A Detailed Study on the Spatial Characteristics of Heavy Metal Pollution and Ecological Risk of Mining Area
Introduction
Heavy metals from the mining industry will enter the soil mainly as a result of human activities such as mining. Over time, such heavy metal accumulation in the soil would inhibit plant growth and have a negative impact on the environment. At the same time, these heavy metals are highly injurious to human health (Li et al., 2014; Zhao et al., 2012; Wuana and Okieimen. 2011). Severe effects have been found on the liver, kidneys and central nervous system due to the heavy metals copper and zinc, w'hich are mainly emitted as the result of industrial activities (Sani et al., 2017). Severe neurological impacts were also observed, along with skin disorders and other cancers in the body, by the presence of arsenic (Adio et al., 2017). Hence, it has become extremely important to analyze the effects of soil pollution due to heavy metals.
Heavy Metal Pollution
A large number of methods have been suggested for the analysis of heavy metals, including the single factor evaluation method, the Nemero index, the Hakanson method, the pollution load index, the geographical accumulation index, the fuzzy coefficient method, and the gray clustering method (Hakanson, 1980; Muller, 1969; Fan et al., 2012). The type of method to be used for any particular analysis depends upon several factors, such as the content of the heavy metals, toxicity, migration and transformation behaviors (Hakanson, 1980). This particular procedure for evaluation has been applied in several areas of China and other countries with a very high heavy metal pollution. The Simple risk index (£,) and Comprehensive risk index (/?/,) needs to be evaluated and adjusted based on the type of heavy metal present in the soil and their corresponding toxicity. As per the Hakanson method, a few standard values are prescribed for the toxicity coefficient
(jpcb _ 7Н» _ 40) anci the total amount of toxicity coefficients among eight pollutants (133 > the maximum toxicity coefficient, THs = 40). For a variation of content and type of heavy metals, there arises different assessment standards. In order to have accurate results, it is necessary to modify the evaluation zone (Peng et al., 2007; Fu et ah, 2009; Jian-Hua et ah, 2011). The results obtained as per the latest modification in the evaluation process gives more accurate results than the old ones. The present chapter also investigates the accurate location of high-risk level areas, identified by GIS technology. A visual interpretation of the risk dispersal by determining the value of unspecified points helps in conducting an evaluation of soil samples by the spatial interpolation method (Chabukdhara and Nema, 2013; Sun et ah, 2010). All the previous data on the detection of heavy metals was found to be focused mainly on large areas and very little research has been carried out on the quantitative research. This chapter focuses on the extensive research on the towmships with the risks being divided based on the area and area ratio. The results thus obtained from the case study in this chapter are more precise and relatable to the existing scenarios compared to the older researches; thus, the outcomes can provide new dimensions for gaining more details on the soil pollution by heavy metals. A large polymetallic mining area in Suxian, China w'as selected for the case study in this chapter, focusing mainly on Zn, As, Cu, Pb and Hg as the main contaminants. This chapter considers the effect of these metals in soil along with their distribution and environmental threats to the nearby villages and towns.
Sample Collection and Analysis
Location
The Suxian district in China was selected as the location for the carrying out of the impact of heavy metal pollution in soil. Located in the southern hemisphere of Hunan province, the district is characterized by monsoon humidity with heavy rainfalls. The average rainfall is around 1470 mm and the annual average temperature remains somew'here between 17-18°C.
The district is also characterized by the large-scale availability of minerals such as the ShiZhuyuan metal, Qiao Kou Pb-Zn ore, Ma Nao Mn ore, and Xu Jiadong, Jie Dong and Qi Fengdu coal mines. The region also boasts of around 50-51 different varieties of non-ferrous metals, mainly Lead (Pb), Zinc (Zn), Tin (Sn), Bismuth (Bi) and Molybdenum (Mo). It also has around 18 deposits, of w'hich Tang Xi, Da Kuishang, Ao Shang and Bai Lutang are the largest. The Shizuyuan mine is said to be the largest polymetallic mine, w'ith around 143 different minerals.
Sample Collection and Analysis
The samples were collected in 2015 for a time period from July to September. The obtained soil samples were then analyzed and tested as per the follow'ing procedure described by Bao.
An intermediary area was established based on the location of the mining area and soil samples were collected from the surface layer (0-20 cm) from different points.
For each 9 m2 area around 5-6 soil samples were collected. 100 g of the soil samples was taken out and mixed properly before being packed and labelled with date, type of soil, location etc.
The present case study focused on accumulating around 167 soil samples with different contents of Zn. Pb, As, Cu and Hg and testing them as per the GB 15618- 1995 method.
0.1-0.2 g of dried and powdered soil samples were taken in a digestion tank and cooked with HC1-HN0,-HC104-HF.
