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Journal of Soils and Sediments

, Volume 19, Issue 12, pp 4042–4051 | Cite as

Risk assessment, spatial distribution, and source identification of heavy metal(loid)s in paddy soils along the Zijiang River basin, in Hunan Province, China

  • Zhaoxue Zhang
  • Nan Zhang
  • Haipu LiEmail author
  • Yi Lu
  • Qiang Wang
  • Zhaoguang YangEmail author
Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • 129 Downloads

Abstract

Purpose

This study aimed to reveal spatial distribution of As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, V, and Zn in paddy soils in the Zijiang River basin and to evaluate its pollution status and potential ecological risks, and thus to provide basic information for rational utilization of paddy soils in the study area.

Materials and methods

The heavy metal(loid) concentrations in one hundred and thirty-five paddy soil samples (these samples were collected from the top 0–20 cm layer) were measured by inductively coupled plasma-optical emission spectrometry. The spatial distribution characteristics of the heavy metal(loid)s were depicted by the Ordinary Kriging interpolation analysis. The contamination degree and potential ecological risks of the heavy metal(loid)s in paddy soils were assessed by Nemerow’s comprehensive index, geoaccumulation index, potential ecological risk factor, and potential ecological risk index. The potential sources of the heavy metal(loid)s were deduced by Pearson’s correlation analysis, hierarchical cluster analysis, and principal component analysis.

Results and discussion

The mean concentrations of the heavy metal(loid)s decreased in the order of Mn > V ≈ Zn > Cr > Ni ≈ Pb > Cu ≈ Sb > As > Cd. Except for Cd and Sb, the mean concentrations of As, Cr, Cu, Mn, Ni, Pb, V, and Zn were close to the background reference values. The concentration of Cd in 94.8% of samples exceeded the soil quality standard value (grade II, 5.5 < pH < 6.5, GB 15618–1995). According to the assessments of pollution and potential ecological risks for the heavy metal(loid)s, 45.2% and 46.7% of samples were severely polluted and moderately polluted, respectively. The potential sources analysis indicated that Cd, Sb, and Zn mainly originated from agricultural, mining, and smelting activities; As, Cu, and Pb mainly originated from agricultural activities, while coal combustion by-products was another major source of these heavy metal(loid)s in paddy soils near the thermal power plant in the southwest corner of the study area; Cr, V, Mn, and Ni mainly originated from natural source.

Conclusions

Cadmium and Sb are the main contaminants in paddy soils in the study area, and there are hot-spot pollution areas.

Keywords

Antimony Cadmium Heavy metal(loid)s Paddy soils Risk assessment 

1 Introduction

With the wide application of heavy metal(loid)s in industrial and agricultural production activities, a large amount of heavy metal(loid)s accumulated in soils were very difficult to be removed (Rogan et al. 2009; Römkens et al. 2009; Gao et al. 2016; Marrugo-Negrete et al. 2017). Reports have shown that excessive heavy metal(loid)s can significantly affect the community structure and distribution of microorganisms in soils (Ouyang et al. 2016), thus, in turn, will affect the soil ecosystem structure, and ultimately leads to a series of problems (Lambers et al. 2009; Lü et al. 2018), such as soil energy and matter fluxes associated with the microbial metabolic activities (Leff et al. 2015; Marrero et al. 2015; Wang et al. 2018). Correspondingly, the productivity of the soil might be reduced. In addition, the yield and safety of the crops would be affected since heavy metal(loid)s could be accumulated in crop tissues (Huang et al. 2007; Sridhara Chary et al. 2008) and pose an adverse impact on human health. For example, long-term ingestion of rice contaminated by heavy metal(loid)s can cause many debilitating diseases and psychotic disorders (Coen et al. 2001; Zhao et al. 2011). Thus, heavy metal(loid) contaminants in soils have attracted great attention from the governments, farmers, and researchers internationally.

