UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Capacity expansion of urban water supplies : a case of Accra-Tema metropolitan area, Ghana Alhassan, Hadisu 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


24-ubc_2015_february_alhassan_hadisu.pdf [ 2.04MB ]
JSON: 24-1.0074398.json
JSON-LD: 24-1.0074398-ld.json
RDF/XML (Pretty): 24-1.0074398-rdf.xml
RDF/JSON: 24-1.0074398-rdf.json
Turtle: 24-1.0074398-turtle.txt
N-Triples: 24-1.0074398-rdf-ntriples.txt
Original Record: 24-1.0074398-source.json
Full Text

Full Text

Capacity Expansion of Urban Water Supplies:  A Case of Accra-Tema Metropolitan Area, Ghana  by  Hadisu Alhassan  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan)  November 2014 © Hadisu Alhassan, 2014 ii  Abstract  The supply-demand gap of potable water in rapidly growing urban cities in developing countries has become a major challenge. This is potentially worsened by poor and superficial assessment of the complex parameters of water resources and treatment technologies regarding capacity expansion. The estimated daily demand of 150 million gallons in the Accra-Tema Metropolitan Area (ATMA) region of Ghana far outweighs the daily supply of 94 million gallons. In this study, the Analytic Hierarchy Process (AHP) approach, through the inputs of experts in the water industry in Ghana, is used to assign weights to the identified significant factors that impact on selecting the best alternative source for urban water supply capacity expansion in the ATMA region. Three alternative plants, alongside their respective sources, are considered — the Weija, the Kpong, and the Teshie Desalination plants. The decision criteria considered are environmental, economic, technical, and socio-cultural criteria, with each having sub-criteria. In analysing the pairwise comparative judgments by the experts, the environmental criterion was found to be the most important criterion with the highest priority weight, followed by the economic, the technical and the socio-cultural criteria. In the analysis, the Kpong treatment plant ranked first with a score of 36.1%. This was followed by the Weija and Teshie desalination plants, which scored 33.8 and 30.2% respectively. Sensitivity analysis on the model revealed that the model is sensitive to the environmental and economic criteria while being robust to the technical and socio-cultural criteria. Sensitivity, in relative terms, indicated that the resource availability sub-criterion is the most critical, while that of the energy sub-criterion proved the most critical in absolute terms.   iii  Preface  The lack of potable water supply seriously affects socio-economic development, and facilitates the growth of poverty and diseases. This study, which borders on water supply capacity expansion, is therefore conducted to shed light on how expert scientific judgment and decision-making tools could provide the road map to making robust decisions of national interest. The study was conducted to choose the best alternative source of supply, through a collaboration of the University of British Columbia (UBC), the Ghana Water Company Limited (GWCL) and the Public Utility Regulatory Commission of Ghana (PURC). Data was gathered from experts of GWCL and PURC and analyzed with the Analytic Hierarchy Process (AHP) algorithm to develop priorities in order to rank the alternatives.  The dissertation is the independent work of the author, Mr. Hadisu Alhassan, under the supervision of Dr. Bahman Naser. The questionnaire involved in the study, as reported in the appendix, was approved by the UBCO Behavioural Research Ethics Board (BREB) with a BREB certificate number of H14-01795. The core contribution of the dissertation has been structured in publication format. The materials of this work are based on a research article that is in the final stage of preparation for publication. The article is listed below:  Alhassan, H., Naser, Gh., Milani, A., Nunoo S., (2014), “Capacity Expansion of Urban Water Supplies: A Case of Accra-Tema Metropolitan Area, Ghana”, To be submitted for Journal of Water Supply: Research and Technology-AQUA, Submission Date: October 10, 2014.      iv  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents .......................................................................................................................... iv List of Tables ................................................................................................................................ vii List of Figures ............................................................................................................................. viii Acronyms, Abbreviations and Mathematical Notations ........................................................... ix Acknowledgements ........................................................................................................................ x Chapter 1 Introduction ................................................................................................................. 2 1.1 Research Significance ....................................................................................................... 5 1.2 Research Objectives .......................................................................................................... 5 1.3 Thesis Organization .......................................................................................................... 6 Chapter 2 Literature Review ........................................................................................................ 8 2.1 Brief Review on Capacity Expansion Studies .................................................................. 8 2.2 Environmental Criterion ................................................................................................. 11 2.2.1 Resource Availability (Quantity) ............................................................................. 11 2.2.2 Resource Quality ..................................................................................................... 13 2.2.3 Treatment Waste Management and Ecological Impacts ......................................... 16 2.3 Economic Consideration ................................................................................................. 18 2.3.1 Construction Cost .................................................................................................... 19 2.3.2 Energy Cost ............................................................................................................. 19 2.3.3 Chemical and Maintenance Costs ............................................................................ 21 2.3.4 Financial Viability ................................................................................................... 22 2.4 Technical and Socio-Cultural Perspectives..................................................................... 24 2.4.1 Operational Flexibility ............................................................................................. 24 2.4.2 Land Area ................................................................................................................ 25 v  2.4.3 Storm Drainage ........................................................................................................ 25 2.4.4 Aesthetics and Acceptability ................................................................................... 25 2.4.5 Security of Transmission and Cultural Resource .................................................... 26 2.5 Multi-Criteria Decision Analysis (MCDA) .................................................................... 27 2.5.1 Analytic Hierarchy Process (AHP) .......................................................................... 31 Chapter 3 Research Methodology .............................................................................................. 34 3.1 AHP Algorithm ............................................................................................................... 34 3.2 AHP Steps ....................................................................................................................... 34 3.3 Sensitivity Analysis ............................................................................................................. 37 Chapter 4 Case Study .................................................................................................................. 41 4.1 Synopsis .......................................................................................................................... 41 4.2 Significance ..................................................................................................................... 42 4.3 Accra-Tema Metropolitan Area (ATMA)............................................................................ 45 4.4 Alternatives, Criteria, Sub-criteria and their Comparative Judgments ................................ 46 4.4.1 Data Collection ........................................................................................................ 46 4.4.2 Alternatives .............................................................................................................. 47 4.4.3 Criteria ..................................................................................................................... 47 Chapter 5 Results and Discussion .............................................................................................. 50 5.1 Estimation of Priorities in Hierarchy .............................................................................. 50 5.2 Environmental Criterion ................................................................................................. 54 5.3 Economic Criterion .............................................................................................................. 56 5.4 Technical Criterion .............................................................................................................. 56 5.5 Socio-cultural Criterion ....................................................................................................... 57 5.6 Sensitivity Analysis ............................................................................................................. 58 Chapter 6 Conclusion and Recommendation ........................................................................... 66 6.1 Conclusion ........................................................................................................................... 66 6.2 Limitations of the Study ....................................................................................................... 67 vi  6.3 Recommendations for Future Work ..................................................................................... 67 References .................................................................................................................................... 69 Appendix A: Research Questionnaire ....................................................................................... 84 vii   List of Tables  Table 2.1   GWCL Tariff Structure by PURC ................................................................... 24 Table 3.1   Gradation scale for quantitative comparison of    alternatives (Saaty 1980) ................................................................................. 35 Table 4.1   Definition of criteria ........................................................................................ 49 Table 5.1   Aggregated Pairwise comparative judgment of criteria .................................. 50 Table 5.2   Priorities of criteria and sub-criteria with respect to the alternatives .............. 53 Table 5.3   Summary statistics of consistency indeces ...................................................... 61 Table 5.4   Priorities of sub-criteria and alternatives from the pairwise   comparative judgments .................................................................................... 64 Table 5.5   Absolute changes in sub-criteria weights (        .......................................... 64 Table 5.6   Relative changes in sub-criteria weights (         .......................................... 64 Table 5.7   Criticality degrees and sensitivity coefficients (D´k) ....................................... 65     viii  List of Figures  Figure 1.1   Organization of thesis....................................................................................... 7 Figure 2.1   Outline of literature review ............................................................................ 10 Figure 3.1   Flow diagram of the AHP methodology ........................................................ 36 Figure 4.1    ATMA water distribution system     (Source: Ghana Water Company Limited) .................................................... 44 Figure 4.2   Tanker water supply and sachet water    vending (http://edmingle.blogspot.ca) ............................................................ 46 Figure 4.3   The AHP hierarchy for the capacity expansion project ................................. 48 Figure 5.1   Overall ranking of alternatives ....................................................................... 58 Figure 5.2   Performance sensitivity graphs (weights of listed criteria were increased    with the others simultaneously reduced) ......................................................... 62 Figure 5.3   Gradient sensitivity graphs (weights of listed criteria were increased    with the others kept unchanged) ..................................................................... 63 Figure 5.4   Graphical Representation of Relative changes in sub-criteria    weights (         ............................................................................................. 65  ix   Acronyms, Abbreviations and Mathematical Notations  AHP Analytic Hierarchy Process AMCOW African Ministers’ Council on Water ATMA Accra-Tema Metropolitan Area AWWA American Water Work Association BBC British Broadcasting Corporation CEC Commission for Environmental Cooperation CI Consistency Index CR Consistency Ratio DDT Dichlorodiphenyltrichloroethane  ELECTRE Elimination and Choice Expressing Reality GWCL Ghana Water Company Limited  IWS Intermittent Water Supply MASc Master of Applied Science MCDA Multi-criteria Decision Analysis NRCS Natural Resources Conservation Service NTU Nephelometric Turbidity Units PROMETHEE Preference Ranking Organization METHod for Enrichment of Evaluations PURC  Public Utility Regulatory Commission RO Reverse Osmosis  SSA Sub-Saharan Africa  US United States UBC University of British Columbia UN Unites Nations UNICEF United Nations International Children Emergency Fund VEI Vitens Evides International WHO  World Health Organization WMO World Meteorological Organization A Matrix A n Number of factors aij Relative weight determined by the pairwise comparison for the relative importance of the ith factor over the jth factor (i, j = 1, 2, 3, ..., n ) wi weight of the ith factor      Principal eigenvalues   minimum change C Criterion Pi Priority of alternative i (i = 1, 2, 3, ..., n ) Ai Alternative i (i = 1, 2, 3, ..., n )    x  Acknowledgements After acknowledging the Almighty God’s guidance, my sincere acknowledgement goes to my supervisor, Dr. Bahman Naser, who dedicated his time and energy to academically groom me beyond the attainment of a master’s degree. I would also like to acknowledge the work of my academic committee for the invaluable contribution they made towards improving this study. Finally, the assistance of management of the Ghana Water Company Limited and the Public Utility and Regulatory Commission of Ghana is also acknowledged.  2  Chapter 1 Introduction “Only 2.5% of the world's water is not salty, and two-thirds of that is trapped in the icecaps and glaciers. Of what is left, about 20% is in remote areas and most of the rest comes at the wrong time and in the wrong place, as with monsoons and floods. The amount of fresh water available for human use is less than 0.08% of all the water on the planet. About 70% of the fresh water is already used for agriculture, and the report says the demands of industry and energy will grow rapidly. The World Water Council report estimates that in the next two decades the use of water by humans will increase by about 40%, and that 17% more water than is available will be needed to grow the world's food. The commission concludes that only rapid and imaginative institutional and technological innovation can avoid the crisis.”                             (BBC News, “Water arithmetic doesn’t add up”, 13 March 2000) Water is a necessity for the sustenance of life and for the socio-economic development of every nation. As the above quotation indicates, water scarcity will be at alarming levels, if it has not been already. The United Nations (UN) indicated that about half of the world will live under situations of high water stress by 2030 (United Nations 2011). Potable water availability has become a global challenge due to the increasing constraints on water supply facilities. The rise in global human population growth and rapid urbanization greatly contributes to the stress on water resources. The United Nations estimates a rise in world population to 8.9 billion in 2050. Relatively, much of this demographic change will occur in developing countries. The population of the developing region is estimated to increase by 58% of its current population over 50 years, as compared to 2% for the developed region (United Nations 2004). Urbanization continues to show no signs of slowing. For example, China’s urban population stood at 47% in 2010. These frightening statistics point to the fact that there will be an increase in competition for most natural resources, among which water is the most essential (Zoppou 2001), and governments are expected to respond appropriately to combat the problem.   Recently, global statistics regarding access to improved water sources has been encouraging. About 87% of the world population has been projected to have access to 3  improved water sources (World Health Organization 2010). Despite the progress made, a staggering population of 884 million still live without access to improved sources. “Improved sources” has been described to mean “household connection, public standpipe, borehole or protected spring, dug well or rain water catchments” (Rosenberg et al. 2008).   Currently, with the developed world facing low economic and population growth, water demand only increases marginally, consistent with practices of managing the growing water scarcity. However, arid regions of Eastern Australia, California in the United States and Southern Spain are expected to face more water scarcity problems due to climate change. Conversely, the developing world is enjoying rapid economic and demographic growth rates with a concomitant increase in demand for water and other resources. South Asia, already facing serious water scarcity problems due to over-exploitation of water resources, still experiences a rise in population growth and urbanization (Pegram 2010). This spells a very gloomy picture for the future availability of water, considering the variability of climate change.   Africa has been one continent that is heavily hit with inadequate potable water supply. Climate change has already shown signs of what the continent can expect in the near future. The continent experienced massive droughts in the 1980s, which affected some twenty countries and created an urgent humanitarian crisis of famine. With the uneven distribution of the water resources across Africa—75% of the resources lying within eight of the main river basins, and the huge expense of transporting water, proximity is going to be the most vital issue in the context of scarcity (Freitas 2013).   Significantly, Ghana has started experiencing the evident effects of climate change. Tributaries to the River Offin, a major source of water supply to Kumasi (the second largest city in Ghana) and its environs are drying up (Gyampoh et al. 2008). This reflects a reported per capita freshwater availability reduction from 9,204 m3 in 1955 to 3,529 m3 in 1990 (Karikari 1996). Unpredictable climate pattern in the Volta River basin, which drains six riparian countries, of which 42% runs through Ghana, is reflected in the water quantity in the Volta Lake. The Lake, which is used to generate hydroelectricity, has seen low water levels in recent years, triggering an energy crisis (Asare 2004).   4  The current deficit in the supply of water mostly affects the developing world, but ageing infrastructure in the developed world is also an emerging threat to the water industry. In countries with inadequate water supply facilities, water utility companies employ intermittent water supply as an alternative to continuous water supply. This, to some extent, ensures the equitable distribution of potable water. However, intermittent water supply has been established as being associated with serious water quality challenges (Totsuka et al. 2004; Thompson and Cairncross 2002). Intermittent water supply features prominently in the low-income and middle-income countries where water is provided for a limited time in a day due to supply-demand constraints. For instance, there is the practice of intermittent water supply in South Asia to a population of over 350 million people, and nearly 50 million people to 10 major Latin American cities. Nearly every water supply facility in India practiced intermittent supply with an average supply period of 4.8 hours per day in 2005 (Vairavamoorthy et al. 2007; Kumpel 2013; McKenzie and Ray 2009; Desai et al. 2008). Similarly, the Ghana Water Company Limited (the sole urban water supplier) rations water in most parts of the country for equitable distribution due to a wide supply-demand gap (Stoler et al. 2012a).   Research has established a link between basic services, public health and poverty (World Bank 2000). The need for capacity expansion of water supply facilities is crucial, as inadequate and intermittent supply of water can culminate in problems of low pressure, deterioration of water quality, as well as increased cost in consumer spending regarding the purchase of more storage tanks (Vairavamoorthy et al. 2007). The emergence of numerous water-borne diseases and other complications, arising from irregular and/or inadequate water supply, makes the idea of capacity expansion more compelling. With plans to meet the nation’s potable water demand seriously advancing in Ghana, a critical look into how such ambitious capacity expansion projects will be executed is vital. Such decisions must avoid routes which have facilitated the collapse of very important national projects by being inter-sectoral and involving all stakeholders in the decision-making process. The success of planning the capacity expansion of water supply facilities largely depends on it being apolitical and non-tribal, but one based on the technical consideration of the merits of its location for the national interest. This study rightly employs a technical approach, encompassing inputs of the relevant stakeholders through the use of multiple criteria decision analysis (MCDA).  5  1.1 Research Significance Although major challenges have been encountered with water due to the troubles that it endures as a consequence of climate change and human degradation of its resources, it still remains key to life, and efforts of making it potable for human consumption continue to advance. Urbanization and population growth are the key threatening variables to the very human existence in urban areas, through the huge stress that they exert on water supply facilities. This pressure sometimes pushes decision-makers to take knee-jerk reactions towards providing solutions to the ever-existing water supply-demand gap.  