As, Hg analysis was carried out using atomic fluorescence spectroscopy (AFS), Cu, Pb, Zn were analyzed using inductively coupled plasma atomic emission spectroscopy (ICP-OES). pH detection was done using a pH meter.
Data Source and Processing
Geographic details of the township must be collected, including maps of the township, village map of Suxian district, mining areas of Suxian etc. All these data were collected from Resources and Environmental Science Data Center of the Chinese Academy of Sciences. The exact location of the sampling points was confirmed by the use of GPS technology. Tools such as Microsoft Excel 2010, IBM SPSS Statistics 19, and ArcGISlO.l (ESRI Inc.) were used to process the samples and geographic data.
Research Survey
Data Processing
The mining area, river locations, and soil sampling areas were determined by a digital map acquired from the China resource satellite application center along with the aid of GPS.
Potential Ecological Risk Index Method
The quantitative and qualitative determination of heavy metal pollution in the mining areas of Suxian district in China was carried out using the Hakanson analysis method. The method took into consideration the importance of toxicity to explore the potential risk posed by heavy metal pollution in the Suxian region (Hakanson, 1980),
The comprehensive potential ecological risk index RI4 is calculated as follows:
where E^is the index of a single potential ecological risk of heavy metal p at sampling point q; RIr is the index of comprehensive potential ecological risk at sampling point q Tp is the toxic response coefficient of heavy metal p (As = 10 Cu = 5Hg = 40 Pb = 5 Zn = 1) (Suresh et ah, 2012; Feng et ah, 2017). C[jis the pollution coefficient of heavy metal p at sampling point q CM is the measured value of heavy metal p at sampling point q and CR is the reference content of heavy metal p.
The Hakanson method mainly considers the coefficient of toxicity being determined by eight main types of pollutants/heavy metals. The method of calculation for the toxic coefficient is described in detail by Hakanson. Zheng and Shi and Fernandez and Carballeira. For the present case study, five pollutants were considered; hence the results obtained by the Hakanson method might show a slight deviation from the normal (Hakanson. 1980; Fernandez and Carballeira, 2001). Most research published to date does not take into account the changes that have to be incorporated if the number of pollutants is not as per the prescribed method (Wei, 2010; Hu et ah,
2013). The changes and the adjustments made during such analysis still remain unclear and unverified (Qing and Shu, 2008).
Keeping all these bottlenecks in mind, the present paper tries to address all the possible shortfalls existing with different evaluation methods for determining heavy metal pollution. Evaluation methods have been modified based on the type of heavy metal present (Li, 2016; Jian-Hua et ah, 2011; Fernandez and Carballeira, 2001). The terms Ep and RIp in the Hakanson method is mainly calculated considering the presence of eight pollutants, this adjustment on £;, is done by introducing a first boundary value
Where £,• = non-production index (C = 1) x the maximum toxicity coefficient considering the number of involved heavy metals
The calculations result in grade values at least twice larger than the last. The value of RIp is adjusted as follows:
RI = (Hakanson 1st grade boundary value/total toxic coefficient of 8 pollutants) = 150/133 = 1.13
Considering the case of the most toxic metal in this case study, mercury gives a toxic coefficient of 40. The total toxic contents for Hg, As, Zn, Pb and Cu are found to be 61. The RIr value is (61x1.13) « 70. A detailed comparison of £ and RI (Hankanson and current research) is shown in Table 17.1.
TABLE 17.1
Comparison of Hakanson Classification Standard in this Study in E and RI Grading Standards
|
Hakanson method Present research |
<40 <40 |
|
|
|
>320 >320 |
|
Hakanson method Present research |
<150 <70 |
|
|
>600 280-560 |
->560 |
|
Ecological risk level |
Slight |
Moderate |
Strong |
Quite strong |
Extremely strong |
(Data obtained with permission from Chen et al. Copyright © 2018, Elsevier)
IDW Interpolation of Heavy Metals in Soil
The Inverse distance weighted (IDW) is an interpolation method which is based on a concept that objects in close proximity to one another have similar properties with regard to details such as sampling points, characteristics of data, and so on (Spokas et al., 2003; Gong et al„ 2014; Ferguson, 1996). The aim is to consider the distance between the sample point and the interpolation point as the weighted value which is then averaged. Closer points (sample and interpolation) would give larger weighted value compared to points situated far from each other. The formula for IDW method
considering a series of points represented by Xh Yh Z, (i = 1,2.........n) distributed on
the same plane, then, acquiring Z value through weight value.
where Z(m) is an interpolated predicted value, Zp is the available point, n is the total number of available known points used in interpolation, dp is the distance between predicted value and the point p and wp is the assigned weightage to point p. With an increase in distance between predicted values and known value, the weightage decreases (Shepard, 1968), and u is the weighing power showing that decrease.
