The Scientific World Journal

The Scientific World Journal / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 680798 | 13 pages | https://doi.org/10.1155/2013/680798

Environmental Risk Assessment System for Phosphogypsum Tailing Dams

Academic Editor: M. Yari
Received02 Sep 2013
Accepted20 Oct 2013
Published08 Dec 2013

Abstract

This paper may be of particular interest to the readers as it provides a new environmental risk assessment system for phosphogypsum tailing dams. In this paper, we studied the phosphogypsum tailing dams which include characteristics of the pollution source, environmental risk characteristics and evaluation requirements to identify the applicable environmental risk assessment methods. Two analytical methods, that is, the analytic hierarchy process (AHP) and fuzzy logic, were used to handle the complexity of the environmental and nonquantitative data. Using our assessment method, different risk factors can be ranked according to their contributions to the environmental risk, thereby allowing the calculation of their relative priorities during decision making. Thus, environmental decision-makers can use this approach to develop alternative management strategies for proposed, ongoing, and completed PG tailing dams.

1. Introduction

The rapid industrialization in China has consumed vast amounts of various industrial raw materials and large quantities of industrial solid wastes remain from mining, mineral processing, and smelting processes [1]. Thousands of industrial sites are contaminated by industrial waste, which are significant threats to the environment and human health. The production and storage of industrial solid waste have resulted in land contamination and the loss of natural resources, as well as posing significant environmental risks.

Phosphogypsum (PG) is an acidic by-product of the phosphate fertilizer industry, which is produced during the production of phosphoric acid from phosphate rock. Large amounts of PG have been produced around the world and the production will increase to several hundred million metric tonnes annually [2]. Over 60 million tonnes of PG is produced per annum in China, which poses various environmental and storage problems.

Environmental risk assessments are widespread, including ecological, water, soil, and atmospheric environmental risk assessment [37]. At present, many experts use site-specific evaluation criteria and methods to assess different types of environmental risks in different areas, such as mine sites and urban environments. For example, ecological risk assessment guidelines have been enacted by the USA [8]. Health risk assessment has been used to evaluate brownfield sites contaminated by POPs [9]. The establishment of environmental risk assessments started relatively recently in China. At present, many areas are contaminated and serious pollution problems demand environmental risk assessments, such as PG tailing dams. Thus, we studied PG tailing dams, including the characteristics of the pollution sources and the environmental risk characteristics and identified the evaluation requirements for environmental risk assessment methods.

2. New Risk Assessment Approach

The current environmental risk assessment system (ERAS) is an integrated risk assessment, which considers all of the possible factors that affect the environmental risks due to pollution from PG tailing dams.

The main problem of ERAS for PG tailing dams is the integrated assessment of information from many different pollution sources, including quantitative and qualitative data. Therefore, it is necessary to develop detailed assessment methods based on the risk characteristics. Several methods have been developed for risk assessment, including life cycle assessment (LCA), safety check list (SCL), probabilistic risk assessment (PRA), and the analytic hierarchy process (AHP). AHP is one of the most widely used assessment methods. AHP is based on the premise that decision-making related to complicated problems can be handled using a hierarchical structure that transforms complexity into a simple and comprehensible problem [10, 11]. AHP has a wide range of applications, but the conventional AHP approach may not fully reflect the style of human thinking. For example, human judgment is usually represented as accurate numbers in AHP, but decision-makers usually feel more confident about giving interval judgments, rather than expressing their judgments as numeric values in actual situations [12, 13]. Therefore, AHP and fuzzy logic are used as tools to handle problems where there is high complexity, such as environmental and uncertain data. AHP can support environmental decision-makers by providing quantitative results and this ERAS approach can be applied to PG tailing dams.