It was reported that about 10% of agricultural soils were considerably contaminated by heavy metal(loid)s in China (Ma et al. 2016a). Hunan Province is one of the major rice growing areas in China. The rice yield was 12.45 million tons in 2013, which accounted for 9.5% of national rice yield (Ma et al. 2016a). Fig. S1 (Electronic Supplementary Material—ESM) showed the percentages of rice output and tractor plowed area in cities and prefectures in Hunan Province. In the past several decades, a great number of agrochemicals were applied in this region to increase crop yields. According to the Hunan Provincial Bureau of Statistics (2018), from 2000 to 2016, the usage amount of chemical fertilizers and pesticides increased from 6,954,529 to 8,369,673 tons and 85,611 to 118,661 tons, respectively. Besides, Hunan Province also has abundant mineral resources and is well known as the “hometown of nonferrous metals” (Lei et al. 2015). Many reports about heavy metal(loid) contaminants in sediments or mine soils in Hunan Province have been published (Fu and Wei 2013; Ma et al. 2015; Zeng et al. 2015; Fan et al. 2017), but little attention has been paid to the pollution levels and potential ecological risks of heavy metal(loid)s in paddy soils.

The municipal administrative regions of Loudi and Yiyang are located in the central Hunan Province, with their rice yield accounting for 13.2% of the whole province rice yield in 2016 (Hunan Provincial Bureau of Statistics 2018). The area has abundant nonferrous metal mineral resources. As illustrated in Fig. 1, the Xikuangshan antimony mine (the largest antimony deposit in the world) is located at the Lengshuijiang City (Wang et al. 2011), and the Dengshiqiao gold mine and the Zhazixi antimony mine (large antimony deposit in China) are located at the Yiyang City. Our previous study (Zhang et al. 2018) showed that the Zijiang River sediments in Loudi and Yiyang sections were mainly contaminated by Cd and Sb; however, whether the paddy soils are contaminated by the heavy metal(loid)s still remains unknown in the regions. Accordingly, an extensive investigation was carried out in this study to profile the pollution degree, spatial distribution characteristic, potential sources, and potential ecological risks of the heavy metal(loid)s in paddy soils of the study area. The pollution degrees of heavy metal(loid)s were quantitively evaluated by several indexes including Nemerow’s comprehensive index, geoaccumulation index (Igeo), and potential ecological risk index (RI). The associations among heavy metal(loid)s and their common origins were identified by using multivariate statistical techniques including Pearson’s correlation analysis, hierarchical cluster analysis (HCA), and principal component analysis (PCA).
Fig. 1

Map of the study area and sampling sites in the Zijiang River basin

2 Materials and methods

2.1 Reagents and standards

Nitric acid (HNO3, 70%, guaranteed reagent grade) and hydrochloric acid (HCl, 37%, guaranteed reagent grade) were obtained from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, China). Hydrofluoric acid (HF, 40%, guaranteed reagent grade) and perchloric acid (HClO4, 70%, guaranteed reagent grade) were obtained from Aladdin Reagent Co. Ltd. (Shanghai, China). All these reagents were used without further purification. Deionized water (18.2 MΩ cm, Sichuan ULUPURE Technology Co. Ltd., Sichuan, China) was used for preparing all solution. The multi-element standard solution (100 mg/L, 24 elements) was obtained from the National Analysis Center for Nonferrous Metals and Electronic Materials (Beijing, China), and the working standard solution was prepared daily by appropriately diluting the standard solution in 2% HNO3 solution (v/v). The standard reference materials (sediment, GBW07311; soil, GBW07445) were obtained from the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences.

2.2 Sample preparation

Since this study area belongs to the hilly region, the sampling was carried out in paddy fields with an area of 10 m × 10 m within 40 km from the river bank according to the local topographic features. One hundred and thirty-five paddy soil samples were collected at the depth of 0 to 20 cm from the surface around the paddy field of the study area in October 2017 (Fig. 1). Each sample was composed of 4–5 sub-samples within a 10 m × 10 m rectangular plot. Locations and description of sampling sites along the Zijiang River basin were listed in Table S1 (ESM). The collected paddy soils were packaged in clean plastic bags and then taken to the laboratory for further processing. The samples were air-dried at room temperature and passed through a 2-mm sieve to remove plant fragments and stones. After that, the samples were ground into a powder and passed through a 0.15-mm nylon sieve, and then were stored in a desiccator for further analysis.