This dissertation, thus, derives its significance in attempting to apply an MCDA technique towards water supply capacity expansion to make potable water available for use. It expands on the literature of water supply challenges and develops an integrated approach of group decision-making by the relevant stakeholders using the Analytic Hierarchy Process (AHP). The study also explores into literature to unveil the complicated relationship between urbanization, population growth, poverty, water supply and public health. Even though MCDA techniques have been applied in many areas, including water resources and water supply, this is the first attempt in applying the AHP to facilitate decision-making in urban water supply capacity expansion. The work exclusively explores the broad thematic criteria that ought to be considered in water supply capacity expansion projects, and narrows down to specific sub-criteria pertinent to the study area, to elucidate the evaluation and pairwise comparative judgment of the criteria by the experts who participated in the study. Ultimately, it is hoped that this research will help the authorities make robust water supply capacity expansion decisions and reduce the associated water-related poverty and risks.  1.2 Research Objectives The primary objective of this study is to choose an appropriate decision-making tool to support water supply authorities towards the capacity expansion of water supply facilities. The decision-making tool will help ensure that well-thought, comprehensive and robust decisions are made to improve the planning and development of the water supply industry. Specifically, this will be achieved through the following set of short-term objectives: 1. a comprehensive review of the challenges to adequate water supply, 2. identifying a decision-making framework for water supply capacity expansion, 3. applying the identified framework to the case study of Ghana, and 6  4. drawing lessons on the case study to evaluate the effectiveness of the decision-making tool. 1.3 Thesis Organization This dissertation is structured into six chapters that discuss various aspects of the research. While Figure 1.1 indicates these chapters briefly, they are explained below:  Chapter 1 – Introduction: This chapter introduces the research significance and the objectives of the study.  Chapter 2 – Literature Review: This chapter reviews the relevant literature and provides a more detailed insight to the problem under study, the approaches taken by previous researchers in studying the problem, and the outcome of such studies.  Chapter 3 – Research Methodology: This chapter considers the methodological approach employed in the study. It describes the nature of the participants involved in the study, the data collection process, and the multi-criteria decision tool employed.  Chapter 4 – Case Study: This chapter describes the case studied in this dissertation. It further discusses the application of the proposed methodology (discussed in Chapter 3) on the case study.  Chapter 5 – Results and Discussion: The Chapter provides the results of the analysis for the test case.  Chapter 6 – Conclusion and Recommendation: This chapter concludes the dissertation by summarizing the findings as well as providing some recommendations and suggestions for future study. 7                     LITERATURE REVIEW Chapter 2 – Literature Review  Chapter 1 - Introduction Chapter 4 – Case Study  Chapter 3 – Research Methodology  Chapter 5 – Results and Discussion  Chapter 6 – Conclusion and Recommendation  Conclusion Results and Discussion Economic Consideration Multi-criteria Decision Analysis  Technical and Socio-cultural Considerations Environmental Consideration Organization Objectives Significance AHP Algorithm Sensitivity Analysis AHP Steps Institutional Framework Overall Synopsis Application of Methodology Definition of MCDA Variables Figure 1.1 Organization of thesis 8  Chapter 2 Literature Review  “What everyone in the astronaut corps shares in common is not gender or ethnic background, but motivation, perseverance, and desire - the desire to participate in a voyage of discovery.” Ellen Ochoa (1958 – ) This chapter reviews the literature of critical criteria considered for capacity expansion. It reviews the criteria that border on water supply capacity expansion by grouping them into environmental, economic, technical and socio-cultural perspectives. Finally, the approach employed in the study, multi-criteria decision analysis (MCDA), is reviewed. The specific MCDA analytical tool employed, the analytic hierarchy process (AHP), concluded the MCDA review. 2.1 Brief Review on Capacity Expansion Studies In the process of capacity expansion of a water supply system, a series of factors are considered in the planning process. These include: a comprehensive analysis of the existing system; forecasting water demand of the community being supplied; evaluation of source water quantity and quality; proposing a treatment technology; evaluation of cost functions of the facilities; consideration of the operability of the system; evaluation of land availability and the general socio-cultural impact such an expansion might have on the community (Nakashima 1982). Capacity expansion studies have been conducted since the mid-1900s. Such studies have clearly helped in providing a better insight into making reasonable investments. Capacity expansion problems have taken different forms through different production industries. In many analyses, a forecast is generally made followed by an estimation of production capacity to aid in implementing sound economic policies. As reviewed by Braga et al. (1985), many capacity expansion studies in the fields of water supply and water resources have often taken the form of mathematical modeling. For example, Butcher et al. (1969) developed the optimum capacity expansion sequence considering the factors of water demand-time interdependence, the prevailing interest rates, and the foreseeable comparative costs and capacities of alternative projects, using dynamic programming. With this approach, out of a multitude of alternatives, he was able to prioritise the order of expansion of these facilities for economic investment. 9  Erlenkotter (1973a) elicited the weakness of the approach employed by Butcher et al. (1969), by indicating that their dynamic programming methodology could give less optimal results in some applications, and rather suggested the use of a binary state space dynamic programming. Other studies have developed models regarding minimum annual cost (Erlenkotter 1973b; Tsou et al. 1973).  While this research will focus on the capacity expansion of water supply facilities, it will employ a MCDA approach (unlike the mathematical modeling approaches discussed above) in choosing the best alternative for capacity expansion out of several other supply sources. The major focus of the selection is based on well-considered criteria that affect every capacity expansion decision of a water supply system. They include environmental, economic, technical and socio-cultural criteria. Figure 2.1 briefly highlights these effective criteria. The following sections provide further insight to the criteria. 10  Figure 2.1 Outline of literature review Literature Review  Resource availability Resource quality Treatment waste management and ecological impacts Environmental Aspect Construction cost Energy cost Financial Viability Chemical and maintenance cost Economic  Aspect Analytic hierarchy process Multi-criteria Decision Making Operational flexibility & expertise of employees Land area Aesthetics and acceptability Storm drainage Security of transmission & cultural resources Technical and Socio-cultural Aspect 11  2.2 Environmental Criterion This criterion broadly covers water resource availability, water resource quality, the management of treatment wastes from water supply facilities and the general effect on the ecological setup of the environment as a result of the execution of a treatment facility’s capacity expansion. In the wake of climate change and environmental degradation, engineers conducting water supply capacity expansion projects place much emphasis on environmental challenges of such projects. 2.2.1 Resource Availability (Quantity)  The engineering of water supply is explicably entangled with that of water resources. Without adequate and good quality water resources, the supply of water to the populace becomes a very complicated and an extremely expensive pursuit. The success of a water supply capacity expansion, therefore, is reliant on the ability of engineers to estimate future water budgets to determine if withdrawals will be environmentally appropriate. In this study, a review of water as a resource is important in giving a detailed insight into its impact on capacity expansion. With the vast abundance of water on the earth, one would wonder why potable water supply should not be as cheap and easy to access as have been considered by lay people. The earth is considered a blue planet with a staggering 70% of its surface being covered by water, but in terms of the various kinds of water constituting this percentage, 97% of it exists as salt water in the ocean with less than 3% being fresh water. Close to 70% of fresh water resources is inaccessible in the form of glaciers and permafrost, and the remaining 30% is hidden in deep underground aquifers that are inaccessible. Consequently, less than 1% of the earth’s fresh water (surface and ground water) is accessible for human use. This is the quantity that is regularly replenished through precipitation (Gleick 1998; Seckler et al. 1998).  An estimated annual precipitation of 108,000 km3 occurs on the earth’s surface with 60% (61,000 km3) of it returning to the atmosphere through evapo-transpiration. The remaining 40% (47,000 km3) flows through rivers and finally ends up in the sea. This represents a per capita per year value of 9,000 km3. The quantity flowing through rivers roughly represents an equivalent of the total storage in Lake Baikal in Russia, and Lakes Tanganyika and Victoria in Africa (World Meteorological Organization 1997; Seckler et al. 1998). Just as precipitation varies widely from North America through the Arabian Peninsula to Africa, so does the sparse distribution of water resources on the planet. For 12  example, Canada is estimated to contain 20% of the world’s freshwater (Hendriks 2014) while Saudi Arabia remains arid with virtually no accessible freshwater. This underscores the fact that run-off varies throughout the world. Despite the huge population of Asia (about 69% of the world’s population) it only holds 36% of global run-off, while South America with a population representing 5% of the world’s population has 28% global run-off. Statistics indicate that a vast proportion of global run-off is inaccessible. The Amazon River holds a significant proportion of global run-off (15%) but this is only accessible to 0.4% of world population, rendering it 95% inaccessible. Similarly, the Congo-Zaire River is estimated to be 50% inaccessible. In all, expert estimates reveal that the total run-off volume accessible to humans is 7774 km3 representing 19% of the total run-off (Postel et al. 1996).  In essence, the issue does not only lie with water quantities, but also has to do with the replenishing rate of the resource and its distribution. This buttresses the fact that problems associated with the practical distribution of water resources with respect to space and time, as well as the ability of consumers to afford supplies, has been largely responsible for the wide supply-demand gap in most countries. The increasing noticeable problem of climate change and its effects on climatic factors such as precipitation, temperature and other variables has exacerbated the challenge of inadequate potable water supply in some parts of the world (Kayaga et al. 2007; Stern 2007).   Sub-Saharan Africa is not spared with the impact of climate change. The Sub-Saharan region is experiencing variable freshwater availability. The Sahelian countries suffer from more variable water availability while most West African countries, of which Ghana is a member, have abundant freshwater resources. Increasing urbanization and rising population growth continue to affect freshwater availability in Africa. In 1990, it was reported that eight African nations were water-stressed, and this is projected to rise to eighteen by 2025 with a population of 600 million people affected (World Bank 1995). The lack of adequate potable water in Africa due to low capacity of water supply facilities leaves it as the continent with the worst potable water coverage. This affects rural areas most, and worsens the already precarious situation of poverty and diseases. The rural population without access to sufficient water stands at 65% and that of urban areas is 25% (World Bank 1997).  13  Overall, Africa has more than 100,000 km2 of river basins representing more than a third of the world’s main river basins, serving as an environmental resource. The continent also has other major water supply sources such as Lake Victoria, Lake Tanganyika, Lake Chad and Lake Malawi. However, several countries often share these resources. This is likely to cause serious conflicts between countries when major projects like constructing huge freshwater reservoirs for water supply capacity expansions are undertaken without appropriate consultation among competing countries. To be able to significantly reduce the wide potable water supply-demand gap, Sub-Saharan Africa needs to properly manage its freshwater resources by employing the appropriate environmental management practices to protect water quantity and quality, through strong policy implementation and inter-sectoral participation.  Similar to all West African nations, Ghana is endowed with freshwater bodies, despite the occurrence of seasonal shortages in some areas (Odame-Ababio 2003). Like most parts of the world, there is an uneven distribution of freshwater resources within the country — the south-western part, known as the forest zone, receives more rainfall than the coastal, and northern or savannah zones (Water Resources Commission 2012). The average rainfall is between 2,150mm in the extreme forest zone and gradually reduces to about 800mm in the coastal zone and to 1,000mm in the savannah zone. The country is drained by three major river basins — “the Volta basin, the southwestern basins and the coastal basin river systems, which respectively cover 70, 22 and 8 percent of the total area of Ghana”. Typical of African river basins, the Volta river basin is trans-boundary running through five other countries including Côte d’Ivoire, Burkina Faso, Togo, Benin and Mali (Water Resources Commission 2012). In the present, Ghana will relatively not have much difficulty in terms of water resources to expand its potable water supply capacity. 2.2.2 Resource Quality  Water sustains life in many different forms. However, humans have heavily abused water resources and find them as convenient sinks for depositing wastes. The indiscriminate waste disposal, coupled with the geogenic release of natural chemicals, leads to microbial and chemical pollution of water resources. The apparent vulnerability of water resources has been ignored and their usability as sources for potable water supply has been threatened, mainly by anthropogenic pollution (Sundaray et al. 2006; Törnqvist et al. 2011). Pollution does not only affect water security but also threatens the aquatic 14  ecosystem (Wu and Chen 2013). The recent origins of pollutants appear more complicated and come from different sources including the use of agrochemicals, the release of untreated sewage, oil spills, and other industrial waste releases (Kjellén and Mcgranahan 1997). Water quality has a huge impact on the treatability of water, and is a critically considered factor when water supply facilities are built. For instance, too much alkalinity in water would consume more aluminum- and iron-based coagulants, while a low pH will require the use of more alkaline chemicals like lime to raise the pH (Crittenden et al. 2012).   Water quality degradation is a great concern worldwide (Schwarzenbach et al. 2010). A Gallup poll conducted in 2009 disclosed that the pollution of source waters has become the key environmental worry for the United States citizens (Saad 2009). Many important water resources are being threatened by pollution. For example, in the Amazon River Basin, just like most parts of Africa, some levels of mercury and Dichlorodiphenyltrichloroethane (DDT) have been recorded. The mercury pollution results from local mining activities while the DDT comes from soils that were contaminated by mass spraying exercises carried out in the 1940s and 1990s to combat malaria. Both chemicals have affected water quality and the aquatic ecosystem, making water supply facilities needing extensive treatment technology to eliminate the chemical threat. When these water quality threats are not being considered in the construction or capacity expansion of a water supply facility, there is the potential of ultimately defeating the prime purpose of protecting public health, and this will eventually question the reasonableness of such investment (Brabo et al. 2000; Torres et al. 2002; Kehrig et al. 1998).  Many other important water resources have recorded varying levels of pollution. China is one of the leading countries with high source water pollution. In 2005, a national survey indicated that 59% of the foremost rivers and 72% of lakes and reservoirs were classified under the top two worst water quality categories in the nation’s water quality classification system. This, according to the country’s water quality grading system, implies that such waters are unfit for both human and industrial use (SEPA 2006), and largely affects water supply decisions considering the enormity of the treatment challenges that would be encountered. In view of the surface water quality challenges, some parts of China have resorted to groundwater as an alternative supply source to 15  expand potable water coverage. However, this has also led to over-exploitation of deep aquifer groundwater and results in salt water intrusion from the sea into the aquifers, making the use of such water difficult (Post 2005). This further complicates the precarious water supply problems in the country and has resulted in a quarter of the population being exposed to poor drinking water quality (Klaver and Mulkey 2006; Varis and Vakkilainen 2001). Other major rivers in the world that face varying levels of quality challenges with different impacts on water treatment systems include the Mississippi River in the United States (Devine et al. 2008), the Yamuna River in India (Upadhyay et al. 2011), the Nile (Ali et al. 2011) and the Congo Rivers in Africa (Verhaert et al. 2013).  In Ghana, the Densu River which is the hub of water supply to the capital Accra, has progressively deteriorated in its quality and affects water supply in many ways (Karikari and Ansa-Asare 2006). In 2013, the supply capacity of the treatment plant was reduced by ten million gallons per day due to failure of its sand filters, a problem that has largely been attributed to the source water quality (Daily Graphic 2013). The water quality deterioration of the Densu River has been promoted by indiscriminate waste disposal, crop farming and aquaculture. Upstream, the river is used extensively for the irrigation of crops such as cassava, pineapple, maize and vegetables, which result in contaminant transport downstream (Ansa-Asare and Asante 1998). The Birim River in the eastern region of Ghana, as well as the Pra and Ankobra Rivers in the western part of the country have also been hit with serious pollution from illegal artisanal mining activities. This challenges the sustainability of the existing water supply system with serious implications on future expansion works. In 2011, the Ghana Water Company Limited (GWCL) temporarily stopped treatment at the Kyebi treatment plant in the eastern region due to excessive pollution of the Birim River by illegal artisanal mining activities locally known as “galamsey” (Modernghana News 2011).   The major cause of surface water quality deterioration, as observed in the Densu River of Ghana, is high nutrient load of nitrogen and phosphorous, and organo-chlorines from pesticides. The high nutrient load, which leads to eutrophication, depletes oxygen, increases primary biomass production as well as promote toxic algal blooms known as cyanobacteria (Heisler et al. 2008). Generally, water supply engineers would have to weigh the quality of different source waters to examine their potential threats and complexities of treatment before embarking on treatment plant construction. Different 16  contaminants pose different danger to public health/environment. Thus, they must be carefully be analyzed for the appropriate treatment mechanism to be employed. For example, cyanobacteria and high nitrate values have been detected in most surface water reservoirs in Ghana (Addico et al. 2011). In surface waters, toxigenic species of cyanobacteria can produce hepatotoxins, neurotoxins and dermatoxins which affect the liver, the nervous system and the skin respectively (Carmichael 2001); high nitrate consumption can lead to reduced oxygen in the blood of infants, a condition known as methaemoglobinaemia (Bradberry 2007); heavy metals in water could cause cancer and nervous system damage. Similarly, high fluoride in groundwater can cause dental fluorosis as recorded in the upper east region of Ghana (Firempong et al. 2013); and high levels of arsenic could cause cancer and skin problems as observed in Bangladesh (Aziz et al. 2014). 2.2.3 Treatment Waste Management and Ecological Impacts Water is treated in order to purify it for an intended use. The sources of water for supply include fresh water bodies such as rivers, lakes, groundwater and the oceans. Usually, surface waters would need treatment due to their vulnerability to pollution. This may include physico-chemical and/or microbiological treatment before supply. Most common surface water treatment processes include screening; prechlorination or aeration to remove odor and oxidize iron and manganese; addition of a coagulant and sometimes polymer into a rapid mix basin for coagulation which is often followed by flocculation; sedimentation to remove coagulated debris; filtration; disinfection; storage; and pumping for supply. The wastes residuals produced during treatment are also treated and disposed off (Goldstein and Smith 2002). Conversely, deep aquifer groundwater is usually microbiologically safe. However, groundwater is mostly rich in natural geogenic contaminants such as arsenic, iron, manganese, fluoride and radio nuclides (Hammer and Hammer 2011), and must be extracted for treatment. Seawater, having a high dissolved salt concentration, requires less chemical treatment but undergoes treatment mechanisms that are energy-intensive. Some of the desalination technologies include popular treatment techniques such as reverse osmosis, multi-stage flash and multi-effect flash distillation (Narayan et al. 2012; Plappally and Lienhard V 2012).  