Detailed Analysis of Results

This section deals with the in-depth analysis of the soil such as their heavy metal characteristic value, the heavy metal soil pollution, and their potential ecological risk assessment.

Soil Heavy Metal Pollution

Characteristic Value Analysis of Heavy Metals in Soil

Table 17.2 shows the statistical data of the five heavy metals focused in the current research responsible for soil pollution in the Suxian region mainly by the determination of certain indices and parameters like the Nemero index, the single factor index, the coefficient of variation, and the average metal content. Once these parameters have been determined, the next step is to determine the soil pollution limiting values as shown in Table 17.2. The standard limits for As, Zn and Hg are 40 mg/g, 200 mg/g and 0.3 mg/g, respectively. Analysis studies suggested that As and Zn had already exceeded the prescribed limit with values as high as 78 and 203 mg/g, respectively whereas the mercury content more or less remained the same, suggesting that high pollution risk may exist due to the presence of As and Zn in more than prescribed safety limits. As mainly stays in soil in inorganic form, Pb and Zn are found in non-residual states, whereas Cu is found as a sulfophilic element (Huang et al., 2012; Fang, 2016). The

TABLE 17.2

Pollution of Soil with Heavy Metals

Mean Value (mg/Kg)

Standard

Deviation

Scale (mg/kg)

Skewness

Kurtosis

Co-efficient of Variation

Background Value (mg/ kg)

Two Levels of Environment Quality Standard

(mg/kg)

Index of

Single

Factor

The

Nemerow

Index

As

78.70

98.70

1.31-547.17

2.61

6.92

125.00

14.00

40.00

5.62

11.60

Си

37.96

54.36

3.01-611.46

7.72

78.11

143.00

26.00

150.00

10.41

Hg

0.31

0.24

0.02-1.28

0.71

0.70

77.00

0.096

0.30

1.46

Pb

160.19

265.33

0.29-2566.78

5.42

42.71

166.00

27.00

250.00

5.93

Zn

202.79

201.56

22.02-1686.8

3.63

20.22

99.00

94.40

200.00

2.14

  • (Data obtained with permission from Chen et al. Copyright © 2018, Elsevier) single factor index method, which basically shows the pattern by which heavy metals get deposited in the soil, can be seen in detail, as described by Guo. The pattern for the considered heavy metals is as follows Hg> Pb> As> Zn> Cu, all having a value greater than one. Hg show's the highest accumulation in soil with a single factor index value of 10.41. The Nemero index, on the other hand, focuses on the effect of the heavy metals with very high concentrations impacting the quality of the soil (Kowalska et al„ 2016; Yang et al.,
  • 2014). As per the Nemero index, a value less than one is considered to be non-polluting in nature, metals having values between 1 and 2 pose slight caution with regard to heavy metal polluting the soil and for metals having values greater than 3 are considered to be high-risk metals causing soil pollution. An analysis of the Nemero index in the present case study suggested a value of 12.1 which definitely posed a threat in terms of polluting the soil. Coefficient of variation is also calculated w'hich helps in determining the variation of metals at each sampling point (Table 17.1) (Manta et al., 2002). A value greater than 1 suggests the high influence of human activity on the metal pollution of soil. The metals considered in the present case study follow' the pattern as described Pb> Cu> As> Zn> Hg. The Suxian district, being highly rich in minerals, is w'idely exploited by human activities such as mining, which brings about huge imbalances in the environment (Saleh, 2015b). Results suggest that a very high Single index factor and the Nemero index factor are evidence for a strong soil pollution scenario and that the cause is extensive human activity.

