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Tailings are residual materials of various procedures in metal extraction from different ores, or coal washing processes. Usually milling and hydrometallurgical processes result in a huge volume of residual slurry which may contain heavy metals and many other toxic materials at concentrations higher than environmental standards [1]. Moreover, mining wastes may comprise specific chemical additives, although the concentration levels are generally of no concern [2]. Tailings disposal is an important issue in saving the environment, especially in the case of low grade deposits where the volume of tailing materials is considerable. The size of solid particles of tailing depends on the ore nature and its dressing procedures. As a case in point, tailings in heavy media processes are relatively coarse, whereas those resulted from flotation processes are fine. Usually, in mineral processing plants, tailings are concentrated into high solid percent pulp using thickeners and piled afterwards still having considerable amount of water [3]. Apart from aesthetic imperfections which are caused by stockpiling of tailings, leakage of toxic materials such as reagents and heavy metals may pose serious threats against the environment [4].

EIA is a process aimed to identify, predict, evaluate, and balance the biophysical, social, and other impacts prior to making basic decisions [5]. In fact, it is a tool in environmental administration used to assess the effects of project activities on the environment with an avoidance approach [6]. Regarding the importance and necessity of having full recognition of the area and its environmental status, accurate perception of the impacts caused by project activities, and the need for presentation and classification of the impacts to better demonstrate the results to the decision makers, various techniques are proposed by the researchers [7]. These techniques includes: Ad-Hoc, check lists, matrices, GIS mapping, and media methods [8].

Simple problems having few criteria and options for decision making may be solved with no need to specific methods; however, when the number of criteria and options increase, systematic methods are used to solve the problem and make the proper decisions [9]. Using these techniques help structuring the values and imaginations of decision makers.

One efficient tool in solving multi-objective/multi-criteria problems is multi-criteria decision analysis which is a model of decision making that reasonably optimizes the problem solving using multiple criteria (sometimes heterogeneous [10]. Multi-criteria decision analysis may be performed either by multi-objective decision making or multi-criteria decision making [11]. In multi-criteria decision making of problems, options are prioritized according to various criteria. A decision making problem can be organized in form of classic multi-criteria decision making techniques [12]. These techniques include AHP, SAW, TOPSIS, ANP, and KIKOR that started to develop in 1980s.

In the current study, an attempt was made to identify the best site for dumping tailing in the case study area using TOPSIS method. Additionally, the significant of environmental impact has been assessed using MCDM based on SAW techniques.

In the present study the SAW method was used to assess environmental impact assessment of mining activities in the study area, and TOPSIS method was applied to select the best location for the dumping site. Thus, the two mentioned method was described in this section. It should be noted that the questionnaire (n=15) was used to to obtain the necessary data and weight the criteria.

The method has been first proposed by Hwang and Yoon in 1981 as a weighted linear combination method. In this method after de-scaling of the decision matrix, the weighted de-scaled decision matrix is obtained by applying weight coefficients of criteria; accordingly the score of each option is calculated [13]. In a multi-criteria decision making problem, if n criteria and m options are present, the decision matrix is as follows:

$X=\left(\begin{array}{ccc}{x}_{11}& \cdots & {x}_{1n}\\ \cdots & \cdots & \vdots \\ {x}_{m1}& \cdots & {x}_{mn}\end{array}\right)$

Where $\text{\hspace{0.17em}}{x}_{ij}$ is the operation of option i$\text{\hspace{0.17em}}(i=1,2,\mathrm{...},m)$ in relation to criterionj $\text{\hspace{0.17em}}(j=1,2,\mathrm{...},n)$ .

In order to de-scale the decision matrix, R matrix is defined as

$R=\left(\begin{array}{ccc}{r}_{11}& \mathrm{...}& {r}_{1n}\\ \mathrm{...}& \mathrm{...}& \vdots \\ {r}_{m1}& \mathrm{...}& r{\cdots}_{mn}\end{array}\right)$

Where the elements are calculated as

${r}_{ij}=\frac{{x}_{ij}}{\mathrm{ma}\stackrel{\u2322}{x}\left\{{x}_{ij}\right\}}$

${r}_{ij}=\frac{\frac{1}{{x}_{ij}}}{\mathrm{max}\left\{\frac{1}{{x}_{ij}}\right\}}=\frac{\mathrm{min}\left\{{x}_{ij}\right\}}{{x}_{ij}}$

Regarding the significance coefficient of various criteria in decision making, criteria weight vector is defined as $\text{\hspace{0.17em}}\left[{w}_{1},{w}_{\begin{array}{l}2,\text{\hspace{0.17em}}\cdots \text{\hspace{0.17em}}\text{\hspace{0.17em}},\\ \end{array}}{w}_{n}\right]$ and the best option is selected by

${A}^{*}=\{{A}_{i}/\mathrm{max}{\displaystyle \sum _{j=1}^{m}{w}_{j}{r}_{ij}\}}$

In the present study the TOPSIS method was used to select the best location for the dumping site. In this regards, a negative ideal solution maximizes the cost criteria or attributes and minimizes the benefit criteria or attributes, whereas a positive ideal solution maximizes the benefit criteria or attributes and minimizes the cost criteria or attributes. The TOPSIS method is explained in a succession of six steps as follows:

Step 1: Calculate the normalized decision matrix. The normalized value ${{\displaystyle v}}_{ij}$ is calculated as follows:

${r}_{ij}={x}_{ij}\sqrt{{\displaystyle \sum _{i-1}^{m}{x}_{ij}^{2}}}$ i =1, 2, ..., m and j = 1, 2, ..., n.

where ${{\displaystyle w}}_{j}$ is the weight of the ${{\displaystyle j}}^{th}$ criterion or attribute and $\sum _{j=1}^{n}{{\displaystyle w}}_{j}=1}.$

Step 3: Determine the ideal (${{\displaystyle A}}^{*}$ ) and negative ideal (${{\displaystyle A}}^{-}$ ) solutions.

