ing a master plan for water resources pollution control in Isfahan Province in Iran. Different ... Office of Management and Planning, to develop a master plan for ...
International Water Resources Association Water International, Volume 28, Number 4, Pages 478–490, December 2003
Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study Mohammad Karamouz, Amirkabir University, Tehran, Iran, Banafsheh Zahraie , University of Tehran, Tehran, Iran, and Reza Kerachian, Amirkabir University, Tehran, Iran Abstract: Excessive demand for water due to a growing population, agricultural, and industrial development, along with climate change and depletion of nonrenewable resources have intensified the need for integrated water resources management and water pollution control. This paper presents different aspects of a master plan for water pollution control and the results of a case study for developing a master plan for water resources pollution control in Isfahan Province in Iran. Different components of the water resources system and pollution sources in the study area were identified and the effects of each of the pollution sources on surface and groundwater resources contamination were investigated. Two Multiple Criterion Decision Making (MCDM) techniques, namely Simple Additive Weighting (SAW) method and Analytical Hierarchy Structure (AHP) were used in order to determine the share of agricultural, industrial, and domestic sectors in polluting the water resources. In the application of MCDM techniques, engineering judgments and the information gathered from brain storming sessions with engineering experts and the agencies’ officials have also been incorporated in order to overcome the data deficiency in this region for this type of analysis. Based on this study, several specific major categories of water pollution reduction projects were defined and in each category, several projects were identified. The total cost of implementation of the projects was also estimated and the projects were prioritized based on their potential impact on water pollution control. Keywords: Master Plan, Water Pollution Control, Analytical Hierarchy Process, Multiple Criteria Decision Making, Simple Additive Weighting Method
Introduction Increasing demand for water, higher standards of living, depletion of resources of acceptable quality, and excessive water pollution due to agricultural and industrial expansions have caused intensive social and environmental predicaments all over the world. Since the beginning of the twentieth century, the population of the world has tripled, nonrenewable energy consumption has increased by a factor of 30, and industrial production has multiplied 50 times. Although progress has improved the quality of life, it has caused significant environmental destruction of such a magnitude that could not be predicted (Buchholz, 1993). The question to be answered is whether, in the future, development could be economical and ecologically sustainable. We cannot answer this question unless we have a vision of the future and our planning schemes are environmentally responsible toward the major elements of our physical environment, namely, air, water, and soil. Water, among these elements, is of special importance. Excessive use of rivers and groundwater and misusing and pol478
luting these vital resources by residential, agricultural, and industrial wastewater has threatened our well-being. As stated by Karamouz (1994), planning for sustainable development of water resources means water conservation, waste and leakage prevention, improved efficiency of water systems, improved water quality, water withdrawal and usage within the limits of the system, water pollution within the carrying capacity of the streams, and water extraction from groundwater within the safe yield of the system. More and more, we are seeking a balance between our physical being, ability to manage our resources, and the limitations imposed by the environment. After the United Nation Conference on Environment and Development (UNCED, 1992), many countries have begun to make significant changes in the institutional structures of their government to comply with environmental factors when decisions are made on economic, social, fiscal, energy, agricultural, and other policies. In this paper, major objectives and steps, in line with the above vision, that should be taken for development of a master plan for water resources pollution reduction are discussed. The main objectives of this study have been to
Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study
develop a method for determining how much water is polluted by domestic, industrial, and agricultural activities and also to define the outline of the master plan including the specific projects for pollution reduction and a time table for budget allocation to these projects. Major concerns about the causal effect among different components of the system are also discussed in the paper. The results of an urgent study, directed by Iranian Office of Management and Planning, to develop a master plan for water pollution reduction in Isfahan province in Iran are demonstrated in this paper. The study area has been facing population growth and rapid expansion of agricultural and industrial activities. For many years, this complex system of resources and users has been managed with limited long-term plans that have resulted in considerable environmental problems. Basic characteristics of the study area are studied and different components of the system including surface and groundwater resources and different point and non-point sources of pollution are identified. A ten-year planning horizon for water pollution control was considered and major projects have been defined in order to reduce the current pollution level by 50 percent.
