xxvii
SURFACE AND GROUNDWATER QUALITY OF ENUGU URBAN AREA
By
Mary Nkiru Ezemonye B.Sc (U.N.N), M.Sc (U.N.N) (PG/Ph.D/03/35002)
A Thesis submitted to the School of Postgraduate Studies and the Department of Geography, University of Nigeria, Nsukka in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy
Department of Geography, University of Nigeria, Nsukka.
2009
xxviii
CERTIFICATION
Mrs Mary Nkiru Ezemonye, a postgraduate student in the Department of Geography, specialising in Hydrology and Water Resources, has satisfactorily completed the requirement for course and research work for the degree of Doctor of Philosophy (Ph.D) in Geography. The work embodied in this thesis is original and has not been submitted in part or full for any other diploma or degree of this or any other university.
______Prof. R.N.C. Anyadike (External Examiner) (Supervisor)
______Dr. I.A. Madu Head, Department of Geography)
2009 xxix
ABSTRACT
The central aim of this study is to determine the quality of the surface and ground water and to determine the Water Quality Index (WQI) cum the prevalent water related diseases identifiable in Enugu urban area where rapid population growth has not been matched by development of facilities. The study used primary data: water samples from rivers and wells and patient records from fifty hospitals in Enugu. Ambient monitoring of the water sources was observed for one year. Values for 16 selected physical, chemical and biological parameters were determined from laboratory analysis and these were compared to the World Health Organisation (WHO) Guideline for drinking water. Twelve parameters were within acceptable limits; while four exceeded the WHO maximum permissible levels. It was observed that all the rivers and wells sampled had very high bacteriological contaminations. On the bases of values obtained per parameter, seasonal and spatial variations were observed to exist between rivers and wells. The WQI obtained for the rivers and wells utilizing nine of the sampled parameters showed that generally, there were of average quality i.e. between 50 and 67. In some months WQI ranging from 35 to 47 were obtained indicating that there were months the water sources recorded qualities that were just fair. Water related diseases were treated in all the sampled hospitals in the urban area. The four major water related diseases were detected while the wards showed variations in seasonal prevalence patterns. To ensure the maintenance of already existing water quality and to reduce the rate of further deterioration of the rivers and wells, it was suggested that the National Water Policy should be reviewed and the overlap of functions of the Ministries mandated to manage the water quality properly redefined. There is a dare need to monitor the water bodies, create data base and utilize them in the management of water quality. The enforcement of existing laws needs better planning so as to achieve compliance to set standards. Community involvement in water quality management is also advocated for. This will be achieved through formal and informal education. The need for sensitization of the populace as regards sterilization of all sources of water abstracted can not be over emphasized.
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LIST OF FIGURES
FIGURES PAGE Fig 1 Location of Enugu urban in Enugu state……………. 13 Fig 2 Enugu urban area showing the L.G.As………………….. 14 Fig 3 Geological map of Enugu urban area…………………… 15 Fig 4 Map of Enugu showing the rivers……………………… 16 Fig 5 Soils of Enugu urban area………………………………. 24 Fig 6 Map of Enugu showing major wards……………………. 25 Fig 7 Map of Enugu showing sample sites……………………. 30 Fig 8 Comparison of river temperatures to WHO’s MPL ……… 48 Fig 9 Comparison of river pH to WHO’s MPL ……. ……………..48 Fig 10 Comparison of river turbidity levels to WHO’s MPL ……… 50 Fig 11 Comparison of river total dissolved solids to WHO’s MPL ….. 51 Fig 12 Comparison of river conductivity levels to WHO’s MPL ………53 Fig 13 Comparison of river total hardness levels to WHO’s MPL …… 54 Fig 14 Comparison of river dissolved oxygen levels to WHO’s MPL...57 Fig 15 Comparison of river biochemical oxygen demand levels to WHO’s MPL ……… …………………………………………… 57 Fig 16 Comparison of river phosphate levels to WHO’s MPL ……….60 Fig 17 Comparison of river sodium levels to WHO’s MPL ………... 60 Fig 18 Comparison of river sulphate levels to WHO’s MPL ……….. 62 Fig 19 Comparison of river iron levels to WHO’s MPL …………. 63 Fig 20 Comparison of river Ammonia levels to WHO’s MPL ……. . 65 Fig 21 Comparison of river calcium levels to WHO’s MPL ………… 68 Fig 22 Comparison of river nitrate levels to WHO’s MPL …………… 68 Fig 23 Comparison of river fecal coliform bacteria levels to WHO’s MPL ……………………………………………………………………69 Fig 24 Comparison of temperature of wells to WHO’s MPL ………. 71 Fig 25 Comparison of pH of wells to WHO’s MPL ………………. 72 Fig 26 Comparison of Turbidity levels of wells to WHO’s MPL …… 73 Fig 27 Comparison of total dissolved solids levels of wells to WHO’s MPL … 74 Fig 28 Comparison of conductivity of wells to WHO’s MPL ……….. 75 xxxi
Fig 29 Comparison of total hardness levels of wells to WHO’s MPL … 76 Fig 30 Comparison of dissolved oxygen levels of wells to WHO’s MPL …………………………………………………………77 Fig 31 Comparison of biochemical oxygen demand levels of wells to WHO’s MPL…………………………………………………….78 Fig 32 Comparison of Phosphate levels wells to WHO’s MPL …………79 Fig 33 Comparison of sodium levels of wells to WHO’s MPL …………80 Fig 34 Comparison of sulphate levels of wells to WHO’s MPL ……… 81 Fig 35 Comparison of Ammonia levels of wells to WHO’s MPL …… 82 Fig 36 Comparison of calcium levels of wells to WHO’s MPL ……… 83 Fig 37 Comparison of nitrate levels of wells to WHO’s MPL ………. 84 Fig 38 Comparison of well fecal coliform bacteria levels of WHO’s MPL………………………………………………………… 85 Fig 39 Rainy season temperature variation pattern of the rivers……. 88 Fig 40 Dry season temperature variation pattern of the rivers………. 88 Fig 41 Rainy season pH variation pattern of the rivers……………… 90 Fig 42 Dry season pH variation pattern of the rivers……………….. 90 Fig 43 Rainy season turbidity variation pattern of the rivers………... 92 Fig 44 Dry season turbidity variation pattern of the rivers…………... 92 Fig 45 Rainy season total dissolved solids variation pattern of the rivers 94 Fig 46 Dry season total dissolved solids variation pattern of the rivers 95 Fig 47 Rainy season conductivity variation pattern of the rivers…….. 95 Fig 48 Dry season conductivity variation pattern of the rivers………. 96 Fig 49 Rainy season hardness variation pattern of the rivers………… 97 Fig 50 Dry season hardness variation pattern of the rivers…………... 97 Fig 51 Rainy season dissolved oxygen variation pattern of the rivers…..98 Fig 52 Dry season dissolved oxygen variation pattern of the rivers…....99 Fig 53 Rainy season biochemical oxygen demand variation pattern of the rivers………………………………………………………. 101 Fig 54 Dry season biochemical oxygen demand variation pattern of the rivers……………………………………………………….. 101 Fig 55 Rainy season phosphate variation pattern of the rivers……. 102
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Fig. 56 Dry season phosphate variation pattern of the rivers ------102
Fig. 57 Rainy season sodium variation pattern of the rivers ------103
Fig. 58 Dry season sodium variation pattern of the rivers ------104
Fig. 59 Rainy season sulphate variation pattern of the rivers ------105
Fig. 60 Dry season sulphate variation pattern of the rivers ------105
Fig. 61 Rainy season iron variation pattern of the rivers ------107
Fig. 62 Dry season iron variation pattern of the rivers ------107
Fig. 63 Rainy season ammonia variation pattern of the rivers ------109
Fig. 64 Dry season ammonia variation pattern of the rivers ------109
Fig. 65 Rainy season calcium variation pattern of the rivers ------110
Fig. 66 Dry season calcium variation pattern of the rivers ------111
Fig. 67 Rainy season nitrate variation pattern of the rivers ------112
Fig. 68 Dry season nitrate variation pattern of the rivers ------112
Fig. 69 Rainy season fecal coliform bacteria variation pattern of the rivers -----114
Fig. 70 Dry season fecal coliform bacteria variation pattern of the rivers ------114
Fig. 71 Seasonal temperature pattern of the rivers ------116
Fig. 72 Seasonal pH pattern of the rivers ------117
Fig. 73 Seasonal turbidity pattern of the rivers ------119
Fig. 74 Seasonal total dissolved solids pattern of the rivers ------120
Fig. 75 Seasonal conductivity pattern of the rivers ------122
Fig. 76 Seasonal total hardness pattern of the rivers ------122
Fig. 77 Seasonal dissolved oxygen pattern of the rivers ------124
Fig. 78 Seasonal biochemical oxygen decimal pattern of the rivers ------125
Fig. 79 Seasonal phosphate pattern of the rivers ------127
Fig. 80 Seasonal sodium pattern of the rivers ------127 xxxiii
Fig. 81 Seasonal sulphate pattern of the rivers ------128
Fig. 82 Seasonal iron pattern of the rivers ------130
Fig. 83 Seasonal ammonia pattern of the rivers ------131
Fig. 84 Seasonal calcium pattern of the rivers ------132
Fig. 85 Seasonal nitrate pattern of the rivers ------133
Fig. 86 Seasonal nitrate fecal pattern of the rivers ------134
Fig. 87 Rainy season temperature variation pattern of the rivers ------135
Fig. 88 Dry season temperature variation pattern of the rivers ------135
Fig. 89 Rainy season pH variation pattern of the rivers ------136
Fig. 90 Dry season pH variation pattern of the rivers ------137
Fig. 91 Rainy season turbidity variation of the rivers ------138
Fig. 92 Dry season turbidity variation of the rivers ------138
Fig. 93 Rainy season total dissolved solids variation of the rivers ------139
Fig. 94 Dry season total dissolved solids variation of the rivers ------140
Fig. 95 Rainy season conductivity variation of the rivers ------141
Fig. 96 Dry season conductivity variation of the rivers ------141
Fig. 97 Rainy season total hardness variation pattern of the rivers ------142
Fig. 98 Dry season total hardness variation pattern of the rivers ------143
Fig. 99 Rainy season dissolved oxygen variation pattern of the rivers------144
Fig. 100 Dry season dissolved oxygen variation pattern of the rivers ------145
Fig. 101 Rainy season biochemical oxygen demand variation pattern of the rivers -146
Fig. 102 Dry season biochemical oxygen demand variation pattern of the rivers ---147
Fig. 103 Rainy season phosphate variation pattern of the wells------148
Fig. 104 Dry season phosphate variation pattern of the wells ------148
Fig. 105 Rainy season sodium variation pattern of the wells ------149 xxxiv
Fig. 106 Dry season sodium variation pattern of the wells ------150
Fig. 107 Rainy season sulphate variation pattern of the wells ------151
Fig. 108 Dry season sulphate variation pattern of the wells ------151
Fig. 109 Rainy season ammonia variation pattern of the well ------153
Fig. 110 Dry season ammonia variation pattern of the wells ------153
Fig. 111 Rainy season nitrate variation pattern of the wells ------154
Fig 112 Dry season nitrate variation pattern of the wells ------155
Fig. 113 Rainy season fecal variation pattern of the wells------156
Fig. 114 Dry season fecal variation pattern of the wells ------156
Fig. 115 Seasonal temperature pattern of the wells ------159
Fig. 116 Seasonal pH pattern of the wells ------160
Fig. 117 Seasonal turbidity pattern of the wells ------161
Fig. 118 Seasonal total dissolved solid pattern of the wells------162
Fig. 119 Seasonal conductivity pattern of the wells ------163
Fig. 120 Seasonal hardness pattern of the wells ------164
Fig. 121 Seasonal dissolved oxygen pattern of the wells ------165
Fig. 122 Seasonal pattern of biochemical oxygen demand of the wells ------166
Fig. 123 Seasonal phosphate pattern of the wells ------167
Fig. 124 Seasonal sodium pattern of the wells ------168
Fig. 125 Seasonal sulphate pattern of the wells ------169
Fig. 126 Seasonal ammonia pattern of the wells ------170
Fig. 127 Seasonal nitrate pattern of the wells ------171
Fig. 128 Seasonal fecal coliform bacteria pattern of the wells ------172
Fig. 129 January water-related diseases prevalence pattern in the Enugu urban--197
Fig. 130 February water-related prevalence pattern in the Enugu urban ------198 xxxv
Fig. 131March water-related diseases prevalence pattern in the Enugu urban ----198
Fig. 132 April water-related diseases prevalence pattern in the Enugu urban ---199
Fig. 133 May water- related diseases prevalence pattern in the Enugu urban ----200
Fig. 134 June water- related diseases prevalence pattern in the Enugu urban ----201
Fig. 135 July water- related diseases prevalence pattern in the Enugu urban -----201
Fig. 136 August water- related diseases prevalence pattern in the Enugu urban--202
Fig.136 September water- related diseases prevalence pattern in the Enugu urban------
------203
Fig137 October water- related diseases prevalence pattern in the Enugu urban--203
Fig139 November water- related diseases prevalence pattern in the Enugu urban------
------204
Fig. 140 December water- related diseases prevalence pattern in the Enugu urban------
------205
Fig. 141 Monthly percentage of water-related diseases in Enugu urban ------206
Fig. 142 transmission routes of water-borne disease in Enugu urban ------207
Fig. 143 Seasonal pattern of water-borne diseases in Enugu urban ------219
Fig. 144 Seasonal pattern of water-washed disease in Enugu urban ------232
Fig. 145 Seasonal pattern of water-based diseases in Enugu urban ------236
Fig. 146 Seasonal pattern water-related vector diseases in Enugu urban ------250
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CHAPTER I INTRODUCTION 1 .1 Background of the Study. Water, a colorless, tasteless, and odorless liquid is one of the most important natural resources, solely because it has no other substitute and without it, life is impossible. Man can exist for many days; even weeks without food but cannot survive for more than two or three days without water. Water is thus an essential raw material for human life and the presence of a reliable source of water is a very important factor in the establishment and smooth running of any community. When water is absent or scarce people would have to adopt a life style, which requires moving from place to place in search of water especially as the available water supply gets exhausted or the quality becomes compromised. In an ideal situation, water of good quality should be readily available for consumption by each household. In the same vein the taps should run on hourly and daily basis such that water when ever it is needed can be utilized. This is because access to safe drinking water is essential to health, a basic human requirement and a component for health protection. This is why the United Nations General Assembly declared the period from 2005 to 2015 as the International Decade for Action, Water For Life. At present, regular water supply is not the situation in Nigeria. Water supply is grossly inadequate. The absolute and relative scarcity of water supplied in urban areas of developing countries is further compounded by the inequality of water supply within the urban areas. As has been identified by Ezenwaji (2003), water supplies in urban areas are at two extremes. The two extremes are:- 1 .The high-income district where the rich and economically well/off live and virtually every water consumer has an in-house connection. 2 .The low income district where households demand low quantity of water and lack in-house connection. In the high-income district, the quantity of water demanded is very high, while the quantity supplied is low. Also the low-income districts are supplied with little or nothing. This definitely leaves much to be desired as there is always a big difference between the amount of water supplied to the urban rich and the urban poor. xxxvii
The quantity of water therefore readily available at least effort to households in developing countries of Africa is usually inadequate and of very low quality. This is because the populace tends to depend on and to use water abstracted from very poor quality sources such as ponds, flood waters and highly polluted rivers in times of scarcity. An earlier estimate by Tebutt (1983) indicated that as many as 200 million people are without safe water supply and adequate sanitation. A World Bank report of 1996 specified that more than five million people in developing countries of the world do not have access to safe and potable water supplies. And Cech (2005) is also of the opinion that 1.1 billion people were still using water from unimproved sources in sub- Sahara Africa and 42% of the population is still without potable water supply. The water supply situation has thus hardly improved over the years. Instead water inadequacy has continued to prevail, while the residents of these urban areas spend long hours in search of water. A lot of money is also spent to purchase water some of which are of highly compromised quality. The implication of the above facts, as has been observed by Agberemi (2003), is that over 200,000 deaths occur annually due to water and sanitation related diseases. It is well known that clean water and adequate sanitation are pre-requisites for a healthy living. The links between water quality and health risks are also well established .Where potable and safe water are unavailable water-related diseases will continue to increase at a frightening rate and a lot of human activities will be unable to take place. Safe and potable water availability is thus a very critical factor in all forms of socio-economic development of any country. Its unavailability, will limit progress to a very large extent. The challenge now is not only the problem of obtaining minimum quantity of water necessary to sustain life; rather it is also that of the quality of water available(WHO, 2002).The value of water is a function of the water quality. However, human population is currently pressing against the limits of available water resources in many parts of the world such that the quality of water is put at a risk. Unless very efficient and effective measures are put in place and taken, the quality will continue to deteriorate. Even if the United Nations Millennium Development Goal which aims to cut the proportion of those without safe access to water by half is met, many will still perish in the next 5 to 10 years if the quality of water sources that serve as intake sources are xxxviii not monitored. There is therefore always a major need for proper water monitoring and management as man must continue to use his water resources but cannot continue to compromise the quality of this resource. The compromise of water quality maybe possible only when the population of the area is very low and the resource is limitless. All too often, water is considered quite adequate for man as long as there has been no obvious mortality, which can be ascribed to known pollutants. Thus the degradation of water quality often passes unnoticed. To ensure sustainability of our urban water resources, we must ensure that the quality and quantity are properly monitored. Whether our water resources will provide the required services depends on how well we employ quality monitoring as a management tool. Water quality is the physical, chemical and biological characteristics of water. Water quality monitoring is a fundamental tool in the management and planning of water. It can be used to define existing water quality status, detect trends, or establish causes and sources of water quality problems that serve management needs (Mbajiogu, 2003).Water quality monitoring gives rise to more information driven management programmes that can be implemented. It is also necessary to be able to enforce laws developed on the basis of water quality. To even evaluate the efficiency of any management programmes instituted on the bases of existing water quality, further quality monitoring is a needed step. It is clear that ensuring adequate water supply will necessitate continuous monitoring of water quality of our urban areas as urbanization increases. Water monitoring offers a measure of hope for identifying, planning and managing our water resources.
1.2 Statement of the Research Problem. The search for fresh water to drink, to bath in, to irrigate crops etc, etc is as old as civilization. Across the ages, cities have thrived where the supply is abundant and have collapsed in the face of water scarcity. It is noteworthy that the amount of water on earth is constant and cannot be increased or decreased. For land-based forms of life however about 97% of water is not available for consumption because of its salinity(Davie,2002).Even the 3% that is fresh water often is not readily available for human use as much of it is either locked in glacial ice or is stored underground. It is also important to point out that water as a geographical entity is not distributed uniformly over the surface of the earth. This xxxix uneven distribution of surface and groundwater means that many parts of the world exist without reliable sources of water. Man in every corner of the globe is however making increasing demands upon the water resources in his surrounding and thereby altering it. This demand is constantly increasing not only because of the rapid population growth, but also due to the increase in the standard of living. Despite the technological progress characterizing the modern era and the fact that most of the earth’s surface is covered by oceans, the availability of fresh water remains a pressing concern throughout the world. This is because water may be in abundance in an area, but safe water sources may not readily be accessible to the people as the unsafe nature of the water will make meeting supply difficult. In Nigeria, water supply for public consumption and use is the constitutional responsibility of the three tiers of government (i.e. the Federal, State and Local Governments).There are also supplementary supplies by private individuals necessitated by the inadequacy of supply by the governments. The water inadequacies stem from the fact that most of the water works, established before 1920, have experienced no expansion and are dysfunctional (Ibeziako, 1985; Anyadike and Ibeziako, 1987; Agberemi, 2003; Ezenwaji, 2003). This situation of dysfunctional water works has resulted in the old water works still supplying about 10-15% of the entire demand for many urban areas. Even with the external support to State governments over the years, full capacity functioning of water projects has remained unachieved in Nigerian urban centers. Thus many more people have been using the same amount of water/ facilities for different purposes. This means that urban population demands on its water resources have been on the increase, and the water quality has experienced remarkable changes. Thus obtaining water in Nigerian urban areas is becoming increasingly more of an issue of quality rather than just that of quantity available for use. Water shortages can occur not only from the standpoint of quantity, but also that of quality. This is especially true in situations where the quality of water is so poor that its utilization for any meaningful purpose is highly reduced or impossible. Enugu which is currently the capital of Enugu State of Nigeria has served as the Headquarters of Eastern Nigeria as well as the capital of the former East Central State, and Anambra State. It started as a mining town and has gradually become an important administrative, educational, commercial and industrial centre. Enugu has xl experienced and is still experiencing increased migration from the rural areas of Nigeria. This has resulted in increased population of fewer than 100 in 1909 to a population of 772,664 in 2006(Hair, 1962; National Population Commission, 2006) Rapid urbanization, industrialization and urban development with their attendant environmental problems have continued in Enugu and have created stress on water availability and quality. The supply situation has continued to deteriorate, and some sections of the urban area no longer receive water from the public water supply. In these sections of the town experiencing acute water supply shortage, the residents have resorted to intensive utilization of any available surface and ground waters. To ensure they meet their needs, the residents tend to compromise on standards. They utilize whatever quality of water is available. This utilization of compromised water has intensified incidents of water-related and induced diseases among the urban dwellers. A healthy environment is one in which the water quality supports and protects health. Ensuring adequate water supply and the protection of surface and ground waters of Enugu urban area will necessitate continuous monitoring of water quality as urbanization and industrialization increases. Poor water quality usually becomes a major constraint on development if not adequately considered within a given development programme. This is because water resource conditions are complementary to many other development inputs. It is this normally neglected aspect of water management/water quality monitoring that necessitated this study. This study is considered important because the quality of water and its suitability for use is a function of its physical, chemical and biological properties. Also reversing the damage done to any water resource is usually complicated and expensive. It is thus very important to minimize further harm through quality monitoring. One way of achieving this is by ascertaining what the quality of the urban waters is and taking measures to ensure that the quality is not further reduced. To even enlighten the populace on the state of their water bodies, enforce laws and evaluate the effectiveness of any management programmes developed, water quality monitoring is absolutely necessary. Water quality monitoring offers a measure of hope for planning and management of our water resources.