Wastes, often known as residuals, are generated in the process of water treatment. The residuals come in different forms — solids, liquids and gases — and are classified 17  according to their nature and the treatment stage at which they are released. Generally, treatment residuals and the specific treatment process are considered together, as the two are intertwined (Water Research Foundation 2007; Edzwald 2010). Residuals are often categorized as follows (Water Research Foundation, 2007):  Sludge — waste generated during clarification or lime softening stages in conventional treatment plants,  Backwash Waste — wastewater generated from cleaning filters in conventional treatment plants,  Membrane Concentrate — wastewater containing contaminants that are rejected at the membrane surface in membrane filters,  Brine Residuals — wastewater produced by desalination membranes through ion exchange process,  Spent Carbon — activated carbon that has lost its adsorption properties, and  Off Gases — gases produced through air stripping.  A carefully designed and well-operated water treatment system should have an appropriate way of handling treatment wastes to prevent the negative impacts that they would have on the environment if they were released in their raw state. Generally, the stages of handling sludge consist of thickening, conditioning, dewatering, drying and coagulant recovery (Aldeeb 1999). Treated residuals in the form of coagulant sludge have been used in a lot of applications, including filling landfills and as manure for crops. Cornwell (1992) evaluated the constituents of leachate from sludge in landfills and recommended the appropriate mechanism of handling their disposal to prevent groundwater contamination. Novak (1995) recommended the appropriate loading rate of treatment residual to crops. Water treatment residuals can modify the quality and sediment content of the surface waters into which they are released, as well as deteriorate air quality. Most residuals contain toxic wastes and would need further treatment before being released into the environment. George et al. (1991) determined the possible harm that alum sludge could pose to receiving waters. The advent of industrial pollution, increasing environmental hazards and the need to positively utilize treatment wastes has made the consideration of treatment residual management in the design and construction of new water treatment facilities imperative. Water treatment residual management has become more compelling as new threats such as the management of radionuclides and arsenic have emerged (Edzwald 2010). For instance, the inappropriate handling of wastes 18  containing arsenic can release the contaminant back into the environment. This is particularly so because of the extreme sensitivity of arsenic with respect to pH. The processes of dewatering and land application of such wastes must be handled with care (MacPhee et al. 2001).   Another important consideration is the ecological impact of these residuals on aquatic life. Residuals containing toxic chemicals that are released, without the appropriate treatment mechanism employed, into receiving waters can kill and destroy the diversity of aquatic species. For instance, the release of wastes containing chlorine can be lethal to aquatic life. These insidious effects on aquatic biota have been recorded in wastes containing more than 0.02 mg/l total residual chlorine (CEPA 2004). The presence of chlorine in residuals when released into surface waters can result in the production of chloroalkyls such as trihalomethanes that have been suspected to be carcinogenic to human health (Glaze and Henderson 1975; Grove et al. 1985). Other chemicals, such as surfactants, in surface waters can react with chlorine to form non-volatile and biodegradable-resistant  halogenated substances such as alkylphenols polyethoxycarboxylates (Ball and Reinhard 1985). For the desalination process, drawing water from the sea can have serious effect on the ecosystem. This is particularly so when large organisms like fishes and birds are impinged and killed at intake screens. Additionally, the withdrawn water usually contains eggs, larvae and tiny organisms like fishes that eventually grow in the withdrawn water. In the process of desalination, however, these organisms are killed and then deposited back into the sea, a practice that facilitates oxygen depletion (Cooley et al. 2006; York and Foster 2005). Brine of desalinated water can contain twice as much salinity as is in the seawater, higher concentration of inorganic metals and residuals of the pre-treatment chemicals. This can cause salinity-balance problems for organisms, bioaccumulation of heavy metals in fishes, increase in inorganic mineral concentration and the release of toxic chemicals into the water (Amalfitano and Lam 2005; Chesher 2010).  2.3 Economic Consideration Constructing a water supply facility is an arduous task. It consists of the building of different unit processes, depending on the technology that is employed. The economics of constructing a treatment plant is a function of the quality of the source water, the treated 19  water volumes required, the treatment technology and the equipment procured for the construction. 2.3.1 Construction Cost Literature on conventional water treatment plant construction economics varies widely from modern techniques like reverse osmosis (RO). This has been due to the fact that conventional treatment technology has not significantly changed since the 1970s, and this usually makes economic comparison relatively difficult (Rogers 2008). However, in a detailed report, Gumerman et al. (1979) provided an approach to conventional water treatment plant construction economy by categorizing the cost into eight components. This was done to ease the difficulty in updating the various cost components in case there is a change. The identified components included: “ (1) excavation and site work; (2) manufactured equipment; (3) concrete; (4) steel; (5) labour; (6) pipe and valves; (7) electrical equipment and instrumentation; and (8) housing”. Gumerman et al. (1979) found the cost of constructing a 19,000 m3/day conventional treatment plant was $2,364,000 at the time of their study. The production cost was found to reduce as the size increases. For instance, they established that the unit cost for 19,000, 151,000 and 492,000 m3/day plants represented 8, 5 and 3 cents/m3 respectively, on the basis of a 70% capacity utilization. Comparatively, the cost estimated for a 19,000 m3/day capacity reverse osmosis plant gave a unit cost of 21 cents/m3 on 70% capacity utilization. Similarly, Jurenka et al. (2001) gave the total production cost for a 3,800 m3/day to be 26 cents/m3. Karagiannis and Soldatos (2008) revealed that the total production cost of a desalination plant is a function of plant size and the quality of the feed water. Different estimated costs have been recorded for reverse osmosis treatment of seawater and brackish water. Production cost of seawater reverse osmosis ranges from 600 to 800 $/m3/day (Reddy and Ghaffour 2007; Sauvet-Goichon 2007) while that of brackish water is between 240 and 400 $/m3/day (Vince et al. 2008;  Yun et al. 2006). 2.3.2 Energy Cost Energy and water use are interconnected. Huge amount of energy is used in producing water, and the reverse of that happens for energy production as well. Quite apart from being used to produce water, energy also affects the resource quality and availability of water through the emission of greenhouse gases. Energy can pollute water and also impart significant effects on water through climate change (Cohen et al. 2004). Griffiths-Sattenspiel and Wilson (2009) have indicated that the use of water-efficient devices can 20  cut carbon dioxide emission by 38.3 million tons. Most of the developing countries, especially those in Africa do not have reliable data on energy use in relation to water, while the developed countries have data with a common trend. The energy used for water pumping and treatment facilities, water heaters and boilers correspond to 12% of Ontario’s entire electricity demand (Canada), as well as a staggering 40% of that of natural gas. Comparably, statistics in California represent 19% of total electricity demand and 30% of that of natural gas (Cohen et al. 2004). It has been observed that saving water reflects in significant energy saving. The energy required to pump and treat one liter of water will rise by 5-10% in the United States (Goldstein and Smith 2002).   Plappally and Lienhard V (2012) in a comprehensive review of the energy demand of water supply systems re-affirmed the knowledge that different technologies of water production consume varying amount of energy, and in accordance to the economy of scale. All the water treatment stages consume some form of energy. Examination of the energy consumption in a surface water treatment facility of the capacity of 37,850 m3/day, the approximated energy consumption was 14,057 kWh/day, which in terms of unit consumption becomes 0.371 kWh/m3. The consumption varies from plant to plant by economies of scale. Most of the electricity consumption in surface water treatment plants come from the high-lift pumps that pump water into the distribution systems. This constitute about 80 - 85% of the total consumption (Goldstein and Smith 2002).  The relationship between the energy employed and the depth of the water in groundwater pumping is linear at a specific pressure as per established scientific equations (Reardon et al. 2012). The energy expended in the pumping process depends on the flow rate, the suction and discharge elevations, frictional losses, and the efficiency of the pump (Ahlfeld and Laverty 2011). In California, groundwater pumping requires energy in the range of 0.14 - 0.69 kWh/m3 (Bennet and Park 2010). Similar to the reported unit electricity consumption value of surface water, (Goldstein and Smith 2002) reported a groundwater consumption figure of 0.482 kWh/m3. This is seen to be 30% higher than that reported for surface water. In ground water treatment and supply, about one-third of the electricity consumption comes from well pumping with about 0.5% used in treatment (mainly disinfection), while the rest is used by booster pumps that distribute the water (Goldstein and Smith 2002).   21  An alternative to freshwater treatment in arid and water-scarce countries is desalination. The cost for desalinating water is reducing due to the progressive advancement of technology. However, when compared to the other treatment technologies discussed above, it is still an expensive option of treating and supplying water (Cooley et al. 2006). With respect to desalination, the largest cost component in the processing of purifying water is energy that constitutes nearly half of the total production cost. Furthermore, thermal desalination plants consume more than half the total production cost (Wangnick 2002). The energy consumption of desalting water depends on the feed water and the type of desalination mechanism employed. Theoretically, the minimum needed energy for desalting seawater through reverse osmosis is 1.0 kWh/m3, without the inclusion of losses in converting thermal energy to electrical energy (Cooley et al. 2006). However, highly efficient plants utilize between 4 and 25 times the calculated value of 1.0 kWh/m3. Herndon (2013) indicates that the unit cost for a desalination plant was about 3.96 kWh/m3, while Djebedjian et al. (2007) indicated a consumption value of 7.8 kWh/m3 in Egypt (Africa). 2.3.3 Chemical and Maintenance Costs Chemical and microbiological pollution of water make it impure, prompting the requirement of treating the water for its intended use. Varying levels of pollution dictate the degree of treatment and the technology employed in treatment. While groundwater might employ only disinfection, surface water would usually employ the treatment train of conventional methods (Goldstein and Smith 2002). The use of different technology and treatment methods for different waters make unit chemical and maintenance costs vary widely. Natural organic matter, a major component of surface water, differs for various sources of waters depending on the biochemical interaction of the water and its environment. Accordingly, the designs of treatment plants vary and plants would have different treatment stages and installed equipment.   In Ghana, the observation of the author revealed that the Weija and Kpong treatment plants (in Accra), which both utilize conventional surface water treatment, slightly differ in their design. While the Weija treatment plant utilizes coagulation due to the polluted nature of the Densu River, the Kpong treatment by-passes this stage of treatment due to the relatively good quality of the Volta River (Water Resources Commission 2014), causing a huge difference in unit chemical and maintenance costs. Even within a specific 22  plant, chemical and maintenance costs vary seasonally, as well as annually, unlike energy consumption that is relatively stable. For instance, in the Lukunga water treatment plant in Kinshasha (Democratic Republic of Congo) chemical dosages in treating water from the Lukunga River has increased exponentially since the 1940s due to deterioration in water quality. The turbidity range which used to be 15 - 25 NTU in 1940, has risen to 100 - 120 NTU, with very high turbidities of up to 1000 NTU recorded during heavy rainstorms (Musibono 2014). High turbidity translates in high chemical usage, and may cause a rise in the cost of maintenance as some of the treatment equipment would be stressed. In 2006, the McAllen Northwest facility in McAllen (Texas, USA), a conventional surface water treatment plant with a capacity of 8.25 million gallons per day, recorded unit chemical cost of 4 cents/m3 and a maintenance-related cost of 2 cents/m3 representing 7% and 4% of total production cost (Rogers 2008). Public Utility and Regulatory Commission (PURC 2004) reported chemical and maintenance costs to be 6.57 and 4.90% of the total direct expense. Unlike the case of seawater, which has a fairly constant quality, these figures, especially, those for the chemical cost can rise sharply if the raw water quality deteriorates. Other forms of treatment such as membrane filters and desalination plants have relatively lower unit chemical and maintenance costs when compared with conventional treatment process of poor water quality. The general unit chemical cost of desalination ranges from 3.5 to 6.5% of the total cost for multi-stage flash and reverse osmosis technologies, respectively (Carlos 2010).    2.3.4 Financial Viability Huge capital and human resources are invested in water supply facilities world-wide to improve both supply coverage and public health. In the developing world where water supply is particularly challenging, many capacity expansion projects have been successful while others have not been able to meet their intended objectives (Singh et al. 1993). Most of the problems associated with the failures arise from issues of financial challenges culminating in poor maintenance and service delivery (Raje et al. 2002). In the deliberations of stakeholders to either construct new water supply facilities or embark on capacity expansion projects, the financial viability of such projects should be scrutinized in terms of the total cost of construction and operation, and the estimated available income of consumers (Piper and Martin 1999). Thus, the issues of willingness- and ability-to-pay deserve attention. Piper and Martin (1999) described ability-to-pay as “the maximum amount households can pay for water given their income and other household 23  expenses” and willingness-to-pay as “the monetary value an individual places on a good or service”. While ability-to-pay mainly considers the financial constraints consumers are faced with, willingness-to-pay concerns both preferences and financial constraints of consumers. The basic factors that affect consumers’ willingness-to-pay are mainly issues of water quality, service reliability, water tariff, and affordability (Ntengwe 2004). According to Engel et al. (2005),  empirical evidence has shown that willingness-to-pay for better water supply services is high among educated households, and is also gender-dependent. With a very low willingness-to-pay in a water supply community, the supply system is bound not to be financially viable.  For example, consumers in Conakry (Guinea) preferred water from wells to the public water supply system due to high water tariffs from the water utility company (Ménard and Clarke 2000). Conversely, Ntengwe (2004) reported that consumers in village communities in China, on the basis of deteriorated water quality, abandoned their groundwater sources and opted for the public water supply system due to its good quality. Expressing willingness-to-pay does not translate into the ability-to-pay. Ainuson (2009) found that 76.1% of respondents in a survey in Accra (Ghana) expressed willingness-to-pay, while about 66.2% of these participants earned income below an amount of $89 (or GH₵ 100 Ghana cedis); an amount that most consumers could not afford to pay for water supply services. In Ghana, the cost of water is subsidised in accordance with the usage category, with domestic consumers usually paying less than commercial and industrial consumers. An increasing block tariff system, divided in tiers of 1000 L, is employed in the country. The lowest tier (otherwise known as the lifeline tier) is made affordable for the poor (Ainuson 2009). According to Ainuson (2009), the water tariff structure as per the tariff regulatory institution (the PURC) is summarised in Table 2.1 below for the years of 1998 to 2006.         24  Table 2.1 GWCL Tariff Structure by PURC Date Quantity of water used Approved rates in cedis (per 1000 Liters) 1998 0-13 400  13-45 1000  45+ 1400 1999 0-10 500  10-40 1300  40+ 1820 2001 0-10 990  10+ 3600 2002 0-20 3000  20+ 4500 2003 0-20 3500  20+ 4800 2004 0-20 4031  20+ 5528 2005 0-20 4031  20+ 5528 2006 0-20 4850  20+ 6750  2.4 Technical and Socio-Cultural Perspectives  The technical and socio-cultural perspectives may include operational flexibility, land area, storm drainage, aesthetics and acceptability, as well as security of transmission and cultural resources. 2.4.1 Operational Flexibility Recently, many companies and organizations concentrate keenly on how to optimize productivity in a very competing world. The pressures of competition and efficiency have pushed industries into finding ways of making operations more flexible. In the water supply industry, automating the processes in the treatment train makes operation and maintenance more flexible (Dayal et al. 2001). By increasing the flexibility of the process treatment, a competent and elastic system is created that efficiently responds to changing environmental conditions and emerging treatment challenges (Qi and Luo 2007). Harris et al. (1998) indicated that an effective firm with operational flexibility should have some in-built mechanisms that allow a large degree of changes of sequencing, scheduling, etc., so that in the event that vital equipment breaks down, the entire system of operation does 25  not grind to a halt. D’Souza (2002) explains flexibility to mean making the required adjustments to react to varying environmental situations without compromising on performance. Technical training and familiarity improves the expertise of employees. With the ubiquitous nature of conventional water treatment plants, many operators are likely to have more knowledge, experience and troubleshooting skills in dealing with conventional treatment systems as compared to relatively new technologies such as reverse osmosis. 2.4.2 Land Area Land is a critical factor as the entire treatment facility will be sited on it. In the consideration of land as a criteria, its elevation and flood levels are main parameters to be considered (Kövári 1984). In the planning stage, the shape and size of the land should be properly assessed to ensure the design plan can properly fit in it. The consideration of shape is necessary for the efficient flow of water and waste products through the treatment train, with rectangular shapes preferred to long narrow ones (Spokane County 2003). The land should be expansive enough to make room for future expansion when the need arises, as well as provide a buffer zone of protection for the treatment facility to reduce the potential impact that the facility can have on the surrounding environment. A wider buffer zone requires less mitigation measures on site, and a length of a 30-meter buffer is usually considered ideal (Spokane County 2003). 2.4.3 Storm Drainage  The factor of storm drainage assesses how the installment of the treatment facility would negatively impact on the drainage of the construction site and its immediate surroundings. Spokane County (2003) indicates that whenever the construction of a treatment plant on a site demands “significant rerouting” of water drainage, or requires significant creation of new drainage systems, such as site, on the basis of suitability, it should be less preferred. 2.4.4 Aesthetics and Acceptability The purpose of improving water quality is to significantly reduce the undesirable contaminants in the water to prevent any harmful effect they might have on public health. Generally, consumers are unable to determine chemical and microbiological aspects of water quality. However, once they are able to identify problems with the physical and aesthetic quality of potable water, a considerable attitudinal change will be observed (World Health Organization, WHO, 2011). According to Postawa and Hayes (2013), 26  potable water providers must ensure that the water provided is not only safe but also aesthetically pleasing, otherwise consumers might turn to alternative sources that might not be safe.   Aesthetics boarder on acceptability, but acceptability can be wider. For example, drinking water might be safe with good aesthetics but can still be rejected by consumers on the basis of their perception of the source water, especially if fecal matters and other related substances are discharged into it. When potable water containing iron and manganese is exposed to the atmosphere, the ions get oxidized and may pose discoloration risks beyond a certain concentration. Similarly, corrosion within cast iron pipelines can also impart color on potable water (Husband and Boxall 2011; Vreeburg et al. 2008). Apart from the problem of discoloration, iron imparts a metallic taste on water that has a concentration of more than 0.3 mg/l (WHO 2011). Odour, which can be caused by the presence of many chemicals including methyl tert-butyl ether and chlorine, can also be precipitated by iron. The presence of some iron-utilizing bacteria in water distribution systems has been widely cited to be an odour source. When these bacteria die they leave a reddish-brown slime that has a very offensive odor (Postawa and Hayes 2013). 