Integrated Approach in Environmental Management of Water Resources: Major Concerns Decision-making systems in many countries tend to separate the economic, social, and environmental factors at the engineering, planning, and management levels. This influences the actions of all groups in the society including decision-makers in the government and in the public and private sectors. It also has had important implications for the efficiency and sustainability of the development process. Some fundamental reshaping in institutional and legal frameworks is needed to place environmental concerns at the center of decision-making process. Problem identification is the first step for obtaining a framework of a master plan for water pollution reduction. For this purpose, the components of the system and the interaction among them as well as the causal effect should be identified. Improving the use of data and information at all stages of environmental planning and management is another issue that should be considered. Making systematic and simultaneous use of social, economic, ecological, and environmental data will provide a significant impact on the effectiveness of development and water pollution control plans. Design and operation of a monitoring and sampling system not only can be used for obtaining sufficient environmental data to improve the modeling schemes, but also can play an important role in evaluating progress towards achieving goals. This will lead to identifying indicators that measure changes across the environment. The above steps will provide data and knowledge about
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a system. Data deficiency, which is usually caused by inadequate resources and/or poor project planning, is a common problem in environmental decision making in developing countries. These problems can be summarized as follows: • Lack of information about qualitative and quantitative characteristics of water resources systems components and pollutants and about the economic costs associated with the pollution of the system; • The development projects are funded by different organizations responsible for environment and natural resources protection; • The budget allocated to environmental protection projects is usually insufficient due to unstable economical priorities in most of the developing countries; • The absence of coordination among different organizations that are responsible for water supply, distribution, allocation, and use; and • Lack of public awareness programs about environmental issues. Defining objectives and selecting tools to achieve the targets is the second step for development of master plans for water resources pollution reduction. Flexible planning approaches should be adopted to allow the consideration of multiple goals and enable adjustments considering the changing needs. Some important tools, which can be considered in development of these plans, are: • Providing an effective legal framework for environmental protection in general and water pollution control in particular; • Making effective use of economic and market incentives for participants in water development and pollution control projects; and • Establishing a system of environmental and economic accounting to guide the decision makers and general public to assess the behavior and functionality of water-related systems in order to make them environmentally responsible. Considering the above items, the process of development of an integrated master plan for water pollution control can be summarized in three phases that are shown in Figure 1. As it can be seen in this figure, the problem identification is the main concern of first phase. Major steps for identification of the system and interactions among different components as summarized earlier are included in the first phase. In the second phase, the main objectives of the master plan should be defined. The following parameters should be assessed in the third phase in order to determine the impact of proposed projects to reduce water pollution in the future: • Water consumption figures for different sectors; • Share of water pollution of different sectors (domestic, agricultural, industrial, etc.); • Efficiency of treatment of different contaminants; and
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Identification of Systems Components Identification of Interactions among the Components of the System Identification of Cause and Effects Analysis of Economic, Social, and Political Consequences of Water Pollution
First Phase: Problem Identification
Major Steps
Flexible Planning Approaches Definition of Projects for Water Pollution Control Impacts of Current and Ongoing Projects on Water Pollution and Budget Allocated to them Socio-Economic Impacts of Proposed Projects
Determination of Share of Pollution Reduction by Each Project Budget Associated with Each Proposed Project
Priority of Implementation of Projects
Main Objec tive Time Table for Budget Spending
Institutional Changes Providing Effective Legal Framework Making Effective Use of Economic Incentives
Selecting Tools
Establishing Integrated System of Environmental and Economic Accounting Making Systematic and Simultaneous Use of Social, Economic, Ecological, and Environmental Data
Second Phase: Definition of Objectives and Selecting Tools
Water Resources Pollution Reduction
Consequences Flexible Planning Approaches
Adjustments Considering Changing Needs
Consideration of Multiple Goals
Figure 1. Major phases for development of a master plan for water pollution control
• Measure of pollution reduction by proposed projects. The major step is to determine the sources of the pollution and share of contamination of domestic, agricultural, and industrial sectors. This is an important step in developing a master plan because in many countries, organizations do not accept the full responsibility for polluting water. At the same time, in the process of implementing the master plan, a budget will be allocated to each sector to reduce pollution. Therefore, agencies are having a more realistic approach in defining their share of water pollution if for nothing else but to absorb their fair share of the budget. Therefore, in the case of this study, they had full participation and cooperation and many conflicts in the assessment of their responsibilities and shares have been easily resolved. Development of action items and details of the master plan is determined in this phase and are presented in Figure 2. In the following sections, results of a case study for Isfahan province in Iran are presented. Development of a master plan was needed that could help the decision-makers allocate necessary resources to curb the current state of water pollution disorder in Isfahan. It was not possible to implement all of the factors and schemes at this time due to limitations of time and resources, but they were part of a general vision that was used to develop a master plan, which is easy to assess and implement.
Major Characteristics of the Study Area Long-term average annual rainfall over Iran is esti-
Time Table of Action Items
Figure 2. Details of the third phase of development of a master plan for water resources pollution control
mated as 250 to 300 millimeters. Isfahan province, with 160 millimeters average annual rainfall, is located in an arid part of the country. In addition, with 105,805 square kilometer area, Isfahan province is the fifth largest province in Iran. Population of the province is estimated as more than 4.2 million, of which 75 percent are residing in the Isfahan Metropolitan Area and its suburbs. The Zayandeh-rud River, with an average annual flow of 1630 million cubic meters including water transfer from Karoon River, is the major artery in Isfahan Province in the central part of Iran. This river with a drainage basin of 4200 square kilometers enters Zayandeh-rud reservoir some 190 kilometers west of Isfahan. The river-reservoir system supplies water for 120,000 hectares of agricultural lands, major industries such as petrochemical and steel factories, and to the city of Isfahan and some other towns near Zayandeh-rud River. The river eventually enters Ghavkhooni wetland (lake) about 130 kilometers down-
Agricultural lands Industrial Complex Sub-basin boundary
Figure 3. Isfahan Province and location of major dams and rivers
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Table 1. Breakdown of water consumption from Zayandeh-rud River in current situation and projected water demands in 2020 Current water consumption Volume Percent Demand (MCM/year) Domestic 200 12 Industrial 100 6 Agricultural 1350 82
Projected demand in 2020 Volume Percent of (MCM/year) increase 490 145 250 150 1950 45
sectors.