1.3 Aim and Objectives of the Study. xli
The aim of this study is to determine the quality of surface and ground waters of Enugu Urban in Enugu State. To achieve our stated aim, the following objectives have been set to i. Investigate the quality of surface and ground water sources in Enugu urban area in relation to the World Health Organization standard. ii. Determine seasonal and spatial quality variation patterns of the surface and ground water sources in the area of study. iii. Develop a water quality index for surface and ground waters of Enugu urban. iv. Identify the common water-related diseases prevalent in the study area. v. Suggest appropriate measures for improving and managing the quality of the water resources of the urban area.
1.4 Literature Review. Water is an essential element for survival. It is a vital resource in all spheres of human endeavor. For instance, a person needs to drink about three litres of fresh water per day in order to maintain adequate hydration. According to Bartram and Helmer (1996), aquatic ecosystems throughout the world are threatened or impaired by a diversity of pollutants as well as destructive landuse and water management practices. The contamination of drinking water has become a major challenge to the environmentalist and water resource managers in the rapidly developing countries. Also as indicated by Commission for Sustainable Development (1997), the world faces a worsening series of local and regional water problems. These problems intensify as rivers, groundwater and lakes are being severely contaminated by human, industrial and agricultural wastes. A growing number of regions according to Giles and Brown (1997) face increasing water stress because more people are polluting water and demanding more of it for various uses. Water quality is thus declining in many places as the resource is being damaged, in most cases irreparably by human socio-economic activities. Demand for fresh water however will continue to rise even as the water quality deteriorates. According to Robarts (1998), the world faces worsening water quality problems. Water quality is degraded as pollutants are added to water bodies. Fried (1975) defined water pollution as a phenomenon, which is the modification of the physical, xlii chemical and biological properties of water; restricting its use in various applications it normally plays a part. Novotny (2003) also expressed the view that water pollution can be defined as the introduction by man directly or indirectly of substances or energy into rivers and estuaries, which results in such deleterious effects as harm to living resources, hazardous to human health, hindrance to marine activities, impairment of quality of use of water and reduction of amenities. Strahler and Strahler (1974) also defined pollution as the artificially induced degradation of natural groundwater quality. A definition of the term water pollution usually reflects degradation in water quality. Water quality is usually measured in terms of the concentration of constituents in the water and it is classified relative to intended use. The concentration of the constituents simply expresses the status of water in physical, chemical and biological terms (Fried, 1975; Bartram and Helmer, 1996; Fishburne, 1999). Many factors have been suggested by authors as the reason for water quality degradation. For instance, Mitchell (1989) is of the opinion that increased volume of industrial and domestic waste pollutes the water course. Strahler and Strahler (1974) are of the view that rapid urbanization makes radical physical changes in water flow and it also pollutes surface water with a large variety of wastes. Leaky (1970) attributed water pollution to increase in population and rapid urbanization while Ogboru (2001) warned that the greatest hazard in Nigeria today is that of water borne diseases whose neglect he attributed to the ignorance of people about water quality. Egboge (1971) states that stream water runoff is a major source of water pollution. This non-point source also pollutes urban areas. Water pollution sources can thus be either point or non-point sources. Various studies have been carried out by researchers and some of these are aimed at river quality assessment and monitoring in various parts of the world. Some have also developed models for predicting river quality downstream for better water management and control. Some others have targeted understanding the spatial and temporal changes of water quality. Thoman (1972) studied the quality of Delaware River and identified zones of pollution along the river. He also developed a water quality model for the river. Harkins (1972) developed a river quality index, which can be used to assess water quality; while O’ Conner (1972) applied multi-attribute scaling procedures for the development of indices of water quality. xliii
Gleick (1993) reported that drinking water with cadmium was toxic and usually results in anemia, poor metabolism or death at high concentration. Also Bowell et al (1996) observed that high concentrations of magnesium and sulphates in water have laxative effects on human beings. Miller et al (1998) studied the impact of dairy production on Utah waterways. They identified bacterial pollution as the major source of the waterway’s pollution. Zekster et al (1993) are of the opinion that groundwater is generally a very good drinking water source because of the natural purification properties of the soil. It is used for various activities especially in areas where surface water is scarce. There is no limit to the possible pollutants in groundwater and the causes of groundwater pollution are closely associated with man’s use of water (Agar and Langmuir. 1971, Berk and Yare; 1977; Noss, 1989). United States Environmental Protection Agency (1975) warned also that once contaminated groundwater is difficult to clean because groundwater moves slowly and contaminants do not spread or mix quickly. Crane and More (1984) suggest therefore that the prevention of contamination is the best way for protecting groundwater quality. According to Dillion (1997) the pollution of groundwater supplies by sanitation is a universal problem and it is particularly severe for communities in low- lying islands. Falkland (1991) studied the Fecal pollution of groundwater by sewage from septic tanks. He concluded that Fecal contamination of groundwater caused the closure of well at Kiritimatic and Majuro, Marshall Island. Beswick (1985) reported that the density of on-site disposal was creating a groundwater risk for intensively occupied parts of Cayman Islands. Lenonard (1982) working in Australia, indicated that pollution arises from disposal of waste in disused sand pit in south East Melbourne and this constituted a major local problem. Thomas and Foster (1986) reported concentration of nitrates in groundwater in Bermuda. Other studies such as Bryson (1988), Andrews (1988) and Nemickas et al (1989) have all identified elevated nitrate concentrations in groundwater as being due to infiltration from septic tanks. Canter et al (1988) have reported that septic tanks used by about 70 million people discharge large volumes of domestic wastewater annually into groundwater. Dillion (1997) is also of the opinion that septic tanks are the leading contributors to the total volume of wastewater discharged into the subsurface and these are strongly linked to the incidences of water borne diseases. xliv
Yates and Yates (1988) identified septic tanks as causes of water borne diseases such as Gastroenteritis, Hepatitis A and Typhoid. While Rafique et al (2003) worked on groundwater of Thar Desert, Pakistan and investigated the water quality parameters and noted that most of the water samples do not meet the World Health Organization (WHO) standard for drinking water especially in respect of chemical contents of the water. Bokar et al (2003) working in Changchun China, mapped contaminant index based on Chinese standard for groundwater quality. They found out that the groundwater is not suitable for drinking due to the presence of high concentration of nitrate. Murad and Krishnamruthy(2003) working in eastern United Arab Emirates, identified factors controlling groundwater quality using a chemical isotopic approach found that agricultural practices are a possible source. Wallis et al (1996) surveyed raw and treated water samples from 72 municipalities in Canada and found that Gravid cysts were present in 21% of raw water samples and that human- infective Gravid cysts are commonly found on raw surface water and sewage. Different aspects of water quality have been studied in Africa .For instance, Bowel et al (1996) working in Tanzania assessed the biogeochemical factors affecting groundwater in Makutuapora aquifer and noted that the water was affected by chemicals than by microbial activity. Gyan-Boakye and Dapaach- Siakwan (1999) are of the opinion that the most prominent water quality problem in Ghana’s groundwater is excessive iron concentration. Also Keraita et al (2003) studying waste management and its effect on water quality of Ghana, concluded that the level of Fecal contaminations in streams of the city were exceptionally high due to the city’s waste water poor management. Iwugo et al (2003) studied pollution management approach in South Africa and concluded that river forum is the basis of catchment’s management. Egboka (1983) assessed the aquifer performance of groundwater in Nsukka environment utilizing pump tests and grain size analysis. Akujieze (1984) identified lack of adequate toilet facilities as a factor that affects safe groundwater quality. Ezeigbo (1987) specified that urbanization process and waste disposal systems are some of the factors that help in deterioration of water quality in Anambra State. Also Iloputaife (1988) working in central Anambra State, discovered that the quality of groundwater is highly controlled by the geology and human activities in the area. Ogboru (2001) examined the nature of environmental pollution and its effect on shallow wells as a source of water to Ondo town. The result indicated that the xlv shallow nature of wells in Ondo aid pollution of the wells. Erah, Akujieze and Oteze (2002) studied the levels of chemical and microbial contamination of boreholes and open wells in Benin and concluded that indiscriminate location of septic tanks, soak- away pits and pit latrine plus poor waste disposal constitute major health concern. Andrew (2000) working in Kaduna found out that 80% of water samples analyzed did not conform to the WHO standard for drinking water. He maintained that the major sources of groundwater contamination were pit toilets, stagnant dirty water in gutters and heaps of refuse. Egbulem (2003) also worked in Kano, Nigeria and identified sources of groundwater contamination as being mainly from human activities and that bacterial counts were generally higher in rainy season. He also pointed out that water quality in terms of bacterial count did not confirm to the WHO standard. Omenano et al (2003) studied water quality in Nigeria. They concluded that government owned public water utilities (GPWU) do not adhere to WHO water quality standards, while privately owned water enterprises (POWE) are forced to conform to these standards. Studies on surface water quality assessment and other issues have been undertaken in several parts of Nigeria. These include studies by Egboge, 1971; Faniran, 1981, Akintola et al, 1980; Akhionbare, 1980; Oluwande, 1980; Ajayi and Osibanyo, 1981; Ndiokwere, 1984; Ude, 1984; Udeze,1990; KaKulu et al, 1992; Udonsi, 1992; Martins et al, 1996; Nwachukwu et al, 1988; Efobi, 2001; Ogboru, 2001; Ovuawah and Hymore, 2001; Ikhile, 2002; Phenol et al, 2002; Bashire et al, 2002, Nnodu et al ,2002; Bayou, 2003; Imoobe et al, 2003; Ahmed, 2003, Omenano, 2003, Akpabio et al, 2004. Stott (1979) is of the opinion that generally, due to increasing pollution of water resource by man’s activities, it can be argued that the problem of water quality are now much more difficult and demanding than that of quantity. Although water quality studies have been handled from various dimensions and on different water bodies, water quality decline is still very significant and continuous. Most water resource studies in Nigeria although they concentrate on either of these aspects –river water quality or groundwater quality the sampling strategy employed is one period oriented sampling. Works such as this (ambient monitoring programme) geared towards evaluating the quality of the water resources of an urban area and drawing conclusions regarding the situation of the water bodies through the utilization of standard water quality indexing method have not been done for any Nigerian urban center. An ambient monitoring programme when employed helps to xlvi describe conditions or long term trends in water quality monitoring parameter over a period of one year or more. This study provides information on the water quality of an urban area based on an ambient monitoring of her water resources. It further provides water quality index for the surface and ground water thus providing an overview of the state of urban water quality.
1.5 The Study Area. Enugu is the capital and major city of Enugu State of Nigeria. The city is located approximately between latitude 06 30 and 06˚ 40΄ North and longitude 070 20 and 0 7 35 East of the equator (Fig 1) at an altitude of 209.3 meters above sea level. It covers an area of about 145.8 sq kms. Enugu urban is made up of three Local Governments namely Enugu North, Enugu South and Enugu East. It is bounded in the north east by Isi-Uzo Local Government Area, in the north west by Igbo Etite local government area, in the west by Udi Local Government Area, in the south by Nkanu West Local Government Area, and east by Nkanu West Local Government Area( Fig 2) 1.5.1 Relief, Drainage and Geology. The topographical features of Enugu can be classified into two: to the west is the escarpment, which is erosional and is continually eroded by the east flowing river and to the east are the Cross River plains and lowlands that are generally low and of monotonous relief (Jennings 1959).Enugu lies at the foot of the escarpment, on the Cross River plains (Ofomata 2002). The geology of Enugu consists of false bedded sandstones, which are associated with the top of the escarpment; sandstones and rocks of the lower coal measures (Mamu formation) dominate the middle and lower slopes of the escarpment facing the city, while the plain is underlain by sandstones and shales (Umeji,2002) (Fig 3). In this economically important coal-bearing horizon round Enugu, five coal seams varying in thickness from a few centimeters to 3.5 meters crop out at the upper reaches of the Asata river, Ogbete river, Aria river, Ekulu river (Umeji,2002). According to Otiji (1988), the coal/bearing part of the formation is mainly of fresh water and low salinity sandstones, shale, mudstone and sand-shale. Both features (the escarpment and the plains) are dissected by streams. These streams with their deeply incised valley upstream, take their source from the eastern slopes of the escarpment. The main streams flowing through Enugu are the Ogbete, xlvii
Aria, Asata, Immaculate and Ekulu together with their numerous tributaries (Fig 4). These streams provide a good drainage system for the city and also depict a Badly permanent water-table level in the highly porous sandstones and shales of the plains (Jennings, 1959).
FIG 1
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FIG 2
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FIG 3
l
FIG 4
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The headwaters of Ekulu river with a length of about 27km, take their source from the northeastern part of the Enugu escarpment at a height of about 330m above sea level. It drains the northern outskirts of the city thereby draining Ekulu and Abakpa Nike areas. It flows east for about 9.5km and then turns north-eastward for almost 8.0km distance after passing the bridge at Abakpa Nike (Chukwu, 1995). Asata river has its headwaters in the scarp slopes at an elevation of approximately 300 meters in the western part of Enugu. It flows Northeast wards for almost 5km before receiving its tributaries-Aria, Immaculate and Ogbete rivers. The rivers drain the Government Reserved Area (GRA), Coal Camp (Ogbete layout), Uwani and the Central Business District (CBN) area of Enugu urban area. The rivers experience extreme seasonal fluctuations in volume because they receive their main supplies of water during the rainy season. The drainage pattern is controlled by the nature of the rocks over which the rivers flow and because the rocks are composed of homogenous strata of similar resistance to erosion the drainage network is dendritic. Water is drawn by gravity through the pore spaces in rocks to the zone of saturation. Coarse-grained, poorly-cemented and porous sandstone is a suitable medium through which the groundwater body receives its recharge and replacement. lii
Some parts of Enugu are underlain by coal and shale beds with low permeability, thus flow of groundwater is virtually prevented (Umeji, 2002).
1.5.2 Climate: The climate of Enugu is a tropical wet and dry type according to Koppen’s classification system with a clear cycle of seasons. Rainfall over the city is high, with annual totals ranging from 1,600mm to more than 2,000mm. Rainfall normally occurs during the rainy season and the onset of rainy season on average is March and the end is October (Anyadike, 2002). The average length of the rainy season months is 260 days. The dry season lasts from late October to mid March. There is thus a pronounced wet and dry season and this affects the river regimes, with lower-water flow in the dry season. According to Anyadike (2002), the period of soil moisture deficiency lasts from late December to April; soil moisture recharge lasts from May to September; soil moisture is surplus September to October; and soil moisture utilization lasts from November to December. This seasonal rainfall pattern has a lot of implication for water resources in the area. It also dominates the agricultural calendar of the peri urban environment. Temperatures are high, usually varying between 25º C and 29º C, reaching the maximum with the approach of the rainy season. The hottest months are February, March and April. During these months, the temperature gets as high as 30.50 C. Enugu also experiences a short spell of harmattan sub-season occurring between December and February. The main vegetation of the study area is the derived savanna with fringing forests along the river courses (Adejuwon, 1971; Igbozurike, 1978; Areola, 1980).
The types of soil found in Enugu urban were derived from underlying rock formation and according to Ofomata (1975, 1978) three types of soils can be distinguished within the urban area. These include the ferrallitic soils, Lithosols and the hydromorphic soils (Fig 5).
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FIG 5
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1.5.3 Growth and development of Enugu. The modern city of Enugu dates from the discovery and development in 1909 of coal in the sandstones (Jennings, 1959). The present site of the city was a wooded tract of farm land belonging to and separating farm settlements in Udi district: Ngwo, Akagbe, Abor and Nike (Okoye, 1977). The first settlers were Mr. W. J. Leck, a British mining engineer and a group of Laborers from Onitsha. Their settlement in Enugu in 1917 resulted in two separate residential quarters being built; one for the Europeans and the other for the indigenous settlers, these two settlement separated by the Ogbete river were later known as the Government Reserved Area (lying north of the river) and Ogbete or Coal Camp (lying to the south of the river). The opening of the mines (Iva mine, 1971; Hayes mine, 1951; and Ekulu mine, 1960) however attracted new miners and tradesmen into Enugu. As the population of miners in the settlement increased, more trades were attracted to it mainly from the former Eastern Region. In this manner, the population of the town gradually increased (Hair, 1962). Although coal mining provided the initial impetus for the growth of the town, the function Enugu has performed as an administrative headquarters in the last 60 (sixty) years, has helped in her population growth. The introduction of regionalization in Nigeria in 1956 resulted in Enugu becoming the capital of the former Eastern Nigeria. After the creation of 12 states out of the former regions in 1967, Enugu lv became the capital of East-Central State of Nigeria. It became the capital of Anambra State in 1976 after the creation of 19 States and also maintained this of state capital after the creation of 36 States in 1996. It is to date still the state capital of Enugu State. With this its continued administrative function, many workers have continued to be attracted to the town. Table 1 shows the progressive population of Enugu from the year 1921 to 2006.
TABLE 1: Population of Enugu: 1921 to 2006 Year Population % increase 1921 3,170 - 1931 13,000 76 1953 63,764 80 1963 138,457 54 1973 168,641 18 1991 465,072 63 2004 586,284 21 2006 772,664 24 Sources: i) Hair (1962): ii) Federal Republic of Nigeria (1963): iii) Anambra State Ministry of Finance and Economic Development (1980): iv) National Population Commission (1991), National Population Commission (2006).
The population figures for Enugu show that the city increased from 3,170 in 1921 to 63,764 in two decades, a period when mining was the dominant occupation in the town. The town’s population also grew from 63,764 in 1953 to 138,457 in 1963, the decade in which industrial development took off in the city. Further increase in the population of the city is also noted for the period between 1973 to 1984, thus in a study carried out by Concept Eco-design International (1980), it was indicated that Enugu is one of the most densely populated cities in the Eastern States of Nigeria with an annual growth rate of about 5%. The 1991 census figures showed the population of lvi
Enugu urban to be 465,072 people. The population of Enugu urban has continued to grow till date and is estimated to be 586,284 people in 2004, while the 2006 population is 772,664. Enugu has experienced high rate of population growth as the National Population Commission 1991 stipulated that an annual growth rate of 3% has been experienced between 1991 and 2004. The implication of the high rate population growth in Enugu urban is excessive pressure on the water resources of the urban area.
1.5.4 Residential Structure of Enugu Urban. For the purpose of this study, Enugu is divided into 10 major residential wards as was utilized for the 1991 census (Fig 6). These residential areas are as follows: 1. Ogbete (Coal Camp): This is one of the oldest residential areas in Enugu. It occupies the southern sector to the extreme west of the city. It is bounded to the north by the Ogbete stream and occupies an area of about 5.2 square kilometers. The houses in this area are mostly bungalows and have outhouses, separately detached kitchens and toilet houses. Most of the people residing in this area are of the low-income group being mainly petty traders and artisans.
2. Ogui: Ogui is a high-density old residential area and it has at the eastern part of the city. It occupies an area of about 3.6 sq kms. Most of the houses found in this area are bungalows, but a good number are storey buildings. It is inhabited by petty traders, artisans, teachers and low-income civil servants.
3. Asata: Asata lies east of Ogui residential area and is separated from the latter by a small tributary of the Asata River. It occupies an area of about 5.0 sq kms. Some of the houses in this residential area are fenced and each contains the main house, a kitchen, bathroom and toilet. Most of the residents are mainly civil servants and petty traders. It however houses mostly the middle-income earners. Hand dug wells abound in this area.
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4. Uwani: This residential layout lies to the southern part of the city. It occupies an area of about 3.3 sq kms. Storey buildings and bungalows are predominant in this area. Some of the houses possess modern amenities like water taps and water closet systems, while some possess no modern facilities. Generally, this ward is built up and devoid of open spaces. Senior civil servants and other professional reside in this area. Residents of this area, depend a great deal on hand-dug wells found in most compounds.
5. Achara layout: Achara layout lies south of Uwani. It occupies an area of about 5.0 sq kms. Most houses in this area primarily are storey buildings of modern architectural design with modern facilities. A few bungalows are however found in this area and some of these bungalows have no modern facilities like taps and water closet systems. People from all walks of life reside in this area. Generally, Achara layout is noted for the fact that the residents depend heavily on groundwater supply.
6. New Haven: This is a medium density area, located at the extreme northern section of the city. It is bounded to the north by a railway line and to the south by the Asata River. It occupies an area of about 3.3 sq kms. It comprises both storey buildings and bungalows. Some of the houses are self-contained. Hand dug wells are uncommon here. Both the low and upper income people reside in this residential area.
7. Iva Valley: Iva valley is an old residential area located in the eastern part of the city. It occupies an area of about 10.9 sq kms and it is the third largest ward in Enugu. It consists mainly of bungalows and make shift houses that possess no form modern facilities like water closet systems and ‘boy’ quarters. Most of the residents in this ward are mainly petty traders, ex-coal mineworkers, and artisans.
8. Abakpa Nike: Abakpa Nike lies to the extreme north of the city and is bounded to the south by the Ekulu River. The government low cost housing estate is located in this area and it is occupied by the high and middle class civil servants. lviii
It occupies an area of about 11.8 sq km, the second largest ward in Enugu. It is also very densely populated. Some of the bungalows and storey buildings in this ward are of modern architectural design with modern facilities. Both the low, middle and high income workers reside in this residential area.