Heavy Metal Pollution in the Case Study Area

The heavy metal content in the soil is basically analyzed using the IDW interpolation in ArcGIS (Figure 17.1). The area in which mining activities are conducted is marked as red. Mercury has been found to have a very high accumulation rate compared to the other four metals. This accumulation is basically found at the point of intersection of three towns: Tangxi. Bailu and Da Kuishan. Smaller regions had a high distribution of arsenic, lead and zinc, whereas the mercury content could be found mainly in the regions of Qi Fengdu, Wu Lipai, Ma Touling, Qiaokou, Aoshang and Liangtian w'ith extremely high values in the Shi Zhuyuan-Manaoshan area. Smaller areas such as Qi Fengdu may also be influenced by the presence of mercury, but relatively less in comparison to the above-mentioned areas. Heavy metal accumulation is as a result of various human activities, including the processing of minerals and transportation, especially in mining areas. The Suxian mining industry saw a wide-scale development during the early 1980s; however, these developments were mainly substandard. As a result, large numbers of bottlenecks w'ere observed, which proved extremely harmful for the environment at large. The riverbeds were polluted due to the wastewater from the mining industry finding its way to various rivers; moreover, during monsoons the rain would also wash away the accumulated heavy metals into farmlands, thereby destroying crops in the neighboring area. The wastewater and the waste ores were found to be the main source of all these causes. Thus the junction points of various towns in the township were mainly found to be the primary location for heavy metal soil pollution.

Ecological Risk Assessment of Heavy Metals

Single Factor Ecological Risk Assessment of Heavy Metals in Soil

The various ecological risks and impacts of the heavy metals considered for the case study w'as carried out using the interpolation method known as the ArcGIS

(a) The distribution of As

FIGURE 17.1 (a) The distribution of As (left) and Cu (right) contents in soil, (b) The distribution of Hg (left) and Pb (right) contents in soil, (c) The distribution of Zn contents in soil(Re- produced with permission from Chen et al. Copyright © 2018. Elsevier).

(Continued), (a) The distribution of As

FIGURE 17.1 (Continued), (a) The distribution of As (left) and Cu (right) contents in soil, (b) The distribution of Hg (left) and Pb (right) contents in soil, (c) The distribution of Zn contents in soiKReproduced with permission from Chen et al. Copyright © 2018. Elsevier).

technique. Figure 17.2 well presents the different results of the risk assessment for the study. Mercury was found to pose a profound risk on the townships of the Suxian district, whereas metals such as zinc and copper posed medium-level threats. High-risk level areas which show the Hg pollution is represented by a deep colour. A pollution risk of level 3 for As and Pb has been observed in townships

(a) Single-factor ecological risk index of Zn in soil,

FIGURE 17.2 (a) Single-factor ecological risk index of Zn in soil, (b) Single-factor ecological risk index of As (left) and Cu (right) in soil, (c) Single-factor ecological risk index of Hg (left) and Pb (right) in soiKReproduced with permission from Chen et al. Copyright © 2018. Elsevier).

(Continued), (a) Single-factor ecological risk index of Zn in soil,

FIGURE 17.2 (Continued), (a) Single-factor ecological risk index of Zn in soil, (b) Singlefactor ecological risk index of As (left) and Cu (right) in soil, (c) Single-factor ecological risk index of Hg (left) and Pb (right) in soiKReproduced with permission from Chen et al. Copyright ©2018, Elsevier).

near Bai Lutang. Tang Xi and Da Kuishang. The risk posed by heavy metal deposition is estimated by the ArcGis interpolation method. Each township considered in this study had different levels of pollution with Cu and Zn posing the least threat amongst the five considered metals for the present case study in the Suxian district.

Risk Assessment and Its Adjustment at the Township Scale

Potential Ecological Risk Assessment of Arsenic

The risk posed by arsenic at different levels for the townships in Suxian district is shown in Table 17.3. The area was subdivided into five levels of risk: 59.4% of the area was exposed to very low pollution risks (level 1); 24.5% posed a level 2 risk; 10% had a level 3 risk; 6.1 % had level 4 risk; and 0.02% had a level 5 risk. The moderate-risk regions (level 2) are mainly distributed in the regions of Bai Ludong, Ma Touling, Bai Lutang and Tang Xi. Level 3 risk areas are located in Bai Lutang, Tang Xi and Da Kuishang. Local pollution is also observed in areas surrounding the mining areas, such as Ma Touling, Gang Jiao, Bai Ludong. Moderate-risk zones were identified as Bai Ludong, Tang Xi and Bai Lutang, whereas the strong risk areas were identified as Bai Lutang, Tang Xi and Da Kuishang. The villages with level 4 risks were identified to be:

Ma Touling town-Ban Zilou

Tang Xi town-Heng Long, Wu Malong, Shang He, Shi Hu

Bai Lutang town-Dong Bo, Shi Zhuyuan, Bai Lutang, Xiang Shan-ping

Da Kuishang town-Liang Sangoing, Tai Pingtou.