2.3 Determination of heavy metal(loid) concentrations in paddy soils

The heavy metal(loid) concentrations in the paddy soil samples were determined according to the Chinese Ministry of Environment Protection (CMEP) method HJ 781-2016 (CMEP 2016a) with minor modifications (for determination of Cd, Cr, Mn, Ni, Pb, Sb, V, and Zn) and the CMEP method HJ 803-2016 (CMEP 2016b) for determination of As, and detailed digestion and determination procedures were described in our previously published paper (Zhang et al. 2018). Briefly, to determine the concentrations of Cd, Cr, Cu, Mn, Ni, Pb, Sb, V, and Zn in samples, about 0.2 g soil sample was digested in Teflon beaker with HCl (5 mL), HNO3 (8 mL), HF (8 mL), and HClO4 (4 mL) at a hot plate, and the digestion temperature was 140 °C. After that, the digestion solution was evaporated to about 1 mL, and then was diluted to 100 mL with 5% nitric acid solution. To determine the concentration of As in samples, about 0.2 g soil sample was digested with aqua regia (HCl + HNO3, 3:1, v/v) at a hot plate, and the digestion temperature was 100 °C. Quality assurance and control were conducted by using the standard reference materials (GBW07311 and GBW07445), and the recoveries of targeted heavy metal(loid)s ranged from 85 to 118%. The relative deviation of the duplicate samples was within ± 5%.

2.4 Assessment of heavy metal(loid) contaminations

2.4.1 Nemerow’s comprehensive index (Ps)

The Nemerow’s comprehensive index (Ps) (Chen et al. 2015, 2018) was employed to evaluate the soil comprehensive contamination status for all the heavy metal(loid)s, and it is calculated by the following formula:
$$ {P}_s=\sqrt{\frac{{\left(\frac{1}{n}{\sum}_{\mathrm{i}=1}^n\frac{C_s^i}{C_n^i}\right)}^2+{\left(\frac{C_s^i}{C_n^i}\right)}_{\mathrm{max}}^2}{2}} $$
(1)
where \( {C}_s^i \) and \( {C}_n^i \) are the measured concentration and corresponding background reference value of the element i, respectively; n is the number of targeted heavy metal(loid)s in paddy soils. Table S2 (ESM) showed the classification of Ps.

2.4.2 Geoaccumulation index (Igeo)

The pollution levels of each heavy metal(loid) in paddy soils were evaluated through the Igeo and the formula is shown as follow (Antoniadis et al. 2017):
$$ I geo={\log}_2\left(\frac{C_s^i}{1.5{C}_{\mathrm{n}}^i}\right) $$
(2)
where \( {C}_s^i \) and \( {C}_n^i \) hold the same meanings as that in Formula 1. Factor 1.5 is the correction coefficient. Table S3 (ESM) summarized the categories of Igeo.

2.4.3 Potential ecological risk index

TheRI proposed by Hakanson (1980) was used to evaluate the potential ecological risks of heavy metal(loid)s in paddy soils. The following formulae are used to calculate the RI:
$$ {E}_r^i={T}_r^i\times \frac{C_s^i}{C_n^i} $$
(3)
$$ RI={\sum}_{i=1}^n{E}_r^i $$
(4)
where \( {E}_r^i \) represents the potential ecological risk factor for a single element. \( {T}_r^i \) refers to the toxic response factor (Cd = 30; As and Sb = 10; Cu, Ni, and Pb = 5; Cr and V = 2; Mn and Zn = 1) which mainly provide information about the potential transport routes of toxic substances and the threat to human (Hakanson 1980; Zhao et al. 2012; Chai et al. 2017; Zhang et al. 2018). Table S4 (ESM) summarized the categories of RI and \( {E}_r^i \).