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IGBO ETITI ISI UZO
ABAKPA NIKE GRA
IVA - VALLEY NEW HEAVEN ASATA
OGUI
INDEPENDENCE LAYOUT COAL CAMP
UWANI
ACHARA LAYOUT/ MARY LAND
N
LEGEND
Local Government Boundary Urban Boundary
0 1 2 3 4 5 Ward Boundary
Rivers
FIG 6: MAP OF ENUGU SHOWING MAJOR WARDS Source: Fieldwork, 2006.
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9. Independence layout: This residential area lies in the eastern part of the city. It occupies an area of about 9.8 sq kms. Most of the buildings in this area are storey buildings, while some are duplexes with modern facilities. The houses are mostly of modern architectural designs and are often set in the midst of large lawns. It is a low density area occupied mostly by the upper income workers. Hand dug wells are hardly found in this residential area.
10. Government Reserved Area (G.R.A): this area lies in the northern part of the city. It is bounded to the north by the Ekulu River and occupies an area of about 15.5 sq kms. It is the largest ward in this city. The initial buildings were of British Colonial architectural designs. The houses are predominantly bungalows and storey buildings set in the midst of large lawns and gardens. The outstanding character of this ward is its openness and low housing density. Some government offices also exist here and it is the senior civil servants, business proprietors and the upper income class that reside here. Hand dug wells and bore holes are found in few houses.
1.5.5 Industrial and Institutional Structure of the City Even though Enugu started as a coal mining settlement, coal mining is no longer its primary industry (Okoye, 1975). At present, its dominant economic function is administrative and commerce. From the nineteen fifties, Enugu began to expand its industrial base by adding manufacturing industries to its extractive industry. The first industrial estate for Enugu was established at the satellite town of Emene in 1961. A smaller industrial zone also exists at the Ogbete industrial estate. Apart from these, other small manufacturing industries are located in different parts of the city. Educational institutions in Enugu have increased in number over the years. The town has more than 1,000 primary schools and 320 secondary schools (private schools inclusive). It also has over eight higher institutions and these educational institutions are located in different parts of the town.
1.5.6 Sample Location for Surface Waters. lxi
The five major surface water sites (Fig 7) were as follows:-
1. Asata river (Designated as SWI). This sample site is located on the Asata river and the sample collection point was from the location point with GPS reading of N06.27.336,E007.30.430.This site is at the point where the Asata river flows across the Ogui road( close to the Ogui road Zenith bank)(Plate 1). Domestic and municipal effluents especially those generated from the Artisan market are deposited into the water at various points around this site. A block industry, fuel station, and car washing centre exist close to the bank of this river at this sample site. Urban agriculture where animal dung is utilized is also practiced along the banks of the river by the inhabitants of the railway quarters.
2. Aria river (Designated as SW2). This sample station located on the Aria which flows through the Government Reserved Area and has a GPS reading of N06.28.941, E007.28.925.This sample site is located where most of the human activities take place along the river. A lot of residential buildings (e.g. the Onitsha Road flats), a filling station, market and a mechanic workshop are located close to this sample site, while a market also exists close by. The river serves as a waste dump site for the waste originating from the market and neighboring residential areas (Plate 2).
3. Ekulu river (Designated as SW3) This river which has its source outside the Enugu urban area flows through the Abakpa Nike residential area. The sample station with a GPS reading of N06.51.647, E007.24.448 is situated where most of the activities such as collection of water for domestic activities, sand quarrying, washing of cloths take place. Waste dump sites also exist at the river banks (Plate 3).
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Plate 1: Sample site: Asata River (SW1)
Plate 2: Sample Site: Aria River (SW2) lxiii
Plate 3: Sample Site: Ekulu River (SW3) 4. Ogbete river (Designated as SW 4) This river flows through the Coal Camp ward. The sample station with a GPS reading of N06.26.940, E007.28.923 is situated were a shanty residential area bothers the river (Plate 4). The University of Nigeria teaching hospital and the Ogbete market are also major facilities along the banks of this river. These facilities all utilize the river as waste dump sites. 5. Immaculate river (Designated as SW 5) This river serves as a divide between the Coal Camp and Uwani wards. It has its origin from outside the urban area. The sample station with a GPS reading of N06.25.401, E007.29.769., has a lot of residential buildings and motor mechanic workshops close to its banks. It is utilized by both wards for various domestic activities. At times of scarcity it serves as the only source of water supply especially for the Coal Camp ward where the use of hand dug wells is not practiced (Plate 5).
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Plate 4: Sample Site: Ogbete River (SW4)
Plate 5: Sample Site: Immaculate River (SW5) lxv
FIG 7
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1.6 Research Methodology. The survey universe consists of: All the surface water bodies that flow through the urban center namely: Asata River, Aria River, Ekulu River, Ogbete River, and Immaculate River. Ground water sources i.e. hand dug wells located in different wards where they are found and utilized. Hospitals in the urban area. Data that was used in this thesis was sourced form primary and secondary sources. Comprehensive field work was carried out from January 2006 to December 2006.
1.6.1 Field Work Procedure for Collecting Surface and Ground water Samples. In selecting the sample stations, the researcher was guided strictly by the objectives of the study, the human activities that take place within the urban area, wards through which the rivers flow, accessibility of the stations and the cost of analyzing each selected parameter. Based on previously mentioned facts, the surface water bodies in Enugu were identified and a sample site was selected per river. The sample station was selected in such a manner that the site is located about 2 kms away from the area of concentration of human activities. On the issue of selecting sample sites for ground water survey, wards in the urban area that have hand dug wells were first identified. The selection of a sample site in each identified ward was governed by the acceptance of such residents to allow the monthly samples to be collected from their hand dug wells. Five wards(namely: Asata, Abakpa, Achara Layout, Ogui and Uwani) were identified as being dependent on hand dug wells are major sources of their water supply, while the other wards did not have hand dug wells at all. This entailed having one major sample site in each of the five wards where hand dug wells are utilized, and then sampling from four(4) other sites that lie three(3) kilometers radius( north, south, east , west )of the major sample site(Fig 7). The sample sites are as follows: HDW 1: The sample sites represent Abakpa Nike ward. The major sample site was selected from one of the houses on Ugbene Street (N06.28.868, E007.30.877) (Plate 6). In this part of the ward, virtually every house has a hand dug well as they find it lxvii very easy to intercept water at shallow depths. The houses in this area also occur very close to each other such that the wells are dug in any available space regardless of how close it is to the septic tank.
Plate 6: Sample Site: Abakpa(representing Abakpa ward) (HDW1)
HDW 2: The sample sites represent Achara Layout ward. The major sample site was selected from one of the houses on Igbaram Street (N06.25.401, E007.29.779)(Plate 7 ).This area has a lot of high rise houses and because of sever water shortages arising from the fact that they are rarely supplied water by the Water Board each compound has its own hand dug well( Plate 7).
HDW 3: The sample sites represent Uwani ward. The major sample site was selected from one of the houses on Edozien Street (N0625.401. E007.29.779).This area experiences water scarcity such that the residents of this ward depend on hand dug wells for their regular water supply (Plate 8). lxviii
Plate 7: Sample Site: Achara layout (Representing Achara layout ward) (HDW2)
Plate 8: Sample Site: Uwani (Representing Uwani ward) (HDW3)
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HDW 4: The sample sites represent Ogui ward. The major sample site was selected from one of the houses on Edinbury Road (N06.25.763, E007.31.420).This area experiences water scarcity and hand dug wells are common features in this ward ( Plate 9).
Plate 9: Sample site: Ogui (Representing Ogui ward) (HDW 4)
HDW 5: The sample sites represent Asata ward. The major sample site was selected from one of the houses on Udi Road (Plate 10). Due to the inability of the residents of this ward to obtain water from the Water Board, they depend on hand dug wells found in their various compounds for their water supply.
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Plate 10: Sample Site: Asata (Representing Asata Ward) (HDW5) Note: The well is attached to a toilet house.
On the whole, 10 sample stations were maintained (five surface water sites and five ground water sites (Fig 7).The water samples were preserved and analyzed at the Edo State Environmental Laboratory, Benin City. In Nigeria, the Federal and States Governments are guided in their environmental policy by the recommendations of the WHO and FAO (McDonald and Kay, 1988). WHO drinking water quality guidelines have thus formed the basis for testing. The full range of parameters have not been utilized in this work because of resource constraints and also based on the fact that this is permissible. The selected physico-chemical and biological parameters are as:- Temperature, Turbidity, Total Dissolved solid, pH, Conductivity, Dissolved Oxygen, Biochemical Oxygen Demand, Hardness, Phosphates, Nitrate, Sulphate, Ammonia, Calcium, Iron, Sodium.
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1.6.2 Method of Laboratory Analysis of Water Samples.
1.6.2.1 Temperature: The surface water temperatures were determined in situ using a mercury-in-glass thermometer lowered at a depth between 0.5 and 1 meter until a constant reading is attained (approximately 2 minutes). The temperature is then recorded in Celsius. The recording was done between 3-4 pm.
1.6.2.2 pH: The pH was determined in situ using a Suntex Digital pH Meter. The meter probe was immersed into the sub-surface water (6 meters) and the pH read from the meter.
1.6.2.3 Conductivity (µSCM): The water conductivity values were determined in situ using the Suntex 120 Conductivity Meter. The meter probe was immersed into the surface water (6 meters) and the values were read from the conductivity meter.
1.6.2.4 Turbidity: The turbidity (optical clarity of water) was measured using a simple device called a turbidimeter (a Suntex Digital Turbidity Meter Model was used).The turbidimeter is an optical device that measures the scattering of light and provides a relative measure of turbidity in Nephelometer Turbidity Units (NUT).
1.6.2.5 Total Dissolved Solids: The gravimetric method was used to determine surface water total dissolved solids (TDS) with a filter membrane apparatus in accordance with APHA 2540D Protocol. A 100ml aliquot of the water sample was filtered through a dry pre-weighed 0.45 µm filter paper. The filter was then oven dried at 105C for one hour ( i.e. evaporated to dryness). After drying, the filter paper was cooled and weighed. The difference in weight gives the total dissolved solids (TDS).
1.6.2.6 Dissolved Oxygen: Dissolved oxygen (DO) was determined by the Azide modification of Winkler’s method adapted for the HACH DR 2010equipment for standard methods. Clean 60ml glass-stopper BOD bottle was filled to over flowing with water samples directly from source. Fixation in the field was carried out by adding the contents of Dissolved Oxygen 2 powder pillows. The bottle stoppers were restored and the content was thoroughly mixed by rotation and inversion until a lxxii flocculent brownish precipitate was produced. The bottles were stored away in darkened containers under water until their contents were titrated in the laboratory. Before titration, the contents Dissolved Oxygen 3 powdered pillow (sulphamic acid) was added, thoroughly mixed, and aliquots of 20ml with 0.200n sodium thiosulphate using the HACH Digital Titration, until the sample changed from yellow to colourless. Using starch indicator towards the end of the titration remarkably improved the end point from deep blue to colourless. The number of digits from the digital counter window multiplied by 0.1 gave the concentration of dissolved oxygen in mg/l.
1.6.2.7 Biochemical Oxygen Demand: A Darkened bottle was used to collect the water sample. The sample was incubated for 5 days at 20(i.e. room temperature) in a light-tight drawer. After 5 days, the level of dissolved oxygen was determined by conducting the dissolved oxygen test. The biochemical oxygen demand level was then determined by subtracting this dissolved oxygen from the dissolved oxygen level found in the original sample taken 5 days previously. The values are expressed in mg/l.
1.6.2.8 Total Hardness: This was determined using ETDA titrating procedure (i.e. using the solution of the sodium salt of ethlinediaminetetraacetic acid as the titrating agent and Epitome Black T (a dye which serves as an indicator to show when all the hardness ions have been complexed. The hardness is then calculated from the titration result and expressed as mg/l.
1.6.2.9 Calcium: This was determined using the tritimetric EDTA method. Eriochrome black was used as the indicator. A known volume of sample was titrated with EDTA titrant to reach from pink to purple end point. The calcium content is calculated after the tirantand and the results are expressed in mg/l.
1.6.2.10 Sulphate: Sulphate was determined turbidimetrically with UV/Visible spectrophotometer at a wavelength of 425nm in accordance with ASTM D4130. The method is based on precipitated of sulphate with barium chloride (precipitating agent). Prior to analysis of the samples the equipment was calibrated with sulphate working standards prepared in-house from neat sulphate salts. The result was recorded in mg/l. lxxiii
1.6.2.11 Phosphate: Water and Sulfuric Acid were added to a 50 ml flask and it was swirled; then Ammonium Persulfate was added and boiled. Sodium hydroxide was added and it was swirled until it turned faint pink. Sulfuric acid was added until the pink colour disappeared. The solution was then diluted using deionized water. Phosphate Acid Regent was added and mixed. The test tub was placed in the phosphate comparator with Axial .The sample colour was matched to a colour standard and the result was recorded in mg/l.
1.6.2.12 Nitrate: This was determined using the phenol disulphric acid method. A known volume of sample was evaporated. Phenol disulphuric acid, distill water and ammonia was added. The nitrate developed was measured using a spectrophotometer. Nitrate was subsequently determined using nitrate standard. The result was recorded in mg/l.
1.6.2.13 Iron: Iron was determined using the phenathronic method. The result was recorded in mg/l.
1.6.2.14 Ammonia: Ammonia was detected by colorimetric nesslerization i.e. the use of Nesslers’s Reagent which reacts with ammonia to form a yellow and the amount of colour developed is directly proportional to the amount of ammonia present. The result was recorded in mg/l.
1.6.2.15 Sodium: This was analyzed by flame photometry. the result was recorded in mg/l.
1.6.2.16 Fecal Coliform Bacteria: The method used for the detection of coliform bacteria is the multiple agar plate method. A medium in the form of a jelly called agar is prepared on agar plates also called Petri dishes. The agar is a special diet for coliform bacteria-Escherichia coli. A certain amount of water sample on the surface of the agar-this is the inoculation stage. The inoculation stage is followed by the incubation stage when the Petri dishes are incubated in an incubator or oven for 48 hours at about 35-40.The bacteria begins to grow, feed and multiply if present in the lxxiv water. Colonies of the bacteria are counted under the microscope and the number recorded.
1.6.3 Field Work Procedure for Hospital Sampling. The main procedure for obtaining information from hospitals was through hospital records generated from hospitals in Enugu urban area. To qualify as a sample site, a hospital had to have the facility for admitting patients or be a clinic that treats up to 100 patients per month. This criterion was decided upon by the researcher to ensure the possibility of working with hospitals with high patronage. However, only hospitals that were willing (after being promised by the researcher never to mention the hospital name) were utilized for the study. On the whole, five hospitals were selected per ward, making a total of 50. In each month therefore these hospitals were visited. Information regarding patients was extracted from the hospital cards. To determine patients that qualified for the study, the residential address of each patient played a major role. Each visit, the researcher would identify from the hospital records the patients who reside in any of the wards in Enugu that visited the hospital. Such patients then were considered to be qualified. The researcher, working with the help of the nurses, extracted the illness the patient was diagnosed as suffering from. The number of patients that were treated for water- related diseases per month was recorded. The most frequently occurring water-related illnesses were determined.
1.6.4 Documentary Materials. Relevant documents from the State Water Corporation, Ministries, and hospitals were collected and reviewed. Additional information was also obtained from library and internet search.
1.6.5 Field Observations. Field observation helped a great deal in keeping the researcher informed about the actual situation of things on ground. For instance, we were able to identify the various types of wastes normally deposited directly into the surface water bodies especially those that flow close to residential areas and market places. This enabled us to appreciate the water analysis (results) obtained from the water samples. lxxv
It was also possible to determine the wards that were utilizing ground water resources due to regular water shortages. The GPS readings of the sample sites were determined using GPS 12 Garmin Model (Serial Number 36209488) obtained from the Faculty of Life Science, University of Benin, Benin city.
1.6.6 The Water Quality Index Methodology utilized Traditional approaches to assessing water quality are based on a comparison of experimentally determined parameter values with existing guidelines as has been conducted in Chapter Two of this work. The use of this methodology allows for proper identification of contamination sources. However it does not readily give an overview of the spatial and temporal trends in the overall water quality in a watershed (Debels, Figueroa, Urrutia, Barra, and Niell (2005). One of the major difficult tasks also facing environmental and water managers is how to transfer the interpretation of the complex physical, chemical and biological parameters into information that is understandable to technical and policy individuals as well as the general public. The general public, political decision-makers and non-technical water managers usually do not have the time and the training to study and understand traditional, technical review of water quality data (Brown,1970).This is because the quality of water is defined in terms of its physical, chemical and biological parameters (Sargaonkar and Deshpande, 2003). Internationally, there have been a number of attempts to produce a method that meaningfully integrates the data sets and converts them into information (Nagels, Colley and Smith, 2001).Thus a number of indices have been developed to summarize water quality data in an easily expressible and understood format (Couillard and Lefebvre, 1985). Water Quality Index (WQI) according to Boyacioglu (2007) was first proposed by Horton in 1965 and since then a lot of consideration has been given to the development of ‘water quality index’ methods with the intent of providing a tool for simplifying the reporting of water quality data (Liou, Lo and Wang, 2004). Thus various methods of calculating water quality index have been developed e.g. the standard method developed by the National Sanitation Foundation (NSF) (developed in the early 1970s by the Environmental Protection Agency (EPA) Region 10) (NSFWQI) (Mitchell and Stapp, 1993) and the Universal Water Quality Index lxxvi
(UWQI) developed by Boyacioglu, 2007). All methods are targeted at utilizing physical, chemical and biological parameters to arrive at a value that indicates the state of the health condition of the water body. The Water Quality Index (WQI) is a useful tool for communicating water quality information to the lay public and to legislative decision makers; it is not a complex predictive model for technical and scientific application (McClelland, 1974). It is basically a mathematical means of calculating a single value from multiple test results. It thus provides a single number like a grade that expresses overall water quality at a certain location and time based on several water quality parameters(Veerabhadram,1998).The index result represents the level of water quality in a given water basin e.g. river or stream, lake etc. This can give an indication of the health of the water shed at various points and can be used to keep track of and analyze changes over time; compare a water supply’s quality with other water supplies in the region or from around the world. To determine our WQI, the standard method developed by the National Sanitation Foundation (NSFWQI) was utilized. The Water Quality Index (WQI) is a unit less number ranging from 0-100. A scale rates the quality of the water as follows: Range(Water Quality(Rank) Quality Index value) 91-100 Excellent 71-90 Good 51-70 Average(Medium) 25-50 Bad 0-25 Very Bad
To determine the WQI, nine parameters were measured and used thus creating a worksheet presented as appendix A. The parameters are Biochemical Oxygen Demand, Dissolved Oxygen, Fecal coliform, Nitrates, pH, Temperature, Total dissolved solids, Phosphate and Turbidity. The Q-value for each parameter was calculated and recorded on the worksheet (Appendix A). The Q-value for each parameter was then multiplied by the weight factor and recorded as Total column (Appendix A). The total column values were lxxvii added up to determine the overall WQI for the water sources tested (Appendix B to Y). The index result obtained was compared to the WQI categorization scheme to determine the water quality rating for the water sources tested.
1.6.7 Methods of Data Analysis. In handling the data generated from the water samples and hospital records, appropriate statistical and cartographic techniques were used to analyze and present the results of the study. The figures (diagrams) utilized in this research gave the opportunity of portraying the visual image of the phenomenon under discussion. Basic statistical parameters such as percentages mean standard deviations, standard errors of estimates, line graphs, Cluster bars etc were used to deduce patterns and relationships. The major analytical tools were the ANOVA and the National Sanitation Foundation Water Quality Index Method.
1.6.8 PLAN OF THE THESIS. This thesis is presented in seven chapters. Chapter one is the introduction devoted to the discussion of the background of the study, the research problem, aim and objectives of the study, the area of study, literature review and research methodology. Chapter two reflects what the physical, chemical and bacteriological conditions of the surface and groundwater sources are like. Under this part, the comparison of the laboratory results of surface and groundwater samples to the World Health Organization guideline for drinking water was carried out. Chapter three discusses the detected variations between the parameters on seasonal bases and the spatial variations evidenced by the field study are highlighted. This provided information on the quality variation patterns between surface and groundwater. In chapter four, the monthly and annual water quality indices of surface and groundwater resources are calculated and the findings discussed. Based on the resulting water quality indices, inferences are made regarding the quality (health) of the water resources of Enugu urban area. lxxviii
Chapter five deals with the incidents and prevalence of water-related diseases identified in the study area. The seasonal and spatial dimensions of the water-related diseases are discussed. In chapter six the environmental and policy implications of the detected water quality were discussed. Emphasizes were laid on the likely implication of disregarding the resultant water quality in the urban area. Chapter seven summaries the various findings. Also measures that can be taken to ensure quality improvement of the urban water resources were indicated.
CHAPTER TWO.