2.4.5 Security of Transmission and Cultural Resource Like many similar systems, water infrastructure is vulnerable to both natural and human-induced threats. The importance of such infrastructure to the sustenance of life make utilities devote much time and capital on addressing potential security threats on water supply facilities. Following the September 11, 2001 terrorist attack on the United States, the security consideration in constructing new water supply facilities has taken a different dimension. Prior to the attack, researchers foresaw this dimension and called for increased funding in protecting water supply infrastructure. Haimes et al. (1998) stated that “Obviously, there are costs associated with hardening water supply systems. The costs of [water] system hardening can be viewed as analogous to paying for means of countering potential terrorist threats against the airline industry”. A physical, chemical or biological attack on water transmission mains can have dire consequences on public health, irrigation, food production and firefighting (Leuven 2011). Terrorist and enemy attacks on water systems add to the existing traditional threats of adverse weather conditions such as hurricanes, rapid ageing of mains due to aggression from soils, floods and earthquakes (Seger 2003). The target of intentionally contaminating water has been 27  an age old problem. History indicates that adversaries viewed attacking water systems as an asymmetric advantage in warfare. Hickman (1999) detailed some historical examples of such attacks including: 1) the use of cyanide by Nero in ancient Rome to kill his enemies; 2) the contamination of ponds with cadavers in the United States civil war; 3) the intention of Japanese soldiers to biologically poison Chinese water supplies with anthrax, cholera, etc. during World War II; 4) the contamination of the Bohemia reservoir with sewage by Hitler; and 5) the poisoning of Kosovo’s water wells by Yugoslav forces in 1998.  In Africa, water transmission mains are unlikely to be attacked in the manner anticipated by western nations. However, they are also prone to threats. For instance, water vendors in Kibera and Mathare (Nairobi) forms cartels to extort money from local consumers through selling water at exorbitant prices. They facilitated their actions by first denying the communities the opportunity of water supply through vandalizing transmission mains that served the communities (Primus 2011). Similar cases of vandalism have been reported in Zambia (Lusakatimes 2011). The author has also witnessed some kind of vandalism on the water transmission mains along Kpong-Tema route (Ghana) by farmers to access water for irrigation and for grazing animals.   Cultural resources are a critical part of society and help preserve some historical sites or artifacts. They offer unique details of past communities and environment, and help modern societies find ways of tackling emerging environmental problems (Natural Resources Conservation Service, NRCS, 2014). In the event that a water supply facility is to be established, a critical look into how these specially-preserved areas might be affected by such projects is very important. LaBudde (2009) indicated that in planning to construct water supply projects, its effect on cultural resources will have to be adequately addressed with clear mitigating measures. In the weighting of alternatives, he argued that projects that will significantly affect such resources should be giving relatively low scores. 2.5 Multi-Criteria Decision Analysis (MCDA)  With the complexities of the modern era, water authorities, engineers, and designers are faced with making difficult decisions between various available alternatives. Considering the diverse impact and future ramifications that these decisions might have, it behooves on the decision-makers to make acceptable and reliable decisions through a rational 28  approach, considering the multitude of constraints accompanying the alternatives. Decisions are usually made among alternatives evaluated by a set of diverse criteria. These criteria can sometimes present substantial decision-making conflicts and would require a carefully balanced analysis before final decisions are made. Decisions that defy basic rationality, experience, knowledge of subject matter and well-structured analysis mostly end with unsuccessful results. According to Bhushan and Rai (2004), a typical decision-making process involves the following: 1. comprehensive analysis of the problem, 2. identifying a plethora of criteria associated with the decision-making, 3. assessing the criteria, 4. evaluating alternatives in relation to criteria, 5. ranking alternatives on a priority scale, and 6. incorporating expert judgments to arrive at a decision.  Multi-criteria decision analysis (MCDA) is a discipline that simultaneously considers multiple criteria and different alternatives and can be used as a decision aid in complex decision making problems. MCDA consists of a plethora of approaches and is by no means a perfect panacea to the pain of decision making (Bhushan and Rai 2004). This is because the various tools have their inherent weaknesses and only complement knowledge and experience in making decisions. MCDA is broadly characterized into two classes: multi-attribute and multi-objective decision making. The widely accepted practice of this classification fits the categorization into two facets of problem-solving: multi-attribute decision making is often used for selection (evaluation) problems and multi-objective decision making is often used for design with continuous variables. A clear distinguishing feature is that while multi-objective decision making is not associated with problems with pre-determined alternatives, multi-attribute decision making is usually limited to a few predetermined alternatives (Yoon and Hwang 1995). Some of the most known models are those based on utility theory such as the multi-attribute utility theory, models based on outranking such as Elimination and Choice Expressing Reality or ELECTRE (Roy 1968) and Preference Ranking Organization Method for Enrichment of Evaluations or PROMETHEE (Brans and Vincke 1985), distance-based models such as Technique for Order of Preference by Similarity to Ideal Solution or TOPSIS (Yoon and Hwang 1995), and the analytic hierarchy process or AHP (Saaty 1980).   29  Choosing a method among the many MCDA techniques can be a very arduous task and difficult to justify because the various techniques have their inherent limitations, concepts and perspectives. In choosing an MCDA method, one has to consider the details of the input information, the cumbersomeness of the modeling process and the outcome. For example, in considering every criterion in the perspective of their “utility function”, the multi-attribute utility theory will be more desirable. However, when the time and energy needed in using the multi-attribute utility theory is considered to be laborious, one can employ the use of pairwise comparison of criteria through the use of the AHP. The AHP can also pose problems when substantially large amount of information is considered. In the case when “only ideal and anti-ideal options are required”, a distance-based model such as TOPSIS is more efficient (Ishizaka and Nemery 2013).   Despite the diversity of MCDA techniques, they tend to have a lot in common in many respects (Chen and Hwang 1992). Some underlying mathematics is common in some MCDA methodologies but what is seen to be even more common is the notation and terminology. Common among different MCDA approaches are the terminologies “goals”, “alternatives”, “criteria” and “sub-criteria” (Triantaphyllou 2000). “Alternatives” refers to the different options available in choosing the best decision through prioritization and ranking; “criteria” refers to attributes associated with an MCDA problem through which the different alternatives are assessed; and “goal” normally refers to the main object of the problem. An MCDA might have many criteria and sub-criteria and this often leads to them being presented in a hierarchical order (Triantaphyllou 2000).  The interdisciplinary nature of MCDA has been evident in its diverse application in different fields. This includes (but not limited to) applications in transportation  (Sayyadi and Awasthi 2013), in forest management planning (Brukas et al. 1999), in supply chain management (Cruz 2009), and in the banking sector (Pasiouras et al. 2010). In the field of water supply and water resources management, MCDA has also been applied in numerous studies, with examples of recent applications being the establishment of rescue policies for water utility businesses in Indonesia (Peniwati and Brenner 2008), the evaluation of management alternatives for urban water supply (Okeola and Sule 2012a), decision making in urban water management (Abrishamchi et al. 2005), prioritization of water management for sustainability (Chung and Lee 2009), and wetland ecosystem stability (Zhang et al. 2013). 30  The AHP has been chosen as the preferred MCDA method for this study. Even though it has its inherent weaknesses such as rank reversal and the exponentially large number of judgments needed when dealing with numerous criteria and sub-criteria, it has an overriding advantage over most MCDA methods. Some of the advantages of the methodology include the following (Nadja and Karlheinz n.d.): 1. It employs the concept of decomposing a complex decision problem into various simplified components (i.e goal, criteria, sub-criterion, etc.) in a hierarchical order. This helps decision-makers make more informed judgements while avoiding committing errors. 2. It is relatively simple, flexible, and easy to understand. The concept of pairwise comparative judgement and its potential of checking inconsistency in judgment even makes it more appealing. 3. It has the ability of utilising both qualitative judgments and quantitative factors. 4. It supports both individual and group decision-making. Within a group, individual participants’ subjective judgments can be aggregated through the calculation of the geometric mean. 5. It has the ability of being used along with other MCDA techniques. As indicated earlier, despite the above-mentioned advantages, the AHP also comes with its weaknesses, among which include the following: 1. Having many criteria and sub-criteria result in multiple pairwise comparisons. An n number of criteria or sub-criteria results in n(n-1)/2 pairwise comparisons. This situation can make questionnaire lengthy, time consuming and frustrating to participants. More pairwise comparisons can lead to greater inconsistency in judgement.  2. Its compensatory (additive) property of good scores compensating for bad scores for some criteria can lead to the loss of very relevant information. 3. The AHP has a peculiar problem of rank reversal. When an identical alternative to any of the less-optimum alternatives is introduced, the ranking of the alternatives can alter.   31  2.5.1 Analytic Hierarchy Process (AHP) AHP is a popular MCDA tool that is used in making decisions across a spectrum of disciplines. Developed by Thomas Saaty (1980), the technique relies on a strong mathematical and logical foundation. It is particularly unique from other MCDA techniques and has the flexibility of being used conjunctively with most MCDA methods in making robust decisions (Vaidya and Kumar 2006). The method also makes room for potential inconsistency in human judgments and provides avenues for improving the consistency (Saaty and Vargas 2001). The AHP methodology is based on the fundamental principles of problem decomposition, pairwise comparative judgments, and the synthesis of priorities (Saaty and Vargas 1994). Cardinal preferential judgements, based on a linear scale of relative importance or intensity, are made in a pairwise comparative manner to arrive at priorities for the ranking of alternatives. Sargaonkar et al. (2011) used the AHP in combination with geographic information system (GIS) to prioritize and rank sites for groundwater recharge. The conjunctive use of the AHP with other decision-making tools enhances the attainment of optimum benefits from both the AHP and its complementary MCDA technique. The AHP makes provision for the capturing of both technical and non-technical variables within the same structure for analysis. This structural flexibility makes it a more suitable tool for use in the water sector where there is the involvement of inter-sectoral and multi-institutional  collaboration (Lafontaine 2012).   According to Braunschweig (2001), the AHP is based on three underlying principles: 1. Problem Structuring: The problem is analyzed and decomposed into different components and then structured in a hierarchical order of homogenous clusters and sub-clusters of factors (goals, criteria, sub-criteria, alternatives). This is to improve understanding of the problem and allow decision makers focus on the problem parts systematically. It helps in deriving local priorities of the units within a cluster in relation to the mother cluster. The priorities indicate how dominant (important, preferable or likely) one element is over the other. 2. Pairwise Comparative Judgments: This involves the pairwise comparison of criteria, as identified in the hierarchical structure of problem, to determine their relative importance with respect to the goal. It also involves the pairwise comparison of the relative strength of alternatives in relation to each criterion. The 32  comparison is done with reference to a linguistic fundamental scale used to weigh the strength of preference between two considered units. 3. Synthesis: This is to prioritize various elements through an eigenvector approach. All the individual priorities are then aggregated into a composite priority for every alternative before the alternatives are ranked for a preferred decision to be made.  According to Saaty (1986), four axioms form the mathematical foundation of the AHP. They are summarised below without their mathematical representations: 1. The reciprocal axiom: The AHP is fundamentally founded on the pairwise comparison of elements in a matrix as represented in the structured problem. Thus if elements A and B are compared, and A is six times more important than B, then B is one-sixth times as important as A. 2. The homogeneity axiom: This axiom ensures the uniformity, for the sake of comparison, of elements within a cluster. It stresses on the possibility of errors in judgement within a cluster when the elements to be compared vary widely. Therefore, elements found to be largely disparate should be categorised under different clusters, or even different levels if deemed appropriate. 3. The synthesis axiom: Within the structured hierarchy of a problem, judgements yielding priorities for top level elements in the hierarchy should not depend on their respective lower level elements. Thus, how important a top level element is (priorities of criteria) should not depend on lower level elements (priorities of sub-criteria). 4. The axiom of expectation: This axiom is about the representation of thoughts within a hierarchy. Decision-makers with strong belief in their ideas can, and should incorporate them into the hierarchy. The AHP has been successfully employed in many other areas including choosing the best alternative among many others, forecasting, prioritization, resource allocation, balanced scorecard and total quality management (Forman and Gass 2001). These applications have generally bordered on engineering design, appropriate technology selection, and portfolio management (Forman and Gass 2001). The application of the AHP even stretches to the medical field (Dolan et al. 1993; Suner et al. 2012) as well as project management (Al-Harbi 2001), manufacturing industry for machine tool selection 33  (Yurdakul 2004), multimedia authorizing systems selection (Lai et al. 1999), manufacturing firms for credit evaluation (Yurdakul and İç 2004), and in forecasting the expected economic recovery strength and likely turn around period (Saaty and Vargas 2012). Okeola and Sule (2012) used the AHP application in evaluating management alternatives for urban water supply systems. They considered a set of management alternatives to make the best choice from. The set included public ownership and operation, private ownership and operation, and public ownership and private operation. In the outcome of the study, stakeholders opted for public ownership and operation, rejecting any form of private participation. This confirmed their belief that government ownership will provide a better sustainable and efficient service delivery. 34  Chapter 3 Research Methodology “Research is what I'm doing when I don't know what I'm doing.”     Werner von Braun (1912 – 1977)  The tool employed in analyzing the problem is the AHP. The AHP is an MCDA solution tool that utilizes both logical and intuitive approaches to select the “best” choice from a plethora of alternatives based on the evaluation of multiple criteria. With the AHP, decision-makers are able to make pairwise comparative judgments that enable them develop overall priorities in ranking alternatives. This chapter reviews the AHP technique and its steps in solving a decision-making problem.  3.1 AHP Algorithm The purpose of this study is to employ the AHP in decision-making regarding the capacity expansion of facilities supplying drinking water to a large group of consumers. The AHP decision-making process is illustrated in Figure 3.1 below. The following sections provide further details about the technique. 3.2 AHP Steps Following Figure 3.1, Bhushan and Rai (2004) provided the following step-by-step methodology for AHP:  1. Structuring the decision problem into a hierarchy of goals, criteria, sub-criteria and then alternatives. This gives a general view of the complexity in the relationships within the problem and allows the decision-makers to assess if the entities compared are of the same magnitude. In the hierarchical tree, each element is connected to, at least, another element in the hierarchy. 2. Collection of data from decision-makers in accordance with the hierarchical structure and the pairwise comparison of the various alternatives as per the qualitative scale in Table 3.1 (Saaty 1980). To successfully employ the AHP in decision-making, questionnaires should be sent to the experts relevant to the field of study (e.g., water supply) for their individual qualitative judgments.   35  Table 3.1 Gradation scale for quantitative comparison of alternatives (Saaty 1980) Judgment Options Intensity of Importance  Equal 1 Marginally strong 3 Strong 5 Very Strong 7 Extremely strong 9 Intermediate values to reflect fuzzy inputs Reflecting dominance of second alternative compared with the first 2,4,6,8 Reciprocals    3. The pairwise comparisons of the criteria are arranged in a square matrix with diagonal elements being 1. The ith row criterion is better than the jth column criterion if element (i, j) has a value greater than 1, and vice versa. The value of the (j, i) element is the reciprocal of the value of the (i, j) element. For example, the pairwise comparison matrix A, in which aij = wi/wj within the matrix is the pairwise comparison between elements i and j and can be represented by an n x n matrix as:                                                                                      3.2-1     where n is number of factors, aij is the relative weight determined by the pairwise comparison for the relative importance of the ith factor over the jth factor, wi is weight of the ith factor, and i, j = 1, 2, 3, ..., n. 4. The computation of the principal eigenvalue and the corresponding normalized right eigenvector of the comparison matrix gives the relative importance of the various criteria being compared. The elements of the normalized eigenvector are termed weights with respect to the criteria or sub-criteria and ratings with respect to the alternatives. Mathematically this is represented as:            3.2-2  36                      Structure problem in a hierarchy Gather data from experts Calculate weights of criteria and ratings of alternatives Arrange pairwise comparisons in a matrix Calculate local and global ratings and rank the alternatives Is consistency acceptable? No Yes Figure 3.1 Flow diagram of the AHP methodology 37  where w is the normalized right eigenvector of the matrix A and      is the principal eigenvalues. Symbolically this becomes:                                                                                                   3.2-3  and            3.2-4   5. The consistency of the matrix of order n is evaluated based on the consistency index (CI) defined by:                   3.2-5   Comparisons made by this method are subjective and the AHP tolerates inconsistency through the amount of redundancy in the approach. If this consistency index fails to reach a required level then answers to comparisons may be re-examined. Comparing the CI with an average random index, RI, (derived from a sample of randomly generated reciprocal matrixes using the scale 1/9, 1/8, …, 8, 9) gives the consistency ratio (CR). The CR values are supposed to be less than 0.1, otherwise the process would have to be repeated until an acceptable value is obtained. Following Saaty (1980), this dissertation assigned the RI values of 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41, 1.45, and 1.49 for matrices of size 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, respectively. 6. The rating of each alternative is multiplied by the weights of the sub-criteria and aggregated to get local ratings with respect to each criterion. The local ratings are then multiplied by the weights of the criteria and aggregated to get global ratings.  3.3 Sensitivity Analysis Sensitivity analysis describes how sensitive the final outcome is to changes in the input data. In sensitivity analysis, the usual assumption is that the weight of a criterion explains 38  how sensitive it is with respect to reaching the final goal. However, this is not always the case as research has proven the criterion with the smallest weight can be the most sensitive (Triantaphyllou 2000). Relevant literature shows applications of various techniques of sensitivity analysis in many areas ( DeRoberts and Hartigan 1981; Berga 1984; Von Winterfeldt and Edwards 1986; French and Insua 1989; ; Ríos Insua 1990; Armacost and Hosseini 1994; Triantaphyllou 2000). Masuda (1990) conducted a comprehensive study on sensitivity analysis on the AHP. He identified the extent to which modifications in local and global priority can cause rank reversal in the alternatives. Later, Triantaphyllou (2000) modified Masuda’s approach by taking into account the smallest modification in the weight of a criterion that can change the ranking of the alternatives. This could include a change in criterion weight that can cause any of the alternatives to change its ranking, or a change in criterion weight that can cause the best (top) alternative to lose its ranking. This research conducted the sensitivity analysis in two ways: 1) by applying the approach proposed by Triantaphyllou (2000); 2) by using the built-in sensitivity analysis function in the Expert Choice software. With the Expert Choice software, graphical alteration of the criteria weights was performed to observe the corresponding change in the rankings of the alternatives to determine the robustness of the decision made. This is particularly important since explicit considerations of potential uncertainties in decision maker’s mind have not been accounted for.  Following Triantaphyllou (2000), the approach considers the change in either absolute or percentage terms, and therefore coins the terminology Absolute Any (AA), Absolute Top (AT), Percent Any (PA), and Percent Top (PT). It proceeds with the following definitions for m alternatives and n decision criteria as explained in section 3.2: 1. Let                                denote the minimum change in the current weight    of criterion    such that the ranking of alternatives    and    will be reversed.  Also let                                            3.3-1  where         expresses changes in relative terms. 