2.5 Statistical analysis

The One-Sample Kolmogorov-Smirnov (K-S) test, Pearson’s correlation analysis, PCA, HCA, and Dunnett’s C test were performed by SPSS software v.19.0. The probability distributions of heavy metal(loid) concentrations were tested by the One-Sample Kolmogorov-Smirnov (K-S) test. The relationships among the heavy metal(loid)s in paddy soils were identified by Pearson’s correlation analysis. PCA and HCA were employed to identify the associations among the heavy metal(loid)s and their potential sources. The validity of PCA was evaluated by the Bartlett sphericity tests (p < 0.001) and the Kaiser-Meyre-Olkin (KMO) value (> 0.5) (Zhang et al. 2018). During the principal component analysis, the option of “correlation matrix” was chosen, which means that the data normalization was automatically carried out by the SPSS software. The principal components were extracted based on the eigenvalues (> 1.0) in PCA and the cumulative variance (> 70%) contribution rate of principal components (Zhang et al. 2018). HCA was conducted by Ward’s method (Varol 2011; Zumlot et al. 2013). The Euclidean distance was selected as a measure of homogeneous subgroups of heavy metal(loid)s with the standardized data sets (Z-scores). The cluster relationships between the ten heavy metal(loid)s were visually demonstrated by the HCA. The difference among concentrations of the heavy metal(loid)s in paddy soils was determined with Dunnett’s C test, and p < 0.05 was used to indicate the statistical significance.

The Ordinary Kriging interpolation analysis was performed by the geography information system (GIS) software, which has been widely applied to predict heavy metal(loid) contents at unsampled or unmeasured locations (Zhao et al. 2010). The applicability of the interpolation analysis was evaluated by the predictive value error analysis function of the software. The interpolation analysis can be used if the results of the error analysis meet the requirements that the absolute value of the mean error and the mean standardized error are approximately equal to 0, the average standard error is approximately equal to root mean square error, and the root mean square standardized error is approximately equal to 1 (Xu 2009).

3 Results and discussion

3.1 Concentrations and spatial distribution characteristic of heavy metal(loid)s in paddy soils

3.1.1 Concentrations of heavy metal(loid)s

Table 1 summarized the descriptive statistics of heavy metal(loid) concentrations in paddy soils. The heavy metal(loid) concentrations had a large variation in all sampling sites. The levels of Mn were the highest in all targeted heavy metal(loid)s, which ranged from 113.07 to 977.02 mg/kg. The mean concentration of Cd was the lowest, and the concentration ranged from 0.21 to 8.40 mg/kg. The mean concentrations of the targeted heavy metal(loid)s decreased in the order of Mn > V ≈ Zn > Cr > Ni ≈ Pb > Cu ≈ Sb > As > Cd. The concentrations of Cd and Sb at all sampling sites were higher than their background reference values (China National Environmental Monitoring Center 1990), while only some of the sampling sites exceeded their background reference values for other heavy metal(loid)s. The maximum concentrations of Cd and Sb were 66.7 and 134.2-fold as high as their background reference values, respectively. Based on the categories of coefficient of variation (CV) (Gao et al. 2016; Zhang et al. 2007), the concentrations of As, Cr, Cu, Mn, Ni, Pb, V, and Zn had a moderate variability (the CV values were between 10 and 90%), and Cd and Sb had an extensive variability (the CV values were greater than 90%). These results suggested that there might exist external inputs for the heavy metal(loid)s in the study area and different degrees of enrichment at some sampling sites. In addition, according to the soil quality standard values proposed by the Ministry of Ecology and Environment of the People’s Republic of China (1995), the concentration of Cd in 94.8% of samples exceeded the soil quality standard value, indicating that the Cd pollution exists in paddy soils of the study area.
Table 1

Descriptive statistics analysis for heavy metal(loid) concentrations (mg/kg) in paddy soils of the study area