Since a large area in the Suxian district is surrounded by mining areas, the release of the minerals due to mining into the soil might result in pollution caused by arsenic. Hence, locations which are closely related to the areas near the mining industry need to be analyzed properly in order to improve the soil quality (Saleh et al„ 2011).

Potential Ecological Risk Assessment of Mercury

Suxian district had a serious risk of mercury (Hg) pollution, with 56% of its total area posing a level 3 risk and 26% a level 4 risk. Table 17.4 gives details of the various risk levels in the townships.

Areas like Gang Jiao, Tang Xi, Da Kuishang and Bai Lutang are at a level 3 risk for Hg contamination. Areas excluding the Na Tub, Liao Jiawan, Gang Jiao and Su Xianling subdistricts of Suxian had very high Hg risks. Level 4 risk exists for 40% of the total areas for Ao Shang, Wu Lipai and Qiao Kou, whereas level 5 risk exists for nearly 85% of the total areas for Wu Lipai, Liang Tian and Aiao Kou. Hg leakage has been one of the major causes for such a high rise in risk level of different areas in Suxian (Saleh, 2015a). Nearly 50% of its area is at either moderate or very high risk

Township

Level 1 (Slight)

Level 2 (Moderate)

Level 3 (Strong)

Level 4

(Quite Strong)

Level 5

(Extreme Strong)

Area, in2

%

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

Liao Wangping

1612.30

2.28

582.81

2

Gang Jiao

996.98

1.41

2348.97

8.06

346.13

2.89

Qi Fengdu

2002.84

2.83

1242.53

4.27

Tai Ping

2588.60

3.66

2.96

0.01

Wu Lipai

6822.07

9.65

106.50

0.37

Ma Touling

550.26

0.78

3579.66

12.29

562.10

4.70

349.09

4.8

He Yeping

4819.23

6.82

2218.80

7.62

159.76

1.33

Xu Jiadong

4413.93

6.24

1136.03

3.90

5.92

0.05

Qiao Kou

8987.62

12.71

745.52

2.56

174.55

1.46

Bai Ludong

251.46

0.36

3121.11

10.71

739.60

6.18

Bai Lutang

402.34

0.57

4819.23

16.54

4848.82

40.50

3121.1

42

29.5

1

Tang Xi

2597.48

3.67

5262.99

18.06

2479.14

20.70

3242.4

44

Su Xianling

165.67

0.23

Nan Та

109.46

0.15

Ao Shang

7567.59

10.70

1251.40

4.29

325.42

2.72

130.17

1.7

Liang Tian

10700.5

15.13

2387.43

8.19

2322.34

19.39

417.13

5.7

Da Kuishang

7303.25

10.33

112.42

0.39

Deng Jiatang

4523.39

6.40

82.84

0.28

Liao Jiawan

4271.93

6.04

124.25

0.43

Total

70690.9

59.37

29125.4

24.46

11963.7

10.05

7259.9

6.1

29.5

0.02

Note: % (except the "Total” column) represents the proportion of the area for a certain risk level to the total area of the same risk level: the % in the "Total” column represents the proportion of area for a certain risk level to the total area in research zone.

(Data obtained with permission from Chen et al. Copyright © 2018, Elsevier)

Township

Level 1 (Slight)

Level 2 (Moderate)

Level 3 (Strong)

Level 4

(Quite Strong)

Level 5

(Extreme Strong)