Systems Components The major components of the Zayandeh-rud River system can be summarized as follows: • Surface water resources include Zayandeh-rud River, local rivers, drainage and local canals that are called Madee. Madees have been used for water diversion from different parts of the Zayandeh-rud River to agricultural lands and different parts of the city and are used as a part of surface runoff collection system within the city. Underground inter-basin water transfer projects are currently under operation and two new transfer systems are under construction. They are considered as part of the surface water resources system. • Groundwater resources include six different aquifers in the Zayandeh-rud River basin. • Point sources of pollution include: o Towns / Villages waste water discharge; o Industrial waste water discharge (industrial units and industrial complexes); o Plumes generated by solid waste disposal (domestic, medical, and hazardous wastes); o Non-point sources of pollution; o Return flow from agricultural lands;
250 Average concentration (mg/L)
stream of the city of Isfahan (Figure 3). A high percentage (more than 50 percent) of major national industries in the country are located in this province out of which about 60 to 70 percent are located near Isfahan occupying about 5 to 6 percent of the total province area. Some strategic industries such as petrochemical and steel factories have increased the share of industrial water demand in this area. Thus, the master plan mostly concentrated on the Isfahan Metropolitan Area in the vicinity of the Zayandeh-rud River and major irrigation projects. Table 1 shows the current breakdown of water consumption from the Zayadeh-rud River and the projected water demand in year 2020. As it can be seen in this table, 82 percent of consumed water is allocated to agricultural sector. A projected 45 percent increase in the agricultural water demand will have a significant effect on water allocation within the system. An increase of 145 and 150 percent in domestic and industrial demands, respectively, are also an indicator of more wastewater generated by these
Ca+
Na
COD
BOD
200 150 100 50 0 30
60
90
120
130
145
160
168
190
235
265
278
300
Distance from Zayandeh-rud Dam (km)
Figure 4. Variation of Certain Quality Indicators in Zayandeh-rud River
o
Soil erosion and sedimentation.
Analysis of water quality samples from surface and groundwater resources in the study area shows that the current situation is critical. Analysis of Zayandeh-rud River water quality has shown that it has a good quality at the origin, but the situation is devastating downstream of Isfahan city. Figure 4 shows variation of some of the quality indicators in Zayandeh-rud River. As this figure shows, downstream of station number 6 (downstream of Isfahan city), the concentration of contaminants increases significantly. The adverse water quality in Zayandeh-rud River near historical bridges has affected tourism. Electric conductivity (EC) of the Zayandeh-rud River at Varzane, downstream of Isfahan, shows 32 times the EC downstream of the reservoir. Heavy metals in the Zayandeh-rud River are higher than maximum accepted values for irrigation. The bypass of Southern Isfahan Wastewater Treatment Plant has caused a jump in heavy metals content of the Zayandeh-rud River. There are also three major drainage systems, which are the major sources of pollution of industrial and agricultural return flows in the study area: · Zobe-Ahan Drainage System transfers the sewage directly from some industries like Slaughterhouse, Leather Factory, and part of sewage from a Steel Factory directly to the Zayandeh-rud. · Shah-Karam Drainage System transfers drainage from agricultural lands downstream of Isfahan. · Sagzi Drainage System transfers drainage from agricultural lands to an area north of the Zayandeh-rud River near Gav-Khoni Wetland. In a study done by Water and Wastewater Consulting Engineers (1996), urban runoff of Isfahan City was found to be another source of water pollution. A comparison between concentration of contaminants in Isfahan urban runoff and standards established by Iranian Environmental Protection Agency shows that even the minimum observed values have been higher than the maximum accepted standard for irrigation or discharge to surface water. Excessive groundwater discharge from aquifers near Isfahan has created a critical situation especially in Mahyar and Jarghooyeh Aquifers. Major subsidence in Mahyar
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Table 2. Selected water quality indicators and sub-indicators Water Quality Indicators Total dissolved and suspended solids Heavy Metals Nutrients Organic Material Hardness Other parameters
Related sub-indicators TSS TDS Zn Cd TP TN BOD COD Ca+2 Mg+2 ClTS
TP: Total Phosphorous; TN: Total Nitrogen; TS: Total Sulfur
Plain has created sinkholes. There are also serious quality problems in these aquifers because of infiltration of agricultural effluents and excessive use of pesticides and fertilizers. Many local industries have also polluted groundwater resources with chemical or hydrocarbon contaminants that seldom can be treated naturally. Gas stations with underground single-layered tanks are leaking and have polluted soil and groundwater resources. The depth of saturated soil with petroleum contaminants is several meters in certain locations. These types of pollution are hazardous to public health and a major threat to groundwater resources. In the next sections of the paper, an outline of the master plan and the proposed projects for water pollution reduction in Isfahan are presented. One of the most important factors in integrated water resources development and management is to see the causal effects among different components of the system. The effects of the sources of pollution on surface and groundwater resources and the interactions among different elements of the water cycle should be considered. In this study, the quality of water in different parts of the system was analyzed and the effects of each pollution source were estimated. Subjective information as well as engineering judgments have been used to overcome the data shortage for this type of analysis, which is explained in the following section. Different organizations that are responsible for water supply, distribution, allocation, and use do not coordinate well with each other when it comes to their development plans and how the generated wastewater in their system could be curbed.
