for As is × 10 mg kg−1. Lowercase letters indicate difference significant at P b 0.05 by Duncan's tests.

pollution source and the cross point is the range. While a trend line does not cross the background value, we assume the range is the distance from the pollution source to a point where the gradient of the trend line (derivative) reaches a very small value (k). We used a value of 0.1 for k in this study. The range of influence (drange) can be calculated by Eq. (3):

f



 background−y0 −β if background ≥ y0 α   k = ln −β if background b y0 −αβ

=

drange = ln drange

ð3Þ

=

The ranges of influence for Cu, Ni, Pb and Zn are 2105, 990, 3560 and 1219 m respectively. The influence ranges are useful in determining a safe distance from emission sources for cultivated land to minimize metal accumulation in food crops.

The first four principal components were then used as the independent variables in multiple regression analysis to extract the natural component of heavy metal concentration. Multiple regression equations are shown in Table 4. The F-tests showed that the regression models of Cu, Ni, Pb, and As were statistically significant. The determination coefficient for Cu is 63% which means that the regression model explains 63% of the variance in the RAC. Determination coefficients of Ni, Pb and As are 54%, 56% and 57%, respectively. The degrees of fitness highly indicate that environmental variables can explain the distribution characteristics of RACs using the regression equations.

3.5. Predicting heavy metal accumulation The predicted values (sum of the anthropogenic component and natural component) of Cu, Pb, Zn and As were compared with the

3.4. Natural component Remainder of anthropogenic component (RAC) is controlled by the joint effects of soil parent materials and its physico-chemical properties. Therefore, element geochemical characteristics and soil physicochemical properties were used to extract the natural component. Because soil environmental variables are generally correlated, it is improper to conduct regression analysis with them directly. Therefore, we first applied a principal component analysis (PCA) on the environmental variables to eliminate correlations among them. Zeromean unit-variance was made before principal component analysis. The PCA extracts four principal components with their eigenvalues over 1, which account for over 87.5% of the total variation of data set. The factor loading matrix of the principal components after rotation is shown in Table 3. Principal component 1 (PC1) accounts for 47.2% of the total variance, and have high loadings on elements Li, V, Fe, Mg Ti and K indicating the influence of soil parent material and element background level in soils. PC2 explains about 19.4% of the total variance and has high loadings on Mn, P and Ca. PC3, accounts for 14.5% of the total variance with a high loading on SOM. PC4 (6.4% of the total variance) with a high negative loading of clay indicates the influence of soil mineral on the component. Table 2 Coefficients of the exponential equations for Cu, Ni, Pb and As, and the ranges of influence. Element

y0

α

β

r2

Range (m)

Cu Ni Pb As

21.03 26.82 37.42 8.39

7.08 6.8 16 3.7

−0.00124 −0.00245 −0.00064 −0.00296

0.79 0.73 0.76 0.81

2105 990 3560 1219

Fig. 4. Decreasing concentrations of Cu, Pb, Ni and As with the increase of distance from the pollution source.

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Table 3 PCA factor loadings of environmental variables. SOM stands for soil organic matter and PC1, PC2, PC3 and PC4 represent the first, second, third and fourth principal component respectively. Loadings in bold are statistically significant.

pH SOM Clay Li Ti V Fe Mn P Mg Ca Na K

PC1

PC2

PC3

PC4

−0.51 0.41 0.66 0.97 0.81 0.95 0.92 −0.37 0.22 0.94 −0.19 −0.43 0.80

0.67 0.25 0.07 0.11 0.07 0.09 −0.13 0.76 0.72 0.11 0.83 0.18 0.39

−0.42 −0.77 0.31 0.01 −0.03 −0.19 0.20 0.40 0.18 0.02 −0.26 0.77 0.34

−0.16 0.29 −0.62 0.01 0.22 0.10 −0.07 0.11 −0.23 0.07 −0.04 0.39 0.21

Table 4 Multivariate regression equations for the natural components of Pb, Cu, Zn and Cd. Element

Regression equation

R2

P

Cu Ni Pb As

Cu = 20.84 + 0.252PC1 + 0.711PC2 − 376PC3 + 251PC4 Ni = 25.82 + 0.506PC1 − 0.326PC2 − 0.188PC3 + 0.279PC4 Pb = 36.05 − 0.234PC1 + 1.37PC2 − 0.836PC3 + 0.987PC4 As = 8.39 + 0.161PC1 − 0.260PC2 + 0.005PC3 + 0.151PC4

0.63 0.54 0.56 0.57

b0.05 b0.1 b0.1 b0.1

measured values to test the accuracy of the partition method (Fig. 5). The predicted concentrations are consistent with the measured values for the metals. According to scatter diagrams, the predicted concentrations of Cu, Pb, Zn and As are very close to their measured values, and the correlation coefficients between the measured and the

predicted for Cu, Ni, Pb and As are 0.968, 0,896, 0.943 and 0.945, respectively. The good match between the predicted and the measured values indicates that the component partition approach reasonably captures the distribution patterns of heavy metals accumulation around the industrial town. The component partition approach not only could model the spatial distribution of heavy metals near an industrial town, but also, more important, could explain the accumulating processes of heavy metals by anthropogenic and natural components. 4. Conclusions Cu, Ni, Pb and As have high concentrations compared with their natural background levels that indicate some heavy metals have accumulated in the agricultural soils surrounding Xushe as the result of industrial activities in the past three decades. There are some differences in the directional distribution of heavy metal concentrations. The prevailing wind direction may explain the differences. Component partition approach was used to extract the anthropogenic component from the total concentration using trend analysis. Results show that the distributions of anthropogenic components of Cu Ni, Pb and As in the study area decrease with increasing distance following exponential functions. The natural components were described as multiple regression functions of soil physico-chemical variables. Results from the analysis show that the predicted values of Cu, Ni, Pb and As are consistent with their measured values, and the partition method can be used to predict the spatial distribution of heavy metal accumulations. This method provides a useful tool to explain the processes of heavy metal accumulations under anthropogenic influence. Anthropogenic component extracted from the total concentration is useful for modeling heavy metal accumulations affected by industrial activities. However, there are many uncertainties using statistical analysis mythology to determine the anthropogenic contribution of heavy metal accumulations. Finding new and effective auxiliary variables to enhance model accuracy is still much needed in future study. References

Fig. 5. Predicted and measured concentrations of Cu, Ni, Pb and As.

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