${{\displaystyle A}}^{*}=\{(\underset{i}{\mathrm{max}}{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_{b}),(\underset{i}{\mathrm{min}}{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_{c})\}=\left\{{{\displaystyle v}}_{j}^{*}\right|j=1,2,\mathrm{...},m\}\text{(2)}$

${{\displaystyle A}}^{-}=\{(\underset{i}{\mathrm{min}}{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_{b}),(\underset{i}{\mathrm{max}}{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_{c})\}=\left\{{{\displaystyle v}}_{j}^{-}\right|j=1,2,\mathrm{...},m\}\text{(3)}$

Step 4: Calculate the separation measures using the m-dimensional Euclidean distance. The separation measures of each alternative from the positive ideal solution and the negative ideal solution, respectively, are as follows:

${{\displaystyle S}}_{i}^{*}=\sqrt{{\displaystyle \sum _{j=1}^{m}{({{\displaystyle v}}_{ij}-{{\displaystyle v}}_{j}^{*})}^{2},j=1,2,\mathrm{...},m}}\text{(4)}$

${{\displaystyle S}}_{i}^{-}=\sqrt{{\displaystyle \sum _{j=1}^{m}{({{\displaystyle v}}_{ij}-{{\displaystyle v}}_{j}^{-})}^{2},j=1,2,\mathrm{...},m}}\text{(5)}$

Step 5: Calculate the relative closeness to the ideal solution. The relative closeness of the alternative ${{\displaystyle A}}_{i}$ with respect to ${{\displaystyle A}}^{*}$ is defined as follows:

${{\displaystyle RC}}_{i}^{*}=\frac{{{\displaystyle S}}_{i}^{-}}{{{\displaystyle S}}_{i}^{*}+{{\displaystyle S}}_{i}^{-}},i=1,2,\mathrm{...},m\text{(6)}$

Step 6: Rank the preference order

The processing plant of refractory gold ore in As-Sbsulfurcontaining deposits in Zarshoran Gold Mine, Takab, WestAzarbaijan was selected as the case study area.

To choose the best siting dump, 9 locations and 10 criteria were defined. The questionnaire was applied to provide quantitative data to compare the sites locations and criteria. The dumping site distance from the mine is the first criteria that should be weighted in the questionnaire. Moreover, the distance from the residential area and sensitive ecosystems are the second and third criteria. The vulnerability to flood and earthquake estimate the vulnerability of the selected sites (A1 to A9) to flood or earthquake based on the expert opinions and distance from the faults. The energy consumption was estimated through the access to water and electricity based on the expert opinions. The ease of access to the local employees is categorized ad supply of human resource that weighted based on the expert opinions. The results of the applied MCDM methods are more described as follows.

This stage consisted of the following steps:

(1). First, 9 options (A1-A9) were selected as suggested options for the unit establishment.

(2). Important technical and locational sections were determined surveying the state of the art, and a specific score was assigned to each [14].

(3). A specific score (within 0-10) was assigned to each selected point in each notified item (Table 1).

(4). Next, the input data should be de-scaled using the vector method (Table 2).

(5). The criterion weights were normalized by vector method as they are listed in table 3.

(6). The weighted normalized decision matrix may be constructed at this stage (Table 4).

(7). The maximum and minimum values in each column were determined (Table 5).

(8). Next, the ideal and non-ideal values in each column were determined (Table 6).

(9). A matrix is constructed for the distance from ideal and non-ideal values (Table 7).

(10). Then the similarity index was constructed for each option as it is noted in table 8.

(11). Finally, prioritizing of each option was performed according to the value of options (Table 9).

As can be seen in Table 9, A9>A6>A5>A8>A4>A3>A2>A1>A7 with A9 as the most ideal and A7as the least proper options.

Determination the significance of the environmental impacts has been always a challenging issue in EIA process. In this regard, 40 negative impacts of gold ore processing were extracted (Table 10).

The selected environmental impacts from gold ore processing is derived from Iranian Leopold matrix. The classification of this matrix has been done with numbers -1 to -5 into 4 classes (Table 11).

Table 12 carries the available criteria from national and international references. These include impact nature, magnitude, spatial extent, and duration as main criteria [15-19] and probability of occurrence, ease of implementing mitigation measures as complementary criteria.

Results of sensitivity analysis for SAW model is delivered in figure 1.

After the options were outranked, the values are classified into classes: 1-10 having very high impacts (VH), 11-20 having high impacts (H), 21-30 having medium impacts (M), 31-40 having low impacts (L). Results of this model is given in table 13.

The achievements of current study could be summarized as follows:

In order to locate the dumping site for tailings, prioritizing of locational options was performed using TOPSIS technique. Prior to this, the decision matrix is constructed after the criteria and their weights are determined.

Determination of environmental impacts significance is performed by specifying the value of each option in each criterion and determination of weight for each criterion before constructing a matrix providing the raw data for decision making techniques.

Subsequently, SAW, TOPSIS, and ELECTRE-TRI methods were applied to classify the options. Sensitivity of each method was analysed and revealed that sensitivity of TOPSIS is maximum (20%) and ELECTRE-TRI has the minimum sensitivity (5%). In other words, ELECTRE-TRI has higher potential ability to determine the environmental impacts significance.

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