Elements

Minimum values

Maximum values

Mean values

Median values

Standard deviation

CV (%) a

Skewness

Kurtosis

Background reference values b

The soil quality standard values c

Percent of exceeding the background values (%) d

Percent of exceeding the soil quality standard values (%) e

As

0.91

39.58

8.88

7.91

5.44

61.25

2.97

13.45

15.70

30

7.4

1.5

Cd

0.21

8.40

0.96

0.69

0.96

99.33

5.07

33.60

0.126

0.3

100

94.8

Cr

49.47

179.70

90.30

84.49

26.03

28.83

1.72

2.95

71.4

250

79.3

0

Cu

7.90

75.33

22.39

20.70

9.19

41.04

1.97

7.59

27.3

50

21.5

1.5

Mn

113.07

977.02

345.46

329.49

157.66

45.64

1.26

2.35

459

NV f

19.3

Ni

15.52

104.29

31.37

27.98

10.99

35.03

3.21

16.47

31.9

70

37.0

1.5

Pb

18.96

62.94

29.47

28.67

5.98

20.31

1.94

7.35

29.7

100

40.0

0

Sb

3.84

251.02

18.10

9.72

32.71

180.68

5.46

32.01

1.87

NV

100

V

62.81

152.77

103.54

103.18

16.99

16.41

0.27

- 0.11

104.4

NV

43.0

Zn

59.52

275.66

102.25

94.00

28.58

27.95

2.59

10.64

94.4

200

48.9

0.7

aCoefficient of variation. bSoil background reference values for heavy metal(loid)s in Hunan Province (China National Environmental Monitoring Center 1990). cEnvironmental quality standards for soils of China, grade II (5.5 < pH < 6.5) (Ministry of Ecology and Environment of the People’s Republic of China 1995). dPercentage of samples which heavy metal(loid) contents in paddy soil samples exceed the background reference values. ePercentage of samples which heavy metal(loid) contents in paddy soil samples exceed the soil quality standard values. fNV is the no criterion value

3.1.2 Spatial distribution characteristic of heavy metal(loid)s

For visualizing the spatial distribution of the targeted heavy metal(loid)s in paddy soils, GIS was used to perform the Ordinary Kriging interpolation analysis for predicting the concentrations of heavy metal(loid)s at those unsampled sites. And the error analysis was carried out to evaluate the applicability of the interpolation analysis. The errors were presented in Table S5 (ESM). According to the judging criterion, the Ordinary Kriging interpolation analysis of the heavy metal(loid) concentrations in this study was acceptable except for the element of Mn. The results of the interpolation analysis were illustrated in Fig. 2, Cd had a hot-spot area in the southeast (close to the Dengshiqiao gold mine area) and in the south (close to the Xikuangshan antimony mine area) of the study area; Sb had a hot-spot area in the south (close to the Xikuangshan antimony mine area); As, Ni, and Pb had a hot-spot area in the southwest; Cu and Zn showed similar spatial distribution with three hot-spot areas in the three different districts of the study area (middle part, southeast, and south); Cr showed spatial distribution with a hot-spot in the north; V had a different spatial distribution pattern from the other heavy metal(loid)s, which showed a uniform distribution trend, and the concentration of V was close to the regional background reference value. These results suggested that the hot-spot areas of the heavy metal(loid)s might be derived from point pollution source except for V.
Fig. 2

Spatial distributions of heavy metal(loid) concentrations in paddy soils

3.2 Assessment of heavy metal(loid) contaminations

Nemerow’s comprehensive index (Ps) and geoaccumulation index (Igeo) were employed to evaluate the contamination levels of the targeted heavy metal(loid)s in paddy soils. As illustrated in Fig. S2 (ESM), Ps values ranged from 2.10 to 95.89. According to the categories of Ps values (Chen et al. 2015; Huang et al. 2018) in Table S2 (ESM), most sampling sites were contaminated by the heavy metal(loid)s (i.e., 45.2%, 46.7%, and 8.1% of samples were severely polluted (Ps > 3.0), moderately polluted (2.0 < Ps ≤ 3.0), and slightly polluted (1.0 < Ps ≤ 2.0), respectively). In addition, the sampling sites nearby the Xikuangshan antimony mine area were subjected to more serious heavy metal(loid) pollutions than other areas, and S58 was the most heavily polluted sampling site.

Figure 3 showed the Igeo values of the heavy metal(loid)s in all sampling sites. The mean Igeo values decreased in the order of Sb > Cd > Cr > Zn ≈ Pb > Mn ≈ V ≈ Cu > As, in addition, the mean Igeo values of Pb and Mn were not significantly different, as well as Mn and Ni. Based on the categories of Igeo values (Liao et al. 2016; Ma et al. 2016b; Antoniadis et al. 2017) in Table S3 (ESM), it is clear that this study area was moderately polluted by Cd and Sb (1 < Igeo < 2), while it was practically unpolluted by As, Cr, Cu, Mn, Ni, Pb, V, and Zn. Table 2 showed the class distribution of Igeo for the heavy metal(loid)s, a small number of sampling sites were heavily polluted or extremely polluted. For Cd, most of the sampling sites were moderately polluted (44.4% and 30.4% of samples were moderately polluted and moderately to heavily polluted (2 < Igeo < 3), respectively). In addition, as shown in Table 1, Cd concentrations in 94.8% of samples exceeded the soil quality standard value. For Sb, also, most of the sampling sites were at levels of moderately polluted (53.3% and 24.4% of samples were moderately polluted and moderately to heavily polluted, respectively). Therefore, it could be concluded that the paddy soils in this study area were mainly contaminated by Cd and Sb.
Fig. 3