In addition, the environmental supervision of industrial solid waste is a tremendous responsibility. Thus, the establishment of ERAS for PG tailing dams is very important for solid waste management and technology systems. Due to the characteristic requirements of ERAS for PG tailing dams, fuzzy logic and AHP can be combined to provide a more comprehensive analysis. This method may be extended to the development of an ERAS for solid waste management [13]. The proposed approach is shown in Figure 1.

2.1. Preliminary Stage

An abundance of risk data and information are related to PG tailing dams, so the establishment of an ERAS requires a range of experts from different disciplines with essential experience in construction. During the preliminary stage, the risk assessment group collected data related to risk, determined the risk criteria, identified the characteristics of tailings, obtained data related to tailing dams and the environments of tailing dams, analyzed the backgrounds of experts, identified potentially affected areas, and identified the final discharge media, and so forth.

2.2. Establishment of a Factor Index (FI) Stage
2.2.1. Establishment of the Factors in the FI Hierarchy

Many previous studies have shown that the AHP method can be used for multiobjective decision-making. The main sections of the overall hierarchy structure are based on the expert opinion and the qualitative analysis of the environment in the study area. The ERAS used for PG tailing dams is shown in Figure 2. In this section, we will explain the details of each level.

At the first level in the hierarchy, the ERAS of PG tailing dams is the aim of the analysis. The second level includes the solid waste characteristics, environmental characteristics, tailing dam risk, risk management, and utilization prospects.

The solid waste characteristics refer to the characteristics of PG, which are used to evaluate the risk of the solid waste itself. The environmental characteristics include the geographical position, local hydrogeology conditions, and the aspects of the surrounding environment that are sensitive to tailings. These are significant factors, which are used to evaluate the level of environmental risk, and they are also the most closely related to the production activities of humans. The tailing dam risk refers to the tailing dam’s interactions with its surroundings and human activities, which is used to measure the risk of security issues related to PG tailing dams. Risk management is related to the management and maintenance of PG tailing dams. The level of risk in this system is affected by risk management, and many accidents that occur in tailing dams are due to poor supervision. The utilization prospects refer to the comprehensive utilization of PG and government support.

At the third level, the characteristics of the solid waste are subfactors based on the physical and chemical characteristic. These factors are the inherent potential risks of the pollution source. The tailing dam risk factors comprise the characteristics of the dam body, drainage installation, and flood drainage facility. The risk management factors comprise risk prevention and emergency responses. The utilization prospect factors comprise government support and using mature technology, which are vital for this system. Thus, they are placed at the third level.

Solubility, volatility, and radioactivity were selected as the indexes for the physical characteristics at the fourth level. The solubility index reflects the water solubility of the tailings and the risk of leachate outflows, which are harmful to the environment. Radioactivity reflects the risk to the environment from the radioactivity for solid waste, such as radium-226 and its subfield, thorium-232 and its subfields, and potassium-40. Corrosiveness, acidity, alkalinity, acute toxicity, and extraction toxicity were used as subfactors for the chemical characteristics. Corrosiveness reflects the possibility of impermeable membranes being corroded by pollutants. The alkalinity reflects the extent of transfer in the environment. Therefore, the chemical characteristics are very important. The acute toxicity reflects the harmful effects of pollutants on organisms in the short-term, which directly reflects the harmful extent of pollutants. The extraction toxicity is an estimated index for solid wastes, which reflects the negative extent and transfer of pollutants after solid wastes have been leached by water.

The environmental characteristics are very important because they are related to the natural environment and the social environment. The natural environment in different regions has significant effects on the migration and transformation of pollutants. For example, acidic soils will increase heavy metal pollution in most cases, while different regional environments may have different levels of risk due to pollutants with respect to the possible loss of life and property. The natural environment includes the air, soil, geology, hydrology, and ecology. The social environment includes the population density, industry, agriculture, tourism, animal husbandry, and distances between communities.