Determining the Share of Contamination from Different Pollution Sources As mentioned before, calculating the share of con-
tamination from different pollution sources is important for determining the priority and budget allocation for different pollution reduction projects. Therefore, an attempt was made to determine the contamination load resulting from different sources/sectors based on available data such as measurements of quantity and quality of domestic sewage, industrial wastewater, and agricultural return flows. Based on the existing data and information, the quality variables (indicators) shown in Table 2 are used. The pollutant sources have been considered as four main groups: domestic, industrial, agricultural, and others. The category of “other” pollutant sources consists of leakage from underground oil tanks, urban surface run-off, and plumes generated under solid waste disposal sites. Water quality data is only available for the first three groups; therefore the relative weights and shares of these groups in water pollution are first determined and then, the share of other sources is estimated. The contamination load of different sectors is determined based on the share of water use, the available data, engineering judgment, and intensive brain storming of experts in different agencies. For this purpose, the following existing data and information have also been used: • Volume and quality indicators of return flows from irrigation lands; • Volume and quality of municipal effluents; • Volume and quality of industrial effluents; • Water consumption by different sectors. Table 3 shows the composition of contamination load for each sector. As it can be seen in this table, the agricultural sector, which consumes over 82 percent of the total water supply, imposes the highest pollution load in the study area. Even though the pollution load from different sectors are comparable using each of water quality indicators presented in Table 3, a general comparison between the contamination loads of different sectors needs a unified definition of contamination based on all available water quality indicators. For this purpose, the relative weights of different water quality indicators are defined based on engineering judgment and intensive brainstorming of experts in different agencies in charge of supply, distribution, and use of water and collection of wastewater. Because of the multiplicity of quality indicators, the decision-makers and the experts
Table 3. Contamination load resulted from different sources Water Quality Variables Industrial ( ton / year ) Domestic ( ton / year ) Agricultural ( ton / year )
TSS 10,550 55,360 94,500
BOD 20,200 47,520 94,500
TS 132,847 63,360 374,220
TDS 848,980 80,000 1,2663,000
Zn 530 320 1,890
Water Quality Variables Industrial ( ton / year ) Domestic ( ton / year ) Agricultural ( ton / year )
COD 377,320 80,000 189,000
TP 14,440 1,200 283
TN 244 6,400 3,780
Cl73,500 168,000 6,709,500
Ca+2 64,000 128,000 283,500
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Cd 8 8 47 Mg+2 38,400 76,800 343,035
Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study
are asked to fill pair-wise comparison matrices. As stated by Pomerol and Barba-Romero (2000), the idea of introducing pair-wise comparisons between different criteria is because for a decision maker it is easier to make comparisons between a pair at a time rather than assigning weights to the whole set of criteria. In order to incorporate the engineering judgments of the group of decisionmakers and experts, a group decision-making method developed by Aczel and Saaty (1983) was used. In this method, the geometric mean of each element of different pair-wise comparison matrixes is estimated as the group judgment. More details about the geometric mean method TDSS
HM
N
OM
H
OP
Total Dis. & Susp. Solids(TDSS) 1 2 .2 1 1 9 1 .8 0.45 Heavy Metals (HM ) 1 1 1 .2 4 1 Nutrients (N ) 1 1 1 1 7.6 2.5 Organic Materials (OM ) 0.83 1 1 9 3 1 2.2 0.25 0.13 0.11 1 Hardness(H ) 1 Other Parameters(OP) 0 . 55 1 0 . 4 0 . 33 1 1
are presented in Appendix II. The following matrix shows the group judgments for all basic water quality indicators used in this study: As it can be seen in this matrix, called MATQUAL, the group judgments are inconsistent. For example, the relative importance of heavy metals compared with total dissolved and suspended solids is 0.45 and compared with organic materials, it is 1.2. If the decision-makers comparisons were consistent, the relative importance of total dissolved and suspended solids compared with organic materials would be 1.2/0.45=2.6 but it is 1. In this study, the inconsistency of the MATQUAL matrix is quantified using the inconsistency index proposed by Saaty (1990) presented in the next section of the paper. The pair-wise comparisons are also made by decision-makers and experts for all sub-indicators. As it is shown in Table 2, there are only two sub-indicators for each indicator. In case of 2 x 2 matrices, they are always consistent. The decision-maker can easily assign a consistent weight when only two variables are involved. But when there are several water quality variables involved, the weighting and judgments about their importance in polluting water resources are rather difficult and could result in inconsistent assessments. The geometric mean method is used in this study to find the group judgment about relative importance of sub-indicators, which resulted in the Table 4. Relative weight of water quality sub-indicators Sub-indicators TSS to TDS Zn to Cd TP to TN BOD to COD Ca+2 to Mg+2 Cl- to TS
Relative weight 1.0 0.59 0.87 1.06 1.0 1.49