2. The Percent-Top (or PT) critical criterion is the criterion which corresponds to the smallest                                    39  3. The Percent-Any (or PT) critical criterion is the criterion which corresponds to the smallest                                    4. The criticality degree of criterion    denoted as    , is the smallest percent amount by which the current value of    must change, such that the existing ranking of the alternatives will change. That is, the following relation is true:                                          3.3-2  5. The sensitivity coefficient of criterion    denoted as sens(   ), is the reciprocal of its criticality degree. That is, the following relation is true:                               3.3-3 If the criticality degree is infeasible (i.e., impossible to change any alternative rank with any weight change), then the sensitivity coefficient is set to be equal to zero. In the sensitivity analysis tables that will follow, the infeasibility of the criticality degree is indicated as N/F. If one wishes to change the rating of two alternatives (e.g A1 and A2) through weight   of criterion C1, the following relation is true:                                                         3.3-4                                          3.3-5  Where Pi represents the final preference of alternative Ai, and aij represents the performance value of the respective alternative. The following condition should satisfy the weight               to be feasible                             3.3-6                      3.3-7              3.3-8  40  The quantity                                , by which the current weight    of criterion    needs to be modified (after normalization) so that the ranking of the alternatives Ai and Aj will be reversed, is given as follows:                                                                3.3-9                                                 3.3-10  Furthermore, the following condition should also be satisfied for the value of         to be feasible:                    3.3-11 Criterion    is considered to be robust if all of its                                 quantities are infeasible. In that case, any change in the weight of the criterion will not affect the original ranking of the alternatives. Such a criterion can be eliminated from further consideration because it does not provide any discriminatory ability in ranking. In this research, the main object of the sensitivity analysis is to identify the most critical criterion, as well as the relative sensitivity of all criteria with respect to the decision model. Insensitive criteria will not be removed from the analysis. However, the presence of the insensitive criteria will guide future researchers on the impacts of the various criteria on the analysis, and to further hint them on the possibility of considering other possible criteria and/or sub-criteria.  41  Chapter 4 Case Study “Whatever happens, we are the architect of the fortune or misfortune that befall us in life. Stop! Take time to make a critical analysis of yourself and the situation you find yourself. Better to learn from other people's mistake and experiences, rather than becoming a case study to others.” Kemmy Nola This Chapter introduces the test case studied in this dissertation. The Chapter explains the research area and provides the hydrological/meteorological characteristics, land uses, population and some climatic information relevant to the research area. The Chapter also discusses the field-data collection techniques.  4.1 Synopsis Ghana, a former British colony, was the first sub-Saharan African country to gain independence in 1957. It is situated in West Africa, shares its southern border with the Gulf of Guinea, northern border with Burkina Faso, eastern border with Togo and the western border with Cote d’Ivoire (World Factbook 2014). The country recorded population of 24,658,823 in the 2010 census (Dodoo, 2013). It ranks 33rd by landmass in Africa and 82nd in the world, with a total landmass of 238,533 km2 and about 11,000 km2 covered by water. It has a tropical climate: hot and humid in the south and hot and dry in the north. In 2011, the total renewable water resources were estimated at 53.2 km3. It records a freshwater withdrawal rate of 0.98 km3/year of which 24, 10 and 66% is used for domestic, industrial and agricultural activities, respectively (World Factbook 2014)  Ghana is burdened with challenges in its water supply system. Poor decision-making usually driven by sub-standard and politically-based assessment of serious national issues have played a major role in the country’s current economic and social woes. World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) reported that access to improved water sources in Ghana is about 82% as of 2008 (WHO and UNICEF 2010). This, in accordance with the Millennium Development Goal target of 77% coverage, would be interpreted as Ghana having met its target. However, this is in stark contrast to provider-based figures, which are significantly low. The water supply providers are the main sources of improved water. The Community Water and Sanitation 42  Agency, which has the responsibility of rural water supply, reported a coverage of 57% as of 2008. On the other hand, the Ghana Water Company Limited (GWCL), which has the responsibility of urban water supply, reported a coverage of 58% as of 2008. According to the African Ministers’ Council on Water (AMCOW), the planned strategic intervention measures forecast a rural and urban water coverage of 76 and 80 percent, respectively, by 2015 (AMCOW 2011). While Ghana has been blessed with the abundance of fresh water bodies, they have unfortunately not been optimally utilized efficiently and most of these resources flow into the sea.  The first potable water supply system in Ghana was installed in Accra, in the Gold Coast era, just before the occurrence of the World War I. This was followed by the establishment of similar facilities in the other urban areas of the country. The occurrence of extensive water shortage in 1959 led to the signing of an agreement between the World Health Organization and the Government of Ghana for the commencement of a comprehensive study into the development of the water sector. The outcome of the study facilitated the formation of Ghana Water and Sewerage Corporation in 1965, under an act of parliament (Act 310) as a legally instituted public utility company. Among other mandates, it was to provide water supply and sanitation services to both rural and urban communities (GWCL 2013). An economic downturn, the lack of capital investment, and the deteriorating infrastructure in the early 1980’s resulted in abysmally low operational efficiency of the Ghana Water and Sewerage Corporation. This called for massive capital investment from the World Bank, other external agencies and donor countries. The Water Sector Restructuring Project, aimed at improving operational efficiency and the efficient management of the water sector was launched as a result of a “Five Year Rehabilitation and Development Plan”. This led to the decentralization of water and sanitation in small towns, decoupling them from Ghana Water and Sewerage Corporation. Eventually, in 1999, Ghana Water and Sewerage Corporation metamorphosed into Ghana Water Company Limited (GWCL), which is solely mandated to provide potable water to urban areas in the country (GWCL 2013). 4.2 Significance  Figure 4.1 indicates the map of the Accra-Tema Metropolitan Area’s (ATMA) water distribution system. This dissertation studies ATMA as the test case due to the unique challenges it encounters in respect of water supply. Some of these challenges include 43  illegal connections, over 50% non-revenue water, inability to fully utilize capacity, inadequate supply to meet demand, lack of bulk metering, deteriorating distribution systems and water quality issues arising from intermittent production of treated water.  In order to help manage the above-stated challenges, the World Bank and the Government of Ghana in 2005 requested GWCL to sign a five year management contract with a private operator called Aqua Vitens Rand Limited (AVRL). The private operator was formed from a public Dutch company, Vitens Evides International, and a public South African Water Company, Rand Water. The role of AVRL was to act as the operator for, and on behalf of GWCL. With the key performance indicators set out in the contractual agreement, AVRL was to improve systems’ reliability and water quality, ensure financial sustainability, improve customer service and provide water at affordable rates. A fund of $1.2 million was invested for the improvement of the various systems such as chemical dosing equipment, pumps and laboratory equipment. While some progress was made, the contract was not renewed in 2011 due to unsatisfactory results (Vitens Evides International, VEI 2012). Increasing the water supply capacity was proposed as the ultimate solution to overcome the problem. 44     Figure 4.1 - ATMA water distribution system (Source: Ghana Water Company Limited) 45  4.3 Accra-Tema Metropolitan Area (ATMA) ATMA mainly consists of the capital city Accra and its suburbs, the industrial city Tema, Ashaiman, and other communities. ATMA has a population of about 4 million (GSS 2013). Ideally, the urban water supply system is supposed to serve this population. The city of Accra is the anchor of the ATMA region and has a total area of 200 km2. The region typically has a tropical savannah climate with average annual rainfall in the region of 730 mm occurring between the two rainy seasons in the country. The average monthly temperature ranges between 24 and 28 degrees Celsius, with the highest temperature occurring during the dry harmattan season (Okyere et al. 2013). For administrative convenience, ATMA is divided into three regions — the Accra East region, the Accra West region and the Tema region. These regions are further divided into districts for effective water distribution and revenue collection.   ATMA has two main sources of water supply, the Weija Treatment Plant, the Kpong Treatment Plant, and the nearly-completed Teshie Desalination plant. ATMA also has two other small water supply systems: the Dodowa wells and the Keseve Adafoa plant. The Weija treatment plant has its source from the Densu River while the Volta River feed the Kpong treatment plant (GWCL-Kpong 2012; GWCL-Weija 2012). The Teshie Desalination plant receives water from the Atlantic Ocean.   The Weija Treatment Plant is located 4 km off the Accra-Winneba road. It is a conventional water treatment plant sited about 120 m above the sea level. It has a total designed capacity of 60 million gallons per day. The Kpong treatment plant is situated at Kpong along the Kpong-Akosombo road close to the Kpong Dam. It has an installed capacity of about 48 million gallons per day. Unlike the Weija plant, it has no advantage of elevation and pumps water twice (low- and high-lift pumping) (GWCL-Kpong 2012). The Weija and Kpong treatment plants employ the multiple-barrier conventional approach for treating surface water. These two treatment plants currently produce a total of 94 million gallons per day out of the current estimated demand of 150 million gallons per day in the ATMA region. The deficit of 57 million gallons per day is putting a serious strain on customers and the economic productivity of the region (Dzakah 2013).   The ever-growing gap between water supply and demand in the region has become a “normal” problem for some decades now. GWCL has put in place a water rationing 46  program to help in the equitable distribution of water. The huge deficit has effectively made some areas “no-water-supply-zones”. High altitude and distant areas from the treatment plants have distribution systems that have not experienced the flow of water for years. This has been mainly due to low pressures as a result of inadequate supply — consumers in low altitude areas take much of the water, leaving little water with low pressures unable to reach consumers in distant and high altitude locations. This situation greatly affects productivity of the region and has promoted the rampant water vending business of tanker and sachet (Figure 4.2) water services with very doubtful water quality (Dodoo et al. 2006).   Figure 4.2 Tanker water supply and sachet water vending (http://edmingle.blogspot.ca) 4.4 Alternatives, Criteria, Sub-criteria and their Comparative Judgments This research employed an MCDA technique to assist the local authorities in making a systematic decision for the water treatment plants expansion of ATMA. The alternative water supply candidates alongside their respective environmental, economic, technical, socio-political factors are evaluated under an AHP procedure.  4.4.1 Data Collection As per the AHP framework, literature was reviewed and expert advice solicited to identify the alternatives, criteria and sub-criteria as presented in the hierarchical structure in Figure 4.3. Considering the limited availability of local experts in the water supply industry in Ghana, 17 participants were recruited for the study. The participants are highly rated engineers and scientists drawn from the Ghana Water Company Limited and 47  the Public Utility and Regulatory Commission (the regulatory body of urban water systems in terms of performance and pricing). The questionnaire was initially evaluated and then approved by the two participating institutions before the data was collected, upon the approval of the UBC Okanagan Behavioral Research Ethics Board. Due to the limited number of experts to technically evaluate the questionnaire, selection of the participants was facilitated with the help of the management of the two institutions. Potential participants were initially contacted through emails to solicit for their interest in participating in the study. The researcher designed the questionnaire, upon a comprehensive review of literature. He had to undergo human ethics training as outlined in the University of British Columbia’s regulations, through the university’s Behavioral Research Ethics Board before the participating institutions granted approval for the commencement of the research. Appendix A provides the questionnaire and consent forms. The questionnaire and consent forms were then sent to the interested participants in ATMA via email. The participants were asked to complete and return the forms to the researcher via email. Following the various analytical steps of the AHP (discussed Chapter 3) and using the Expert Choice software (http://expertchoice.com), the collected data was then analyzed. 4.4.2 Alternatives Considering the statistics given by the African Ministers’ Council on Water (AMCOW 2011), greater efforts in reducing non-revenue water, which mostly comes through pipe bursts, leakages and illegal connections, as well as embarking on major water conservation campaigns, would help improve the water supply situation of the ATMA region. However, the ultimate panacea to the persistent struggle for potable water is to increase the supply. With current plans of alleviating the struggle for potable water, three potential options have been presumed. The options include 1) expanding the Weija Water Treatment Plant, 2) expanding the Kpong Water Treatment Plant, and 3) adding more capacity units to the desalination plant at Teshie under construction. These alternatives were examined under a set of defined criteria, and then were subjected to the AHP procedure to suggest the best alternative. 4.4.3 Criteria Table 4.1 provides definitions for the various criteria and sub-criteria used in this research. 48     Water Supply Capacity Expansion Economic Energy Cost Chemical Cost Maintenance Cost Construction Cost Financial Viability Environmental Resource Quantity Resource Quality (Treatability) Treatment Waste Management Ecological Impact Technical Operational Flexibility Expertise of Employees Land Area Storm Drainage Socio-cultural Treated Water Aesthetics Security of Transmission Acceptability Cultural Resource Figure 4.3 The AHP hierarchy for the capacity expansion project Goal Criteria S u b c r it e r i a TESHIE PLANT KPONG PLANT WEIJA PLANT 49  Table 4.1 Definition of criteria Criteria Sub-criteria Definition     Economic Energy Cost The cost of energy from low-lift pumping, through treatment, to high-lift pumping. Chemical Cost The cost of chemicals employed in treatment.  Maintenance Cost The cost of repairing and replacement of equipment and their parts, as well as the rehabilitation of treatment structures.  Construction Cost The cost of constructing the facility including land acquisition, procurement and installation of equipment, construction of treatment structures, pipe laying etc.  Financial Viability How sustainable the facility will be vis-à-vis operational cost, water pricing and consumer affordability.    Environmental Resource Quantity The availability of resource to support treatment for the design period. Resource Quality (Treatability) The physico-chemical (e.g. dissolved salts) and bacteriological (e.g. coliforms load) quality of raw water and how easily treatable it is. Treatment Waste Management The magnitude of treatment waste and their effect on the environment if not properly managed. Ecological Impact Effect of capacity expansion on aquatic life.     Technical Operational Flexibility The ease with which the facilities can be operated.  Expertise of Employees Knowledge of employees on the likely technology to be employed, and their familiarity with such a system.  Land Area The area of land on which the facility will be constructed must be large enough. The useable land must not occupy the entire site, and should provide the opportunity for the creation of a buffer zone around the facility.  Storm Drainage This looks at the degree to which constructing a water treatment facility on a site would modify the storm drainage in the locality and the ability to construct storm water facilities on the site.   Socio-cultural  Treated Water Aesthetics The taste and odor of treated water. Security of Transmission The likelihood of vandalism on the transmission mains. Acceptability The likelihood of consumers accepting product, based on perception of the source.  Cultural Resource The substantial adverse change to historical and archaeological resources and facilities. 50  Chapter 5 Results and Discussion “The beauty that is in the results of your hard work, is not worth any compromise or delay”  Unarine Ramaru  In trying to make the best decision in the expansion of a water treatment facility in the ATMA region for urban water supply, the alternatives considered included the Weija Treatment Plant, the Kpong Treatment Plant and the Teshie Desalination Plant. The main criteria considered bordered on environmental, economic, technical and socio-cultural perspectives. To enable a deeper evaluation of each criterion, the criteria were divided into sub-criteria. AHP was employed to find the best capacity expansion alternative.  5.1 Estimation of Priorities in Hierarchy The mechanism of the AHP decision-making employed in this study is group decision-making. As stated in section 4.4.1, experts from GWCLL and the PURC made individual inputs by making pairwise comparative judgments on the various criteria and sub-criteria, and with respect to the three alternatives. In order to arrive at a sound decision within the group, the AHP requires that individual judgments are aggregated using the geometric mean method before computing the priorities. A manual computation of the priorities is illustrated in Table 5.1 below, for the criteria:   Table 5.1 - Aggregated Pairwise comparative judgment of criteria  Environmental Economic Technical Socio-cultural Environmental 1.0000 2.4495 5.4772 6.7354 Economic 0.4083 1.0000 4.4721 5.7327 Technical 0.1826 0.2236 1.0000 3.0000 Socio-cultural 0.1485 0.1744 0.3333 1.0000    51  This gives us a pairwise comparison matrix as indicated below:  1.0000 2.4495 5.4772 6.73540.4083 1.0000 4.4721 5.73270.1826 0.2236 1.0000 3.00000.1485 0.1744 0.3333 1.0000  Squaring the matrix yields the following matrix, the sum of the rows in the matrix, and the normalized values (eigenvectors) of the sum respectively:  Matrix Sum Eigenvectors     4.0000 7.2987 24.1539 43.94462.4841 4.0000 13.0911 27.63140.9018 1.4177 4.0000 8.51160.4290 0.7871 2.2600 4.0000       79.3972 47.2067 14.8312  7.4761          0.53320.31700.09960.0502  5.1-1   This process is iterated repeatedly until the derived eigenvectors do not change from the previous eigenvectors.  Matrix Sum Eigenvectors    74.7666 127.2218 388.0928 758.818043.5333 74.4390 227.1764 441.641514.3882 24.6236 75.5790 146.89847.4254 12.6320 38.7459 75.8370       1348.8991 786.7902 261.4892  134.6403          0.53280.31080.10330.0532 5.1-2   One more repetition yields the following priorities: 0.53280.31100.10310.0531 As outlined in the methodology, calculating Aw gives: 0.5328  1.00000.40830.18260.1485  0.3110  2.44951.00002.44950.1744  0.1031  5.47724.47211.00000.3333  0.0531  6.73545.73273.00001.0000   2.21691.29400.42920.2208  5.1-3    52  Given that Aw = maxw, thus:   max  2.21690.5328 1.29400.3110 0.42920.1031 0.22080.05314  4.1608  5.1-4  and  CI   ( max n)(n 1)   4.1608 44 1   0.054 5.1-5  From section 3.2, the RI is 0.9. Thus,                          5.1-6  The pairwise comparative judgments of the parent elements (the criteria) produced “global” priorities. Further down the hierarchy within each criterion, “local” priorities were obtained for the various sub-criteria with respect to their parent criteria. The entire hierarchy was then synthesized by multiplying “local” priorities of sub-criteria by the “global” priorities of the criteria to generate global priorities of the sub-criteria, which were then summed for their respective alternatives as indicated in Table 5.2. The following sections discuss the findings in the table in more details.     53  Table 5.2 - Priorities of criteria and sub-criteria with respect to the alternatives  Criteria Sub-Criteria with  Global (G) and Local (L) Weights Alternatives Weija Kpong Teshie  Environmental (53.3%) Resource Availability (RA) Resource Quality (RQ) Treatment Waste Management (TWM) Ecological Impact L: 0.507 and G: 0.265 L: 0.286 and G: 0.150 L: 0.082 and G: 0.043 L: 0.125 and G: 0.066 0.0212 0.0421 0.011 0.0143 16.9 0.0404 0.0984 0.0289 0.0445 40.5 0.2038 0.009 0.0032 0.0069 42.6   Economic (31.1%) Energy Cost (EC) Chemical Cost (CC) Maintenance Cost (MC) Construction Cost (CoC) Financial Viability (FV) L: 0.302 and G: 0.092 L: 0.100 and G: 0.031 L: 0.049 and G: 0.015 L: 0.147 and G: 0.045 L: 0.403 and G: 0.123 0.0654 0.0024 0.0092 0.0312 0.0834 62.6 0.0201 0.0086 0.0041 0.01 0.03 23.8 0.0069 0.0195 0.0016 0.0037 0.0097 13.5    Technical (10.3%) Operational Flexibility (OF) Expertise of Employees (EE) Land Area (LA) Storm Drainage (SD) L: 0.339 and G: 0.038 L: 0.139 and G: 0.016 L: 0.405 and G: 0.046 L: 0.117 and G: 0.013 0.013 0.0063 0.0105 0.0063 32.0 0.0217 0.0078 0.0187 0.0048 47.0 0.0035 0.0016 0.0166 0.0021 21.1   Socio-Cultural (5.3%) Treated Water Aesthetics (TWA) Security of Transmission (ST) Acceptability (A) Cultural Resource (CR) L: 0.227 and G: 0.013 L: 0.308 and G: 0.018 L: 0.205 and G: 0.012 L: 0.261 and G: 0.015 0.0018 0.0097 0.0032 0.0065 36.8 0.0082 0.0024 0.0075 0.0046 39.4 0.0031 0.0057 0.0011 0.0039 24.0   54  5.2 Environmental Criterion As Table 5.2 - Priorities of criteria and sub-criteria with respect to the alternatives .2 indicates, the environmental criterion had a priority score of 53.3% with respect to the goal, and this is followed by economic (31.1%), technical (10.3%) and socio-cultural (5.3%) criteria. Within the environmental criterion, the Teshie desalination plant ranked first with a score of 42.6%, followed by the Kpong treatment plant with a score of 40.5% and the Weija treatment plant has a score of 16.9%. Considering the environmental sub-criteria with respect to the goal, resource availability scored 26.5%, resource quality scored 15.0%, treatment waste management 4.3% and ecological impact 6.6%. This is explicable in that the sustainability of the entire water supply system rests mainly on environmental issues.   