COMPARATIVE ANALYSES OF SURFACE AND GROUNDWATER RESOURCES OF ENUGU URBAN AREA.
lxxix
2.1 Selected parameters for laboratory analysis and the World Health Organization guidelines for drinking water. Various authors such as Rodier (1975), Grower (1980), Ellis (1989) and Uzoukwu, Ngoka and Nneji (2004) are of the opinion that water quality is commonly defined by its physical, chemical, biological and aesthetic (appearance and smell) characteristics. Water quality is determined by the kinds and amounts of substances dissolved and suspended in the water and what these substances do to inhabitants of the ecosystem (Johnson, Holmanand and Holniquist, 2000; Davie, 2002).It is thus the concentration of these substances that determine the water quality and its suitability for a particular purpose(Curtis,2001).Water quality standards are thus objectives that are recognized in enforcing environmental laws, regulation and comprehensive water management. In the case of Nigeria, the government is guided in its environmental policy by the recommendations of the World Health Organization (WHO) and Food and Agricultural Organization (FAO) (McDonald and Kay, 1988). Nabila and Kehinde (2003) are also of the opinion that the WHO standards serve as guideline for Nigeria policy issues. The Nigerian National Water Supply and Sanitation policy objectives also justify this assertion. The policy which has as its aim ensuring that good water quality standards are maintained by water supply undertakings has six objectives to achieve this aim. Two of these are as follows: i) Monitor and protect the quality of raw water sources for drinking water. ii) The WHO drinking water quality guidelines shall be the baseline for the National drinking water quality standard. The WHO International Standard for drinking water was first prepared in 1958 and revised in 1963 and 1971. In the 1980s its philosophy and content were changed significantly to become the global guideline for drinking water quality (WHO1993, 1998).Till date the WHO produces international norms on water quality and human health in the form of guidelines that are used as the basis for regulation and standard setting in developed and developing countries world-wide. The water quality criteria, objective and standards had to be developed based on vigorous scientific knowledge. The WHO guidelines for drinking water prescribe minimum numerical guideline values for constituents of water as indicators of water quality. lxxx
Based on the fact that this study is an ambient monitoring programme (monitoring of a few parameters on a routine monthly basis for at least one year), the results of this research are limited to sixteen (16) of the standard parameters stipulated by the WHO. The standard parameters utilized are shown in Table 2. Table 2: Selected parameters for laboratory Analysis Parameter Unit of measurement Maximum Permissible Level Temperature ºC 25 Ph 7.0-8.5 Turbidity NTU 5 Total Dissolved Mg/l 500 Solid(TDS) Conductivity µ/SCM 1 Total Hardness Mg/l 500 Dissolved Oxygen Mg/l 3.0 Biochemical Oxygen Mg/l 2 Demand Phosphate Mg/l 5.0 Sodium Mg/l 100 Sulphate Mg/l 200 Iron Mg/l 0.3 Ammonia Mg/l 45 Calcium Mg/l 75 Nitrate Mg/l 10 Fecal Coliform Bacteria cfu/100ml 0 Source: WHO (1988, 2004)
2.2 Comparison of Laboratory Results of Surface and Groundwater Sources to the World Health Organization (WHO) Guideline for Drinking-Water. The surface water sources under study are the five major rivers in the study area (Fig 4). They are namely: Asata river (SW I ) Aria river (SW 2 ) Ekulu river (SW 3 ) Ogbete river (SW4 ) Immaculate river ( SW 5 ) 2.2.1 Temperature of the urban rivers. The water temperature of a river is very important for water quality. It is often a good indicator of contamination. Any sudden change in temperatures of say groundwater suggests that the water is contaminated, possibly from industrial discharges (Dixey, 1972). It is thus an important indicator of the overall quality of a lxxxi body of water (Mitchell, Stapp and Bixby, 2002).Many of the physical, chemical and biological characteristics of a river are directly affected by temperature. Temperature influences: the amount of oxygen that can be dissolved in water (solubility of gases), the rate of photosynthesis by algae and large aquatic plants, the metabolic rates of aquatic organisms, the sensitivity of organisms to toxic wastes, parasites and diseases. From the result of the field study, the highest temperature value recorded in any of the rivers was 27ºC, while the lowest was 23ºC (Table 3). TABLE 3: Temperature values of rivers in Enugu urban area.(C)
SAMPLE SITES Months SW 1 SW 2 SW3 SW4 SW5 January 25 25 25 25 25 February 26 26 26 26 26 March 27 27 27 27 27 April 23 23 23 23 23 May 23 23 23 23 23 June 26 26 26 26 26 July 25 25 25 25 25 August 24 24 24 24 24 September 23 23 23 23 23 October 23 23 23 23 23 November 25 25 25 25 25 December 25 25 25 25 25 Source: field work (2006) The temperatures of the rivers were generally within the WHO Maximum Permissible Level (MPL) of 25ºC except for some months when the temperature exceeded the WHO’s MPL.The rivers/months in which temperatures exceeded the WHO’s MPL are shown in Figure 8 and they include the following: Asata river (SW 1): February, March and July. Aria river (SW 2): March and July. Ekulu river (SW 3): February, March and June. Ogbete river (SW 4): February, March and July. Immaculate river (SW 5): February, March and July.
2.2.2 pH of the urban rivers. The pH vale is used to describe the intensity of acidity of a solution (Mitchell and Stapp, 1993). Water contains both hydrogen ions (H+) and hydroxide ions (OH-). To determine the pH of a solution, the relative concentration of both ions must be measured. The pH is a measure of the concentration of hydrogen ions lxxxii
of liquids and substances. It is derived from the manner in which the hydrogen ion concentration is calculated. It is the negative logarithm of the hydrogen ions (H+) concentration. Each measured liquid has a pH value on a scale that ranges from 0 to 14(Clescen, Greenbery and Eaton, 1998); with neutral solutions at pH7 and acidic solution from 0-7 and alkaline solution from 7 -14. Most natural waters have a pH from 4 to 9 and majority are slightly alkaline, above 7 due to bicarbonates of calcium and magnesium dissolved in the water. From the laboratory analyses, the highest level of pH concentration for the 12 months under study was recorded in Ekulu river (SW 3) with a pH value of 7.29 in February, while the lowest level of concentration was recorded in Asata river in September with a pH value of 4.45(Table 4). TABLE 4: pH values of rivers in Enugu urban area. SAMPLE SITES Months SW 1 SW 2 SW 3 SW 4 SW 5 January 6.53 5.557 6.2 6.11 6.15 February 6.75 6.78 7.29 6.91 6.97 March 6.49 6.5 6.34 6.31 6.65 April 6.52 6.62 6.67 6.73 6.56 May 6.5 6.51 6.88 6.61 7.15 June 6.31 6.25 6.35 6.41 6.39 July 6.6 6.69 6.47 6.5 6.5 August 6.06 6.11 5.95 6.02 6.22 September 5.21 5.33 4.76 4.54 5.36 October 6.47 7 5.49 5.41 6.02 November 4.91 5.05 5.37 5.3 5.51 December 6.25 6.51 6.32 6.05 6.75 Source: Field work (2006). A comparison of the pH values obtained from laboratory analyses to the WHO’s MPL of 7/8.5 indicate that the pH values for all the rivers fall within the WHO’s MPL for the 12 months of study (Fig 9)
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28 27 26 25 WHO’sWHO(MPL) MPL 24 23 Temperature( c) 22 21 SW1 J F M A M J J A S O N D SW2 Months of the year SW3 SW4 Fig 8: Comparism of river temperatures to WHO(MPL) SW5 Fig 8: Comparison of River temperature to WHO’s MPL
WHO’sWHO(MPL) MPL
8
7
6
5
4 pH level pH 3
2
1
0 SW1 J F M A M J J A S O N D SW2 Months of the year SW3 SW4 FIG 9:Comparism of river pH levels to WHO(MPL) SW5
Fig 9: Comparison of river pH levels to WHO’s MPL.
2.2.3 Turbidity of the urban rivers. Turbidity is a measure of relative clarity of water (Davie, 2002).Turbidity of natural waters is caused by the presence of components such as clay, mud, organic materials, bacteria, lime or rust held in colloid at suspension. The greater the turbidity, the murkier the water is. Turbidity increases as a result of suspended solids in the water that reduce the transmission of light (Hach, 1979). lxxxiv
Table 5 and Figure 10 indicate that generally, the turbidity level for all the rivers exceeded the WHO’s MPL of 5 NTU in most of the months. Low levels were however recorded in some months. The low turbidity months were as follows: Asata river (SW 1): February, May. Aria river (SW 2): July, August, September. Ekulu river (SW 3): March, May, July, August and December. Ogbete river (SW 4): May, June, July, August and October. Immaculate river (SW 5): August, September and November. TABLE 5: Turbidity values of rivers in Enugu urban area.(NTU) SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 5 53 5 10 5 February 2 7 23 6 20 March 5 11 4 7 8 April 7 58 7 13 33 May 3 13 1 2 23 June 6 74 9 3 16 July 10 5 4 2 8 August 10 2 0 0 3 September 14 3 6 5 3 October 10 12 7 2 14 November 7 10 5 5 2 December 12 17 3 10 9 Source: Field work (2006).
2.2.4 Total Dissolved Solids of the urban rivers. Total dissolved solids are the solid matter in water. Dissolved solids in a natural water usually composed of the sulphate, bicarbonate and chloride of calcium, magnesium and sodium (Michaud, 1991). Table 6 indicates that the highest value for total dissolved solids in all the rivers in Enugu urban area was 260 mg/l. The value occurred in November in Immaculate river (SW 5), while the least occurred in Aria river in August.
TABLE 6: Total Dissolved Solids of rivers in Enugu urban area.(mg/l) lxxxv
SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 67 53 86 87 54 February 68 7 97 103 43 March 56 11 79 82 100 April 100 58 60 100 81 May 67 13 92 81 37 June 60 74 78 87 47 July 60 5 75 87 43 August 57 2 61 81 39 September 78 3 68 92 58 October 78 12 92 68 58 November 28 10 150 250 260 December 30 17 156 134 220 Source: Field work (2006). Generally for all the rivers, the dissolved solids levels were within the WHO’s MPL of 500 mg/l (Fig 11).
80
70
60
50
40
30 Turdidity(NTU) 20
10 WHO’sWHO(MPL) MPL 0 J F M A M J J A S O N D Months of the year SW1 SW2
SW3
SW4 SW5 Fig 10: Comparism of rivers turbidity levels to WHO(MPL) SW5 Fig 10: Comparison of rivers turbidity levels to WHO’s MPL
lxxxvi
WHO(MPL) 500 WHO’s MPL 300
250
200
150
100
50 Total Dissolved solidsTotal Dissolved (Mg/l)
0 SW 1 J F M A M J J A S O N D SW 2 Months of the year SW 3 SW 4 Fig 11: Comparism of rivers total dissolved solids to WHO(MPL) SW 5 Fig 11: Comparison of rivers total dissolved solids to WHO’s MPL
2.2.5 Conductivity of the urban rivers. Conductivity is an index of the total ionic content of water. This is the capacity of water for conveying electrical current and is directly related to the concentration of ionized substances in the water. A rapid method of estimating the dissolved salts in water sample is by measurement of its electrical conductivity (Holden, 1970). It gives useful indication of the total concentration of ionic solutes and therefore measures the freshness or otherwise of a water body. Changes in conductivity of a sample, according to Hutton (1983) may signal changes in mineral composition of raw water, seasonal variations in reservoirs, intrusion of sea or saline waters by over pumping and pollution from industrial wastes. Potable water should therefore not have high conductivity. From the laboratory analysis showing the conductivity level in each of the rivers (Table 7) and depicted in Figure 12, it can be seen that all the rivers have values that are within the WHO’s MPL of 75 mg/l.
TABLE 7: Conductivity values of rivers in Enugu urban area.(µSCM) lxxxvii
SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 0.16 0.56 0.18 0.19 0.1 February 0.14 0.59 0.21 0.21 0.92 March 0.07 0.03 0.13 0.1 0.05 April 0.07 0.03 0.11 0.16 0.06 May 0.14 0.07 0.19 0.17 0.07 June 0.12 0.09 0.16 0.18 0.09 July 0.1 0.06 0.11 0.12 0.06 August 0.1 0.07 0.09 0.11 0.06 September 0.12 0.08 0.11 0.14 0.09 October 0.1 0.07 0.09 0.12 0.08 November 0.11 0.07 0.45 0.06 0.02 December 0.02 0.01 0.45 0.04 0.03 Source: Field work (2006).
2.2.6 Total Hardness of the urban rivers. The total hardness is the sum of temporary or carbonate hardness and permanent or non-carbonate hardness (Barth, 1990). Water hardness is due to the presence of sulphate, chlorides, calcium and magnesium (Betz and Nell, 1950). Hardness as measured is made up of alkaline earth metals, mainly calcium and magnesium ions. Knowledge of the hardness of water is of great importance especially from the standpoint as hard water consumes excessive quantities of soap, forming curd and depositing on the hair, fabrics and glass ware. In terms of industrial uses, it is the chief source of scale in heating equipment, boilers and pipelines (Barth, 1990). From Table 8 and Figure 13, it is observed that all the rivers have hardness values that are within the WHO’s MPL of 100 mg/l.
TABLE 8: Total hardness of rivers in Enugu urban area. (mg/l) lxxxviii
SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 0.52 0.44 0.72 0.42 0.67 January 0.26 0.19 0.49 0.36 0.29 February 0.20 0.20 0.22 0.49 0.19 March 0.32 0.32 0.56 0.72 0.48 April 0.81 0.24 0.49 0.45 0.36 May 0.47 0.35 0.24 0.47 0.33 June 0.13 0.21 0.30 0.33 0.21 July 0.11 0.11 0.11 0.21 0.10 August 0.86 0.66 0.44 1.24 0.70 September 0.73 0.69 0.78 o.46 0.81 October 0.63 0.10 0.12 0.25 0.42 November 0.63 0.42 0.95 0.97 0.96 December Source: Field work (2006).
1 WHO(MPL) WHO’s MPL 0.9
0.8
0.7
0.6
0.5
0.4
0.3 conductivity level( scm) conductivity 0.2
0.1
0 J F M A M J J A S O N D SW1 Months of the year SW2 SW3 SW4 Fig 12: Comparism of river conductivity levels to WHO(MPL) SW5 Fig 12: Comparison of river conductivity levels to WHO’s MPL
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WHO’s MPL
1.4
1.2
1
0.8
0.6 Hardness(mg/l) 0.4 `
0.2
0 SW1 J F M A M J J A S O N D SW2 Months of the year SW3 SW4 FIG 13:Comparism of total hardness of rivers to WHO(MPL) SW5 Fig 13: Comparison of total hardness of rivers to WHO’s MPL
2.2.7 Dissolved Oxygen of the urban rivers. The oxygen content of water depends on a number of physical, chemical, biological and microbiological processes (IHD-WHO, 1978). Water in contact with air contains a certain quantity of oxygen depending on atmospheric pressure, the temperature, and the content of dissolved salts. Deviation in the concentration of oxygen from the equilibrium maybe caused by sharp changes in the temperature of water, physico/chemical and chemical process such as the use of oxygen for oxidation of substances or the absorption of oxygen during the corrosion of metals, and biochemical processes such as the aerobic biochemical oxidation of organic matter, the breathing of aquatic organisms or the production of oxygen during the process of photosynthesis (Davie, 2002). The measurement of oxygen in water is important because it is one of the practical indications of the purity, indicating its biological state, the predominant process in it, the destruction of organic substances and the intensity of self purification (Ehinger, 1995). Organic polluting materials added to water consume oxygen such that if there are oxygen-consuming pollutants in water, concentration of dissolved oxygen is affected. If there are no oxygen consuming pollutants in water, the concentration of xc
oxygen present will be determined by the water temperature and its salt content (Mara, 1978).Oxygen is the key component for the survival of most aquatic organisms. The concentration varies from species to species. It is also important in determining the corrosiveness of water. Values of dissolved oxygen were determined by laboratory analyses and are presented in Table 9. TABLE 9: Dissolved Oxygen values for rivers in Enugu urban area. SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 1 1.6 6.7 3.1 0.6 February 8.7 9.7 9.7 8.7 8.8 March 4 4 3.6 4 3.8 April 1.7 1.7 1.7 1.4 2.7 May 4.6 5 4.4 3.9 5.1 June 5.9 5.6 4.9 4.5 4.9 July 5.4 5.4 5.4 4.8 5.2 August 5.8 5.6 5.4 3.6 5 September 7.3 7.8 6.3 6 6.8 October 4.7 4.8 5.4 3.6 5 November 7.3 7.8 6.3 6 6.8 December 4.5 5.8 5.5 5.7 5.8 Source: Field work (2006). The dissolved oxygen values for all the rivers in most months of the year exceeded the WHO’s MPL of 3.0 mg/l except in April for all the rivers and in January for Asata, Ogbete and Immaculate rivers (Fig 14).
2.2.8 Biochemical Oxygen Demand (BOD ) of the urban rivers. Biochemical Oxygen Demand is a measure of the amount of oxygen used by microorganisms in the aerobic oxidation of organic matter (Michaud and Noel, 1991). When organic matter decomposes, it is fed upon by aerobic bacteria. In this process organic matter is broken down and oxidized (combined with oxygen).Aerobic bacteria may decompose organic matter at such a fast rate that dissolved oxygen decreases causing a biochemical oxygen demand. Nutrients are prime factor to high biochemical oxygen demand (Carter, 1985). Values of BOD determined by laboratory analyses are presented in Table10.
xci
TABLE 10: Biochemical oxygen demand values of rivers in Enugu urban area. SAMPLE SITES Months SW 1 SW 2 SW 3 SW 4 SW 5 January 7.2 2.8 1 2.5 2.4 February 2.4 2.3 2.4 9.7 3.1 March 1.9 1.9 0.9 2.1 1.4 April 1.4 0.4 0.9 1.6 1.1 May 4.1 4 3.9 4.1 3.9 June 1 1 3.2 1.4 0.3 July 0.4 0.3 0.4 0.9 0.7 August 0.5 0.4 0.3 0.5 0.4 September 0.4 0.7 0.3 0.5 0.3 October 0.5 1 0.4 0.3 0.4 November 3.2 1.7 1.9 0.7 0.7 December 2.1 2.6 0.9 0.7 1.9 Source: Field work (2006). From Table 10, the highest BOD for all the rivers was 9.7 mg/l, while the lowest level was 0.3 mg/l. There were monthly variations in BOD levels in the rivers as regards the months that had values that exceeded the WHO’s MPL of 2.0 mg/l. As depicted in Fig 15, the months in which the BOD levels exceeded the WHO’s level were as follows: Asata river (SW 1): February, May and November. Aria river (SW 2): February, May and December. Ekulu river (SW 3): February, May and July. Ogbete river (SW 4): January and February. Immaculate river (SW 5): January, February and May. On the average each river had a three (3) months period in which the BOD exceeded the WHO‘s MPL, while in the other months of the year, the BOD levels were within the WHO acceptable level.
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12
10
8
6
4 WHO’sWHO(MPL MPL)
Dissolved oxygen(mg/l)Dissolved 2
0 J F M A M J J A S O N D SWS 1 Months of the year SWS2 SWS3 Fig 14: Comparism of river dissolved oxygen levels to SWS4 WHO(MPL) SWS5 Fig 14: Comparison of river dissolved oxygen levels to WHO’s MPL
12
10
8
6
4
2 WHO(MPL)WHO’s MPL Biochemical oxygen Biochemical demand(mg/l)oxygen 0 J F M A M J J A S O N D Months of the year SW1 SW2 SW3 Fig 15: Comparism of river Biochemical oxygen Demand levels to SW4 WHO(MPL) SW5 Fig 15: Comparison of river biochemical oxygen demand levels to WHO’s MPL
2.2.9 Phosphate of the urban rivers. xciii
Phosphorous is usually present in natural water as phosphate. In unpolluted bodies of water, phosphates are formed mainly during biological processes of transformation of organic substances to inorganic phosphates (Michaud, 1991). Organic phosphate is a part of living plants and animals, their by-products, and their remains (Canter, 1985; Barth, 1990).Inorganic phosphates include the ions bonded to soil particles and phosphates present in laundry detergents. Phosphorous comes from several sources such as human wastes, animal wastes, industrial wastes and human disturbance of the land and its vegetation. Phosphorous is an essential element for life. It is a plant nutrient needed for growth and a fundamental element in the metabolic reactions of plants animals. Plant growth is limited by the amount of phosphorus available. The natural scarcity of phosphorous can be explained by its attraction to organic and soil particles. Any unattached or free phosphorous in the form of inorganic phosphates is rapidly taken up by algae and large aquatic plants. Based on the fact that algae only require small amounts of phosphorous to live, excess phosphorous causes extensive algal growth called “blooms” (Clexen, Greenberg and Eaton, 1998). Algal blooms are a classic symptom of cultural eutrophication.This is the human caused enrichment of water with nutrients, usually phosphorous. Considerable irregular increases in the concentration of phosphates may indicate a presence of pollutants. The values of phosphate determined from laboratory analyses (Table 11) indicate that three rivers (Asata, Aria and Ekulu) had phosphate concentration levels that were within the WHO’s MPL of 5.0 mg/l. While two rivers (Ogbete and Immaculate) had one month each (January and October respectively) in which the phosphate concentration levels exceeded the WHO’s MPL of 5.0 mg/l (Fig 16).