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weights shown in Table 4. The method proposed for estimating the share of different sectors in water pollution of the study area, which is explained in detail in the following section, is capable of considering more detailed criteria representing environmental impacts and health aspects regarding different pollutants. But in the study area, the available data are limited to the concentration of some water quality variables that have been used in this research. Therefore, in selecting the relative weights of quality indicators, the contamination load of each indicator compared with the water quality standards in the study area is considered as an important factor. For example, even though the toxicity of heavy metals is much higher than other indicators, such as TDS, the relative weight of Zn and Cd are lower because their concentration compared with the water quality standards in the study area are negligible. In the next step, the pollution load of different sectors should be estimated using a unified definition of contamination based on all selected water quality indicators. One way to tackle this problem is to use Multiple Criterion Decision Making (MCDM) methods. MCDM methods have been widely used in the area of environmental and water resources planning and management, but only a limited number of these studies consider different aspects of integrated planning. Bella et al. (1996) analyzed the water allocation conflict problem in the Upper Rio Grande Basin by two different MCDM techniques. A criterion was used to rank different alternatives consisting of different economic, environmental, and operational factors. Raju et al. (2000) studied the implementation of MCDM analysis for a case study of an irrigation area in the Huesca Province of Spain. The criteria used to rank different alternatives consisted of economic, environmental, and social factors. Five different MCDM techniques were used and resulted in choosing the alternative strategies. Karamouz et al. (2002) utilized the MCDM technique for monitoring and evaluating pressure and drip irrigation projects in Iran, considering different technical and economic factors. In addition, different methods have been suggested to deal with Multiple Attribute Decision Making (MADM) problems for discrete variables. See Fodor (1995) and Szidarovszky et al. (1986) for details. Among all of the suggested methods, the Simple Additive Weighing Method (SAW) and the Analytical Hierarchy Process (AHP) are the most suitable methods for this study because of the nature of the problem and the structure of relevant criteria. Analytical Hierarchy Process (AHP) The AHP method was first developed by Saaty (1980; 1994) and has been widely used in both fields of theory and practice. In this method, the application of pair-wise comparisons has been used to compare different alternatives relative to each criterion. The final result of this method is an estimation of relative weights of alternatives considering all criteria and sub-criteria defined by the ana-
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lyst. Different steps that should be taken in AHP method are presented in Appendix III. In this study, there are n basic criteria and m1 , L , mn sub-criteria for each of the basic criteria. The overall Inconsistency Index in the AHP method can be estimated as
II = II b + [w1
II 1 L wn ] × M II n
[
(1)
]
where II b and w1 L wn are the Inconsistency index and eigenvector estimated for the pair-wise comparison matrix of the basic criteria. II 1 , L , II n are the inconsistency indices for the pair-wise comparison matrices of sub-criteria for each basic criterion calculated by Equation A-2 in Appendix III where k will be the number of sub-criteria defined for each criterion. The overall CRI for the basic and sub-criteria, CRI , can be then estimated as follows
CRI m1 L wn ] × M CRI m n
CRI = CRI n + [w1
(2)
where CRI n is the Inconsistency Index of an n x n random matrix (n is the number of basic criteria). Table 5 shows the CRI n values calculated for random matrices with different dimensions ( n ). For example for six basic criteria, n is equal to 6, and CRI 6 is equal to 1.24. CRI m1 , L, CRI mn are the inconsistency indices of m1 by m1 , …., mn by mn random matrices, respectively, associated with each sub-criterion. Inconsistency ratio can then be estimated, replacing CRI n with CRI and II with I I in Equation A-3 in Appendix III. The eigenvector of pair-wise comparison matrix is then used for estimating the relative weight (importance or priority) of different alternatives. For this purpose, the following relation can be used mi a wi = ∑ w j × ∑ ci ,k × w j ,k j= 1 k =1
[
n
Wi = a
]
where wia is the weight of alternative (sector) i; w j is the relative weight of basic criterion j which is the j th element of eigenvector for pair-wise comparison matrix of basic criteria; c i,k is the value of sub-criterion k for alternative i divided by the maximum value of that sub-criterion for all alternatives; w j ,k is the relative weight of sub-criterion k of basic criterion j; Wia is the relative weight of alternative i; m/n is the total number of alternatives/basic criteria; and mi is the number of sub-criteria defined for basic criteria i. In this study, the selected quality indicators, sub-indicators such as TDS (see Table 2), and the share of contamination from different sectors are considered as criteria, sub-criteria, and alternatives, respectively. Figure 5 shows the hierarchy structure of water quality indicators. Wia is the load percentage of water pollution generated through the activities of different sectors. Simple Additive Weighing Method (SAW) The SAW Method is one of the classical methods to solve discrete MCDM problems. In the first step of this method, the dimensionless decision matrix should be estimated based on the value of different criteria for different alternatives. Then the weight of each alternative (Wj ) is calculated as follows n
W j = ∑ wi ⋅ r ji i =1
Basic Criteria
Total Dissolved & Suspended Solids (0.25)
Heavy Metal (0.162)
j =1
Source: Saaty (1990)
TDS (0.5)
Cd (0.63)
GOAL
TN (0.533)
INDUSTRIAL
BOD (0.52)
DOMESTIC
COD (0.48)
AGRICULTURAL
Ca+2 (0.5)
(4)
Table 5. Inconsistency index of random matrixes (CRI) n 1 2 3 4 5 6 7 8 9 10 CRI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.45 n: dimension of random matrix
Alternatives
TP (0.467)
m
∑ waj
Sub-criteria
Zn (0.37)
Organic Material (0.233)
wia
(5) TSS (0.5)
Nutrients (0.221)
(3)
∀j
Hardness of Water (0.04)
Mg+ 2 (0.5) Cl (0.6)
Other Parameters (0.094)
TS (0.4)
Figure 5. Hierarchy structure of indicators (The numbers in the parenthesis are the weight of each criteria/sub-criteria)
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where wi is the weight of criterion i; W j is the weight of alternative j; n is the number of criteria; and m is the number of alternatives. In this study, these methods are used for estimating the share of contamination from different pollution sources, namely, industrial, domestic, and agricultural activities and the results are compared with the results of AHP method. In addition, the sensitivity of results to the weights associated with water quality indicators are investigated, which is discussed in the following section.