Box and whisker plots of the geoaccumulation index (Igeo) for heavy metal(loid)s in paddy soils. Boxes represent 25th, 50th (median), and 75th percentiles and whiskers minimum and maximum values. Mean values (□); outliers (×). The green, orange, blue, magenta, and red dashed lines refer to Igeo different values which were 0, 1, 2, 3, and 4, respectively

Table 2

Class distribution of geoaccumulation index (Igeo) for heavy metal(loid)s in paddy soils (%)

Class

As

Cd

Cr

Cu

Mn

Ni

Pb

Sb

V

Zn

Practically unpolluted

95.6

0.0

83.0

93.3

88.1

89.6

95.6

0.0

98.5

91.1

Unpolluted to moderately polluted

4.4

11.9

17.0

5.2

11.1

9.6

4.4

9.6

1.5

8.9

Moderately polluted

0.0

44.4

0.0

0.7

0.0

0.7

0.0

53.3

0.0

0.0

Moderately to heavily polluted

0.0

30.4

0.0

0.7

0.7

0.0

0.0

24.4

0.0

0.0

Heavily polluted

0.0

11.9

0.0

0.0

0.0

0.0

0.0

5.9

0.0

0.0

Heavily to extremely polluted

0.0

0.0

0.0

0.0

0.0

0.0

0.0

4.4

0.0

0.0

Extremely polluted

0.0

1.5

0.0

0.0

0.0

0.0

0.0

2.2

0.0

0.0

3.3 Potential ecological risks

As shown in Fig. S3 (ESM), the RI values in all the sampling sites ranged from 111.93 to 2842.89. Based on the categories of RI (Ma et al. 2016a; Chai et al. 2017; Huang et al. 2018) in Table S4 (ESM), 12 sampling sites (8.9%) had very high potential ecological risks (RI ≥ 600), 37 sampling sites (27.4%) had considerable potential ecological risks (300 ≤ RI < 600), and 86 sampling sites (63.7%) had moderate potential ecological risks (150 ≤ RI < 300). As illustrated in Fig. S2 and Fig. S3 (ESM), the RI values showed a markedly corresponding relationship with the Ps values. In addition, according to the categories of \( {E}_r^i \) (Ma et al. 2016a; Chai et al. 2017; Huang et al. 2018) in Table S4(ESM), the heavy metal(loid)s showed low risks in the study area except for Cd and Sb. The mean \( {E}_r^i \) values were in a decreasing order: Cd > Sb > As > Pb ≈ Ni ≈ Cu > Cr > V > Zn ≈ Mn. The mean concentration of Cd was the lowest, but it had high potential ecological risks in some sampling sites (i.e., 22 samples (16.3%) had very high potential ecological risks (\( {E}_r^i \) ≥ 320), 46 samples (34.1%) had high potential ecological risks (160 ≤ \( {E}_r^i \) < 320), 55 samples (40.7%) had considerable potential ecological risks (80 ≤ \( {E}_r^i \) < 160), and 12 samples (8.9%) had moderate potential ecological risks (40 ≤ \( {E}_r^i \) < 80), respectively). Moreover, the two sampling sites with the highest potential risks of Cd were S76 (the vicinity of the Dengshiqiao gold mine area) and S58 (the vicinity of the Xikuangshan antimony mine area). For Sb, 6 samples (4.4%) had very high potential ecological risks, 4 samples (3.0%) had high potential ecological risks, 23 samples (17.0%) had considerable potential ecological risks, 79 samples (58.5%) had moderate potential ecological risks, and 23 samples (17.0%) had low potential ecological risks (\( {E}_r^i \) < 40). And the sampling sites with the highest potential risks of Sb were located at the vicinity of the Xikuangshan antimony mine area (i.e., S57, S58, and S60). As shown in Fig. 1 and Fig. S3 (ESM), most sampling sites of the study area showed high potential ecological risks which were mainly caused by Cd and Sb. These results suggested that the contamination of Cd and Sb should be controlled by some necessary measures at some sampling sites.