Area, in2

%

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

Liao Wangping

38.46

0.75

641.98

4.42

1514.7

2.26

Gang Jiao

38.46

0.75

3121.11

21.4

532.51

0.80

Qi Fengdu

411.22

8.01

295.84

2.04

502.93

0.75

2032.4

6.4

2.9

0.28

Tai Ping

88.75

1.73

147.92

1.02

1849

2.76

505.89

1.6

Wu Lipai

201.77

3.92

322.47

2.22

1118.28

1.67

5135.7

16

150

14.06

Ma Touling

153.84

3

269.21

1.85

2141.88

3.20

2476.1

7.8

He Yeping

207.09

4.03

491.09

3.38

4848.82

7.25

1650.7

5.2

Xu Jiadong

224.82

4.38

597.60

4.11

3159.56

4.72

1573.8

5

Qiao Kou

124.25

2.42

301.76

2.08

4132.88

6.18

4644.6

14

704

65.38

Bai Ludong

106.50

2.07

307.67

2.12

3124.07

4.67

573.9

1.8

Bai Lutang

372.76

7.25

1529.49

10.5

8946.20

13.37

2372.6

7.5

Tang Xi

461.51

8.98

1470.32

10.1

9694.68

14.49

1955.5

6.2

Su Xianling

165.67

3.22

Nan Та

2.96

0.06

2.96

0.02

53.25

0.08

50.29

0.1

Ao Shang

227.80

4.43

541.39

3.72

4946.44

7.39

3396.2

10

162

15.02

Liang Tian

204.13

3.97

431.93

2.97

4067.80

6.08

2665.5

8.4

50.2

4.64

Da Kuishang

606.47

11.79

1922.96

13.2

11026

16.48

2272

7.2

Deng Jiatang

458.55

8.91

1067.98

7.35

2973

4.44

106.5

0.3

Liao Jiawan

1035.44

20.11

1059.11

7.28

2245.43

3.36

56.21

0.1

Total

5129.86

4.31

14522.7

12.2

66877.6

56.17

31468

26

1070

0.9

Note: % (except the 'Total” column) represents the proportion of the area for a certain risk level to the total area of the same risk level: the % in the "Total” colume represents the proportion of area for a certain risk level to the total area in research zone.

(Data obtained with permission from Chen et al. Copyright © 2018. Elsevier) levels of heavy metal pollution of soil and thus extensive testing must be carried out to curb the occurrence of pollution by Hg and similar heavy metals and to regulate the transportation and exploitation activities near the metal mining areas.

Studies were also performed in order have secure an insight into the pollution scenario caused by the lead (Pb). The risk level for Pb pollution was identical to that of arsenic, i.e. level 3. The majority of the area had lower Pb pollution risk levels. The level 3 risk of pollution was profound in Tang Xi-Wu, mainly Ba Lutang, Tang Xi and Da Kuishang. By comparison, level 4 and level 5 risks were found in Bai Lutang and Da Kuishang, respectively, which are relatively closer to the main mining areas and other activities such as smelting, ore dressing, etc. (Table 17.5). After the analysis of the township areas, the analysis was further continued into villages to identify the potential risks of Pb pollution. Areas like Heng Long, Wu Malong, Shang He, Dong Bo, Shi Zhuyuan, Bai Lutang, Xiang Shanping, Liang San-ping, Tai Pingtou, Bao Anling and BaiXi were identified as areas with considerable risk of such pollution.

Comprehensive Ecological Risk Evaluation of Heavy Metals in Soil

Figure 17.3 gives detail of the risk levels of the considered metals in Suxian district with majority of the areas having a level 3 or above risk. Areas near the mining site of Shi Zhuyuan, such as Tang Xi, Da Kuishang and Bai Lutang, pose a level 4 threat. The meeting junction of all these three townships had a level 5 risk. Table 17.6 shows the area of each township that is threatened by heavy metal pollution. Strongly polluted areas are mainly observed for Ma Touling, Bai Lutang, Da Kuishing. Qiao Kou, Tang Xi and Ao Shang, all of which have a threat of level 4 or more. The main reason for such high-risk pollution scenarios in these areas is because of the fact that large activities occur in the mining area, such as wastewater discharge, leaching, the tailing of heavy metals, other domestic and agricultural activities (Gupta et al„ 2012; Saleh, 2015c). It has thus been suggested that the control of such damaging activities should be given immediate attention and control measures should be implemented, mainly in the Bai Lutang and the Tang Xi areas.

 
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