The magnifying problem of climate change and its effect on climatic factors such as precipitation, has exacerbated the challenge of inadequate potable water supply in some parts of the world (Kayaga et al. 2007; Stern 2007). Climate change has introduced large variability and unpredictability in the water cycle. Sub-Saharan Africa in particular has experienced severe droughts in recent years and is projected to have about eighteen water-stress countries by 2025, which eventually would affect a population of 600 million people (World Bank 1995). In Ghana, the drying up of tributaries of the Offin River (a major water supply source in the Ashanti Region), and the continuous reduction of water levels in the Volta Lake are clear manifestation of dwindling freshwater reserves (Gyampoh et al. 2008; Asare 2004). Karikari (1996) buttresses this fact by his assertion that per capita freshwater availability declined from 9,204 m3 in 1955 to 3,529 m3 in 1990. In the sub-criteria of resource availability, the Teshie plant which has its source from the Atlantic Ocean ranked first (76.9%), followed by the Kpong plant with its source from the Volta Lake weighting 15.2% and the Weija plant with its source from the Weija Dam (Densu River) having a weight of 8.0%. This reflects the real pattern— the Atlantic Ocean has an infinite capacity and the Weija and Kpong sources have capacities of 0.114 and 148 km3, respectively (Kuma and Ashley 2008; VRA 2014).   Pollution of freshwater resources compounds the environmental challenge to urban water supply. Freshwater resources have increasingly become vulnerable to anthropogenic pollution (Sundaray et al. 2006; Törnqvist et al. 2011), affecting the aquatic ecosystem and urban water supply (Wu and Chen 2013). Eutrophication, which seriously impairs the 55  quality of water resources, has become the leading global threat to water quality (UNDESA 2014). Comparing the three source waters, the Kpong treatment plant, having its source from the Volta Lake, has by far a superior source water quality (Uusitalo 2002) when compared to the Weija treatment plant which has its source from the Weija Dam, and the Teshie desalination plant which has the vast Atlantic Ocean as its source. This reflects in the scores of 65.8, 28.1, and 6.0% for the Kpong plant, the Weija plant and the Teshie plant respectively for the resource quality category. Karikari and Ansa-Asare (2006) report that indiscriminate waste disposal into the Densu River has deeply deteriorated its water quality. The poor water quality and relatively low volume of source water are attributable to the low score of the Weija treatment plant. Conversely, the Teshie desalination plant performed better in the environmental category largely due to the unlimited available quantity of its source, despite having the worst quality due to very high dissolved solid content. Resource availability’s high global score boosted the performance of the Teshie plant in the environmental section, and the overall rating process, highlighting the compensatory nature of the AHP.  With respect to the operations of the three systems, the Kpong treatment plant produces less treatment waste. Backwashing of filters and the desludging of the clarifiers are far less frequent when compared to the Weija plant, and this might account for the high priority score of 0.0289 (67.2%) for Kpong treatment plant than the other two plants. At Weija, the poor quality of the raw water requires the use of aluminum coagulants, which produce huge volumes of sludge and frequently clogs filters requiring lots of backwashing. Frequent desludging and backwashing generate large volumes of treatment wastes. The use of the coagulants also increases the dissolved salt concentration of treatment wastes through the introduction aluminum and sulfate ions (as aluminum sulfate is used). This ultimately increases the complexity in managing such wastes and accounts for a score of 25.6% for the plant. In desalination plants, the major waste produced is brine. However, the treatment wastes may contain residuals of chemicals (e.g. chlorine, sulfuric acid, hydrochloric acid, sodium hexametaphosphate, etc.) used in pre-treating the raw water. Brine can contain salinity of about twice its original value in the raw water, making its management relatively complex. Inappropriate handling of brine wastes can contaminate groundwater or affect aquatic lives (due to increased salinity and chemical residuals) when directly discharged into the sea. In addition the discharged brine in thermal desalination processes increases the temperature of the 56  receiving waters. High temperatures significantly reduce dissolved oxygen levels, and this can be lethal to aquatic life (Danoun 2007).  5.3 Economic Criterion The economic criterion also scored a high weight relative to the other criteria. Usually, economic analysis comes next after the consideration of environmental factors. It is a very important criterion that ensures the sustainability of a water supply project. Within the economic criterion, the Weija treatment plant ranked highest (62.7%). The treatment plant is popularly known for its good economic attributes, despite having a relatively high chemical consumption rate. Unlike the Teshie desalination and the Kpong plants which would employ two pumping stages (before and after treatment), the Weija treatment plant utilizes its high altitude to consume less energy by distributing treated water by gravity. In terms of chemical consumption, desalination plants consume less when compared to conventional treatment plants. This might account for the high weight 0.0195 (63.9%) in the chemical consumption sub-criterion for the desalination plant. The Weija plant also recorded the highest weights in maintenance (0.092 representing 61.7%) and construction costs (0.027 representing 69.3%) probably due to the fact that it uses less equipment (e.g. pumps), resulting in less cost in both sub-criteria. Ultimately, the more economically reasonable plant will be more sustainable and financially viable as the results indicate.  5.4 Technical Criterion Under the technical criterion, Kpong treatment plant obtained a weight of 46.9% followed by Weija and the Teshie plants with weights of 32.0% and 21.1% respectively. In terms of the operation of all the alternatives, the Kpong treatment plant seems more flexible. This observation reflected in the judgment of participants — the Kpong treatment plant was adjudged the most flexible in terms of operations with a score of 56.8%, while the Weija and Teshie plants scored 34.0% and 9.2%, respectively. Unlike the Weija and Teshie plants, the Kpong plant does not employ pre-treatment chemicals or coagulants. The raw water is only allowed to settle, filtered and conditioned with disinfectants and an alkali to adjust pH. However, eutrophication in the Weija Dam promotes algal growth, which makes treatment at Weija more cumbersome when compared to the Kpong plant. At Weija, frequent desludging of clarifiers is needed to control sludge volumes in order to stabilize the sludge blanket and optimize sedimentation. Similarly, the rapid sand filters must be continuously monitored and backwashed in order to avoid filter breakthrough 57  due to the large load of suspended solids in the clarified water. Operating desalination plants can be more complex than conventional treatment plants. Some of the factors contributing to the operational complexity of desalination plants include complex process instrumentation and controls; scaling in thermal plants; membrane fouling in reverse osmosis plants; and difficulty in handling waste brine. The complex structure of desalination plants requires that plant operators be well trained to properly operate the plant. Comparatively, due to the age of technology, the simplicity of design and the widespread availability of conventional treatment systems, many operators seem to have adequate knowledge on their operations. This accounts for the scores of 49.7%, 40.1% and 10.2% for Kpong, Weija and Teshie plants, respectively, under the sub-criterion of expertise of employees. In the category of availability of land for construction, the Kpong, Teshie, and Weija plants scored 40.9%, 36.3%, and 23%, respectively. Explicably the available land in the highly scored plants is more than that of Weija. The Weija land allocated for treatment plant capacity expansion is known to be heavily encroached (Awuku-Apaw 2011).  5.5 Socio-cultural Criterion The socio-cultural criterion, in the context of this study, generally covers the areas of treated water aesthetics and acceptability, the risk to water distribution, and the effects of constructing a treatment plant. Within this criterion, the scores were 39.2%, 36.6%, and 24.0% for the Kpong, Weija, and Teshie plants respectively. Due to the non-employment of coagulant salts the Kpong and Teshie plants were weighted higher than the Weija plant. However, the Kpong plant scored higher than the Teshie plant (63.1% vs. 23.8%) due to the excessive demineralization of water during desalination. Heavy pollution of the source water at Weija, coupled with the use of inorganic coagulants, affects the taste of treated water produced at Weija. Most people indicate that treated water from Kpong is “sweeter” than that from Weija (Stoler et al. 2012b). Aesthetics and acceptability are closely linked but could differ widely if customers’ base their judgment on the perception of the source water rather than its quality. In the acceptability sub-criterion, the scores of 63.6, 27.1, and 9.3 percent were recorded for the Kpong, Weija and Teshie plants. The better aesthetics of water produced at Kpong is likely to be the reason for its high scores. Conversely, the “poor” taste of water from the Weija plant accounts for its low score of 27.1%. However, considering the Teshie plant, its low score might be due to public perception of the “untreatable” nature of seawater. Perhaps, consumers do not believe the 58  ocean water can be suitably conditioned for consumption. From the results, the participants indicated that transporting water from Kpong (13.6%) was more risky than those from Weija (54.8%) and Teshie (32.2%). This could be due to the fact that Weija and Teshie are located within Accra Township with distribution mains running through the city, unlike the Kpong treatment plant, which transports water through bushes and remote areas over a long distance to the ATMA region.    Figure 5.1 indicates the overall ranking of the alternatives with respect to the goal of capacity expansion. As the figure reveals the Kpong treatment plant was ranked the first with a score of 36.1%, followed by the Weija treatment plant (the second) with a score of 33.8% and the Teshie desalination plant (the third) with a score of 30.2%.  Figure 5.1 - Overall ranking of alternatives 5.6 Sensitivity Analysis It is informative to test the robustness of the proposed AHP model. This is particularly significant as the ranking of the alternatives are dependent on the weights assigned to the criteria and sub-criteria. Such weights rely on the subjectivity of the participants’ judgments. The sensitivity analysis were conducted by applying the function built in the Expert Choice software, and the technique developed by Triantaphyllou (2000).   Teshie DP Weija WTP Kpong WTP 30.2 33.8 36.1 Synthesis with respect to goal Overall inconsistency = 0.06 59  With the built-in sensitivity analysis function in the software, the alternatives’ ranking and their performance with respect to the various weights for the criteria can be displayed graphically. Figure 5.2 indicates the results of the analysis. As the figure reveals, the environmental and economic criteria were the sensitive criteria. Furthermore, Figure 5.2 (c) shows that it takes an increment of 11.6% of the weight of the environmental criterion with simultaneous reductions of 12.7, 13.3 and 13.85 percent of the economic, technical and socio-cultural criteria respectively to witness a change in the alternatives’ ranking. Similarly, according to Figure 5.2 (b), it takes an increment of 13.4% of the economic criterion with a simultaneous reduction of 5.9, 6.2 and 6.9 percent of the weights of the environmental, technical and socio-cultural criteria to observe any alteration in the ranking of the alternatives. Overall, the ranking of alternatives in the model proves to be robust against changes in the technical and socio-cultural criteria as revealed in Figure 5.2  (c) and (d).   Figure 5.3 (a), (b), (c) and (d) show the gradient sensitivity graphs. In these graphs, the criterion of interest is displayed on the horizontal axis, with the vertical red and blue dotted lines indicating the criterion’s original and altered weights respectively, while other criteria weights are kept unchanged. These lines meet the various alternatives at some point to indicate their ranking. Figure 5.3 (a) and (b) show that changing the priority weights changes the ranking, while that of Figures 5.3 (c) and (d) do not respond to the criteria weight changes. With this sensitivity function in the software, one is able to see a clear visual picture of the response of the model to some changes in criteria weights. It should be emphasized that the impact of the various sub-criteria on the sensitivity of the model is not incorporated in the software by its designers, as only the main criteria are displayed.  Alongside the earlier sensitivity analysis technique employed, the methodology proposed by Triantaphyllou (2000) was used to give a further detailed picture on the sensitivity of the sub-criteria with respect to the alternatives’ ranking.  Employing the methodology as explained in section 3.3, and using the synthesised data of priorities for all sub-criteria in Table 5.3, the minimum change in the weight of Resource Availability (RA) sub-criterion required to change the ranking between the Kpong and the Teshie plants as per equation 3.3-4 is: 60                                        5.1-7  The value -0.096 satisfies equation 3.0-9 because it is less than the resource availability sub-criterion weight of 0.265. This makes the modified weight w1* to be                           Table 5.5 shows the rest of the data for priority changes in absolute terms (       , while that of Table 5.6 represents changes in relative terms (        . As explained earlier, the criticality of the sub-criteria to changes in the ranking of the alternatives depends on whether one views it in absolute or relative terms. To demonstrate these two perspectives, an analysis was done for both relative and absolute criticality.  From Table 5.6, the percent-top critical sub-criterion (the most critical sub-criterion in relative terms) which is the least relative value in rows (or in bars as shown in Figure 5.4) involving the top ranked alternative (the Kpong plant ) is -36.0%, under the resource availability sub-criterion. This indicates that an increase (because of the negative sign) in the weight of the resource availability sub-criterion by 36.0% will make the Teshie desalination plant the preferred destination for capacity expansion in place of the Kpong plant, as seen in the original ranking. The Percent-Any critical sub-criterion can be obtained based on the smallest relative value in Table 5.6, or as seen in Figure 5.4. This is found to be -19.5%, coincidentally also corresponding to the resource availability sub-criterion. It implies that the preference for the best alternative for treatment plant capacity expansion is sensitive to an increase of 19.5% of the resource availability weight. The relative change concept, instead of the absolute change concept, is the preferred approach in this study. Absolute changes can be misleading since the original values from which the change occurred are not factored in the analysis. If the decision makers were to be interested in absolute changes rather than relative changes, the Absolute-Top value will be -0.047 corresponding to the energy cost (EC) sub-criterion. This indicates that the Kpong treatment plant will relinquish its place in the ranking to the Weija treatment plant when the energy cost sub-criterion weight increases by 0.047. Likewise, the Absolute-Any value, by definition, will also be -0.047 under the energy cost sub-criterion. Table 5.7 shows the values for criticality degree (D´k) as described in section 3.3. The table also indicates the results of the sensitivity coefficient. From the table, the resource availability 61  sub-criterion is the most sensitive decision sub-criterion. The robust sub-criteria are the sub-criteria with all the         values indicated as not feasible (N/F).  Out of the 17 experts that participated in the study, a summary statistics of the consistency index of the various responses under each criterion is shown in Table 5.3. The table reveals an overall mean consistency index of 0.06. The socio-cultural criterion had the lowest mean consistency index (0.02) while the technical criterion had the highest mean consistency index (0.10). From the table, it is seen that the economic criterion had the highest standard deviation value of 0.0141, while the technical criterion had the lowest standard deviation. The high standard deviation of the economic criterion could be due to the fact that it had ten pairwise comparative judgment questions as compared to six comparative judgment questions for all the other criteria. Research has shown that higher number of pairwise comparison questions can lead to a higher inconsistency in judgments (Sheth 2009). Table 5.3 Summary statistics of consistency indeces Criteria Mean Standard Deviation Environmental 0.06 0.0071 Economic 0.08 0.0141 Technical 0.10 0.0010 Socio-cultural 0.02 0.0071 62          (a) Environmental criterion                        (b) Economic criterion  (c)  Technical crierion                                                                                         (d) Socio-cultural criterion Figure 5.2 Performance sensitivity graphs (weights of listed criteria were increased with the others simultaneously reduced) Performance Sensitivity for nodes below: Goal: Choosing a Water SupplyFacility for Capacity Expansion. Alt%Weija Treatment PlantTeshie Desalination PlKpong Treatment PlanEnvironmenta Economic Technical Socio-cultur OVERALLObjectives NamesEnvironmenta EnvironmentalEconomic EconomicTe hnical Techni alSocio-cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie Desal Teshie Desalination PlantPage 1 of 123/09/2014 4:39:40 AMhadisu2Performance Se sitivity for nodes below: Goal: Choosing a Water SupplyFacility for C pacity Expansion. Alt%Teshie Desalination PlKpong Treatment PlanWeija Treatment Plantnvir nmenta Economic Technical Socio-cultur OVERALLObjectives NamesEnvironmenta EnvironmentalEconomic EconomicT c nical TechnicalSoci -cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie Desal Teshie Desalination PlantPage 1 of 123/09/2014 4:45:25 AMhadisu2Performance Sensitivity for nodes below: Goal: Choosing a Water SupplyFacility for Capacity Expansion. Alt%Teshie Desalination PlWeija Treatment PlantKpong Treatment PlanEnvir nmenta Economic Technical Socio-cultur OVERALLObjectives NamesEnvironmenta EnvironmentalEconomic EconomicTechnical TechnicalSocio-cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie Desal Teshie Desalination PlantPage 1 of 123/09/2014 4:51:22 AMhadisu2Performance Se sitivity f r nodes below: Goal: Choosing a Water SupplyFacility for Capacity Expansion. Alt%Teshie Desalination PlWeija Treatment PlantKpong Treatment Plannvir nmenta E o omic Technical Socio-cultur OVERALLObjectives NamesEnvironmenta Environmen alEcon mic EconomicTechnical TechnicalSoci -cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie D sal Teshie Desalination PlantPage 1 of 123/09/2014 4:53:34 AMhadisu263   (a)                                                                                                                          (b)                  (c)                                                                                                                           (d) Figure 5.3 - Gradient sensitivity graphs (weights of listed criteria were increased with the others kept unchanged) Gradient Sensitivity for nodes below: Goal: Choosing a Water Supply Facilityfor Capacity Expansion. .1 .2 .3 .4 .5 .6 .7 .8 .9 1EnvironmentalWeija Treatment PlanKpong Treatment PlaTeshie DesalinationObjectives NamesEnvironmenta EnvironmentalEconomic EconomicTechnical TechnicalSocio-cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie Desal Teshie Desalination PlantPage 1 of 123/09/2014 10:00:51 PMhadisu2Gradient Se sitivity f r nodes below: Goal: Choosing a Water Supply Facilityfor Capacity Expansion. .1 .2 .3 .4 .5 .6 .7 .8 .9 1EconomicTeshie DesalinationKpong Treatment PlaWeija Treatment PlanObjectives NamesEnvironmenta EnvironmentalEcon mic EconomicTechnical TechnicalSoci -cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie D sal Teshie Desalination PlantPage 1 of 123/09/2014 9:59:42 PMhadisu2Gradient Sensitivity for nodes below: Goal: Choosing a Water Supply Facilityfor Capacity Expansion. .1 .2 .3 .4 .5 .6 .7 .8 .9 1TechnicalTeshie DesalinationWeija Treatment PlanKpong Treatment PlaObjectives NamesEnvironmenta EnvironmentalEconomic EconomicTechnical TechnicalSocio-cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie Desal Teshie Desalination PlantPage 1 of 123/09/2014 10:01:29 PMhadisu2Gradient Se sitivity f r nodes below: Goal: Choosing a Water Supply Facilityfor Capacity Expansion. .1 .2 .3 .4 .5 .6 .7 .8 .9 1Socio-culturalTeshie DesalinationWeija Treatment PlanKpong Treatment PlaObjectives NamesEnvironmenta Environmen alEconomic EconomicTechnical TechnicalSoci -cultur Socio-culturalAlternatives NamesWeija Treatm Weija Treatment PlantKpong Treatm Kpong Treatment PlantTeshie D sal Teshie Desalination PlantPage 1 of 123/09/2014 10:20:07 PMhadisu264  Table 5.4 - Priorities of sub-criteria and alternatives from the pairwise comparative judgments  Sub-Criteria* Alternatives RA RQ TWM EI EC CC MC CoC FV OF EE LA SD TWA ST A AR Weights 0.265 0.150 0.043 0.066 0.092 0.031 0.015 0.045 0.123 0.038 0.016 0.046 0.013 0.013 0.018 0.012 0.015 Kpong 0.152 0.658 0.671 0.677 0.218 0.282 0.275 0.223 0.244 0.568 0.497 0.408 0.364 0.626 0.135 0.636 0.307 Weija 0.080 0.282 0.255 0.218 0.708 0.079 0.617 0.695 0.677 0.340 0.401 0.229 0.477 0.137 0.545 0.271 0.433 Teshie 0.768 0.060 0.074 0.105 0.075 0.639 0.107 0.082 0.079 0.092 0.102 0.362 0.159 0.237 0.320 0.093 0.260 *The abbreviations of sub-criteria listed in the table represents the respective sub-criteria as listed in Table 5.2 in their respective order. Table 5.5 - Absolute changes in sub-criteria weights (         RA RQ TWM EI EC CC MC CoC FV OF EE LA SD TWA ST A AR K-W N/F 0.061 N/F 0.050 -0.047 N/F -0.068 -0.049 -0.053 N/F N/F N/F -0.203 N/F -0.056 N/F -0.182 K-T -0.095 0.098 N/F N/F N/F -0.165 N/F N/F N/F N/F N/F N/F N/F N/F -0.317 N/F N/F W-T -0.052 N/F N/F N/F 0.056 -0.064 N/F N/F 0.060 N/F N/F -0.268 N/F -0.36 N/F N/F N/F  Table 5.6 - Relative changes in sub-criteria weights (             RA RQ TWM EI EC CC MC CoC FV OF EE LA SD TWA ST A AR K-W N/F 41.0 N/F 76.7 -51.0 N/F -453.2 -108.8 -43.3 N/F N/F N/F -1529.4 N/F -318.4 N/F -1216.6 K-T -36.0 65.7 N/F N/F N/F -539.4 N/F N/F N/F N/F N/F N/F N/F N/F -1791.6 N/F N/F W-T -19.5 N/F N/F N/F 61.0 -208.6 N/F N/F 48.4 N/F N/F -586.1 N/F -2744.4 N/F N/F N/F 65  Table 5.7 - Criticality degrees and sensitivity coefficients (D´k)   RA RQ EI EC CC MC CoC FV LA SD TWA ST AR D´k 19.5 41.0 76.7 51.0 208.6 453.2 108.8 43.3 586.1 1529.4 2744.4 318.4 1216.6 Sens(Ck) 0.0513 0.0244 0.0130 0.0196 0.0048 0.0022 0.0092 0.0231 0.0017 0.0007 0.0004 0.0031 0.0008   Figure 5.4 Graphical Representation of Relative changes in sub-criteria weights (          0 500 1000 1500 2000 2500 3000 RA RQ TWM EI EC CC MC CoC FV OF EE LA SD TWA ST A AR Kpong-Weija Kpong-Teshie Weija-Teshie 66  Chapter 6 Conclusion and Recommendation “I have come to the conclusion, after many years of sometimes sad experience that you cannot come to any conclusion at all.”            