Results of Applying MCDM Techniques In order to quantify the contamination of each sector, the SAW and the AHP methods with uniform (1/6=0.16 for each basic criterion and 1/12=0.083 for each sub-criterion) and non-uniform weights associated with water quality sub-indicators are applied. In applying the AHP method, the pair-wise comparison matrices for six basic criteria and 12 sub-criteria, which were presented in the previous sections, are used. The overall inconsistency index is estimated by combining the inconsistency index of basic criteria and the effects of sub-criteria as follows
II = II b + [w1
w2
w3
w4
w5
II 1 II 2 II 3 w6 ] × II 4 II 5 II 6
CRI = CRI 6 + [w1
w2
w3
w4
w5
Sectors Industrial Domestic Agricultural
Non-uniform weights 0.22 0.23 0.55
Uniform weights 0.27 0.21 0.52
estimating the share of different sectors in pollution of the system. The eigenvector estimated for the basic criteria is shown in Figure 5. The shares of contamination for different sectors are estimated using Equations 3 and 4 and the non-uniform weights (see Table 6). As it can be seen in this table, the agricultural sector produces the highest contamination load (about 55 percent of the total pollution) to the water resources in the study area. In order to check the sensitivity of the results with respect to assigned weights to different basic and subcriteria, the uniform weights are also used. The results of AHP method with uniform weights are also shown in Table 6. As it can be seen in this table, the share of contamination of different sectors has not been changed considerably. The SAW method has also been used. For this purpose, the assigned relative weights of all 12 sub-criteria are shown in Table 7. A number between 1 and 12 was agreed between the players to be assigned to different Table 7. Assigned non-uniform weights of water quality variables (sub-indicators) of SAW method
(6)
where [ w1 , L, w6 ] is the eigenvector for the pair-wise comparison matrix introduced earlier as MATQUAL for six basic criteria, and II1 , L , II 6 are the inconsistency indices estimated for the pair-wise comparison matrixes for each set of sub-criteria. In this case as explained before, all of the matrices are 2 x 2 and they are consistent. CRI is then estimated as CRI 2 CRI 2 CRI 2 w6 ]× CRI 2 CRI 2 CRI 2
Table 6. Share of contamination of domestic, industrial, and agricultural sectors estimated using the AHP method
(7)
where CRI 6 is the Inconsistency Index of a 6 × 6 random matrix (6 is the number of basic criteria), which is equal to 1.24. CRI m1 through CRI m n are equal to CRI 2 because they all have two sub-criteria. The overall Inconsistency Ratio is then estimated using Equation A-3 in Appendix III as 0.04, which is less than 10 percent. Therefore, the group judgment in matrix MATQUAL is used for
Variable
TSS
BOD
TS
TDS
Zn
Weight
0.118
0.159
0.053
0.118
0.039
Variable
COD
Weight
0.145
-
TP
TN
Cl
0.092
0.105
0.079
+2
Cd 0.066
Ca
Mg+2
0.013
0.013
sub-indicators. Then these numbers were normalized to add up to one as shown in this table. Table 8 shows the dimensionless decision matrix for the SAW method, which is estimated by dividing numbers in each column of Table 7 by the largest number in that column. Therefore, one of the values of r ij in each column is one. The values in each column of Table 8 show the relative importance of that criterion for a specific alternative (sector) compared to the others. The final results of SAW method are also presented in Table 9 for both schemes of uniform and non-uniform weights. As it can be seen in Table 9, with both uniform and non-uniform weights, the agricultural sector has the highest rate of pollution. There are also other pollutants, such as leakage from underground oil tanks, surface runoff, and water contamination generated by solid waste collection and disposal, which are not directly attributed to the three sectors but are indirectly related to domestic and industrial sectors. The share of contamination from “other sources” is estimated to be 6 percent based on engineering judgment and
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M. Karamouz, B. Zahraie, and R. Kerachian Table 8. Dimensionless Decision Matrix in SAW Method
Sectors Industrial Domestic Agricultural
TSS 0.11 0.58 1
BOD 0.21 0.50 1
TS 0.35 0.169 1
TDS 0.06 0.006 1
Zn 0.28 0.169 1
Table 9. Share of contamination of domestic, industrial, and agricultural sectors estimated using the SAW method (%) Sectors Industrial Domestic Agricultural
Non-uniform weights 0.23 0.23 0.54
Uniform weights 0.20 0.21 0.59
some limited observed data. To finalize the share of contamination for different sectors, the results of AHP and SWE methods were considered. Furthermore, the results are normalized to incorporate the effects of “other sources.” The final count of the share of contamination (percent of pollution load from different sources) for the current condition is estimated as: Domestic: 20 ± 2 percent; Industrial: 20 ± 3 percent; Agricultural: 54 ± 7 percent; and Others: 6 ± 2 percent. The errors associated with the share of each sector were determined by considering the upper and lower bound of estimates by other methods as well as the uncertainties associated with the dispersion of contaminants in the environment triggered by activities within each sector. The above numbers are estimated based on the analysis of data and the information provided for current situation. Because the planning horizon for the master plan is ten years, the change in the share of contamination according to the development plans of each sector is also considered. As it can be seen in Table 1, the water demands for domestic, industrial, and agricultural sectors in the year 2020 are projected by the authorities and experts to increase about 145, 150, and 45 percent, respectively. Therefore, the rate of development in domestic and industrial sectors is higher than agricultural sector. The rate of increase in contamination rate in three sectors in the tenyear planning horizon are assumed to be directly proportional to increase in water consumption and estimated as half of the figure declared by the authorities for the 20 years time horizon. The above share of contamination (percentage of water pollution) from different sectors is adjusted to take into account the uneven rate of increase in water consumption and wastewater production of the three sectors. Considering the rate of increase in water demand of the three sectors, the average share of contamination of different sectors over the ten-year time horizon of the master plan are: Domestic: 24 ± 2 percent; Industrial: 24 ± 3 percent; Agricultural: 46 ± 7 percent; and Others: 6 ± 2 percent. In estimating the above numbers, the following items are considered at the development stage:
Cd 0.178 0.17 1
COD 1 0.21 0.50
TP 1 0.083 0.019
TN 0.038 1 0.59
Cl0.01 0.025 1
Ca+2 0.22 0.45 1
Mg+2 0.111 0.22 1
• The volume of water withdrawal from water resources estimated for different projects; • The quality and quantity of estimated return flows; and • The demographic changes.
