Results and Discussion

Heavy Metals in Soil Based on Township Scale

The soil around the mining areas is prone to large-scale heavy metal pollution risk. Detailed study of the pollution causing metals still remains a constraint due to shortfalls in the analysis methods and availability of different standards for establishing the specific pollution areas. Specializing in the recognition of small-scale regions for risks in the heavy metal pollution area. To determine the different levels of pollution in the Suxian district, one needs to consider the district into divisions of townships. Such division would help determine the entire condition of soil pollution by heavy metal pollution near the mining areas.

Township

Level 1 (Slight)

Level 2 (Moderate)

Level 3 (Strong)

Level 4

(Quite Strong)

Level 5

(Extreme Strong)

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

LiaoWangping

2195.1

2.30

Gang Jiao

3635.87

3.81

56.21

0.39

Qi Fengdu

2890.36

3.03

355.01

2.49

Tai Ping

2591.56

2.71

Wu Lipai

6928.57

7.25

Ma Touling

4526.35

4.74

514.76

3.61

He Yeping

5943.43

6.22

1254.36

8.79

Xu Jiadong

5555.88

5.82

Qiao Kou

8600.07

9.00

639.02

4.48

656.77

7.25

11.83

5.9

Bai Ludong

3609.25

3.78

502.93

3.52

Bai Lutang

5783.67

6.05

4337.13

30.4

3008.6

33.20

82.84

41

8.88

33.33

Tang Xi

6431.56

6.73

4115.13

28.8

3035.3

33.49

Su Xianling

165.67

0.17

Nan Та

109.46

0.11

Ao Shang

8517.23

8.92

541.39

3.79

215.96

2.38

Liang Tian

7419.67

7.77

Da Kuishang

11617.6

12.16

1943.67

13.6

2144.8

23.65

103.5

50

17.7

66.67

Deng Jiatang

4606.23

4.82

Liao Jiawan

4396.18

4.60

Total

95523.7

80.23

14259.4

11.9

9061.5

7.61

198.21

0.1

26.6

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)

Total potential ecological risk index of the Suxian District (revision evaluation criteria) (Reproduced with permission from Chen et al. Copyright © 2018. Elsevier)

FIGURE 17.3 Total potential ecological risk index of the Suxian District (revision evaluation criteria) (Reproduced with permission from Chen et al. Copyright © 2018. Elsevier).

The case study in this chapter would provide a detailed assessment of the statistics at different levels of the township, thereby aiding the proper efficient management of the soil pollution scenario. In order to give an example of the results that might be useful in the control of soil pollution, arsenic is considered as the metal of importance and in order to determine the various ecological risks associated with it the

Township

Level 1 (Slight)

Level 2 (Moderate)

Level 3 (Strong)

Level 4

(Quite Strong)

Level 5

(Extreme Strong)

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

Area, m2

%

LiaoWangping

692.27

24.84

1502.8

9.89

Gang Jiao

79.88

0.53

3464.29

4.42

147.92

0.6

Qi Fengdu

91.71

3.29

313.59

2.06

2493.93

3.18

346.13

1.5

Tai Ping

44.38

1.59

260.34

1.71

2286.84

2.92

Wu Lipai

106.50

3.82

461.51

3.03

5322.16

6.78

1038.4

4.7

Ma Touling

23.67

0.85

295.84

1.95

3106.95

3.96

1615.2

7.4

He Yeping

260.34

1.71

6830.95

8.72

106.5

0.5

Xu Jiadong

91.71

3.28

967.40

6.36

4372.52

5.58

124.25

0.6

Qiao Kou

147.92

5.29

707.06

4.65

6964.07

8.89

1928.8

8.8

Bai Ludong

100.59

0.66

3748.29

4.78

263.3

1.2

159

17

Bai Lutang

11.83

0.42

387.55

2.55

6896.03

8.80

5390.2

24

Tang Xi

8.88

0.32

775.10

5.09

6925.61

8.84

5872.4

26

535

59

Su Xianling

165.67

0.21

Nan Та

5.92

0.21

76.92

0.51

26.63

0.03

Ao Shang

82.84

2.96

114

7.54

6718.53

8.57

1304.6

5.9

Liang Tian

201.17

7.18

1020

6.71

5810.30

7.41

387.5

1.8

20.7

2.3

Da Kuishang

633.10

22.59

2772

18.21

8937.33

11.40

3304.5

12

Deng Jiatang

236.67

8.44

2159

14.19

2209.92

2.82

180

19

Liao Jiawan

408.26

14.56

1914

12.58

2073.84

2.65

Total

2786.81

2.34

15203

12.77

78353

65.8

21830

18.33

896.39

0.75

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) single factor ecological risk level and the arsenic ecological risk condition for each subdivided township has been considered as shown in Figure 17.2 and Table 17.3. Figure 17.2 shows a visual interpretation of the various risk-level areas in the townships with detailed positions and area. A township with very high-risk level consisting of various subdivision of villages is further distributed into

Level 1 village and Level 2 village

Townships with less or moderate risk levels of heavy metal pollution

Townships and villages are classified into different levels for carrying out the precise treatment based on the extent of pollution.

Level 1: Villages with very high risk level of pollution

Level 2: Villages considerably high risk level of pollution and townships with moderate risk levels

Utilizing a smaller scale for identifying the pollution risk level helps in better management of soil pollution problems and also solves high metal pollution risks in small regions.