3.4 Potential sources of heavy metal(loid)s

One-Sample K-S normal distribution test results indicated that the contents of the heavy metal(loid)s were normally distributed (p > 0.05) in paddy soils. The positive kurtosis values (Table 1) of the heavy metal(loid)s illustrated steeper distribution than a normal distribution. The positive skewness values (Table 1) suggested that the heavy metal(loid)s skewed toward lower levels.

Pearson’s correlation coefficient analysis, PCA and HCA were used to identify the potential sources of the targeted heavy metal(loid)s. The values of Pearson’s correlation coefficient matrix were summarized in Table S6 (ESM). The validity of PCA was verified in view of the p value lower than 0.001 in Bartlett sphericity tests and the KMO value of 0.644. The results of PCA (Table 3) indicated that about 76.139% of the total variance were explained by a total of four principal components whose eigenvalues were greater than 1.0. The results of HCA (Fig. 4) showed that these heavy metal(loid)s could be divided into three different clusters.
Table 3

Rotated component matrix of heavy metal(loid)s

Element

Component

1

2

3

4

As

0.076

0.888

− 0.100

0.103

Cd

0.859

0.076

0.069

0.141

Cr

− 0.192

− 0.193

0.768

0.104

Cu

0.113

0.721

0.313

0.153

Mn

0.004

− 0.030

− 0.010

0.940

Ni

0.130

0.328

0.421

0.729

Pb

0.453

0.627

− 0.102

− 0.141

Sb

0.806

0.068

− 0.228

− 0.107

V

0.151

0.240

0.800

0.070

Zn

0.812

0.409

0.252

0.148

Eigenvalues

3.427

2.027

1.136

1.024

% of variance

23.485

20.829

16.446

15.379

% of cumulative

23.485

44.314

60.759

76.139

The loading stands for a factor loading. The value of loading indicates the relationship between the element and the principal component. Generally, the value is greater than 0.5 indicates that there is a relatively large correlation between the element and the principal component. Factor loadings > 0.6 are shown in italics. Rotation method: varimax with Kaiser normalization. Rotation converged in five iterations

Fig. 4

Dendrogram obtained by hierarchical clustering analysis for heavy metal(loid)concentrations in paddy soils

The first principal component (PC1) accounted for 23.485% of the total variance, which has high factor loading for Cd, Sb, and Zn. Therefore, Cd, Sb, and Zn might come from common sources. Cadmium, Sb, and Zn showed significantly positive correlation (p < 0.01) with each other (r = 0.489 for Cd and Sb, r = 0.736 for Cd and Zn, r = 0.566 for Sb and Zn), and cluster 1 included Cd, Sb, and Zn; the results also indicated that Cd, Sb, and Zn might come from common sources. The Zijiang River basin is an important agricultural production area in Hunan Province, and it has plentiful nonferrous metal mineral resources (i.e., the Xikuangshan antimony mine) (Zhang et al. 2018). Some studies reported that the abuse of pesticides or herbicides might enrich Cd, Sb, and Zn in topsoil (Jørgensen et al. 2005; Pierart et al. 2015; Shomar 2006). For example, the range of Cd contents in the phosphate fertilizers in China and in North America are 0.5–3.2 and 16–45 mg/kg (Hu et al. 2016), respectively, and the range of Zn content in fertilizers is 4.87–348.2 mg/kg (Wang and Ma 2004; Zhao et al. 2010; Hu et al. 2016). In addition, residues produced by mining and smelting activities in the study area typically contain high levels of Sb, Cd, and Zn (Zhang et al. 2018), and can release them into the surrounding environment due to permeating, weathering, and dust. Accordingly, these heavy metal(loid)s would be significantly enriched in the soil around the mining area. As illustrated in Fig. 2, the contents of Cd and Zn in sites close to the Dengshiqiao gold mine area were significantly higher than that other areas, and the concentrations of Sb in sites close to the Xikuangshan antimony mine area were significantly higher than other areas. Submicron particles containing heavy metal(loid)s generated by the smelting processes can enter into atmosphere, and then be deposited into water and soil through atmospheric deposition or rainfall or snowfall (Filella et al. 2009). Fu and Wei (2013) reported that wind effect was the dominant factor in controlling the spatial variation patterns of Cd, Sb, and Zn. In summary, Cd, Sb, and Zn in paddy soils were mainly from agricultural activities and mining and smelting activities.