The tailing dam risk reflects the direct impact on the environment based on its degree of stability. The stability of tailings is a security issue and an environmental issue because security risks can lead to environmental pollution. The factors related to the tailing dam risk include the seepage line and dry beach length, returning reservoir, height and capacity of tailings, drainage conditions, dam construction method, flood discharge trench, and the type of tailings.

Poor risk management is the main cause of accidents. Thus, normative operations and effective management will greatly reduce the likelihood of accidents. Risk management can be divided into risk prevention and emergency responses. Risk prevention considers the prevention capacity before the accident, including revetment construction maintenance, daily dam safety monitoring, and ISO authentication. Emergency responses reflect the handling after the accident, including the emergency capacity of rescue facilities and the capacity for emergency protection and leak elimination.

The utilization prospects are the most important of factors, because they indicate the current and future comprehensive utilization situations, while they also reflect the degree of recognition and the degree of support from governments for PG tailing dams.

2.2.2. Pairwise Comparisons of Factors

We conducted pairwise comparison of the factors at the same level based on their relative contributions to the ERAS. The pairwise comparisons used scores on a scale of 1–9, where 1 denoted factors with equal importance, and 3, 5, 7, and 9 denoted factors with weak, strong, very strong, and the highest importance, respectively. The experts could award scores using a fuzzy scale if necessary. The scores of the pairwise comparisons were in different formats, so we had to convert them into a common form before the calculations. Standard trapezoidal fuzzy numbers (STFN) were also used in this study [11] and the conversion equation is shown in Table 1.


Linguistic variableScale of relative importance (crisp number)Trapezoidal fuzzy number

Equally important1
Weakly important3
Essentially important5
Very strongly important7
Absolutely important9
are intermediate scale( )

2.2.3. Establishment of the Pairwise Comparison Matrix

A matrix was constructed according for the pairwise comparisons using the following: where is the scale of comparing with , while the scale is when comparing with .

2.2.4. Consistency Checking

Before calculating the weights of the index, the consistency of the comparison matrix must be checked. To check the consistency of the comparison matrix in an intuitive manner, the fuzzy numbers are first converted into matching crisp values using the following: (a) Calculate the Largest Eigenvalue of the Matrix. The largest eigenvalue of the matrix can be calculated as follows [14]: where is the principal eigenvector of the matrix.

(b) Consistency Check. The consistency of the comparison matrix can be determined using the consistency ratio (CR) as follows: where CI is the consistency index, RI is the random index shown in Table 2, and is the matrix size. As a rule, the consistency of the matrix is considered as acceptable only if CR < 0.10; otherwise the pairwise comparisons must be revised.


Size ( )123456789

RI0.00 0.000.580.901.121.241.321.411.45

2.2.5. Calculate the Fuzzy Weight Vector

Based on the pairwise comparisons in matrix , the weight vectors can be calculated using the following:

2.2.6. Defuzzification

The crisp value of can be calculated using defuzzification with the following:

2.3. Calculate the Scores of the Evaluation Factors
2.3.1. Scores of the Evaluation Factors and Calculating the Fuzzy Evaluation Vectors

Using the data, that is, field survey and sampling data, the fuzzy evaluation vector of a specific factor is calculated as follows. Assume that there are decision-makers , and is attributed to . Convert the values into STFN, as shown in Table 3.


Linguistic variablesTrapezoidal fuzzy numbers

Very poor
Poor
Medium
Good
Very good

2.3.2. Construct the Fuzzy Evaluation Matrix

The evaluation value of the attribute given by the decision-making group can be obtained as follows:

2.4. Calculate the Evaluation Result

Calculate the risk magnitude (RM) using the following:

3. Case Study

3.1. Preliminary Step

The necessary information collected in the preliminary step by the risk assessment group for a specific test case scenario is summarized in Table 4.