TABLE 11: Phosphate values of rivers in Enugu urban area.(mg/l) xciv
SAMPLE SITES Months SW 1 SW2 SW3 SW4 SW5 January 3.9 4.54 3.65 5.77 3.29 February 2.1 3.92 3.29 3.29 2.96 March 4.16 2.33 3.12 2.62 2.04 April 1.6 1.9 4.8 1.6 2.1 May 0.26 0.53 1.04 0.13 0.25 June 0 0.02 0.13 0.17 0.38 July 0.32 0.02 0.02 0.02 0.13 August 0.11 0.13 0.06 0.09 0.02 September 1.43 0.32 1.76 0.08 0.8 October 0.14 0.16 1.76 4.72 5.19 November 0 0.02 0.01 0.01 0.01 December 0.01 0.01 0.01 0.01 0.01 Source: Field work (2006). It is noteworthy however that despite the fact that the phosphate concentration levels for rivers in Enugu urban area generally fall within the WHO’s MPL, the phosphate levels are however high in some months as the total phosphate concentration of non-polluted waters are usually less than 0.1 mg/l(Egboge, 1971). 2.2.10 Sodium of the urban rivers. Sodium is present in appreciable amounts in almost all natural waters. Under natural conditions, the range of concentrations is quite broad. However, in the majority of rivers, lakes and bodies of water, their content is usually small compared to other chief components (Mitchell, Stapp and Bixby, 2000). Its considerable increase maybe connected with the pollution from industrial and household sewage. Values of sodium obtained from laboratory analyses shown in Table 12 , and depicted in Figure 17, show that the sodium level in all the rivers were within the WHO’s MPL of 100 mg/l. TABLE 12: Sodium values of rivers in Enugu urban area. SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 8.4 1.5 3.3 2.9 26.5 February 9.7 34 1.2 12.1 10.7 March 15.1 15 21 13.8 7.3 April 15.1 15.1 21.6 15.4 8.6 May 9.2 15.1 18 10.6 5.5 June 10.1 10.1 17.5 14.1 8.3 July 1.31 0.76 1.64 1.3 0.71 August 1.21 0.79 1.28 1.25 0.68 September 0.71 0.44 0.63 0.56 0.42 October 0.57 0.86 0.22 3.1 1.66 November 2.25 0.59 1.4 1.22 4.17 December 1.4 0.37 2.05 1.21 0.74 Source: Field work (2006). xcv
7
6
5 WHO’sWHO(MPL) MPL
4
3
2 Phosphate level(mg/l)Phosphate
1
0 J F M A M J J A S O N D SW1 Months of the year SW2 SW3 SW4 Fig16: Comparism of river phosphate levels to WHO(MPL) SW5 Fig 16: Comparison of river phosphate levels to WHO’s MPL
WHO’sWHO(MPL) MPL 40
35
30
25
20
15
Sodium level(mg/l) 10
5 SW1 0 SW2 J F M A M J J A S O N D SW3 Months of the year SW4 Fig 17: Comparism of river sodium levels to WHO(MPL) SW5
Fig 17: Comparison of river sodium levels to WHO’s MPL xcvi
2.2.11 Sulphate of the urban rivers. The supply of sulphate ions in surface, ground and underground waters under natural conditions is due to the reaction of water with sulphate-containing rock and other compounds (Michand and Noel, 1991). Increase in sulphate concentration maybe related to pollution of the body of water by runoff water which contains relatively large quantities of organic and mineral compounds of sulphur (Mitchell, Stapp and Bixby, 2000). The value of sulphate concentration in Enugu urban rivers shown in Table 13 indicates that Asata river (SW 1) had the highest value in the month of March (9.7 mg/l), where Figure 18 indicates further that generally all the rivers had values that are within the WHO’s MPL of 200 mg/l. TABLE 13: Sulphate values of rivers in Enugu urban area SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 0 0.7 0.1 0.6 0 February 2.3 8.4 7.4 6.6 1.8 March 9.7 0 0 0.6 0.6 April 2.4 2.3 1.6 7.4 4.7 May 0.3 0.4 0.4 1.8 5 June 0.3 0 0.4 3 1.9 July 0.1 0.4 0.1 0.6 0.4 August 0.1 0.2 0.1 0.2 0.3 September 0.3 0.7 0.7 0.1 0.6 October 1.2 0.6 2.5 1.6 0.6 November 0.1 0.1 0.1 0.1 0 December 0.6 0 1.1 0.7 0.2 Source: Field work (2006) xcvii
WHO(MPL) WHO’s MPL 12
10
8
6
4 Phosphate level(mg/l)Phosphate
2
SW1 0 SW2 J F M A M J J A S O N D SW3 Months of he year SW4 Fig 18: Comparism of river sulphate levels to WHO(MPL) SW5
Fig 18: Comparison of river sulphate levels to WHO’s MPL
2.2.12 Iron of the urban rivers. Iron is found in a great variety of forms; in solution, colloids and suspensions and in organic and mineral complexes in various states of valence (Mara, 1978). Iron is present in most surface and subsurface waters. In polluted surface water, the concentration of iron varies. For some industrial applications, not even a trace of iron can be tolerated (Davis, 2002). Irregular increases in the concentration of iron indicate a possible pollution by waste waters of metallurgical and metal-processing industries and some mine waters containing iron, when exposed to the air, so that oxygen can enter, become turbid and highly unacceptable from the aesthetic view/point (Michaud and Noel, 1991). Values of iron determined from laboratory analyses are presented in Table 14 and this is depicted in Figure 19.
xcviii
TABLE 14: Iron content of rivers in Enugu urban area. SAMPLE SITES Months SW 1 SW2 SW 3 SW 4 SW5 January 0.01 0.1 0.03 0.03 0.2 February 0 0 0.01 0.01 0.2 March 0 0 0.1 0.1 0.2 April 0.1 0.1 0.1 0.2 0.2 May 0.1 0.1 0.17 0.1 0.1 June 0.1 0.1 0.1 0.1 0.1 July 0.1 0.1 0.1 0.1 0.1 August 0.1 0.1 0.1 0.1 0.1 September 0.1 0.1 0.1 0.1 0.1 October 0.1 0 0.1 0.1 0.2 November 0.1 0 0 0.1 0.1 December 0.1 0 0 0 0.1 Source: Field work (2006).
From Table 14 and Figure 19, it is observed that all the rivers have iron values that are within the WHO’s MPL of 0.3 mg/l.
WHO’s MPL WHO(MPL)
0.25
0.2
0.15
0.1
IronIronlevel(mg/l) level(mg/l)
0.05
SW1 0 J F M A M J J A S O N D SW2 SW3 Months of the year SW4 SW5 Fig 19: Comparison of river iron levels to WHO’s MPL
xcix
2.2.13 Ammonia of the urban rivers. The presence of ammonia ions in unpolluted water is connected with the process of the biochemical decomposition of protein substances (Mara, 1978).An increase in the concentration of ammonium-ions therefore, is observed when aquatic organisms are dying off especially in the zone of aggregation(layers of increased density of phyto- and bacteria plankton(IHD-WHO,1978). Ammonia ions can be formed during the anaerobic reduction of nitrates and nitrites. A product of microbiological activity, ammonia is sometimes accepted as chemical evidence of sanitary pollution when encountered in raw surface waters, which has suffered de-oxygenation and denitrification due to sewage contamination or contamination by industrial effluents and excremental pollution (Barth, 1990). Thus the amplitude of seasonal fluctuations of ammonium ions reflects the nutrition of the body of water and its pollution by organic nitrogen-containing substances contained in household and industrial sewage(especially from the food industry). Values of ammonia determined from laboratory analyses presented in Table15, indicate that the highest level was recorded in Ogbete river (SW4) in the month of June.
TABLE 15: Ammonia values for rivers in Enugu urban area(mg/l) SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 0.93 0.36 0.57 3.23 0.46 February 0.9 0.32 0.22 0.22 0.38 March 0.78 1.58 3.31 1.81 2.12 April 1.69 2.62 1.96 2.42 2.31 May 1.12 0.78 2.31 1.81 1.23 June 6.92 7.1 5.45 8.72 6.57 July 0.06 0.11 0.09 0.01 0.01 August 0.09 0.06 0.12 0.04 0.09 September 0.12 0.03 0.1 0.1 0.04 October 1.23 0.63 2.46 1.63 0.28 November 0.91 0.01 0.02 0.03 0.01 December 0.71 0.01 0.01 0.02 0.01 Source: Field work (2006). The ammonia level in all the rivers were within the WHO ‘s MPL of 45 mg/l as is depicted by Figure 20. c
WHO’s MPLWHO(MPL)
10 9 8 7 6 5 4
3 Ammonia level(mg/l) 2 1 SW1 0 SW2 J F M A M J J A S O N D SW3 Months of the year SW4 Fig 20:Comparism of river ammonia levels to WHO(MPL) SW5 Fig 20: Comparison of river ammonia levels to WHO’s MPL
2.2.14 Calcium of the urban rivers. Calcium ions are present both in surface and in underground waters which they penetrate as a result of the interaction between the water and the minerals in the soil or rock (Maidment, 1993).The natural concentration of calcium maybe influenced by industrial waste. The calcium values obtained (Table16) and depicted in Figure 21, indicate that all the rivers had calcium levels that were within the WHO’s MPL of 75 mg/l. TABLE 16: Calcium values for rivers in Enugu urban area(mg/l) SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 2.01 1.95 2.02 2.41 1.81 February 1.8 1.95 2.09 2.6 1.87 March 2.05 2.08 2.62 2.63 1.78 April 2.62 2.53 2.67 2.82 2.01 May 2.69 2.55 2.93 2.94 2.51 June 2.83 2.73 2.93 3.04 2.59 July 3 2.95 3.05 3.4 2.59 August 2.89 2.98 3.05 3.4 2.51 September 2.91 2.98 3.01 2.94 2.67 October 2.5 2.54 2.76 2.62 2.01 November 2.5 2.02 2.03 2.61 2.05 December 2.21 2.02 2.05 2.55 2.09 Source: Field work (2006)
ci
2.2.15 Nitrate of the urban rivers. Nitrates appear in water chiefly as a result of biochemical oxidation of ammonia or the reduction of nitrates (Barth, 1990; Michaud and Noel, 1991).Nitrates are the end-product of the biochemical oxidation of ammonia and nitrogen from organic matter, and a measure of the original quantity of organic matter with which a water is associated. Nitrates in water can originate from agricultural fertilizers, sewage, industrial and packing house wastes, drainage from livestock feeding areas, farm manures and legumes (Davis, 2002).Increased concentration of nitrates may indicate Fecal pollution of the body of water in the proceeding period. High nitrate content in potable water is harmful for children and cause anaemia (Methaemogloanaemia) (Dasgusta, 2004). Nitrates in conjunction with phosphates stimulate the growth of algae, causing eutrophication with other related difficulties associated with excess algae growth. Values of nitrate presented in Table 17 and also depicted by Figure 22, indicate that with the highest nitrate concentration level of 3.31 mg/l occurring in Ogbete river, the nitrate levels of all the rivers were below the WHO’s MPL of 10 mg/l. Table 17: Nitrate content of rivers in Enugu urban area(mg/l) SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 0.03 0.01 0.01 0.04 0.04 February 0.03 0.07 0.07 0.22 0.04 March 0.04 0.06 0.05 0.27 0.04 April 0.05 0.05 0.05 0.34 0.07 May 0.06 0.04 0.01 0.01 0.01 June 0.01 0.01 0.03 0.01 0.01 July 0.26 1.54 0.11 0.06 0.17 August 1.74 0.2 0.21 0.14 0.2 September 1.77 1.54 1.89 3.31 2.06 October 1.45 1.03 1.04 1.37 0.94 November 1.3 0.06 0.08 0.03 0.03 December 0.02 0.01 0.01 0.01 0 Source: Field work (2006)
2.2.16 Fecal coliform bacteria of the urban rivers. cii
Fecal coliform bacteria are microscopic animals that live in the intestine of warm-blooded animals (Mara, 1978; Micheall, Stapp and Bixby, 2000).They also live in the waste material or faeces excreted from the intestinal tract. When fecal coliform bacteria are present in high numbers in a water sample, it means that the water may have received fecal matter from one source or another. They are living organisms and multiply quickly when conditions are favourable for growth and die in large numbers when they are not. Fecal coliform bacteria indicate the potential presence of disease carrying organism. Organisms such as Escherichia Coli, fecal Streptococcus and Clostridium Perfingens are known as indicator bacteria of which E Coli is the most widely used because it is of undoubted fecal origin(IHD-WHO,1978). The concentration of fecal coliform must therefore be monitored in order to determine the likelihood of contamination by microbiological organisms. The common source of coliforms and pathogenic bacteria is raw sewage. Values of fecal coliform bacteria determined from laboratory analyses are presented in Table18 .The table shows that all the rivers had Fecal coliform counts that exceeded the WHO’s MPL of 0cf/100 ml in all the months under study (Fig 23). TABLE 18: Fecal coliform bacteria content of rivers in Enugu urban area SAMPLE SITES Months SW1 SW2 SW3 SW4 SW5 January 1.3 1 1 3 1 February 0.3 1 1 1 2 March 1 1 1 2 2 April 3 3 2 2 2 May 10 0 2 2 2 June 12 4 7 10 7 July 2 2 2 2 9 August 2 2 2 2 2 September 9 2 2 2 2 October 90 90 25 30 2 November 9 90 20 90 5 December 89 14 43 53 19 Source: Field work (2006). ciii
WHO’s MPL WHO(MPL)
4
3.5
3
2.5
2
1.5
Calcium level(mg/l) 1
0.5
0 SW1 J F M A M J J A S O N D SW2 SW3 Months of the year SW4 Fig 21:Comparism of river calcium levels to WHO(MPL) SW5 Fig 21: Comparison of river calcium levels to WHO’s MPL
WHO’s MPL WHO(MPL)
3.5
3
2.5
2
1.5
Nitrate level(mg/l) Nitrate 1
0.5 SW1 0 SW2 J F M A M J J A S O N D SW3 Months of the year SW4 Fig 22:Comparism of river nitrate levels to WHO(MPL) SW5
Fig 22: Comparison of river nitrate levels to WHO’s MPL civ
100 90 80 70 60 50 40
level(cf/100ml) 30
20 Faecal coliformFaecal bacteria 10 0 WHO’sWHO(MPL MPL J F M A M J J A S O N D ) SW1 Months of the year SW2 SW3 Fig 23:Comparism of river faecal coliform bacteria levels to SW4 WHO(MPL) SW5 Fig 23: Comparison of river fecal coliform bacteria levels to WHO’s MPL
2.2.17 Comparison of Annual Values of the Selected Parameters of Rivers to the WHO’s Guideline for Drinking Water. The annual mean values of all the parameters presented in table 19, show that the mean temperature for all the rivers was 24ºC. These mean values for all the rivers indicate that on average, the temperature values for the year were within the WHO’s MPL. The pH mean values for all the rivers indicate that the rivers in Enugu urban area are slightly acidic for most parts of the year but they were within the WHO’s MPL (Table 19).The annual mean turbidity, dissolved oxygen values and fecal coliform levels for all the rivers exceeded the WHO’s MPL.While the conductivity, hardness, total dissolved solids, phosphate, sodium, sulphate, Iron, ammonia, calcium and nitrate for all the rivers were within acceptable limits. The mean values of the river biochemical oxygen demand also shows that two rivers-Asata (SW1) and Ogbete (SW4) had values that exceeded the WHO’s MPL, while three rivers namely Aria (SW2), Ekulu (SW3) and Immaculate (SW5) had values that were within the WHO’s MPL.
cv
TABLE 19: Annual mean values of selected Parameters of rivers in Enugu urban Parameters SAMPLES SITES SW 1 SW 2 SW 3 SW 4 SW 5 Temperature 24.58 24.58 24.58 24.58 24.58 pH 6.22 6.24 6.17 6.07 6.35 Turbidity 7.58 22.08 6.16 5.41 12.00 Total dissolved solids 62.41 22.08 91.16 104..33 86.66 Conductivity 0.1 0.14 0.19 0.13 0.13 Hardness 0.47 0.32 0.45 0.53 0.45 Dissolved Oxygen 5.07 5.4 5.44 4.60 5.04 Biochemical Oxygen Demanded 2.09 1.59 1.37 2.08 1.38 Phosphate 1.16 1.15 1.63 1.54 1.43 Sodium 6.25 7.88 7.48 6.46 6.27 Sulphate 1.45 1.15 1.20 1.94 1.34 Iron 0.07 0.05 0.07 0.08 0.14 Ammonia 1.28 1.13 1.38 1.67 1.12 Calcium 2.50 2.44 2.60 2.83 2.20 Nitrate 0.56 0.38 0.29 0.48 0.30 Fecal Coliform bacteria 19.05 17.5 9.00 16.58 4.58 Source: Fieldwork, 20006.
2.3 Comparison of laboratory results of hand dug wells to the WHO’s guideline for drinking water. The hand-dug wells under study are hand dug wells found in five residential wards in Enugu urban area (Fig 7). These hand-dug wells exist in wards where groundwater resources serve as the major source of water supply for domestic and other purposes. These hand-dug wells for this study are designated as follows: HDW 1: Abakpa Nike wells. HDW 2: Uwani wells HDW 3: Achara Layout wells HDW 4: Ogui wells. HDW 5: Asata wells.
2.3.1 Temperature of the urban wells. cvi
The result of the laboratory analyses presented in Table 20 indicates that the highest temperature value occurred in all the wells in the month of March (27) and the least was recorded in all the wells in the months of April, May and September. TABLE 20: Temperature of wells in Enugu urban area.(ºC) SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 25 25 25 26 25 February 26 26 26 25 26 March 27 27 27 27 27 April 23 23 23 23 23 May 23 23 23 23 23 June 26 26 26 26 26 July 25 25 25 25 25 August 24 24 25 24 24 September 23 23 24 23 23 October 25 25 25 25 25 November 25 25 25 25 25 December 25 25 25 25 25 Source: Field work (2006) In most of the months, the wells had temperature values that were within the WHO’s MPL of 25ºC except for some months when the wells had various values that exceeded the WHO’s MPL especially in March with 27˚ but also in February and June. The wells in Abakpa Nike (HDW 1), Uwani (HDW 2), Achara layout (HDW 3) and Asata (HDW 5) all had values that exceeded the WHO’s MPL in the same months. However the wells in Ogui and Asata also had high values in January and July (Fig 24).
30
25 WHO’sWHO(MPL) MPL
20
15
10 Temperature ( C) Temperature 5
0 HDW1 J F M A M J J A S O N D HDW2 Months of the year HDW3 HDW4 Fig 24:Comparism of temperature levels of wells to WHO(MPL) HDW5 Fig 24: Comparison of temperature levels of wells to WHO’s MPL
cvii
2.3.2 pH of the urban wells. The highest pH value obtained for the wells (Table 21 ) was 8.20 occurring in the wells in Asata in the month of February, while the least value(3.22) was obtained for the wells in Abakpa Nike in the month of September. TABLE 21: pH values of wells in Enugu urban area SAMPLE SITES Months HDW1 HDW 2 HDW 3 HDW 4 HDW 5 January 3.64 5.21 6.25 3.88 5.59 February 7.13 6.3 7.67 4.92 8.2 March 3.31 5.21 6.56 4.71 6.25 April 5.82 6.64 6.71 5.82 6.32 May 4.45 5.66 6.59 5.3 5.49 June 4.08 5.01 5.51 4.31 5.76 July 4.72 4.32 4.36 4.42 5.85 August 4.99 5.38 4.86 4.92 6.09 September 3.22 4.12 4.55 5.78 5.41 October 3.83 5.49 4.86 4.13 6.01 November 3.33 4.2 3.87 6.06 5.25 December 4.02 5.64 5.87 4.82 6.33 Source: Field work (2006) All the wells had values that were within the WHO’s MPL of 7.0/8.5 except for the wells in Abakpa Nike (HDW1) and Asata (HDW5) that had high values in the month of February (Fig 25).
9 WHO(MPL)WHO’s MPL 8 7 6 5
4 pH level pH 3 2 1 0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 HW4 Fig 25:Comparism of pH levels of wells to WHO(MPL) HDW5 Fig 25: Comparison of pH levels of wells to WHO’s MPL
cviii
2.3.3 Turbidity of the urban wells. From the results obtained (Table 22), the turbidity level for the entire well exceeded the WHO’s MPL of 5 NTU in most months (Fig 26). However, values lower than the WHO’s level was obtained in the following months: HDW I: May-September. HDW 2: June-September, November. HDW 3: January HDW 4: June, October, November. HDW 5: December. TABLE 22: Turbidity values of wells in Enugu urban area(NTU) SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 26 27 3 7 9 February 6 6 6 5 10 March 7 6 5 6 14 April 59 7 6 6 12 May 0 6 10 10 10 June 0 2 10 3 12 July 0 2 10 3 12 August 0 3 10 10 10 September 2 3 9 10 12 October 19 6 9 4 23 November 24 1 10 3 10 December 11 6 1 10 4 Source: Field work (2006)
70
60
50
40
30
20 Turbidity level(NTU) 10 WHO’sWHO(MPL) MPL 0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 HDW4 Fig 26:Comparism of turbidity levels of wells to WHO(MPL) HDW5 Fig 26: Comparison of turbidity levels of wells to WHO’s MPL
cix
2.3.4 Total Dissolved Solid of the urban wells. Table 23 indicates that the highest value recorded for all the wells was 2100 mg/l. This value occurred in the month of December in the wells in Abakpa (HDW1). TABLE 23: Total Dissolved Solids of wells in Enugu urban area(mg/l) SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 541 143 432 363 245 February 359 316 326 334 263 March 50 81 96 560 66 April 74 100 74 100 100 May 585 181 625 192 238 June 347 153 311 352 213 July 442 85 487 313 312 August 869 254 142 203 201 September 68 32 38 320 32 October 60 261 39 770 33 November 1500 250 250 1500 150 December 2100 890 130 360 120 Source: Field work (2006) The total dissolved solids exceeded the WHO’s MPL of 500mg/l (Fig 27) in the wells (except the wells in Asata (HDW 5)) in various months as follows: HDW 1: January, May, August, November, December. HDW 2: December HDW 3: May HDW 4: March, October and November. The values for the wells in Asata (HDW 5) were all within the WHO’s MPL.
2500
2000
1500
1000
500 WHO(MPL)WHO’s MPL Total Total dissolved solid(mg/l) 0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 Fig 27:Comparism of total dissolved solids of wells to HDW4 WHO(MPL) HDW5 Fig 27: Comparison of total dissolved solids levels of wells to WHO’s MPL cx
2.3.5 Conductivity of the urban wells. Conductivity levels as obtained from the laboratory analyses are presented in Table 24. TABLE 24: Conductivity of wells in Enugu urban area(µSCM) SAMPLE SITES
Months HDW1 HDW2 HDW3 HDW4 HDW5 January 1.19 0.32 0.94 0.75 0.56 February 0.77 0.66 0.67 0.7 0.55 March 0.59 0.32 0.66 0.38 0.28 April 0.25 0.2 0.41 0.19 0.1 May 1.21 0.39 1.29 0.4 0.49 June 0.73 0.32 0.65 0.73 0.44 July 0.55 0.13 0.57 0.39 0.38 August 0.92 0.32 0.35 0.26 0.49 September 0.62 0.39 0.4 0.36 0.35 October 0.59 0.34 0.49 0.89 0.43 November 0.03 0.02 0.04 0.03 0.06 December 0.14 0.11 0.06 0.08 0.11 Source: Field work (2006) This shows that the wells had conductivity level that were within the WHO’s MPL of 1µSCM in most of the months in the wells in Uwani, Ogui and Asata. The months in which the WHO’s MPL were exceeded are depicted in Figure 28.