The Adjustment of the Potential Ecological Risk Assessment Domain

The determination of an index such as Risk index (RI) helps in determining the risk levels of pollutants in various levels in a district or a village. One such method is the Flakanson method, which mainly requires the presence of at least eight heavy metals to carry out the risk index analysis. Adjustments were made in the evaluation method and the evaluation standards to have a more accessible approach and obtain results with good practical viability (Figure 17.3 and Figure 17.4). Analysis suggested that identification of risk areas almost remained the same as the original method, but showing a slight lower risk level when standards were not adjusted. Without adjustments in standard adjustment level 3 was also obtained. Table 17.6 shows the comparative results of the risk levels before and after adjustment of assessment domain. As already mentioned, adjustments made in the standards causes a decrease in the level 1 and level 2 areas by almost 15% and 50%, respectively. However, without adjustments the analyzed risk levels were not at par with the real-life scenario. With adjustments, level 3 was the main risk level for 66% of the area of Suxian district with some regions also predicting level 5 risk hazard which could not be found in the original research analysis. Hence, adjustments must be made in the domains to portray the real-life scenario.

Selection of Spatial Interpolation Methods for Heavy Metals in Soil

A large variety of interpolation methods are available for carrying out the analysis of heavy metal soil pollution such as IDW, Spline and Kriging. There exists no definite conclusion on determining the best method from the three; however, literature research suggest that IDW might be one of the best methods (Ferguson, 1996; Weber and Englund, 1992; Laslett et al., 1987). In addition, a definite standard does not exist and a variation in primary research objectives might not produce accurate results

The Hakanson total potential ecological risk index of Suxian District (Reproduced with permission from Chen et al. Copyright © 2018, Elsevier)

FIGURE 17.4 The Hakanson total potential ecological risk index of Suxian District (Reproduced with permission from Chen et al. Copyright © 2018, Elsevier).

when it comes to the scenario of analysis of risk levels for heavy metal pollution (Mcshane et al., 1997; Dlamini and Chaplot, 2012).

It has also been observed that IDW is mainly suitable for non-normal distributions (Xia, 1968). Table 17.7 shows the K-S results to determine if the heavy metals considered in this chapter follow a normal distribution. Results suggest that bilateral

TABLE 17.7

The Significant Remediation Village and Level for as High Pollution Risk

Pollution Remediation Level

Township

Village Name

Level 2 (quite strong ecological risk)

Level 2 (Strong ecological risk)

Tang Xi Bai Lutang Ma Touling Da Kuishang Tang Xi Bai Lutang Bai Ludong Gang Jiao

Heng Long. Ma Wulong. Shang He. Shi Hu Dong Po. shi Zhuyuan. Bai Lutang. Xiang Shanping. Jin Tiancun Ban Zilou

Tai Pingtou. Liang Sanping

Xiao Xi. Zhu Dui. Guan Shan

Guan Shandong, Xia Baishui. Ping Tian. Yang Xi

Suo Shiqiao. Long Menchi

Yun Feng

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

significant values were <0.1, thereby not meeting the requirements for the tests clearly suggesting a normal distribution.

The interpolation method may also be used with smoothing effect, which is comparably less in case of IDW interpolation. A high degree of smoothing may result in deviation of accuracy of the results. A comparison with other interpolation methods suggested that the IDW method gave much accurate and relevant pollution index, thereby making it more prevalent in heavy metal pollution research studies. Another method, known as the Kriging interpolation method, is also said to give a very high accuracy results, which is accompanied with algorithms. The use of continuous variables would give results closer to true values (Kravchenko and Bullock, 1999; Weber and Englund. 1992). It has also been suggested that a combination of different interpolation methods will improve the precision of results (Dlamini and Chaplot, 2012). The interpolation method has an added advantage in comparison to other techniques of analysis. The old interpolation method, along with the high accuracy surface modelling method, would provide a sufficient advantage for maintaining the accuracy related to the interpolation data. At the same time, it should also be kept in mind that the methods described above are still in the nascent stages and cannot guarantee accurate results for all of the studies. The IDW method thus can be used as a good analysis technique for precise results.

Summary

A combination of the Hakanson method for risk analysis and ArcGIS technology helps to explain the heavy metal content at different levels of risk. High-risk metal pollution levels were further detected in the tow'nship level and the risk levels for different areas and the area ratio for each township were also analyzed. The results and analysis obtained from this chapter is as below':

  • 1. Based on the type of heavy metals present in the soil an extensive study w'as done using the Hakanson assessment method and the IDW interpolation method. Pollution analysis suggested that areas w'hich showed a higher content of heavy metals posed higher threats and the data obtained were much more consistent compared to the actual conditions.
  • 2. A detailed study was carried out to identify the areas which poses risk of metal pollution in the township and then locate specifically the areas which have extremely greater risk levels.
  • 3. Mercury was found to show a very high accumulation threats compared to the other four metals. A high concentration of heavy metals was found to exist at the junction of Bai Lutang and Da Kuishang, in Bai Lutang, Tang Xi and to the south of Tang Xi. In addition, a high content of mercury was observed in Ao Shang, Liang Tiang, Ma Touling, Qi Fengdu and Wu Lipai. The risk posed by each metal was basically determined by analyzing the Nemero index and the single factor.
  • 4. Areas of Bai Lutang and Da Kuishang had a high risk of being polluted by arsenic and lead. But the major pollutant in several areas of the Suxain district was found to be mercury, which posed a level 4 threat. In areas of Liang Tian and Qiao Kou it was found to pose a level 5 risk. Suxian region also had other major pollutants like zinc and copper, but these metals posed relatively lower level of threats when compared to mercury. The entire Suxain district as a whole had 83% of its total area under the high-risk region of level 3 and above. Thus, the mining activities posed a very high threat of soil pollution throughout this area.

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.

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.

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