General information
 Total storage capacity9.8 million m3
 Starting date2003.06
 Service period 14.86 years
 Type of tailingsValley Type
 Dam height45 m
 Storage capacity8639737 t
 Damming modeUpstream tailings dam
 Comprehensive utilization6.84 × 105 t
 Disposed quantity1.15 × 107 t
Physical-chemical analysis of phosphogypsum
 ElementContent (%)
 Water content24.20
 CaO27.8
 Fe2O30.10
 Al2O30.53
 MgO0.05
 K2O0.13
 Na2O0.13
33.9
 F0.49
 SiO211.85
 P0.7
Extraction toxicity
 IndexUnit (mg/L)
 pH5.73
 Cu<0.02
 Pb<0.1
 Zn0.126
 Cr<0.05
 Cd<0.005
 Be<0.005
 Ba4
 Ni<0.04
 As0.0088
 Se0.0004
 Ag<0.01
 Hg0.0015
 Cr6+<0.004
 Cyanide<0.001
 Fluoride6.49
 Methyl mercury<10 ng/L
 Ethyl mercury<20 ng/L

3.2. Establishment of the FI Stage

Five highly qualified experts in the subject area were selected to form a risk assessment group and they performed the risk assessment using the proposed methodology. Each risk factor was evaluated at the different levels of the FI hierarchy by the experts who awarded scores. Different experts had different weight (Table 5) and they provided precise numerical values, linguistic terms, numerical value ranges, or a fuzzy number based on their knowledge and the information available. These evaluations were converted into STFNs as shown in Table 1 and (6)–(14).


ExpertBackgroundWeight

E150 years’ experience in solid waste management0.23
E250 years’ experience in environment risk assessment0.23
E3Mine senior engineer0.2
E420 years’ experience in tailings dam management0.18
E510 years’ experience in tailings dam management0.16

3.2.1. Secondary Indices

Five experts graded the secondary indices (solid waste characteristics, environmental characteristics, tailing dam risk, risk management, and usage) in a pairwise manner according to Table 1 to produce Table 6.


A1A2A3A4A5
ScoreConverted STFNScoreConverted STFNScoreConverted STFNScoreConverted STFNScoreConverted STFN

A1E11 1 1/3 1/5
E21 1 1/3
E31 1/2 1/4 1/5 1/7
E41 1 1 1 1
E51 2 1 1/3 1/4

Aggregated STFN , 
, 
, 
, 

A2E1 1 1 1/2 (1, 2) 1/4
E2(1/2, 1) 1 1 1/3 1/5 ,  
E32 1 1/2 1/3 1/5 ,  
E41 1 1 1 1
E51/2 1 1/2 1/6 1/7 , 

Aggregated STFN , 
, 
, 
, 

A3E13 2 1 4 1/3
E21 1 1 1/3 1/3 ,  
E34 2 1 1 1/2
E41 1 1 1 1
E51 2 1 1/3 1/5

Aggregated STFN , 
,  
, 
, 

A4E1 1/4 1 1/6 ,  
E2 3 3 1 1/2
E35 3 1 1 1/2
E41 1 1 1 1
E53 6 3 1 (1/2,1)

Aggregated STFN , 
,  
, 
, 

A5E15 4 3 6 1
E23 5 3 2 1
E37 5 2 2 1
E41 1 1 1 1
E54 7 5 (1, 2) 1

Aggregated STFN , 
,  
, 
, 

3.2.2. Tertiary Indices

Experts from different fields graded the tertiary indices with which they were familiar in a pairwise manner to produce Table 7.

(a)

B1B2

B1
B2

(b)

B3B4

B3
B4

(c)

B5B6B7

B5
B6
B7

(d)

B8B9

B8
B9

(e)

B10B11B12

B10
B11
B12

3.2.3. Quaternary Indices

Experts from different fields graded the quaternary indices with which they were familiar and the results are shown in Table 8.

(a)

C1C2C3

C1
C2
C3

(b)

C4C5C6C7

C4
C5
C6
C7

(c)

C8C9C10C11C12

C8
C9
C10
C11
C12

(d)

C13C14C15C16C17C18

C13