1.4 1.2 1 WHO(MPL)WHO’s MPL
SCM) SCM) µ µ 0.8
0.6
0.4
Conductivity ( SCM) Conductivity Conductivity ( Conductivity ( Conductivity 0.2
0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 HDW4 Fig 28:Comparism of conductivity of wells to WHO(MPL) HDW5
Fig 28: Comparison of conductivity of wells to WHO’s MPL
cxi
2.3.6 Total Hardness of the urban wells. Results of the laboratory analyses shown as Table 25 and Figure 29 indicate that all the wells had values that were within the WHO’s MPL of 100 mg/l. TABLE 25: Total Hardness values of wells in Enugu urban area(mg/l) SAMPLE SITES
Months HDW1 HDW2 HDW3 HDW4 HDW5 January 0.76 0.75 1 0.92 0.84 February 0.02 0.53 0.3 0.72 0.54 March 0.36 0.42 0.48 0.13 0.44 April 0.4 0.7 0.4 0.32 0.56 May 0.36 0.6 0.52 0.46 0.48 June 0.3 0.32 0.35 0.4 0.48 July 0.31 0.09 0.37 0.14 0.49 August 0.8 0.35 0.29 0.2 0.75 September 1.1 1.3 1.89 2.7 3.26 October 0.98 0.45 1.58 3.01 11.29 November 0.18 0.21 0.21 0.38 0.16 December 0.74 0.52 0.51 2.36 1.03 Source: Field work (2006)
WHO’s MPL
WHO(MPL) 12
10
8
6
4 Total Total hardness(mg/l) 2
0 HDW1 J F M A M J J A S O N D HDW2 Months of the year HDW3 Fig 29: Comparism of total hardness levels of wells to HDW4 WHO(HDL) HDW5 Fig 29: Comparison of total hardness levels of wells to WHO’s MPL
cxii
2.3.7 Dissolved Oxygen of the urban wells. Values of dissolved oxygen presented as Table 26, indicate that the dissolved oxygen for all the wells under study exceeded the WHO’s MPL of 3.0 mg/l in all the months of the year except for the month of January when all the wells had dissolved oxygen levels that were within the WHO’s MPL (Fig 30).
TABLE 26: Dissolved Oxygen levels of rivers in Enugu urban area SAMPLE SITES
Months HDW1 HDW2 HDW3 HDW4 HDW5 January 1.3 1.1 2.5 0.8 0.6 February 9.3 9.3 9.5 9.6 9.5 March 3.6 4.5 4.8 3.5 2.6 April 2.5 3.4 1 2.8 3.4 May 5.1 5.3 3.7 4.8 5.4 June 4.8 4.9 5 6.4 5 July 5.3 5.4 5.2 5.4 5.3 August 6.5 5.4 5.4 5.3 5.4 September 4.8 4.5 5.1 4.9 4.7 October 5.1 4.9 5 4.5 4.1 November 6.6 5.3 5.1 4.7 6.9 December 5.9 5.8 6.1 5.7 6.1 Source: Field work (2006)
12 10 8 6 4 WHO (MPL) WHO’s MPL 2
Dissolved Dissolved Oxygen(mg/l) 0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 HDW4 FIG 30: Comparism of dissolved oxygen of wells WHO (MPL) HDW5
Fig 30: Comparison of dissolved oxygen of wells to WHO’s MPL
cxiii
2.3.8 Biochemical Oxygen Demand of the urban wells. The biochemical oxygen demands of the wells are shown in Table 27. TABLE 27: Biochemical Oxygen Demand of wells in Enugu urban area.(mg/l) SAMPLE STATIONS MONTHS HDW1 HDW2 HDW3 HDW4 HDW5 January 4.01 4 5.3 1 1.9 February 1.55 2.82 1.63 0.3 4.93 March 0.63 1.74 2.08 1 1.5 April 0.7 4.1 4.6 0.6 2.4 May 3.9 4 3.8 4.15 4 June 0.14 0.42 0.42 0.3 4.2 July 0.17 0.47 0.24 0.47 0.25 August 0.14 0.5 0.53 0.6 0.75 September 0.3 0.56 0.35 0.41 0.23 October 0.34 0.31 0.35 0.65 0.41 November 0.71 2.14 0.95 2.62 2.14 December 0.71 0.71 0.96 2.14 0.71 Source: Field work (2006) Table 27 shows that the WHO (MPL) of 2.0 MG/L was exceeded in all the wells in different months (Fig 31) as follows: HDW 1: January and May, HDW 2: January, April and May. HDW 3: January, April, and May. HDW 4: May, November, and December. HDW 5: February, May, June and November.
6
5
4
3
2 WHO’sWHO(MPL MPL
1
Biochemical oxygen demand level(mg/l) demand oxygen Biochemical 0 J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 Fig 31:Comparism of biochemical oxygen demand levels of HDW4 wells to WHO(MPL) HDW5 Fig 31: Comparison of biochemical oxygen demand levels of wells to WHO’s MPL cxiv
2.3.9 Phosphate of the urban wells. From Table 28 and Figure 32 it can be seen that the WHO’s MPL for phosphate was exceeded only in the wells in Asata in the month of March. In all the other months the wells had phosphate levels that were within the WHO’s MPL. TABLE 28: Phosphate levels of wells in Enugu urban area.(mg/l) SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 3.29 2.35 1.72 4.39 4.39 February 2.5 2.82 4.08 2.82 3.92 March 7.45 2.06 2.76 2.15 3.01 April 4.1 1.4 2.3 1.1 1.4 May 0.08 0.13 0.19 0.16 0.11 June 0.27 0.11 0.2 0.17 0.2 July 0.06 0.11 0.06 0.19 0.06 August 0.03 0.02 0.09 0.03 0.06 September 1.59 0.63 1.16 0.05 1.53 October 0.33 0.65 1.16 0.05 1.53 November 0 0 0 0 0 December 0.01 0 0.001 0.01 0.01 Source: Field work (2006)
8
7
6
5 WHO’sWHO (MPL)MPL 4
3
2 Phosphate level (mg/l) level Phosphate 1
0 J F M A M J J A S O N D Months of the year HDW1 HDW2 HDW3 HDW4 FIG 32: Comparism of Phosphate levels of wells to WHO (MPL) HDW5
Fig 32: Comparison of phosphate levels of wells to WHO’s MPL
2.3.10 Sodium of the urban wells. cxv
Values of sodium obtained (Table 29 and shown as Fig 33) indicate that all the wells had concentration levels that were within the WHO’s MPL of 100 mg/l. TABLE 29: Sodium concentration levels of wells in Enugu urban area. (Mg/l) SAMPLE SITES Months HDW 1 HDW2 HDW3 HDW4 HDW5 January 2.1 2.1 24 26.4 25.2 February 19.8 4.4 28.7 3.5 25.1 March 61.5 46.7 60.4 41.5 37.3 April 62.1 46.7 60.4 41.5 37.3 May 5.5 28.7 40.2 24.1 25.6 June 38.8 19.8 25.3 24.8 21.3 July 5.13 1.99 5.36 4.89 4.2 August 5.93 3.78 4.24 3.29 4.52 September 3.04 2.31 2.43 1.9 1.87 October 1.78 1.75 0.06 0.32 2.76 November 5.7 5.62 5.81 13.68 2.13 December 5.39 3.72 1.95 3.16 3.91 Source: Field work (2006).
WHO’s MPL WHO(MP)
70
60 50
40
30
20
Sodium level (mg/l) 10
0 HDW1 J F M A M J J A S O N D HDW2 Months of Year HDW3 HDW4 FIG 33: Comparism of Sodium levels of wells to WHO (MPL) HDW5 Fig 33: Comparison of sodium levels of wells to WHO’s MPL
2.3.11 Sulphate of the urban wells. cxvi
The sulphate level in the wells from Table 30 and Fig 34 indicate that all the wells had sulphate levels that were within the WHO’s MPL of 200mg/l. TABLE 30: Sulphate levels of wells in Enugu urban area. SAMPLE SITES Months HDW 1 HDW2 HDW3 HDW4 HDW5 January 0.51 0.51 0.48 0.43 0.51 February 3.81 18.18 3.03 3.05 0.91 March 0.61 0 0 0 0 April 13.39 3.21 8.42 1.33 0.73 May 5.03 1.58 1.82 0.67 2.24 June 2.71 0 0.57 0.57 1.57 July 1.42 0.34 0.63 0.26 0.15 August 0.96 0.13 0.09 0.19 0.29 September 0.44 0.05 0.63 0.72 0.78 October 0.46 0.17 0.54 1.23 1.37 November 0.01 0.01 0 0.04 0.03 December 0.03 0.13 1.13 1.09 0.01 Source: Field work (2006)
WHO’s MPLWHO(MPL)
8
7
6
5
4
3 Sulphate level(mg/l) Sulphate
2
1
HDW1 0 HDW2 J F M A M J J A S O N D HDW3 Months of the year HDW4 FIG 34:Comparism of sulphate levels of wells to WHO(MPL) HDW5
Fig. 34: Comparison of Sulphate levels of wells to WHO’s MPL 2.3.12 Ammonia of the urban wells. cxvii
The laboratory analyses in Table 31 indicate that all the wells had ammonia levels that were within the WHO’s MPL of 45 mg/l (Fig 35). TABLE 31: Ammonia levels of wells in Enugu urban area.(mg/l) SAMPLE SITES Months HDW I HDW2 HDW3 HDW4 HDW5 January 4.23 4.23 3.73 3.54 0.36 February 1.19 0.53 1.78 3.27 0.06 March 2.31 3.04 3.77 1.92 1.23 April 1.23 3.15 9 2.77 2.42 May 1.23 0.8 3.02 0.16 1.23 June 7.3 7.12 7.72 8.7 7.08 July 0.09 0.04 0.04 0.01 0.01 August 0.59 0.05 0.01 0.02 0.01 September 0.19 0.04 0.17 0.09 0.14 October 0.46 0.2 0.54 1.23 1.37 November 0.01 0.02 0.07 0.14 0.01 December 0.01 0.05 0.029 0.01 0.02 Source: Field work (2006)
WHO’sWHO(MPL) MPL
10 9 8
7 6 5 4 3
2 Ammonia level (mg/l) 1 0 HDW1 J F M A M J J A S O N D HDW2 Months of the year HDW3 FIG 35: Comparism of Ammonia levels of the wells to WHO (MPL) HDW4 HDW5
Fig 35: Comparison of Ammonia levels of wells to WHO’s MPL
2.3.13 Calcium of the urban wells. cxviii
From Table 32 and Figure 36, it can be observed that all the wells had values that were within the WHO’s MPL of 75 mg/l. TABLE 32: Calcium levels of wells in Enugu urban area. SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 8.01 8.08 10 9.65 9.77 February 8.05 9.01 8.02 9.71 8.04 March 10.1 9.72 8.54 9 8.54 April 10.01 9.03 10 11.24 10.03 May 11.55 10.22 11.35 11.52 11.25 June 13.11 12.52 11.46 12.72 11.25 July 16.2 17.76 12.56 12.78 15.2 August 18.2 17.94 18.02 18.07 18.32 September 12.01 15.25 18 11.03 12.01 October 12.25 11 12.23 9.72 10.11 November 9.4 11.08 11.42 9.83 10.24 December 9.72 10.01 11.05 9.04 10.04 Source: Field work (2006).
WHO(MPL) WHO’s MPL
20
18
16
14
12
10
8
Calcium level(mg/l) 6
4
2
0 HDW1 J F M A M J J A S O N D HDW2 Months of the year HDW3 HDW4 Fig 36:Comparism of calcium levels of wells to WHO(MPL) HDW5
Fig 36: Comparison of calcium levels of wells to WHO’s MPL
2.3.14 Nitrate of the urban wells. cxix
Values of nitrate obtained (Table 33) indicate that all the wells had values that were within the WHO’s MPL of 10 mg/l. This is depicted also in Figure 37. TABLE 33: Nitrate levels of wells in Enugu urban area SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 0.01 0.01 0.03 0.04 0.36 February 0.03 0.07 0.56 0.01 0.06 March 0.05 0.01 0.46 0.04 1.23 April 0.08 0.01 0.64 0.05 2.42 May 0.01 0.04 0.01 0.02 1.23 June 0.01 0.01 0.01 0.05 7.08 July 0.09 0.09 0.06 0.06 0.01 August 0.09 0.03 0.09 0.06 0.01 September 0.14 1.2 0.17 0.69 0.14 October 1.48 1.03 1.51 1.39 1.37 November 0.02 0.01 0 1.33 0.03 December 0 0.01 0.02 0.01 0.02 Source: Field work (2006).
WHO’s WHO(MPL) MPL 8
7
6
5
4
3
2 Nitrate level(mg/l) Nitrate 1
0 HDW1 J F M A M J J A S O N D -1 HDW2 Months of the year HDW3 HDW4 Fig 37:Comparism of nitrate levels of wells toWHO(MPL) HDW5 Fig 37: Comparison of nitrate levels of wells to WHO’s MPL
2.3.15 Fecal coliform bacteria of the urban wells. cxx
The results of the fecal coliform analysis as in Table 34 and Figure 38, show that all the wells had coliform levels that exceeded the WHO’s MPL of 0cfu/100ml in all the months of the year. TABLE 34: Fecal coliform bacteria levels of wells in Enugu urban area. (cfu/100ml) SAMPLE SITES Months HDW1 HDW2 HDW3 HDW4 HDW5 January 1 1 3 0 2 February 3 3 0 3 1 March 3 3 3 3 3 April 2 1 4 2 4 May 2 1 4 1 1 June 9 9 9 7 7 July 2 2 2 2 2 August 2 2 2 2 2 September 2 9 2 18 2 October 9 2 2 18 9 November 5 2 3 3 17 December 3 2 5 11 12 Source: Field work (2006)
20 18 16 14 12 10 8 6 4
Facal Facal coliformlevel bacteria 2 0 WHO’sWHO(MPL) MPL J F M A M J J A S O N D HDW1 Months of the year HDW2 HDW3 Fig 38:Comparism of well faecal coliform bacteria levels to HDW4 WHO(MPL) HDW5
Fig 38: Comparison of well fecal coliform bacteria levels to WHO’s MPL
2.3.16 Comparison of Annual Values of Selected Parameters of Wells to the WHO’s Guideline for Drinking Water. cxxi
The annual mean values of all the parameters for the wells presented in Table 35 show that on the average, the temperature, pH, conductivity, hardness, biochemical oxygen demand, phosphate, sodium, sulphate, ammonia, calcium and nitrate levels for the year were within the WHO’s MPL.
The annual turbidity, dissolved oxygen and the fecal coliform levels (mean values) for all the wells exceeded the WHO’s MPL.While the total dissolved solids for the wells in four locations were within acceptable limits; one location exceeded the WHO’s MPL.
The annual mean values (Table 35) indicate that the fecal coliform levels
exceeded the WHO’s MPL thus indicating that the wells in Enugu urban area show
high levels of fecal contamination.
TABLE 35: Annual mean values selected Parameters of wells in Enugu Urban Parameters SAMPLES SITES HDW1 HDW2 HDW 3 HDW 4 HDW 5 Temperature 24 24.58 24.58 24.58 24.58 pH 4.3 5.2 5.6 4.9 6.0 Turbidity 12.8 6.2 7.4 6.4 11.5 TDS 582 228 245 447 164 Conductivity 0.63 0.29 0.54 0.43 0.35 Hardness 0.52 0.52 0.62 0.97 1.69 Dissolved Oxygen 5.06 4.98 4.86 4.86 4.91 Biochemical Oxygen Demand 1.10 1.81 1.76 1.18 1.95 Phosphate 1.64 0.85 1.14 0.92 1.35 Sodium 18.06 13.96 21.57 15.75 15.93 Sulphate 2.44 2.02 1.44 0.79 0.71 Calcium 11.5 11.8 11.8 11.1 11.23 Ammonia 1.57 1.60 2.48 1.82 1.16 Nitrate 0.16 0.21 0.29 0.31 1.16 Fecal Coliform bacteria 3.5 3.0 3.2 5.8 5.1
CHAPTER THREE cxxii
SEASONAL AND SPATIAL PATTERNS OF SURFACE AND GROUND
WATER QUALITY VARIATIONS.
3.1 Seasonal and Spatial Patterns of Water Quality Variations in the Urban Rivers. 3.1.1 Temperature Variation Pattern of the rivers. 3.1.1.1 Rainy Season Period. The temperature values for the various rivers for this season range from 23C to 26C as is shown in Table 3. The month of July was the month in which all the rivers had their highest temperature values for this season. It is noteworthy that the temperature values for the five rivers were consistent each month (Fig 39) but inspite of this, there were monthly variations between the rivers.
3.1.1.2 Dry Season period
During this season, the temperature range of the rivers was 23C to 27C
(Table 3).The temperature values for the rivers were consistent at each sample period each month (Fig 40), while monthly variations occurred between the rivers. Based on the temperatures obtained from fieldwork, there are no signs of thermal pollution as the values lie within the values for tropical waters (Egboge, 1971).
A comparison of the temperatures for the two seasons (Figs 39 and 40) indicates that the temperatures were generally higher during the dry season than the rainy season for all the rivers. The variations both monthly and between rivers are indicative of seasonal and spatial variations.
cxxiii
26.5 26 25.5 25 24.5 24 23.5 23
Temperature level( C) Temperature 22.5 22 21.5 A M J J A S SW1 Months of the year SW2 SW3 Fig 39:Rainy season temperature variation pattern of the SW4 rivers SW5
Fig 39: Rainy season temperature variation pattern of the rivers
28
27
26
25
24
23 Temperature level( C) Temperature
22
21 SW1 O N D J F M SW2 Months of the year SW3 SW4 Fig 40:Dry season temperature variation pattern of the rivers SW5
Fig 40: Dry season temperature variation pattern of the rivers
cxxiv
3.1.2 The pH Variation Pattern of the Rivers
3.1.2.1 Rainy Season period
Rainy season pH values of rivers in Enugu urban area as shown in Table 4 indicate that the rivers are generally acidic; while rivers Ekulu (SW3) and Ogbete
(SW4) were highly acidic in the month of September (Fig 41). Monthly variations occurred between the rivers. These rivers experience a lot of influence from runoff from surface areas and the decomposition of wastes from the residential and market areas (Plates 3 and 4).
3.1.2.2. Dry Season period.
The dry season pH values for all the rivers (Table 4) indicate that they are acidic in nature with the acidity being slightly high in river Asata in the month of
November. This high acidity is associated with effluents from the Artisan market. The acidity levels generally vary from river to river and from month to month during this season.
The dry season and rainy season pH values (Figs 41 and 42) do not depict a discernible variation pattern. This is buttressed by an ANOVA test that yielded an
F-critical value of 2.53, indicating that the variation between the pH levels of the rivers is not significant. Spatial variations however exist as pH values for the rivers vary from river to river (as is shown by Table 4).
cxxv
8
7
6
5
4
pH level pH 3
2
1
0 SW1 A M J J A S SW2 Months of the year SW3 SW4 SW4 Fig 41: Rainy season pH variation pattern of the rivers SW5
Fig 41: Rainy season pH variation pattern of the rivers
8
7
6
5
4 pH level pH 3
2
1 SW1 0 SW2 O N D J F M SW3 Months of the year SW4 Fig 42: Dry season pH variation pattern of the rivers SW5
Fig 42: Dry season pH variation pattern of the rivers
cxxvi
3.1.3 Turbidity Variation Pattern of the Rivers
3.1.3.1 Rainy Season period.
The turbidity levels of the rivers during this period range between 0-74 NTU
(Table 5 ).This trend indicates that the turbidity is high in the rivers. Aria river (SW 2) recorded a high turbidity level during this period (Fig 43). From this figure, it can be seen that turbidity levels of the rivers vary from month to month and between rivers.
Very low values were observed in the months of July, August and September.
The high turbidity levels recorded in some of these rivers are attributable to high farming activities along the banks of the rivers, increase in the rate of runoff, excavation and discharge of wastes into the rivers.
3.1.3.2 Dry Season Period.
The turbidity levels ranged from 2-53 NTU (Table 5). The highest turbidity level occurred in the month of January; coinciding with the period of (sometimes) first rain that usually introduces increased solid contents into the river. Monthly variations do exist between the rivers thus depicting spatial and seasonal variations
(Fig 44).
cxxvii
80
70
60
50
40
30
Turbidity level(NTU) 20
10
0 SW 1 A M J J A S SW 2 Months of the year SW 3 SW 4 Fig 43: Rainy season turbidity variation pattern of the rivers SW 5
Fig 43: Rainy season turbidity variation pattern of the rivers
60
50
40
30
20 Turbidity level(NTU) 10
0 SW 1 O N D J F M SW 2 Months of the year SW 3 SW 4 Fig 44:Dry season turbidity variation pattern of the rivers SW 5
Fig 44: Dry season temperature variation pattern of the rivers
cxxviii
3.1.4 Total Dissolved Solids Variation Pattern of the Rivers.
3.1.4.1 Rainy Season Period.
The concentration of dissolved solids in the rivers range from 3mg/l to
100mg/l (Table 6).The rainy season total dissolved solids had the highest values in rivers Asata (SW1) and Ogbete (SW4)(Fig.45) in the month of April. Generally, monthly variations occurred during this season and dissolved solids levels varied among the rivers. The sources of the dissolved solids include urban land runoff, farming activities and solid wastes/sewages dumped along the banks of the rivers.