Outline of the Master Plan for Water Resources Pollution Control in Isfahan The main goal of this plan is to reduce 50 percent of water resources pollution in a ten-year time horizon (Karamouz, 2000). This objective could not be achieved without considering the effects of current development projects. Consideration of environmental issues is addressed providing plans for improvement or restructuring of the design process of the on-going and new projects. Considering the above items, the outline of the plan and the major proposed projects are defined as follows: Source Reduction • Reduction of agricultural pollutants such as fertilizers, herbicides, and pesticides by changing the pattern of use and making farmers use chemical products that are less water pollutants; • Subsidizing industries for implementation of wastewater treatment facilities; • Development of wastewater collection networks and treatment plants for towns/villages along the Zayandehrud River. Demand Management and Capacity Expansion • Increasing irrigation efficiency by subsidizing the implementation of different methods of drip and pressure irrigation; • Decreasing “unaccounted for water” by reducing water leakage from water mains in water distribution networks in Isfahan and improvement of metering equipment; • Development of inter-basin water transfer projects in order to increase available water resources and reduce quality issues due to probable water shortages. Human Resources Development • Capacity building, establishment of new branches of the Environmental Protection Agency in different cities in the province and training their staff; • Offering environmental awareness workshops for farmers; • Producing documentary television and other mass media programs about water pollution and its impact on the environment.
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Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study Water Pollutants' Sectors
Agriculture
Industry
Domestic
Others
Share of Total Contamination: 46±6%
Share of Total Contamination: 24±3%
Share of Total Contamination: 24±3%
Share of Total Contamination: 6±2%
Source Reduction
106 *
15%**
Development of Modern Methods of Irrigation and Increasing Irrigation Efficiency
119 *
12%**
Industrial Wastewater Treatment
138 *
8% **
Reduction of Per Capita Consumption
Domestic Wastewater Treatment
351 *
15%**
63 *
3% **
Replacement/ Maintenance of Petroleum Storage Tanks
25 *
2% **
Improvement of Solid Waste Collection, Recycling and Disposal
40 *
2%* *
Rehabilitation of Isfahan Surface Water Collection System
25 *
2%**
* Budget needed in million U.S. Dollars ** Percentage (%) of contamination reduction after implementation of the project
Figure 6. A schematic of master plan for water pollution reduction in Isfahan
Monitoring and Sampling Network • Expansion of existing network for quality and quantity monitoring and sampling from surface and groundwater resources in the study area; • Development of a new biological monitoring and sampling network for the Zayandeh-rud River; • Development of a program for sampling of domestic and industrial wastewater in the study area. Research and Technology Transfer • Analysis of pesticide and herbicide pollutants in the Zayandeh-rud River; • Development of an integrated quality and quantity model for the Zayandeh-rud River; • Integrated study of quality and quantity of groundwater resources in the study area. Institutional Changes and Improvement of Legal Framework • Establishment of environmental offices in all of the water related agencies; • Establishment of a system for environmental accounting in all governmental agencies in Isfahan Province; • Revisiting the duties of different agencies related to environmental protection.
Proposed Projects and Budget The major projects of the master plan are selected and ranked based on how effective they are in reducing the water pollution in the study area. The projects were proposed by different agencies, then, they are prioritized considering their impact toward the target of 50 percent reduction in water pollution set for this study. Finally the most effective projects are selected as Direct Projects as follows:
• • • • • •
Urban and Rural Wastewater Treatment Industrial Wastewater Treatment Source Reduction of Agricultural Pollution Development of Drip and Pressure Irrigation Reduction of Per Capita Domestic Water Demand Improvement of Isfahan Surface Runoff Collection System • Improvement of Solid Waste Collection and Recycling System • Rehabilitation and Replacement of Underground Tanks of Petroleum Products Other projects, which have an indirect impact on sustainable water supply and demand as well as projects aimed at monitoring and assessment, data collection and sampling, research and technology transfer are proposed as “Indirect and Support Projects” as follows. Indirect Projects • Expansion of inter-basin water transfer • Creating a sustainable balance between water supply and use • Rehabilitation of ecological system • Miscellaneous projects for movement of polluting industries and rerouting the agricultural drainage systems. Support Projects • Integrated sampling and monitoring network • Research and technology transfer • Manpower capacity building and improvement of legal framework • Evaluation and assessment In Figure 6, the breakdown of the share of contamination for each sector (source of pollutants), the cost associated with different major projects, and estimated rate of
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M. Karamouz, B. Zahraie, and R. Kerachian Table 10. Average percentage of pollution reduction in domestic sector based on the proposed wastewater treatment projects TSS 85
Treatment Efficiencies
BOD 88
TS 58
TDS 35
water pollution reduction by implementing the major projects are presented. Certain projects will directly reduce the current level of water pollution. The rate of reduction is related to the extent of pollution reduction by these projects. For example, in the case of domestic wastewater treatment projects, the amount of wastewater treated and the efficiency of treatment are considered in estimating the impact of this project in reducing the water resources pollution. The average domestic wastewater treatment efficiency for different water quality indicators, which are estimated based on the characteristics of the proposed treatment plants, are shown in Table 10. The weighted percentage of contamination reduction for domestic wastewater treatment project is calculated as the sum of the weight of each water quality variable times the efficiency of treatment as expressed in Table 10 (see Figure 6). The proposed MCDM model is also used for assessing the effectiveness of the water pollution control projects. The reduction in concentration or load of different water pollutants is estimated based on the characteristics of the proposed projects (such as details presented in Table 10). Then the effectiveness of the projects in reducing the water pollution load is evaluated using the hierarchical structure of water quality variables and their relative weights, calculated in the first phase of this study. The results of this section provide the details of the pollution load reduction in each sector, which are presented in Figure 6. As it can be seen in Figure 6, the total percentage of contamination reduction after implementation of the proposed projects adds up to 59 percent. Likewise, the impacts of other projects: source reduction, modern irrigation techniques, industrial wastewater treatment, reduction of per capita consumption, replacement of underground tanks, improvement of solid waste disposal, and surface water collection are calculated as 15, 12, 8, 3, 2, 2, and 2 percent, respectively. Considering an increase of 18 percent in pollutants during the period of implementation, the net contamination reduction would be 50 percent after ten years.