The second principal component (PC2) accounted for 20.829% of the total variance, which has high factor loading for As, Cu, and Pb. Therefore, As, Cu, and Pb might come from common sources. Arsenic, Cu, and Pb showed significantly positive correlation (p < 0.01) with each other (r = 0.506 for As and Cu, r = 0.509 for As and Pb, r = 0.275 for Cu and Pb) and cluster 2 included As, Cu, and Pb; the results also indicated As, Cu, and Pb might come from common sources. Agrochemical containing As, Cu, and Pb (Epstein and Bassein 2001; Acosta et al. 2011) have been widely and chronically applied to enhance rice yield and quality, which may cause the accumulation of As, Cu, and Pb in paddy soils. For example, Robinson and Ayuso (2004) reported that persistent pesticide application in Maine might lead to As accumulation in soils. Nziguheba and Smolders (2008) reported that mineral fertilizer application increased the Pb concentrations in cultivated soils. Epstein and Bassein (2001) reported long-term use of pesticides and fungicides contained Cu led to Cu accumulation in soils. In addition, it was worth mentioning that the contents of As, Cu, and Pb in the southwest (where a thermal power plant located at) of the study area (Fig. 2) were significantly higher than other areas. Moreover, many researches showed that heavy metal(loid)s accumulated in topsoil in the vicinity of thermal power plants (Guttikunda and Jawahar 2014; Wang et al. 2015, 2016). Therefore, As, Cu, and Pb in paddy soils of the study area were mainly derived from agricultural activities, besides, coal combustion by-products was another major source of As, Cu, and Pb in paddy soils near the thermal power plant.

The third principal component (PC3) (has high factor loading for Cr and V) and the fourth principal component (PC4) (has high factor loading for Mn and Ni) accounted for 31.825% of the total variance. It was in accordance with the results of cluster 3 (Mn, Ni, Cr, and V). Chromium was significantly correlated (p < 0.01) with V (r = 0.348), and Mn was significantly correlated (p < 0.01) with Ni (r = 0.558). As illustrated in Fig. 2, V showed spatial distribution with a uniform distribution trend. And the concentrations of V, Mn, and Ni were close to corresponding background reference values. Therefore, Cr, V, Mn and Ni were mainly from natural sources (atmospheric deposition and rock weathering).

4 Conclusions

The mean concentrations of the heavy metal(loid)s in paddy soils decreased in the order of Mn > V ≈ Zn > Cr > Ni ≈ Pb > Cu ≈ Sb > As > Cd, and the mean concentration of Cd was significantly higher than the soil quality standard value. Most sampling sites were contaminated by Cd and Sb, and the potential ecological risks of these sampling sites were mainly from Cd and Sb. Point pollution sources of the heavy metal(loid)s were existed in hot-spot areas except for V. Cadmium, Sb, and Zn mainly originated from mining and smelting activities and agricultural activities. Arsenic, Cu, and Pb mainly originated from agricultural activities which were enriched in the surrounding paddy soils of the thermal power plant. Chromium, Mn, Ni, and V mainly originated from natural source. This research could provide basic information for the local government to remediate and use paddy soils with contamination by the heavy metal(loid)s.

Notes

Funding information

This work was financially supported by the Special Fund for Agro-scientific Research in the Public Interest of China (No. 201503108) and Science & Technology Project of Hunan Province (No. 2017WK2091).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11368_2019_2352_MOESM1_ESM.docx (528 kb)
ESM 1 (DOCX 527 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Environment and Water Resources, College of Chemistry and Chemical EngineeringCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Key Laboratory of Hunan Province for Water Environment and Agriculture Product SafetyChangshaPeople’s Republic of China

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