3.1.4.2 Dry Season Period
The levels of dissolved solids in the rivers range from 7 to 260mg/l, with the highest value being recorded in the month of November in Immaculate river (SW5)
(Fig. 46).During this period, variations existed monthly and between rivers in terms of the dissolved solid content of the rivers.
Seasonally as is shown by Figures 45 and 46 the levels of dissolved solids varied for the two seasons among all the rivers.
3.1.5 Conductivity Variation Pattern of the rivers
3.1.5.1 Rainy Season
Conductivity values for the rivers during this period range from 0.03 to
0.19µSCM (Table 7). These values are very low and indicative of the fact that the rivers are fresh water rivers. According to Wrights (1982), low conductivity can be ascribed to highly leached laterite soils. Inspite of these low values, variations occurred between the rivers monthly and spatially (Fig. 47).
cxxix
3.1.5.2 Dry Season Period.
The values were generally low for all the rivers, ranging from 0.01 to
0.1µSCM (Table 7). Inspite of the low values, variations also existed monthly and
among rivers (Fig 48).
Generally, conductivity levels were higher in the dry season months than in
the rainy season months as is depicted by Figures 47 and 48.
120
100
80
60
40
Total dissolved solids(mg/l) 20
0 A M J J A S SW 1 Months of the year SW 2 SW 3 Fig 45:Rainy season total dissolved solids variation pattern of SW 4 the rivers SW 5
Fig 45: Rainy season total dissolved solids variation pattern of the rivers
cxxx
300
250
200
150
100
50 Total Total dissolved solids (mg/l) 0 O N D J F M SW 1 SW 2 Months of the year SW 3 Fig 46:Dry season total dissolved solids variation pattern of the SW 4 rivers SW 5
Fig 46: Dry season total dissolved solids variation pattern of the rivers
0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06
0.04 Conductivity level( SCM ) Conductivity 0.02 0 SW 1 A M J J A S SW 2 Months of the year SW 3 SW 4 Fig 47:Rainy season conductivity variation pattern of the rivers SW 5
Fig 47: Rainy season conductivity variation pattern of the rivers
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 c
0.2 Conductivity level( SCM ) Conductivity 0.1 0 SW 1 O N D J F M SW 2 Months of the year SW 3 SW 4 Fig 48:Dry season conductivity variation pattern of the rivers SW 5
cxxxi
Fig 48: Dry season conductivity variation pattern of the rivers
3.1.6 Total Hardness Variation Pattern of the Rivers
3.1.6.1 Rainy Season Period
Total hardness values for this period ranged between 0.10 to 0.86mg/l (Table
8). The highest value at this period occurred in the month of September in Asata river
(SW1), while the lowest value occurred in the month of August in Immaculate river.
It is observable that there are variations in the monthly values obtained within this season and between the rivers as is shown by Figure 49.
3.1.6.2 Dry Season Period.
The values ranged from 0.10 to 0.97 mg/l (Table 8). The highest value occurred in Ogbete river (SW4) in the month of December, while the lowest value for this period was recorded in the month of November in Aria river. Generally, as is depicted by Figs 49 and 50, four rivers (Asata, Aria, Ekulu and Immaculate) have cxxxii higher levels of dry season hardness, while river Ogbete(SW4) had its hardness mean value (0.57mg/l) being higher in rainy season than in dry season (0.49mg/l).
Spatially, there are thus variations in the total hardness of the rivers for both the rainy and dry season periods. For both seasons, the waters are soft waters.
1.4
1.2
1
0.8
0.6
Hardness(mg/l) 0.4
0.2
0 SW1 A M J J A S Months of the year SW2 SW3 Fig 49: Rainy season hardness variation pattern of the rivers SW4 Fig 49:Rainy season hardness variation pattern of the rivers SW5
1.2
1
0.8
0.6
0.4 Hardnesslevels(mg/l) 0.2
0 SW1 O N D J F M SW2 Months of the year SW3 SW4 Fig 50:Dry season hardness variation pattern of the rivers SW5
Fig 50: Dry season hardness variation pattern of the rivers
3.1.7 Dissolved Oxygen Variation Pattern of the Rivers cxxxiii
3.1.7.1 Rainy Season Period.
The values for this season ranged between 1.4 to7.8 mg/l; with the highest
value occurring in the month of November and the least in the month of April (Table
9). The monthly dissolved oxygen concentrations levels of the rivers were high in all
the rainy season months (Fig 51), except in April, when lower dissolved oxygen
demand is attributable to the fact that the water bodies were utilized extensively as
waste receptacles. Monthly and spatial variations occurred during this season.
3.1.7.2 Dry Season Period
Monthly values ranged from 0.6mg/l to 9.7mg/l during this period (Table
9).The highest value occurred in the month of February in Ekulu river, while the
lowest value occurred in the month of January in Immaculate river.
Monthly and seasonally, there were variations among the rivers in terms of
their oxygen demand levels. The rainy season values were generally higher than the
values obtained for the dry season (Fig 52).
7
6
5
4
3
2
1 Dissolved oxygen level(mg/l)oxygen Dissolved 0 A M J J A S SW1 Months of the year SW2 SW3 Fig 51:Rainy season dissolved oxygen variation pattern of the SW4 rivers SW5 Fig 51: Rainy season temperature variation pattern of the rivers
12
10
8
6
4
2 Dissolved oxygen level(mg/l)oxygen Dissolved
0 SW1 O N D J F M SW2 Months of the year SW3 Fig 52:Dry season dissolved oxygen variation pattern of the SW4 rivers SW5
cxxxiv
Fig 52: Dry season temperature variation pattern of the rivers
3.1.8 Biochemical Oxygen Demand Variation Pattern of the Rivers
3.1.8.1 Rainy Season Period
The biochemical oxygen demand range for this period was between 0.3 to
4.1mg/l (Table 10). The highest value occurred in rivers Asata (SW3) and Ogbete
(SW4) in the month of May (Fig. 53). The months of July, August and September recorded lower values, while all the rivers generally recorded high biochemical oxygen demand in the month of May. The values were however low in all the rivers and this is because chemical and biological constituents requiring oxygen were low during this period. This is attributable to the fact that the rainy season flows aid the dilution of the water.
3.1.8.2 Dry Season Period
The values at this period ranged from 0.3 to 9.7mg/l (Table 10), with the highest value occurring in river Ogbete (SW4) in the month of February and the least also in river Ogbete (SW4) in the month of October (Fig 54). Monthly and seasonal variations occurred in biochemical oxygen demand of the rivers. cxxxv
3.1.9 Phosphate Variation Pattern of the Rivers
3.1.9.1 Rainy Season Period
The phosphate levels during this season had a range between 0mg/l to 4.8mg/l
(Table 11). Figure 55 shows that the highest value occurred in Ekulu river (SW3) in the month of April and the lowest value was recorded in Asata river in the month of
June. The phosphate levels were generally low in the months of June, July, and
August. The monthly values observed also indicate variations among the rivers.
Dry Season Period.
The dry season phosphate concentrations ranged from 0 to 5.19mg/l as is shown in
Table 11 indicating that while the levels of phosphate were relatively low, and had varying concentrations, the levels were very negligible for all the rivers in the months of November and December (Fig 56).
The seasonal pattern as depicted by Figs 55 and 56 show that the dry season phosphate concentration was higher than the rainy season for all rivers in Enugu urban area.
4.5 4 3.5 3 2.5 2
1.5 demand(mg/l)
Biochemical oxygen Biochemical oxygen 1 0.5 0 A M J J A S SW 1 Months of the Year SW 2 SW 3 Fig 53:Rainy season biochemical oxygen demand variation SW 4 pattern of the rivers SW 5
cxxxvi
Fig 53: Rainy season biochemical oxygen demand variation pattern of the rivers
12
10
8
6
4 demand(mg/l)
Biochemical oxygen 2 0 O N D J F M SW 1 Months of the year SW 2 SW 3
Fig 54:Dry season biochemical oxygen demand variation pattern SW 4 of the rivers SW 5
Fig 54: Dry season biochemical oxygen demand variation pattern of the rivers
6
5
4
3
2 Phosphate level(mg/l)Phosphate 1
0 SW 1 A M J J A S SW 2 Months of the year SW 3 SW 4 Fig 55:Rainy season phosphate variation pattern of the rivers SW 5
cxxxvii
Fig 55: Rainy season phosphate variation pattern of the rivers
7 6
5
4
3
2 Phosphate level(mg/l)Phosphate 1
0 SW 1 O N D J F M SW 2 Months of the year SW 3 SW 4 Fig 56:Dry season phosphate variation pattern of the rivers SW 5
Fig 56: Dry season phosphate variation pattern of the rivers
3.1.10 Sodium Variation Pattern of the Rivers
3.1.10.1 Rainy Season Period cxxxviii
The sodium levels of the rivers ranged from 0.42mg/l to 21.6mg/l as is shown in Table 12. The values indicate that river Ogbete (SW4) had the highest value during this season, while Immaculate (SW5) had the least level (0.42mg/l) in the month of
September. The monthly values (Fig 57) indicate a decrease in the monthly sodium level from June to September thus showing that there was variation in sodium concentration within and between the rivers.
3.1.10.2 Dry Season Period
The season’s range was between 0.22 to 26.5mg/l occurring in rivers Ekulu
(SW 3) in the month of October and Immaculate (SW5) in the month of January.
The pattern revealed by the dry season values indicates that the sodium level of the rivers was lower in the months of October, November and December (Fig 58).
Variations occurred within the months and the rivers.
25
20
15
10
Sodium level(mg/l) 5
0 SW1 A M J J A S SW2 Months of the year SW3 SW4 Fig 57:Rainy season sodium variation pattern of the rivers SW5 Fig 57: Rainy season sodium variation pattern of the rivers
40
35
30
25 20
15
Sodium level(mg/l) 10
5
0 O N D J F M SWS 1 Months of the year SWS2 SWS3 SWS4 Fig 58:Dry season sodium variation pattern of the rivers SWS5 cxxxix
Fig 58: Dry season sodium variation pattern of the rivers
3.1.11 Sulphate Variation Pattern of the Rivers
3.1.11.1 Rainy Season Period
The values range between 0 to 7.4mg/l as is indicated in Table 13.The lowest value was recorded in Aria (SW2) river in the month of June, while the highest value occurred in the month of April in Ogbete (SW4) river. Very low values were generally observed in the months of July, August and September. The values indicate monthly variations among the rivers within this season (Fig 59).
3.1.11.2 Dry Season Period
The season’s range was from 0 to 9.7mg/l. The highest value for this season occurred in Asata (SW 1) river in the month of March while the lowest value occurred in various river during different months (Fig 60). From the pattern depicted by this figure, it is observable that variations exist between the levels of sulphate concentration in the rivers.
Generally, the pattern depicted by these two seasons (Figs 59 and 60) shows that sulphate levels are higher in dry season in three rivers (Asata (SW1), Aria (SW2), and
Ekulu (SW3); while two rivers namely Ogbete (SW4) and Immaculate (SW5) had higher values in the rainy than the dry season.
8
7
6
5 4
3
2 Sulphate Sulphate level(mg/l) 1
0 A M J J A S SW1 Months of the year SW2 SW3 SW4 Fig 59:Rainy season sulphate variation pattern of the rivers SW5
cxl
Fig 59: Rainy season sulphate variation pattern of the rivers
12
10
8
6
4 Sulphate Sulphate level(mg/l) 2
SW1 0 O N D J F M SW2 SW3 Months pf the year SW4 Fig 60:Dry season sulphate variation pattern of the rivers SW5
Fig. 60: Dry season sulphate variation pattern of the rivers
3.1.12 Iron Variation Pattern of the Rivers
3.1.12.1 Rainy Season Period cxli
The concentration of iron in the different rivers in this season as is shown in
Table 14, indicate that the range is between 0.1 to 0.2mg/l. Consistent values were recorded in all the rivers in the months of June, July, August, and September (Fig 61).
Variations however occurred in two months (April and May).
From Fig 61 it can be seen that in April, three rivers (Asata (SW1), Aria
(SW2) and Ekulu (SW3)) had consistent values of 0.1 mg/l, while in the month of
May all the rivers had the same iron concentration level, only one river (Ekulu
(SW3)) had a different concentration level.
3.1.12.2 Dry Season Period
Iron concentrations for this period ranges between 0 to 0.2mg/l. Generally,
Immaculate (SW5) had higher values during this season. Consistent values were recorded in three rivers (Asata (SW1), Ekulu (SW3) and Immaculate (SW5) in the months of October and November (Fig 62).
In some of the months, the iron concentration levels were negligible for rivers like Aria (SW2), Ekulu (SW3) and Ogbete (SW4). Inspite of the consistent iron levels in the rivers, variation is still observable monthly and spatially in for both seasons.
0.25
0.2
0.15
0.1 Iron level(mg/l)
0.05
0 SW1 A M J J A S SW2 Months of the year SW3 SW4 Fig 61:Rainy season iron variation pattern of the rivers SW5
cxlii
Fig. 61: Rainy season iron variation pattern of the rivers
0.25
0.2
0.15
0.1 Iron level (mg/l)
0.05
0 SW1 O N D J F M SW2 Months of the year SW3 SW4 Fig 62:Dry season iron variation pattern of the rivers SW5
Fig. 62: Dry season iron variation pattern of the rivers
3.1.13 Ammonia variation pattern of the Rivers
3.1.13.1 Rainy Season Period cxliii
The range of ammonia content of the rivers is from 0.06 to 8.72mg/l (Table
15). The highest value for this season was recorded in Ogbete (SW4) in June, while very low ammonia levels were recorded in the months of July, August and September
(Fig 63).
Monthly and Spatial variations occurred during this season as figure also depicts that negligible ammonia levels were observed for rivers Ogbete (SW4) and
Immaculate (SW5) in July. Very low values were recorded in August and September for all the rivers.
3.1.13.2 Dry Season Period
Ammonia levels for the period range from 0 to 3.311mg/l (Table 15). The dry season highest ammonia level was recorded in Ekulu (SW3) river in the month of
March. Figure 64 shows that four rivers (Aria (SW2), Ekulu (SW3), Ogbete (SW4) and Immaculate (SW5) had negligible ammonia levels in the months of November and December.
Generally, higher ammonia levels occurred at the beginning of the rainy season for all the rivers. There are, however, more months with very low ammonia levels in the rainy than the dry season.
cxliv
10 9 8 7 6 5 4
3 Ammonia level(mg/l) 2 1
0 SW1 A M J J A S SW2 Months of the year SW3 SW4 Fig 63:Rainy season ammonia variation pattern of the rivers SW5
Fig. 63: Rainy season ammonia variation pattern of the rivers
3.5
3
2.5
2
1.5 1
Ammonia level(mg/l) 0.5
0 SW1 O N D J F M SW2 Months of the year SW3 SW4 Fig 64:Dry season ammonia variation pattern of the rivers SW5
Fig. 64: Dry season ammonia variation pattern of the rivers
cxlv
3.1.14 Calcium Variation Pattern of the Rivers
3.1.14.1 Rainy Season Period
Calcium content of rives range between 2.01 to 3.05mg/l (Table 16). The highest value for this season was recorded in river Ogbete (SW4) in two months (July and August), while very low calcium levels were recorded in river Immaculate (SW5)
(Fig 65). Spatial and monthly variations occurred in terms of the calcium content of the rivers.
3.1.14.2 Dry Season Period
The dry season range is from 0.02 to 3.23mg/l.The highest value for this season was recorded in river Ekulu (SW3), while very low calcium levels were recorded in river Immaculate (SW5)(Fig 66). Generally, monthly and spatial variations occurred during this season as depicted by Figs 65 and 66.
4
3.5
3
2.5
2
1.5
Calium level(mg/l) 1
0.5
0 SW1 A M J J A S SW2 Months of the year SW3 SW4 Fig 65:Rainy season calcium variation pattern of the rivers SW5
Fig 65: Rainy season calcium variation pattern of the rivers cxlvi
3
2.5
2
1.5
1 Calcium level(mg/l) 0.5
0 SW1 O N D J F M SW2 Months of the year SW3 SW4 SW4 Fig 66:Dry season calium variation pattern of the rivers SW5
Fig 66: Dry season calcium variation pattern of the rivers
3.1.15 Nitrate Variation Pattern of the Rivers
3.1.15.1 Rainy Season Period
Nitrate levels for this period ranges between 0.01-3.31mg/l (table 17). The highest nitrate level for this season occurred in Ogbete (SW4) river. Figure 67 shows that very low nitrate levels were recorded in the first three months of this season. In the month of June very negligible nitrate levels were observed. Inspite of this, monthly and spatial variations occurred.
3.1.15.2 Dry Season Period
The levels of nitrate in the rivers during this period range from 0-
1.45mg/l (Table 17). The highest value for this season was recorded in river Asata
(SW1). From Fig. 68, it can be seen that all the rivers had their highest nitrate levels for the season in the month of October. Apart from Asata (SW1) in November, all the cxlvii other rivers had very low (and some negligible) nitrate levels in November,
December, January, February and March (Fig 68).
Generally, each season had a month in which all the rivers recorded higher nitrate levels. While it was September for the rainy season it was October for the dry season (Figs 67 and 68).
3.5
3
2.5
2 1.5
Nitrate level(mg/l) Nitrate 1
0.5
0
A M J J A S SW1 Months of the year SW2 SW3 SW4 Fig 67:Rainy season nitrate variation pattern of the rivers SW5
Fig 67: Rainy season nitrate variation pattern of the rivers
1.6 1.4
1.2
1
0.8
0.6 Nitate level(mg/l) Nitate 0.4
0.2
0 SW1 O N D J F M SW2 Months of the year SW3 SW4 Fig 68:Dry season nitrate variation pattern of the rivers SW5
cxlviii
Fig 68: Dry season nitrate variation pattern of the rivers
3.1.16 Fecal Coliform Variation Pattern of the Rivers
3.1.16.1 Rainy Season Period
The rainy season fecal coliform count ranges from 2-12 cfu/100mls. The highest fecal coliform level for this season was recorded in River Asata (SW1) in
June. Figure 69 shows a negligible level in Aria (SW2) in March. The same levels were recorded for all the rivers in August.
Generally three rivers (Ekulu, Ogbete and Immaculate), had the same levels in most of the months with the exception of June. Inspite of the same fecal coliform levels recorded for most months and rivers (Fig 69), spatial and monthly variations occurred during this season.
3.1.16.2 Dry Season Period
Fecal coliform levels during this period ranges form 1-90 per 100mls. The levels were high in different months and rivers (Fig 70). Generally from Fig. 70, it can be seen that the fecal coliform levels of all the rivers were negligible from January to
March in comparism to the other levels recorded during this season.
There are thus higher fecal coliform levels in the dry than the rainy season in the rivers.
cxlix
14
12
10 8
6 (cf/100mg) 4
2 Faecal coliformFaecal level bacteria 0 A M J J A S SW1 Months of the year SW2 SW3 Fig 69:Rainy season faecal coliform bacteria variation pattern SW4 of the rivers SW5
Fig 69: Rainy season fecal coliform bacteria variation pattern of the rivers
100 90 80 70 60 50 40
level(c/100mg) 30 20
colifom Feacal bacteria 10 0 O N D J F M SW1 Months of the year SW2 SW3 Fig 70:Dry season faecal coliform bacteria variation pattern of SW4 the rivers SW5
Fig 70: Dry season fecal coliform bacteria variation pattern of rivers
cl
3.2 Seasonal and Spatial Patterns of River Water Quality. 3.2.1 Annual Temperature variation pattern of the rivers 3.2.1.1 Rainy and dry season variations The rainy season mean temperatures as shown in Table 36 for all the river was 24C, thus indicating no variation in the seasonal pattern of the temperature for all the rivers. The dry season mean temperature for all the rivers is 25ºC also indicating that the rivers had no variation in their temperature readings throughout this season.
Inspite of none seasonal variations, the dry season temperatures were higher in all the rivers as depicted by Fig 71. Also there was no spatial variation in terms of temperature as the rivers generally all the same reading for each season.
TABLE 36: Rainy season mean values of selected parameters for rivers in Enugu urban area
Parameters Sample Sites
SW1 SW2 SW3 SW4 SW5
Temperature 24 24 24 24 24
pH 6.20 6.25 6.18 6.13 6.36
Turbidity 8.33 25.83 4.50 4.16 14.3
Total dissolved Solids 70.33 25.83 72.33 88.00 50.83
Conductivity 0.18 0.06 0.12 0.14 0.07
Hardness 0.45 0.31 0.35 0.57 0.35
Dissolved Oxygen 5.11 5.18 4.68 4.03 4.95
Biochemical Oxygen Demand 1.03 1.13 1.50 1.05 1.11
Phosphate 0.62 0.48 1.30 0.34 0.61
Sodium 6.27 7.04 10.10 7.20 4.03
Sulphate 0.58 0.66 0.55 2..18 2..15
Iron 0.10 0.10 o.11 0.12 0.12
Ammonia 0.91 0.48 1.09 1.15 0.54
Calcium 2..82 2..78 2..94 3.09 2..48 cli
Nitrate 0.64 0.56 0.38 0.64 0.40
Fecal Coliform 31.76 32.83 15.16 29.83 5.16 TABLE 37: Dry season mean values of selected parameters for rivers in Enugu urban area.