Million U.S. Dollars
30 25 20 15 10 5 0 1
2
3
4
5
6
7
8
9
10
Year
Figure 7. Time distribution of budget needed for all proposed projects
Zn 40
COD 60
TP 80
Cl40
Ca+2 70
Mg+2 70
Cd 40
TN 60
The total budget needed to reduce the water contamination/pollution by half in a ten-year time horizon is estimated as 2,068 billion Iranian Rials (US$260 million). Of which, 693, 150, and 1225 billion Rials are allocated to Direct Projects, Support, and Indirect Projects, respectively. The variation of budget depends on an estimate of the process of approval, construction period, and the waiting period for the full operation. Figure 7 shows time distribution of budget needed for all of the proposed projects.
Summary and Conclusion In this paper, different aspects of development of a master plan for water pollution reduction were discussed. Definition of objectives, system identification and defining action items and projects needed for water pollution reduction were considered and described in the context of a case study for Isfahan Province in Iran. The study area has been facing population growth and rapid development of agricultural and industrial sectors that has intensified the environmental problems. In order to estimate the effects of agricultural, domestic, and industrial sectors on water pollution in the study area, two MCDM techniques – SAW and AHP methods – were used. Subjective information about relative importance of different quality indicators, which were assessed, based on engineering judgment and expert opinions are also incorporated in the MCDM analysis. The inconsistency in engineering judgments was also assessed and incorporated in finding the relative importance of different water quality indicators by utilizing the AHP method. The economic analysis can also be used instead of the proposed MCDM method considering the cost associated with the environmental impacts of the pollution load of different sectors and cost effectiveness of the water pollution control projects. The economic-based methods, especially in the developing countries, suffer from some data deficiencies and uncertainties in inflation and interest rates over the long-term planning horizon. The proposed MCDM method can easily incorporate the available limited data, experts’ opinions, and engineering judgments in defining the criteria and their relative weights in many developing countries in which the economic data is not usually available. The main goal of the master plan for the study area has been to eliminate 50 percent of water pollution in a ten-year time horizon. Source reduction, demand management, capacity expansion, human resources development, development of monitoring and sampling network, research and technology transfer, institutional changes, and improvement of legal framework have been the major proposed projects for the master plan.
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Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study
Subjective information as well as engineering judgment has been used to overcome the data deficiency for this type of analysis. Breakdown of the total percentage of water pollution generated through the activities of different sectors, the cost associated with different major projects, and estimated rate of pollution reduction by different projects have been determined. A number of other projects were identified that did not have a direct impact on the pollution reduction but implementing these projects could have a long-term effect on the faith of a sustainable water resources development program for the region. Application of MCDM techniques and incorporating engineering judgments played a significant role in this study. The proposed approach in estimating the share of contamination of different sectors can be used for other regions that are faced with the lack of data and information about pollutants and their effects on water resources systems.
Appendix I. Symbols Used = Eigen value of pair-wise comparison matrix λ λ max = Dominant eigenvalue A = Pair-wise comparison matrix a ij = Elements of pair-wise comparison matrix c ij = Value of criterion i for alternative j C = Maximum value of criterion i for all alternatives CRIn = Inconsistency index for randomly filled matrixes with dimension n II = Inconsistency Index IIb = Inconsistency Index for basic criteria IR = Inconsistency Ratio N = Number of decision-makers w = Eigen vector of pair-wise comparison matrix a = Weight of alternative i wi = Weight of alternative i Wi a = Relative weight of alternative i Wi rij = Elements of row i and column j in the Dimensionless Decision Matrix max i
Appendix II. Group Decision Making using Geometric Mean Method k Consider a ij (k=1, …, N) is relative importance of
indicator i comparing with indicator j defined by decisionmaker k, the group judgement of N decision-makers can be estimated as follows
(
a ij = a 1ij × aij2 × L × aiN, j
)
1/N
(A-1)
where N is number of decision-makers, and a ij is the group judgement of the relative importance of indicator i compared with the indicator j (ij th element of pair-wise decision matrix).
Appendix III. Basic Steps in AHP Method The AHP method consists of the following steps: • In the first step, different criteria and sub-criteria should be defined based on different objectives of the investigation. Knowledge and cognition of the analyst about different elements of the system and their interactions is an important prerequisite for defining the criteria and their hierarchy structure. • This method is based on a pair-wise comparison of the importance of different criteria and sub-criteria. The decision-maker should provide different values in the pair-wise comparison matrixes. For example consider A = aij is an k by k pair-wise comparison matrix for the basic criteria, where k is the number of basic criteria. The principal criterion for accepting the assigned weights/ ratios is to show that the comparisons are consistent. • In the third step, the consistency of comparisons should be verified. The difference between the dominant eigenvalue, λ max , and k is used by Saaty (1980; 1994) in defining the inconsistency Index, II
( )
λ max - k k -1 The inconsistency ratio, IR is then defined as II =
(A-2)
IR = II / CRI k
(A-3)
where CRI k is the Inconsistency Index of random matrix obtained by calculating II for randomly filled n by n matrix. • If IR