Anonymous 6.1 Conclusion Decision-making is a complex process that requires a comprehensive evaluation of the criteria bothering the problem being assessed. The AHP has been widely accepted as a convenient decision-making tool, and this reflects in its wide use in academia and in the business community. In this study, the AHP methodology was employed to select the best water supply facility for capacity expansion in the ATMA region of Ghana.    The criteria considered in making the decision included environmental, economic, technical and socio-cultural criteria. In analyzing the pairwise comparative judgments by the experts who participated in the study, the environmental criterion was found to be the most important criterion, followed by the economic, the technical and the socio-cultural criteria in that respective order. In the analysis, the Kpong treatment plant ranked first with a score of 36.1%. This was followed closely by the Weija and Teshie desalination plants, which scored 33.8 and 30.2 percent, respectively.   Sensitivity analysis conducted on the decision model indicated that the model is sensitive to changes in criteria weights for the environmental and economic criteria, and insensitive to the technical and the socio-cultural criteria. The insensitive criteria could be replaced with other criteria, or reformulated (in terms of the sub-criteria), in subsequent studies. Conversely, much focus could be placed on the sensitive criteria for policy development and standards for water supply capacity expansion projects.  In the sub-criteria category, resource availability proved to be the most critical in relative terms — an increase of 19.5% of its weight altered the ranking of alternatives, while in absolute terms, the energy cost sub-criterion was observed to be the most sensitive. 67  6.2 Limitations of the Study Despite the strengths of the AHP, it comes with some observed weaknesses. The problem of inconsistency in the judgment of the participating experts during the administration of the questionnaire was observed to be associated with questions that had relatively large pairwise comparisons. This often resulted in situations where affected participants were asked to re-evaluate their responses in order to satisfy the acceptable inconsistency limit.  A careful examination of the AHP methodology revealed that it is not suitable for interdependent or interacting criteria at different levels. Additionally, the nature of the decomposition of the problem could also have an effect on the final result of the study. Therefore, when different experts view the same problem from different perspectives, in terms of the problem decomposition, the outcome of such a study could be starkly different. For example, if a sub-criterion from the sensitive criteria — environmental and economic — is placed in any of the insensitive criteria (technical and socio-cultural), the entire ranking could change.   The research also revealed that the depth of answers, or quality of response, from the participating experts depend on the clarity of definition given to the identified criteria and sub-criteria. One expert pointed out that it would help the evaluation process if broader definitions for sub-criteria were narrowed to specific points of interests. For instance, he indicated that it would help if the standard of measure for a sub-criterion like water quality was made specific in terms of the parameters of interests (e.g dissolved and suspended solid, colour, turbidity, microbial load, etc.) rather than being left open for different interpretation. 6.3 Recommendations for Future Work The following recommendations are made for future work: 1. As much as possible, the number of criteria and sub-criteria should be condensed to avoid the development of large pairwise comparisons. Larger pairwise comparisons become mentally challenging and could result in inconsistent judgments and the introduction of wide biases. 2. In future research works where interaction of different level criteria is identified, it is recommended that an advanced methodology like the Analytic Network Process (ANP), an extension of the AHP, is used. 68  3. The study employed a group decision-making methodology where different experts answered the same questionnaire separately, before the final score was calculated based on the geometric mean of the individual responses under every criterion and sub-criterion. If there is the possibility of all participants to congregate for a consensus group decision-making, it would help facilitate the speed of the research, reduce wide personal biases, and avoid multiple re-evaluations of inconsistent responses.  4. Interaction between the researcher and participants yields more informed and constructive discussion when questionnaire are administered personally rather than being transmitted electronically, as was done in this study. In such situations, the researcher could immediately help participants calculate the consistency index to determine the need for any re-evaluation. 5. Future research in similar environments could employ the use of quantitative data rather than the qualitative evaluation to avoid biases in judgment. 6. Uncertainty and imprecision in pairwise comparative judgments could be improved via advanced techniques such as fuzzy logic and Monte Carlo Simulation. 7. This study involved only key experts at the top level of management. The operators of water treatment plants often have first-hand information on the detailed technical operations as well as on the challenges that the treatment plants face. The involvement of operators in future studies could give a different perspective from the one observed in this current study.   69  References Abrishamchi, A., Ebrahimian, A., Tajrishi, M., and Mariño, M. (2005). “Case Study: Application of Multicriteria Decision Making to Urban Water Supply.” Journal of Water Resources Planning and Management, 131(4), 326–335. Addico, G. N. D., Hardege, J. D., Babica, P., Kohoutek, J., and Graft-Johnson, K. de. (2011). “Microcystin-RR Like Toxin Identified in the Cyanobacterium Anabaena flos-aquae Strain CCAP 1403/13B Culture.” West African Journal of Applied Ecology, 19(1), 9-16. Ahlfeld, D. P., and Laverty, M. M. (2011). “Analytical solutions for minimization of energy use for groundwater pumping.” Water Resources Research, 47(6), W06508. Ainuson, K. G. (2009). “Ensuring adequate water supply to disadvantaged urban communities in Ghana.” Ph.D., Clemson University, United States -- South Carolina. Aldeeb, A. A. A. (1999). “Water treatment plant residuals management.” M.S., The University of Texas at Arlington, United States -- Texas. Ali, S. M., Sabae, S. Z., Fayez, M., Monib, M., and Hegazi, N. A. (2011). “The influence of agro-industrial effluents on River Nile pollution.” Journal of Advanced Research, 2(1), 85–95. Amalfitano, A., and Lam, K. (2005). “Power to seawater RO in China.” World water and environmental engineering, 28(4), 16–7. AMCOW. (2011). Water supply and sanitation in Ghana : turning finance into services for 2 1  and beyond. UNDP, 1–36. Ansa-Asare, O. D., and Asante, K. A. (1998). “A comparative study of the nutrient status of two reservoirs in southeast Ghana.” Lakes & Reservoirs: Research & Management, 3(3-4), 205–217. Asare, Y. B. O. (2004). “Household Water Security and Water Demand in the Volta Basin of Ghana.” Ph.D., University of Bonn, Bonn, Germany. Awuku-Apaw, J. (2011). “The Weija Dam Obituary.” Feature, <http://opinion.myjoyonline.com/pages/feature/201112/78373.php> (Sep. 19, 2014). AWWA. (2010). Water Quality & Treatment: A Handbook on Drinking Water. McGraw-Hill Professional, New York. AwwaRF. (2007). “Advancing the Science of Water: AwwaRF and Water Treatment Residuals.” 70  Aziz, S. N., Aziz, K. M. S., and Boyle, K. J. (2014). “Arsenic in Drinking Water in Bangladesh: Factors Affecting Child Health.” Front Public Health, 2(57), 1-6. Ball, H. A., and Reinhard, M. (1985). “Discharge of Halogenated Octylphenol Polyethoxylate Residues in a Chlorinated Secondary Effluent.” Water Chlorination. Chemistry, Environmental Impact and Health Effect, Lewis Publishers, Chelsea, MI. Berga, J. (1984). “The Robust Bayesian Viewpoin.” Robustness of Bayesian Analyses, J. Kadane, ed., North-Holland, Amsterdam. Bhushan, N., and Rai, K. (2004). Strategic Decision Making: Applying the Analytic Hierarchy Process. Springer Science & Business Media. Brabo,  da S. E., Santos,  de O. E., Maura,  de J. I., Mascarenhas, F. S. A., and Faial,  de F. K. (2000). “Mercury Contamination of Fish and Exposures of an Indigenous Community in Pará State, Brazil.” Environmental Research, 84(3), 197–203. Bradberry, S. (2007). “Methaemoglobinaemia.” Medicine, Poisoning Part 1 of 3, 35(10), 552–553. Braga, B. P. F., Conejo, J. G. L., Becker, L., and Yeh, W. W. ‐ G. (1985). “Capacity Expansion of Sao Paulo Water Supply.” Journal of Water Resources Planning and Management, 111(2), 238–252. Brans, J. P., and Vincke, P. (1985). “Note—A Preference Ranking Organisation Method.” other, <http://pubsonline.informs.org/doi/abs/10.1287/mnsc.31.6.647> (Dec. 24, 2013). Braunschweig, T. (2001). Planning agricultural research: a sourcebook. CABI Publishing. Brukas, V., Hjortsø, N., Tarp, P., and Boon, T. E. (1999). “Public Participation in Forest Management Planning Supported by Multi-Criteria Decision-Making Techniques.” Multiple Use of Forests and Other Natural Resources, Forestry Sciences, F. Helles, P. Holten-Andersen, and L. Wichmann, eds., Springer Netherlands, 122–133. Butcher, W. S., Haimes, Y. Y., and Hall, W. A. (1969). “Dynamic programing for the optimal sequencing of water supply projects.” 5(6), 1196–1204. Carlos, C. (2010). “The Economics of Desalination for Various Uses.” Barcelona, Spain. <http://www.rac.es/ficheros/doc/00731.pdf>(Sep. 5, 2014) 71  Carmichael, W. W. (2001). “Health Effects of Toxin-Producing Cyanobacteria: ‘The CyanoHABs.’” Human and Ecological Risk Assessment: An International Journal, 7(5), 1393–1407. CEPA. (2004). ARCHIVED - Canadian Environmental Protection Act: Priority Substances List Assessment Report: Chlorinated Wastewater Effluents. Minister of Supply and Services Canada, Ottawa, Ontario, Canada. Chen, S.-J., and Hwang, C.-L. (1992). “Fuzzy Multiple Attribute Decision Making Methods.” Fuzzy Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical Systems, Springer Berlin Heidelberg, 289–486. Chesher, R. H. (2010). “Biological Impact of a Large-Scale Desalination Plant at Key West.” Tropical Marine Pollution, Elsevier Scientific Publishing Company, New York, 99–181. Chung, E.-S., and Lee, K. S. (2009). “Prioritization of water management for sustainability using hydrologic simulation model and multicriteria decision making techniques.” Journal of Environmental Management, 90(3), 1502–1511. Cohen, R., Nelson, B., and Wolf, G. (2004). Energy Down the Drain, The Hidden Costs of  alifornia’s Water Supply. National Resources Defense Council & Pacific Institute. Cooley, H., GleicK, P. H., and Wolff, G. (2006). Desalination, with a Grain of Salt: a California Perspective. Pacific Institute for Studies in Development, Environment, and Security, Oakland, California. Cornwell, D. A. (1992). Landfilling of Water Treatment Plant Coagulant Sludges. American Water Works Association, Denver. Crittenden, J. C., Trussell, R. R., Hand, D. W., Howe, K. J., and Tchobanoglous, G. (2012). MWH’s Water Treatment: Principles and Design. Wiley, Hoboken, N.J. Cruz, J. M. (2009). “The impact of corporate social responsibility in supply chain management: Multicriteria decision-making approach.” Decision Support Systems, 48(1), 224–236. D’Souza, D. E. (2002). “Toward an Understanding of How Organizations Create Manufacturing Flexibility.” Journal of Managerial Issues, 14(4), 470. Daily Graphic. (2013). “Accra, Tema to face 6 months water rationing exercise.” <http://edition.myjoyonline.com/pages/news/201301/100613.php> (Aug. 31, 2014). 72  Danoun, R. (2007). Desalination Plants: Potential impacts of brine discharge on marine life. Technical Report, The University of Sydney, Sydney, Australia. Dayal, U., Hsu, M., and Ladin, R. (2001). “Business Process Coordination: State of the Art, Trends, and Open Issues.” in Proceedings of the 27th Very Large Databases Conference (VLDB 2001, 3–13. DeRoberts, L., and Hartigan, J. A. (1981). “Bayesian Inference Using Intervals of Measures.” The Annals of Statistics, 9(2), 235–244. Desai, S., Vanneman, R., and National Council of Applied Economic Research, New Delhi. (2008). India Human Development Survey (IHDS), 2005: Version 8. Devine, R., Dorfman, M., and Rosselot, K. S. (2008). Missing Protection: Polluting the Mississippi  iver Basin’s Small Streams and Wetlands. Natural Resources Defense Council, New York. Djebedjian, B., Gad, H., Khaled, I., and Rayan, M. A. (2007). “Reverse Osmosis Desalination Plant in Nuweiba City (case Study).” Proceedings of IWTC11, 315–330. Dodoo, D. K., Quagraine, E. K., Okai-Sam, F., Kambo, D. J., and Headley, J. V. (2006). “Quality of ‘sachet’ waters in the Cape Coast municipality of Ghana.” Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering, 41(3), 329–342. Dzakah, V. (2013). Water Resources, Works And Housing Minister Tours Project Sites. Water Resources Works and Housing. Engel, S., Maria, I., and Useche, M. del P. (2005). Improved Water Supply in the Ghanaian Volta Basin: Who Uses it and Who Participates in Community Decision-Making? Intl Food Policy Res Inst, Washington, D.C. Erlenkotter, D. (1973a). “Sequencing of interdependent hydroelectric projects.” Water Resources Research, 9(1), 21–27. Erlenkotter, D. (1973b). “Sequencing Expansion Projects.” Operations Research, 21(2), 542–553. Firempong, C., Nsiah, K., Awunyo-Vitor, D., and Dongsogo, J. (2013). “Soluble fluoride levels in drinking water-a major risk factor of dental fluorosis among children in Bongo community of Ghana.” Ghana Medical Journal, 47(1), 16–23. Forman, E. H., and Gass, S. I. (2001). “The Analytic Hierarchy Process—An Exposition.” Operations Research, 49(4), 469–486. 73  Freitas, A. (2013). Water as a stress factor in sub-Saharan Africa. European Union Institute for Security Studies. French, S., and Insua, D. R. (1989). “Partial Information and Sensitivity Analysis in Multi-objective Decision Making.” Improving Decision Making in Organisations, Lecture Notes in Economics and Mathematical Systems, P. A. G. Lockett and D. G. Islei, eds., Springer Berlin Heidelberg, 424–433. GEI. (2010). “Embedded energy in water studies.” George, D. B., Berk, S. G., Adams, V. D., Morgan, E. L., Roberts, R. O., Holloway, C. O., Lott, R., Holt, L. K., Ting, R. S., and Welch, A. W. (1991). Alum Sludge in the Aquatic Environment. AWWA Research Foundation and American Water Works Association, Denver, CO. Glaze, W. H., and Henderson, J. E., IV. (1975). “Formation of Organochlorine Compounds from the Chlorination of a Municipal Secondary Effluent.” Journal (Water Pollution Control Federation), 47(10), 2511–2515. Gleick, P. H. (1998). The World’s Water 1  8-1999: The Biennial Report On Freshwater Resources. Island Press. Goldstein, R., and Smith, W. (2002). Water and Sustainability: U.S. Electricity Consumption for Water Supply & Treatment—The Next Half Century, EPRI, Palo Alto, CA: 2000. 1006787. EPRI, Palo Alto, CA. Griffiths-Sattenspiel, B., and Wilson, W. (2009). The carbon footprint of water. River Network, Portland. Grove, R. S., Faeder, E. J., Ospital, J., and Bean, R. M. (1985). “Halogenated Compounds Discharged from a Coastal Power Plant.” Water chlorination: chemistry, environmental impact and health effects, Lewis Publishers, Chelsea, MI. GSS. (2013). 2010 Population and Housing Census National Analytical Report. Ghana Statistical Service, Accra, Ghana. Gumerman, R. C., Culp, R. L., and Hansen, S. P. (1979). Estimating Water Treatment Costs: Volume 1 Summary. Contract, Environmental Protection Agency, Cincinnati, Ohio. GWCL. (2013). “History of water supply in Ghana.” GWCL-Kpong. (2012). “Kpong Treatment Plant.” GWCL-Weija. (2012). “Weija Water Treatment Plant.” 74  Gyampoh, B. A., Idinoba, M., and Amisah, S. (2008). “Water Scarcity Under a Changing Climate in Ghana: Options for livelihoods adaptation.” Development, 51(3), 415–417. Haimes, Y., Matalas, N., Lambert, J., Jackson, B., and Fellows, J. (1998). “Reducing Vulnerability of Water Supply Systems to Attack.” Journal of Infrastructure Systems, 4(4), 164–177. Hammer, M. J., and Hammer, M. J. J. (2011). Water and Wastewater Technology. Prentice Hall, Upper Saddle River, N.J. Al-Harbi, K. M. A.-S. (2001). “Application of the AHP in project management.” International Journal of Project Management, 19(1), 19–27. Harris, A., Giunipero, L. C., and Hult, G. T. M. (1998). “Impact of Organizational and Contract Flexibility on Outsourcing Contracts.” Industrial Marketing Management, 27(5), 373–384. Heisler, J., Glibert, P. M., Burkholder, J. M., Anderson, D. M., Cochlan, W., Dennison, W. C., Dortch, Q., Gobler, C. J., Heil, C. A., Humphries, E., Lewitus, A., Magnien, R., Marshall, H. G., Sellner, K., Stockwell, D. A., Stoecker, D. K., and Suddleson, M. (2008). “Eutrophication and harmful algal blooms: A scientific consensus.” Harmful Algae, HABs and Eutrophication, 8(1), 3–13. Hendriks, E. (2014). “Fresh Water.” World Wildlife Fund, <http://www.wwf.ca/conservation/freshwater/> (Sep. 24, 2014). Herndon, A. (2013). “Energy Makes Up Half of Desalination Plant Costs: Study.” Bloomberg, <http://www.bloomberg.com/news/2013-05-01/energy-makes-up-half-of-desalination-plant-costs-study.html> (Sep. 3, 2014). Hickman, D. C. (1999). A chemical and biological warfare threat: USAF water systems at risk. USAF Counterproliferation Center, Air University. Husband, P. S., and Boxall, J. B. (2011). “Asset deterioration and discolouration in water distribution systems.” Water Research, 45(1), 113–124. Ishizaka, A., and Nemery, P. (2013). Multi-criteria Decision Analysis: Methods and Software. Wiley, Chichester, West Sussex, United Kingdom. Jurenka, R., Martella, S., and Rodriguez, R. (2001). “Water Treatment Primer for Communities in Need.” US Bureau of Reclamation. 75  Karagiannis, I. C., and Soldatos, P. G. (2008). “Water desalination cost literature: review and assessment.” Desalination, European Desalination Society and Center for Research and Technology Hellas (CERTH), Sani Resort 22–25 April 2007, Halkidiki, Greece European Desalination Society and Center for Research and Technology Hellas (CERTH), Sani Resort, 223(1–3), 448–456. Karikari, A. Y., and Ansa-Asare, O. D. (2006). “Physico-Chemical and microbial water quality assessment of Densu River of Ghana.” West African Journal of Applied Ecology, 10(1). Karikari, E. (1996). “Water Supply and Management in Rural Ghana: Overview and Case Studies.” Water Management in Africa and the Middle East: Challenges and Opportunities, E. Rached and D. Brooks, eds., IDRC, Ottawa, Ontario, Canada. Kayaga, S., Smout, I., and Al-Maskati, H. (2007). “Water demand management – shifting urban water management towards sustainability.” Water Science & Technology: Water Supply, 7(4), 49. Kehrig, H. A., Malm, O., Akagi, H., Guimarães, J. R., and Torres, J. P. (1998). “Methylmercury in fish and hair samples from the Balbina Feservoir, Brazilian Amazon.” Environmental Research, 77(2), 84–90. Klaver, I., and Mulkey, M. (2006). “Panel: Regulating China’s Water Resources: Perspectives, Concerns, and Responsibilities of Government, Business, and the Community.” Vermont Journal of Environmental Law, 8, 461. Kövári, J. (1984). “Considerations in the selection of sites for Aquaculture.” Inland Aquaculture Engineering, Food and Agriculture Organization, Rome, Italy, 5–7. Kuma, J. S., and Ashley, D. N. (2008). “Runoff Estimates into the Weija Reservoir and Its Implications for Water Supply to Accra, Ghana.” Journal of Urban and Environmental Engineering, <http://www.redalyc.org/resumen.oa?id=283221787001> (Sep. 18, 2014). Kumpel, E. (2013). “Water Quality and Quantity in Intermittent and Continuous Piped Water Supplies in Hubli-Dharwad, India.” Ph.D., University of California, Berkeley, United States -- California. LaBudde, G. (2009). NID Regional Water Supply Project: Technical Memorandum Water Treatment Plant Siting. Nevada Irrigation District/City of Lincoln, Rocklin, CA, 38–41. Lafontaine, H. (2012). “Integrated Decision Making Tool for Rural Irrigation Applications: A Case in Southern India.” M.A.Sc., University of Windsor (Canada), Canada. 76  Lai, V. S., Trueblood, R. P., and Wong, B. K. (1999). “Software selection: a case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system.” Information & Management, 36(4), 221–232. Leuven, L. J. V. (2011). “Water/Wastewater Infrastructure Security: Threats and Vulnerabilities.” Handbook of Water and Wastewater Systems Protection, Protecting Critical Infrastructure, R. M. Clark, S. Hakim, and A. Ostfeld, eds., Springer New York, 27–46. LLOYD’S. (2010). global water scarcity: Risks and challenges for business. Lloyd’s 360° Risk Insight. Lusakatimes. (2011). “Zambia : LWSC seeks partnership in water management.” MacPhee, M. J., Chales, G. E., and Cornwell, D. A. (2001). Treatment of Arsenic Residuals from Drinking Water Removal Processes. Contract, Environmental Protection Agency, Washington, D.C. Masuda, T. (1990). “Hierarchical sensitivity analysis of priority used in analytic hierarchy process.” International Journal of Systems Science, 21(2), 415–427. McKenzie, D., and Ray, I. (2009). “Urban water supply in India: status, reform options and possible lessons.” Water Policy, 11(4), 442. Ménard, C., and Clarke, G. R. G. (2000). A Transitory Regime: Water Supply in Conakry, Guinea. World Bank, Development Research Group, Public Economics and Regulations and Competition Policy. Modernghana News. (2011). “Kyebi Water Plant Shut Down As A Result Of Evil Effects Of Galamsey.” modernghana.com, <http://www.modernghana.com/news/343119/1/kyebi-water-plant-shut-down-as-a-result-of-evil-ef.html> (Nov. 6, 2014). Nadja, K., and Karlheinz, K. (n.d.). Analytic Hierarchy Process (AHP). 1–6. Nakashima, M. (1982). “A Capacity Expansion Planning Method for a Regional Water Supply System (illinois).” Ph.D., University of Illinois at Urbana-Champaign, United States -- Illinois. Narayan, G. P., McGovern, R. K., Zubair, S. M., and Lienhard V, J. H. (2012). “High-temperature-steam-driven, varied-pressure, humidification-dehumidification system coupled with reverse osmosis for energy-efficient seawater desalination.” Energy, 7th Biennial International Workshop “Advances in Energy Studies,” 37(1), 482–493. 77  Novak, J. T. (1995). Assessment of Cropland Application of Water Treatment Residuals. Amer Water Works Assn, Denver, CO. NRCS. (2014). “Cultural Resources.” Natural Resources Conservation Service, <http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/nra/cultural/>. Ntengwe, F. W. (2004). “The impact of consumer awareness of water sector issues on willingness to pay and cost recovery in Zambia.” Physics and Chemistry of the Earth, Parts A/B/C, Water, Science, Technology and Policy Convergence and Action by All (A Meeting Point for Action leading to Sustainable Development), 29(15–18), 1301–1308. Odame-Ababio, K. (2003). “Putting Integrated Water Resource Management Into Practice–Ghana’s Experience. Chapter 13.” Preparing for the Next Generation of Watershed Management Programmes and Projects, Food and Agriculture Organisation, Rome, 157–166. Okeola, O. G., and Sule, B. F. (2012a). “Evaluation of management alternatives for urban water supply system using Multicriteria Decision Analysis.” Journal of King Saud University - Engineering Sciences, 24(1), 19–24. Okeola, O. G., and Sule, B. F. (2012b). “Evaluation of management alternatives for urban water supply system using Multicriteria Decision Analysis.” Journal of King Saud University - Engineering Sciences, 24(1), 19–24. Okyere, C. Y., YACOUBA, Y., and GILGENBACH, D. (2013). “The Problem of Annual Occurrences of Floods in Accra: An Integration of Hydrological, Economic and Political Perspectives.” Theoretical and Empirical Researches in Urban Management, 8(2), 45–79. Pasiouras, F., Gaganis, C., and Zopounidis, C. (2010). “Multicriteria classification models for the identification of targets and acquirers in the Asian banking sector.” European Journal of Operational Research, 204(2), 328–335. Peniwati, K., and Brenner, W. (2008). “Multi-decisions rating model: Establishing rescue policies for Regional Drinking Water Companies (PDAMs) in Indonesia.” European Journal of Operational Research, 186(3), 1127–1136. Piper, S., and Martin, W. (1999). “Assessing the financial and economic feasibility of rural water system improvements.” Impact Assessment and Project Appraisal, 17(3), 171–182. Plappally, A. K., and Lienhard V, J. H. (2012). “Energy requirements for water production, treatment, end use, reclamation, and disposal.” Renewable and Sustainable Energy Reviews, 16(7), 4818–4848. 78  Post, V. E. A. (2005). “Fresh and saline groundwater interaction in coastal aquifers: Is our technology ready for the problems ahead?” Hydrogeology Journal, 13(1), 120–123. Postawa, A., and Hayes, C. (2013). Best Practice Guide on the Control of Iron and Manganese in Water Supply. IWA Publishing. Postel, S. L., Daily, G. C., and Ehrlich, P. R. (1996). “Human Appropriation of Renewable Fresh Water.” Science, 271(5250), 785–788. Primus. (2011). “Mapping: (No) Big Deal » Blog Archive » ICT and the water sector: Water problems in Kibera and Mathare slums.” PURC. (2004). Annual Report 2004. Public Utility and Regulatory Commission. Qi, Q., and Luo, G. (2007). “Constructing Enterprise Flexibility Competence on the Basis of Product Platform and Collaborative Product Commerce.” 2007 IEEE International Conference on Automation and Logistics, 2857–2861. Raje, D. V., Dhobe, P. S., and Deshpande, A. W. (2002). “Consumer’s willingness to pay more for municipal supplied water: a case study.” Ecological Economics, 42(3), 391–400. Reardon, D. J., Newell, P. L., and Roohk, D. L. (2012). “Recycling Conserves Both Water and Energy.” Proceedings of the Water Environment Federation, 2012(13), 3557–3564. Reddy, K. V., and Ghaffour, N. (2007). “Overview of the cost of desalinated water and costing methodologies.” Desalination, 205(1–3), 340–353. Rogers, C. S. (2008). “Economic costs of conventional surface-water treatment: A case study of the Mcallen northwest facility.” Master’s thesis, Texas A&M University, Texas, USA. Rosenberg, D. E., Talozi, S., and Lund, J. R. (2008). “Intermittent water supplies: challenges and opportunities for residential water users in Jordan.” Water International, 33(4), 488–504. Saad, L. (2009). “Water pollution Americans’ top green concern.” Gallup, Research, <http://www.gallup.com/poll/117079/water-pollution-americans-top-green-concern.aspx>. Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. Mcgraw-Hill, New York ; London. Saaty, T. L. (1986). “Axiomatic Foundation of the Analytic Hierarchy Process.” Management Science, 32(7), 841–855. 79  Saaty, T. L., and Vargas, L. (1994). Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. Rws Pubns, Pittsburgh, PA. Saaty, T. L., and Vargas, L. G. (2001). Models, methods, concepts & applications of the analytic hierarchy process. Kluwer Academic Publishers, Boston. Saaty, T. L., and Vargas, L. G. (2012). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer, New York. Sargaonkar, A. P., Rathi, B., and Baile, A. (2011). “Identifying potential sites for artificial groundwater recharge in sub-watershed of River Kanhan, India.” Environmental Earth Sciences, 62(5), 1099–1108. Sauvet-Goichon, B. (2007). “Ashkelon desalination plant — A successful challenge.” Desalination, EuroMed 2006 Conference on Desalination Strategies in South Mediterranean Countries, 203(1–3), 75–81. Sayyadi, G., and Awasthi, A. (2013). “AHP-Based Approach for Location Planning of Pedestrian Zones: Application in Montréal, Canada.” Journal of Transportation Engineering, 139(2), 239–246. Schwarzenbach, R. P., Egli, T., Hofstetter, T. B., von Gunten, U., and Wehrli, B. (2010). “Global Water Pollution and Human Health.” Annual Review of Environment and Resources, 35(1), 109–136. Seckler, D., Amarasinghe, U., Molden, D., de Silva, R., and Barker, R. (1998). World Water Demand and Supply, 1990 to 2025: Scenarios and Issues. International Water Management Institute, Colombo, Sri Lanka. Seger, K. A. (2003). Utility Security: The New Paradigm. Pennwell, Tulsa, Okla. SEPA. (2006). 2005 report on the state of the environment in China. State Environmental Protection Administration, Beijing, China. Sheth, N. N. (2009). “An energy security and climate change model for residential space heating: An analytic hierarchy process approach.” M.A.Sc., Dalhousie University (Canada), Canada. Singh, B., Ramasubban, R., Bhatia, R., Briscoe, J., Griffin, C. C., and Kim, C. (1993). “Rural water supply in Kerala, India: How to emerge from a low-level equilibrium trap.” Water Resources Research, 29(7), 1931–1942. 80  Spokane County. (2003). Evaluation Criteria for Treatment Plant Site Selection. Spokane County, Washington. Stern, N. (2007). The Economics of Climate Change: The Stern Review. Cambridge University Press, Cambridge, UK ; New York. Stoler, J., Fink, G., Weeks, J. R., Otoo, R. A., Ampofo, J. A., and Hill, A. G. (2012). “When urban taps run dry: sachet water consumption and health effects in low income neighborhoods of Accra, Ghana.” Health & Place, 18(2), 250–262. Stoler, J., Weeks, J. R., and Fink, G. (2012). “Sachet drinking water in Ghana’s Accra-Tema metropolitan area: past, present, and future.” Journal of water, sanitation, and hygiene for development : a journal of the International Water Association, 2(4). Sundaray, S. K., Panda, U. C., Nayak, B. B., and Bhatta, D. (2006). “Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of the Mahanadi river–estuarine system (India) – a case study.” Environmental Geochemistry and Health, 28(4), 317–330. Thompson, J., and Cairncross, S. (2002). “Drawers of water: assessing domestic water use in Africa.” Bulletin of the World Health Organization, 80(1), 61–62. Törnqvist, R., Jarsjö, J., and Karimov, B. (2011). “Health risks from large-scale water pollution: trends in Central Asia.” Environment International, 37(2), 435–442. Torres, J. P. M., Pfeiffer, W. C., Markowitz, S., Pause, R., Malm, O., and Japenga, J. (2002). “Dichlorodiphenyltrichloroethane in soil, river sediment, and fish in the Amazon in Brazil.” Environmental Research, 88(2), 134–139. Totsuka, S. N., Trifunovic, N., and Vairavamoorthy, K. (2004). “Intermittent urban water supply under water starving situations.” People-Centered Approaches to Water and Environmental Sanitation, Vientiane, Lao PDR. Triantaphyllou, E. (2000a). Multi-criteria decision making methods: a comparative study. Kluwer Academic Publishers, Dordrecht; Boston, Mass. Triantaphyllou, E. (2000b). Multi-Criteria Decision Making Methods: A Comparative Study. Springer. Triantaphyllou, E. (2000c). Multi-criteria Decision Making Methods: A Comparative Study. Springer, Dordrecht ; Boston, Mass. 81  Tsou, C. A., Mitten, L. G., and Russell, S. O. (1973). “Search Technique for Project Sequencing.” Journal of the Hydraulics Division, 99(5), 833–839. UN. (2011). “Water Quality.” Policy Brief, <http://www.unwater.org/downloads/waterquality_policybrief.pdf>. UNDESA. (2014). “International Decade for Action ‘Water for Life’ 2005-2015. Focus Areas: Water quality.” Water for Lif Decade, <http://www.un.org/waterforlifedecade/quality.shtml> (Sep. 18, 2014). UNEP. (2014). “Watershed degradation increases water treatment costs.” United Nations Environment Programme: Democratic Republic of the Congo, <http://postconflict.unep.ch/congo/en/content/watershed-degradation-increases-water-treatment-costs>. United Nations. (2004). “The United Nations on World Population in 2300.” Population and Development Review, 30(1), 181–187. Upadhyay, R., Dasgupta, N., Hasan, A., and Upadhyay, S. K. (2011). “Managing water quality of River Yamuna in NCR Delhi.” Physics and Chemistry of the Earth, Parts A/B/C, Science, Technology and Policy for Water Pollution Control at the Watershed Scale: Current issues and future challenges, 36(9–11), 372–378. Uusitalo, K. (2002). “An Evaluation of Urban Water Systems using Environmental Sustainability Indicators - A case study in Adenta, Ghana.” Chalmers University of Technology. Vaidya, O. S., and Kumar, S. (2006). “Analytic hierarchy process: An overview of applications.” European Journal of Operational Research, 169(1), 1–29. Vairavamoorthy, K., Gorantiwar, S. D., and Mohan, S. (2007). “Intermittent Water Supply under Water Scarcity Situations.” Water International, 32(1), 121–132. Varis, O., and Vakkilainen, P. (2001). “China’s 8 challenges to water resources management in the first quarter of the 21st Century.” Geomorphology, The Yangtze River: an Introduction, 41(2–3), 93–104. VEI. (2012). Management Contract 2006-2011 for urban water supply in Ghana A Partnership – in and for – development. Vitens Evides International. Verhaert, V., Covaci, A., Bouillon, S., Abrantes, K., Musibono, D., Bervoets, L., Verheyen, E., and Blust, R. (2013). “Baseline levels and trophic transfer of persistent organic pollutants in 82  sediments and biota from the Congo River Basin (DR Congo).” Environment International, 59, 290–302. Vince, F., Marechal, F., Aoustin, E., and Bréant, P. (2008). “Multi-objective optimization of RO desalination plants.” Desalination, European Desalination Society and Center for Research and Technology Hellas (CERTH), Sani Resort 22 –25 April 2007, Halkidiki, Greece European Desalination Society and Center for Research and Technology Hellas (CERTH), Sani Resort, 222(1–3), 96–118. VRA. (2014). “Akosombo Hydro Plant.” Our Mandate, <http://www.vraghana.com/our_mandate/akosombo_hydro_plant.php> (Sep. 18, 2014). Vreeburg, J. H. G., Schippers, D., Verberk, J. Q. J. C., and van Dijk, J. C. (2008). “Impact of particles on sediment accumulation in a drinking water distribution system.” Water Research, 42(16), 4233–4242. Wangnick, K. (2002). Wangnick, K  2  2  DA Worldwide Desalting Plants  nventory   :, 2  2  Produced by Wangnick Consulting for the International Desalination Association, Gnarrenburg, Germany, 55–59. Water Resources Commission. (2014). “Basins.” Water Resources Commission of Ghana, <http://www.modernghana.com/news/343119/1/kyebi-water-plant-shut-down-as-a-result-of-evil-ef.html> (Aug. 31, 2014). WHO. (2011). “Guidelines for Drinking-water Quality-4th ed.” World Health Organisation. WHO, and UNICEF. (2010). “WHO | Progress on sanitation and drinking-water 2010 update.” WHO, <http://www.who.int/water_sanitation_health/publications/9789241563956/en/> (Aug. 15, 2014). WMO. (1997). Comprehensive assessment of the freshwater resources of the world. World Meteorological Organization, Stockholm, Sweden. World Bank. (1995). World development report 1    : workers in an integrating world. Oxford University Press Inc, New York. World Bank. (1997). Water management in roadworks design in the Sahel. The World Bank, 1–83. World Bank. (2000). World development report 2000/2001 - attacking poverty : overview. The World Bank, 1–24. 83  World Factbook. (2014). “Ghana.” The World Factbook, <https://www.cia.gov/library/publications/the-world-factbook/geos/gh.html>. WRC. (2012). “National Integrated Water Resources Management (IWRM) Plan.” Water Resources Commission. Wu, Y., and Chen, J. (2013). “Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China.” Ecological Indicators, 32, 294–304. Yoon, K. P., and Hwang, C.-L. (1995). Multiple Attribute Decision Making: An Introduction. SAGE. York, R., and Foster, M. (2005). Issues and Environmental Impacts Associated with Once-Through  ooling at  alifornia’s  oastal Power Plants. California Energy Commission, Sacramento, California. Yun, T. I., Gabelich, C. J., Cox, M. R., Mofidi, A. A., and Lesan, R. (2006). “Reducing costs for large-scale desalting plants using large-diameter, reverse osmosis membranes.” Desalination, Selected paper from the 10th Aachen Membrane Colloquium 10th Aachen Membrane Colloquium, 189(1–3), 141–154. Yurdakul, M. (2004). “AHP as a strategic decision-making tool to justify machine tool selection.” Journal of Materials Processing Technology, 146(3), 365–376. Yurdakul, M., and İç, Y. T. (2004). “AHP approach in the credit evaluation of the manufacturing firms in Turkey.” International Journal of Production Economics, 88(3), 269–289. Zhang, R., Zhang, X., Yang, J., and Yuan, H. (2013). “Wetland ecosystem stability evaluation by using Analytical Hierarchy Process (AHP) approach in Yinchuan Plain, China.” Mathematical and Computer Modelling, 57(3–4), 366–374. Zoppou, C. (2001). “Review of urban storm water models.” Environmental Modelling & Software, 16(3), 195–231. 84  Appendix A: Research Questionnaire In this survey, you are asked to rank the various criteria and sub-criteria in a pairwise manner, and relative to the three established alternatives, consistent with the AHP methodology. The ranking will be done using the gradation scale in table 1 above. Every respondent is to do the ranking based on individual objectivity, and professional and technical expertise. Different people will most likely respond in different ways and that is where the beauty of decision-making comes in. I would illustrate with examples on how to proceed with the questionnaires. Example 1. Compare the relative importance of the following criteria with respect to water supply capacity expansion.   Score 1  Economic 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Environmental                   Score 2 Economic 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Technical                   In score 1, the respondent, in his opinion and expertise, ticked box 3 to the right towards environmental, indicating that the environmental criteria is marginally stronger than the economic criteria. In score 2, he indicated that both the economic and the technical criteria are equally important by ticking box 1. 85   Example 2. Compare the relative preference of the following with respect to energy cost.  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                   Between the Weija and Kpong plants the respondent ticked box 5 towards Weija, indicating that on the issue of energy Weija is strongly preferred because it consumes less. Between Weija and Teshie desal., the respondent ticked box 7 indicating that energy-wise the Weija plant is strongly preferren to Teshie desal`. Between Kpong plant and Teshie desal. the respondent ticked box 3, indicating that the Kpong plant is marginally preferred to the Teshie desal. on matters of energy. Note: For your decisions to be consistent, note that Teshie desal cannot be preferred to Weija, when Weija is preferred to Kpong and Kpong is also preferred to Teshie desal. Thus if A>B and B>C then C cannot be greater than A. Now let`s begin answering the questions. 86   1. Compare the relative importance of the following criteria with respect to water supply capacity expansion.   Economic 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Environmental                   Economic 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Socio-cultural                    Economic 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Technical                   Environmental  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Socio-cultural                    Environmental 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Technical                   Socio-cultural 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Technical                  87    2. Compare the relative importance of the following sub-criteria with respect to the environmental criterion. Res. Quantity 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Res. Quality                   Res. Quantity 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Waste Mgt.                   Res. Quantity 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Ecological Imp.                   Res. Quality  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Waste Mgt.                   Res. Quality  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Ecological Imp.                   Waste Mgt.  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Ecological Imp.                  88   3. Compare the relative importance of the following sub-criteria with respect to the technical criterion Operational Flexibility 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Expertise of Employees.                   Operational Flexibility 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Land Area                   Operational Flexibility 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Storm Drainage                   Expertise of Employees  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Land  Area                   Expertise of Employees  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Storm Drainage                   Land Area  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Storm Drainage                  89    4. Compare the relative importance of the following sub-criteria with respect to the socio-cultural criterion. Treated Water Aesthetics 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Security of Transmission                   Treated Water Aesthetics 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Acceptability                   Treated Water Aesthetics 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Cultural Res.                   Security of Transmission 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Acceptability                   Security of Transmission 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Cultural Res.                    Acceptability 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Cultural Res.                  90    5.  Compare the relative importance of the following sub-criteria with respect to the economic criterion.  Energy Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9   Chemical Cost                   Energy Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Maintenance Cost                   Energy Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Construction Cost                    Energy Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Financial Viability                   Chemical Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Maintenance Cost                   Chemical Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Construction Cost                   91   Chemical Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Financial Viability                   Maintenance Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Construction Cost                    Maintenance Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Financial Viability                   Construction Cost 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Financial Viability                    6. Compare the relative preference of the following with respect to energy cost.  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                  92    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    7. Compare the relative preference of the following with respect to chemical cost.  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    8. Compare the relative preference of the following with respect to maintenance cost.  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                  93    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    9. Compare the relative preference of the following with respect to construction cost.  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                     94   10. Compare the relative preference of the following with respect to Financial viability  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    11. Compare the relative preference of the following with respect to Resource quantity  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                   95   Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    12. Compare the relative preference of the following with respect to resource quality (treatability)  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    13. Compare the relative preference of the following with respect to treatment waste management  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                   96   Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    14. Compare the relative preference of the following with respect to ecological impact  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                     15. Compare the relative preference of the following with respect to Operational Flexibility 97   Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    16. Compare the relative preference of the following with respect to expertise of employees  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                   98   17. Compare the relative preference of the following with respect to land area  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    18. Compare the relative preference of the following with respect to storm drainage  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                   99   Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    19. Compare the relative preference of the following with respect to Treated Water Aesthetics  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                     20. Compare the relative preference of the following with respect to security of transmission  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                  100    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    21. Compare the relative preference of the following with respect to acceptability  Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    22. Compare the relative preference of the following with respect to cultural resource 101   Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Kpong Plant                    Weija Plant 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                    Kpong Plant  9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9  Teshie Desal.                          102  103   


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items