PARAMETERS SAMPLE SITES
SW1 SW2 SW3 SW4 SW5
Temperature 25 25 25 25 25
pH 6.23 6.23 6.16 6.01 6.34
Turbidity 6.83 18.33 7.83 6.66 9.66
Total Dissolved Solids 54.5 18.33 110 120.66 122.5
Total Hardness 0.49 0.34 0.54 0.49 0.55
Dissolved Oxygen 5.03 5.61 6.20 5.18 5.13
Biochemical Oxygen Demand 2.88 2.05 1.25 2.66 1.65
Phosphate 1.71 1.83 1.97 2.73 2.25
Sodium 6.23 8.72 4.86 5.72 8.51
Conductivity 0.01 0.22 0.25 0.12 0.02
Sulphate 2.31 1.63 1.86 1.70 0.53
Iron 0.05 0.01 0.04 0.05 0.16
Ammonia 1.23 0.63 2.46 1.63 0.28
Calcium 2.50 2.54 2.76 2.62 2.01
Nitrate 1.45 1.03 1.04 1.37 0.94
Fecal coliform 4 2.1 2.6 6.3 7.3
SW5
SW4
SW3 Rivers
SW2
SW1
23.4 23.6 23.8 24 24.2 24.4 24.6 24.8 25 25.2 TemperatureTemperature °C C
DRY Fig 71:Seasonal temperature pattern of the rivers RAIN
clii
Fig 71: Seasonal temperature pattern of the rivers
3.2.2 Annual pH variation pattern of the rivers
3.2.2.2 Rainy and Dry season annual variations
The mean pH values for the rainy season (table 36) for the rivers varied with river Immaculate (SW5) having the highest value and Ogbete (SW4) the lowest value. This indicates that the river acidity in decreasing order is as follows:
Immaculate (SW5), Aria (SW2), Asata (SW1), Ekulu (SW3) and Ogbete (SW4).
The mean pH values for the dry season (table 37) indicate that acidity level in decreasing order is as follows: Immaculate (SW5), Aria (SW2), Asata (SW1), Ekulu
(SW3) and Ogbete (SW4). For the two seasons there were no variations in terms of the acidity levels of the rivers except for Ogui river (Fig 72).
SW5
SW4
SW3 Rivers
SW2
SW1
5.8 5.9 6 6.1 6.2 6.3 6.4 pH level DRY Fig 72:Seasonal pH pattern of the rivers RAINY
Fig 72: Seasonal pH pattern of the rivers
cliii
3.2.3 Annual turbidity variation pattern of the rivers
3.2.3.1 Rainy and dry season variations
The mean values obtained for the rainy season (Table 36) indicate that turbidity levels are highest in Aria river (SW2) and lowest in Ogbete (SW4) river. In decreasing order, the turbidity level in the rivers is as follows: Aria river (SW2)
Immaculate river (SW5), Asata river (SW1), Ekulu river (SW3) and Ogbete (SW4).
The dry season mean values (Table 37) indicate that river Aria (SW2) had the highest turbidity levels, while the turbidity levels were lowest in Ogbete (SW4) river.
In decreasing order, the turbidity level of the rivers is as follows: Aria river (SW2)
Immaculate river (SW5), Asata river (SW1), Ekulu river (SW3) and Ogbete (SW4).
Spatial variation is thus observable in terms of turbidity amongst all the rivers.
There was no particular season in which all the rivers had either high or low turbidity (Fig 73). Rather variations occurred in terms of the seasons of high or low turbidity spatially and seasonally. Three rivers namely rivers Asata (SW1), Aria
(SW2) and Immaculate (SW5) located in different parts of the urban area, had higher turbidity in the rainy season, while the two rivers (Ekulu(SW4) and Ogbete(SW4) others had higher turbidity in the dry season(Fig 73).
SW5
SW4
SW3 Sample sites SW2
SW1
0 5 10 15 20 25 30 Turbidity level(mg/l)
DRY Fig 73: Seasonal turbidity pattern of the rivers RAINY
cliv
Fig 73: Seasonal turbidity pattern of the rivers
3.2.4 Annual Total dissolved solids variation pattern of the rivers
3.2.4.1 Rainy and Dry season variations
The rainy season mean values (Table 36) indicate that River Ogbete
(SW4) had the highest level of total dissolved solids within this season, while Aria river (SW2) had the lowest level. In decreasing order therefore the levels of dissolved solids in the rivers were as follows: Ogbete (SW 4), Ekulu (SW3), Asata (SW1),
Immaculate (SW5) and Aria (SW 2).
The dry season mean values (Table 37) show that Immaculate river (SW5) had the highest mean value with the lowest value occurring for Aria river. In decreasing order the level of dissolved solids in the river are as follows: Immaculate
(SW5), Ogbete (SW4), Ekulu (SW3), Asata (SW1) and Aria (SW2).
For the rainy and dry seasons, as depicted by the seasonal cluster bars
(Fig 74), three rivers (Ekulu (SW3), Ogbete (SW4) and Immaculate (SW5) had, the dissolved solid levels that were higher in the dry season; while two rivers Asata clv
(SW1) and Aria (SW2) had higher dissolved solids in the rainy season. This indicates variations seasonally and spatially.
SW5
SW4
SW3
Sample sites SW2
SW1
0 20 40 60 80 100 120 140 Total dissolved solids(mg/l)
DRY Fig 74: Seasonal total dissolved solids pattern of the rivers RAINY
Fig 74: Seasonal total dissolved solids pattern of the rivers
3.2. 5 Annual Conductivity variation pattern of the rivers
3.2.5.1 Rainy and dry season variations of the rivers
The rainy season mean values (Table 36) indicate that conductivity was highest at this period in River Asata (SW1) and lowest in River Aria (SW2).
Conductivity levels at this period in decreasing order were thus as follows: Asata
(SW1), Ogbete (SW4), Ekulu (SW3), Immaculate (SW5) and Aria (SW2).
The dry season mean values (Table 37) indicate that River Ekulu (SW3) had the highest values, while river Asata (SW1) had the lowest values. Conductivity in deceasing order in the rivers in this season, were as follows: Ekulu (SW3), Aria
(SW2), Ogbete (SW4), Immaculate (SW5) and Asata (SW1).
A comparison of the conductivity levels among the rivers for the dry and rainy seasons indicate that three rivers (Aria (SW2), Ekulu (SW3) and Immaculate clvi
(SW5)) all had higher conductivity levels in the dry season (Fig 75).River Ogbete
(SW4) and Asata (SW1) had higher conductivity in the rainy season. Season and spatial variation was thus observed.
3.2. 6 Annual total hardness variation pattern of the rivers
3.2.6.1 Rainy and Dry season variations of the rivers.
For both seasons the water are soft water.
The mean values for the rainy season total hardness for all the rivers (Table 36) indicate that there were slight variations among the rivers. Ogbete (SW4) river with a mean value of 0.57mg/l had the highest total hardness, while Aria (SW2) river had the lowest (0.31mg/l). In decreasing order, the levels of hardness in the river were as follows: Ogbete (SW4), Asata (SW1), Immaculate (SW5), Ekulu (SW3) and Aria
(SW2).
The highest dry season mean value for total hardness occurred for river Ekulu
(SW3) and the lowest for Aria (SW2). Rainy and dry season comparism (Fig 76) shows that seasonal and spatial variations occurred between the rivers in terms of the river hardness as four rivers) had higher hardness during the dry season. River Ogbete
(SW4) had the highest hardness value in the rainy season.
clvii
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.05 0.1 0.15 0.2 0.25 0.3 Conductivity level(SCM)
DRY Fig 75: Seasonal conductivity pattern of the rivers RAINY
Fig 75: Seasonal conductivity pattern of the rivers
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.1 0.2 0.3 0.4 0.5 0.6 Toatal hardness level(mg/l)
DRY Fig 76: Seasonal total hardness pattern of the rivers RAINY
Fig 76: Seasonal total hardness pattern of the rivers
clviii
3.2. 7 Annual dissolved oxygen variation pattern of the rivers
3.2.7.1 Rainy and dry seasons annual variations of the rivers.
The mean values for the rainy season dissolved oxygen content for all the rivers (Table 36) indicate that there were slight variations among the rivers. Aria
(SW2) river with a mean value of 5.18mg/l had the highest dissolved oxygen content, while Ogbete (SW5) river had the lowest (4.03mg/l).In decreasing order, the dissolved oxygen concentrations for the rivers in the rainy season were as follows:
Aria (SW2), Asata (SW1), Immaculate (SW5), Ekulu (SW3) and Ogbete (SW 4).
The dry season dissolved oxygen mean values (Table 37) indicate that River
Ekulu (SW3) had the highest oxygen demand, while Asata (SW1) had the lowest.
This also reveals that in decreasing order, the dissolved oxygen content was as follows: Ekulu (SW3), Aria (SW2), Ogbete (SW4), Immaculate (SW5) and Asata
(SW1).
Rainy and dry season comparism (Fig 77) shows that seasonal and spatial variations occurred between the rivers. As four (4) rivers (Aria (SW2), Ekulu (SW3),
Ogbete (SW4), and Immaculate (SW 5) had higher dissolved oxygen demand levels in the dry season than in the rainy season. Only river Asata (SW1) had higher oxygen demand level in the rainy season.
SW5
SW4
SW3 Sample sites SW2
SW1
0 1 2 3 4 5 6 7 Dissolved oxygen(mg/l)
DRY Fig 77:Seasonal dissolved oxygen pattern of the rivers RAINY
clix
Fig 77: Seasonal dissolved oxygen pattern of the rivers
3.2. 8 Annual biochemical oxygen demand variation pattern of the rivers
3.2.8.1 Rainy and dry season variations of the rivers.
The mean values for biochemical oxygen demand for all the rivers in the rainy season (Table 36) indicate little variations among the rivers; Ekulu (SW3) having the highest biochemical oxygen demand and Asata (SW1) having the least. In decreasing order, the biochemical oxygen demand levels in the rivers were as follows:
Ekulu (SW3), Aria (SW2), Immaculate (SW5), Ogbete (SW4), and Asata (SW1)
Mean values for the dry season period (Table 37) indicate that in decreasing order, the biochemical oxygen demand of the rivers is as follows: Asata (SW1)
Ogbete (SW4), Aria (SW2), Immaculate (SW5) and Ekulu (SW 3).
A comparison of the biochemical oxygen demand between the rivers for the dry and rainy seasons( Fig 78) indicate that four rivers (Asata, Aria, Ogbete and
Immaculate had higher dry season biochemical oxygen demand; while only Ekulu river had higher rainy season values. These indicate seasonal and spatial variations.
SW5
SW4
SW3
Sample stations SW2
SW1
0 0.5 1 1.5 2 2.5 3 Biochemical oxygen demand level(mg/l)
Fig 78:Seasonal biochemical oxygen demand pattern of the DRY rivers RAINY
clx
Fig 78: Seasonal biochemical oxygen demand pattern of the rivers
3.2. 9 Annual Phosphate variation pattern of the rivers
3.2.9.1 Rainy and dry season variations of the rivers.
The rainy season mean values of phosphate for the rivers range between
0.34 to 0.62mg/l (Table 35) indicating that there was slight variation among the rivers.
The mean values also indicate a pattern that shows that the rivers had decreasing phosphate concentration in this order: Ekulu (SW3), Asata (SW 1), Immaculate
(SW5), Ogbete (SW4) and Aria (SW2).
The dry season mean values of phosphate concentrations in the rivers
(Table 36) indicate that river Ogbete (SW4) recorded the highest level while Asata
(SW1) recorded the lowest level. It also shows that the rivers had a decreasing phosphate concentration in the following order: Ogbete (SW4), Immaculate (SW5),
Ekulu (SW3), Aria (SW 2) and Asata (SW1).
A comparison of the phosphate levels between the rivers for the dry and rainy seasons (Fig 79) indicate that the dry season concentration was higher than the rainy season concentration for all the rivers in Enugu urban area. This also indicates that the rivers had no spatial variation in terms of phosphate concentration for the seasons. clxi
3.2. 10 Annual Sodium variation pattern of the rivers
3.2.10.1 Rainy and dry season variations of the rivers
The rainy season mean values (Table 36) indicate that sodium level was highest at this period in river Ekulu (SW1) and lowest in river Immaculate (SW5).
Sodium levels at this period in decreasing order were thus as follows: Ekulu (SW3),
Ogbete (SW4), Aria (SW2), Asata (SW1), and Immaculate (SW5). However the dry season mean values (Table 37) reveal that in decreasing order, the sodium levels were as follows: Aria (SW2), Immaculate (SW5), Asata (SW1), Ogbete (SW4) and Ekulu
(SW3). The pattern revealed by the rainy and dry season’s sodium levels indicates that variations occurred in the two seasons. Two rivers, Ekulu (SW3) and Ogbete
(SW4), had higher rainy season mean values, two rivers Aria (SW2) and Immaculate
(SW5), had higher dry season mean values; while river Asata recorded no variation in the mean values of the two seasons(Fig 80 ).
Spatial variation thus does exist among the rivers in terms of sodium concentrations for both seasons.
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.5 1 1.5 2 2.5 3 Phosphate level(mg/l)
DRY Fig 79:Seasonal phosphate pattern of the rivers RAINY
clxii
Fig 79: Seasonal phosphate pattern of the rivers
SW5
SW4
SW3
Sample sites SW2
SW1
0 2 4 6 8 10 12 Sodium level(mg/l) DRY Fig 80:Seasonal sodium pattern of the rivers RAINY
Fig 80: Seasonal sodium pattern of the rivers
3.2. 11 Annual sulphate variation pattern of the rivers
3.2.11.1 Rainy and dry season variations of the rivers
The rainy season mean values for the rivers range between 0.55 to 2.18 mg/l (Table 36) indicating that there was variation among the rivers. The mean values also indicate a pattern that shows that the rivers had decreasing sulphate concentration in this order: Ogbete (SW4), Immaculate (SW5), Aria (SW2), Asata (SW1) and Ekulu
(SW3).The dry season sulphate mean values (Table 37) indicate a level of clxiii
concentration in this decreasing order: Asata (SW1), Ekulu (SW3), Ogbete (SW4),
Aria (SW2) and Immaculate (SW5).
Generally, the pattern depicted by these two seasons (Fig 81) shows that
sulphate levels were higher in dry season in three (3) rivers (Asata (SW1), Aria
(SW2), and Ekulu (SW3); while 2 rivers namely Ogbete (SW4) and Immaculate
(SW5) had higher values in the rainy than the dry season (Fig 81).This is indicative of
the fact that there were seasonal variations in the sulphate levels of rivers in Enugu
urban area.
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.5 1 1.5 2 2.5 Sulphate(mg/l)
DRY Fig 81:Seasonal sulphate pattern of the rivers RAINY
Fig 81: Seasonal sulphate pattern of the rivers
3.2. 12 Annual iron variation pattern of the rivers
3.2.12.1 Rainy and dry season variations of the rivers.
The rainy season mean values for the rivers range between 0.10 to 0.12
mg/l (Table 36) showing that the rivers all have values below 1 mg/l during thus
season. The mean values also indicate a pattern that shows that the rivers had
decreasing iron content in the following order: Ogbete (SW4) and Immaculate (SW5)
have the same concentration level; Ekulu (SW3), Aria (SW2) and Asata (SW1) have clxiv the same concentration level. Minimal variations however do occur among the rivers in terms of their iron content during this period.
The dry season mean values the iron content of the rivers (Table 37) range from 0 to
0.1mg/l. Four of the rivers( Asata (SW1), Aria (SW2),Ekulu (SW3), Ogbete (SW4) have a mean value of 0 mg/l, while only Immaculate river (SW5) had a value of
0.1mg/l.
Seasonal and spatial variations occurred in the iron content levels of the rivers. Four rivers (Asata (SW1), Aria (SW2), Ekulu (SW3) and Ogbete (SW4)) had higher rainy season values while Immaculate (SW5) river had higher values in the dry season than the rainy season (Fig 82).
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.05 0.1 0.15 0.2 Iron(mg/l) DRY Fig 82:Seasonal iron pattern of the rivers RAINY
clxv
Fig 82: Seasonal iron pattern of the rivers
3.2. 13 Annual ammonia variation pattern of the rivers
3.2.13.1 Rainy and dry season variations of the rivers.
The rainy season ammonia mean values range from 0.48mg/l in Aria
(SW2) to 1.15mg/l in Ogbete river (SW4) to 0.48 mg/l in Aria (SW2) rver (Table 36).
In decreasing order the pattern of ammonia level in the rivers is as follows: Ogbete
(SW4), Ekulu (SW3), Asata (SW1), Immaculate (SW5) and Aria (SW2).
The dry season mean values for the rivers range from 0.28 mg/l in
Immaculate (SW5) river to 2.46 mg/l in river Ekulu (SW3) (Table 37). The mean values indicate a pattern of decrease as follows: Ekulu (SW3), Ogbete (SW4), Asata
(SW1), Aria (SW2), Immaculate (SW5). This indicates that spatial variation occurred between the rivers.
The pattern revealed by the two seasons indicates that ammonia levels of rivers were higher in the rainy season than the dry season (Fig 83). Seasonal spatial variation occurred among the rivers also.
SW5
SW4
SW3 Sample sites SW2 clxvi SW1
0 0.5 1 1.5 2 2.5 Ammonia(mg/l)
DRY Fig 83:Seasonal ammonia pattern of the rivers RAINY
Fig 83: Seasonal ammonia pattern of the rivers
3.2. 14 Annual calcium variation pattern of the rivers
3.2.14.1 Rainy and dry season variations of the rivers.
The rainy season mean values for calcium range from 2.48 mg/l in
Immaculate (SW5) river to 3.09 mg/l in Ogbete river (SW4) to (Table 36). The rainy season mean value (Table 36) for the rivers indicate a calcium concentration in decreasing order as follows: Ogbete (SW4), Aria (SW2), Immaculate (SW5), Ekulu
(SW3) and Asata (SW1).The dry season mean values of calcium concentrations in the rivers(Table 37) in decreasing order is as follows: Ekulu (SW3), Ogbete (SW4), Aria
(SW2), Asata (SW1) and Immaculate (SW5).
Generally, the rainy season calcium concentrations for all the rivers were higher than the dry season concentrations (Fig 84).
3.2. 15 Annual nitrate variation pattern of the rivers
3.2.15.1 Rainy and dry season variations of the rivers.
The rainy season mean values for nitrate range from 0.38 mg/l in Ekulu
(SW3) river to 0.64mg/l in Ogbete (SW4) and Asata (SW1) (Table 36).These rainy season mean values obtained (Table 36), indicate that nitrate level is highest in Asata
(SW1) and Ogbete (SW4) river and lowest in Ekulu (SW3) river. Thus the level of clxvii nitrate concentration among the rivers at this period in decreasing order is as follows:
Asata (SW1) and Ogbete (SW4), Aria (SW2), Immaculate (SW5), Ekulu (SW3).
The pattern revealed by the two seasons indicates that the nitrate levels of all the rivers were higher in the rainy season than in the dry season (Fig 85). There was thus seasonal spatial variation among the rivers.
SW5
SW4
SW3
Sample sites SW2
SW1
0 0.5 1 1.5 2 2.5 3 3.5 Calcium level(mg/l) DRY Fig 84:Seasonal calcium pattern of the rivers RAINY Fig 84: Seasonal calcium pattern of the rivers
SW5
SW4
SW3 Sample sites SW2
SW1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Nitrate level(mg/l) DRY Fig 85:Seasonal nitrate pattern of the rivers RAINY
clxviii
Fig 85: Seasonal nitrate pattern of the rivers
3.2. 16 Annual Fecal Coliform Variation Pattern of the Rivers
3.2.15.1 Rainy and dry season variations of the rivers.
The mean rainy season values obtained for the rivers (Table 36) indicate that Aria (SW2) river had the highest coliform levels, while Immaculate
(SW5) river had the lowest. In decreasing order, the coliform levels in the rivers were as follows: Aria (SW2), Asata (SW1), Ogbete (SW4), Ekulu (SW3) and Immaculate
(SW5).
While the dry season mean values (Table 37) indicate that the coliform levels in the rivers in decreasing order are as follows: Asata (SW1) and Aria(SW2), Ogbete
(SW4), Ekulu (SW3) and Immaculate (SW5).
Seasonal variation pattern (Fig 86) depicts that the fecal coliform levels were higher in the dry season than the rainy season.
SW5
SW4
SW3
Sample sitesSample
SW2
SW1
0 5 10 15 20 25 30 35 Feacal coliform bacteria level( cfu/l) DRY Fig 86:Seasonal feacal pattern colifom bacteria level RAINY
clxix
Fig 86: Seasonal fecal coliform bacteria pattern of the rivers
3.3 Seasonal and spatial patterns of water quality variations in the urban wells.
3.3.1 Temperature variation pattern of the urban wells.
3.3.1.1 Rainy season period The rainy season temperatures for the wells as is shown in Table 20 indicate that the temperatures range between 21ºC to 25ºC. The lowest temperature of
21ºC recorded occurred in this season in the month of June for the wells in Uwani
(HDW2). There were monthly variations between the wells at this period (Fig 87).
Temperature values for the wells were not consistent at each sample period for each month.
3.3.1.2 Dry season period
The temperatures of the wells within this period had a range from 25ºC to 27ºC indicating higher temperatures (Table 20). The temperature values for the wells were consistent at each sample site for each month, while monthly variations occurred between the wells especially in the months of January, February and March.
Figure 88 further depicts that the temperatures were generally higher in the dry season than the rainy season.
30
25
20 15
10 Temperature(C)
5
0 HDW1 A M J J A S HDW2 Months of the year HDW3 HDW4 Fig 87:Rainy season temperature variation pattern of the wells HDW5
clxx
Fig 87: Rainy season temperature variation pattern of the wells
27.5
27
26.5
26
25.5
Temperature(C) 25 24.5
24 O N D J F M HDW1 Months of the year HDW2 HDW3 HDW4 Fig 88:Dry season temperature variation pattern of the wells HDW5
Fig 88: Dry season temperature variation pattern of the wells
3.3.2 The pH variation pattern of the urban wells.
3.3.2.1 Rainy season period.
The pH of the wells ranges between 3.0 to 6.0 (Table 21) and these values are indicative of the fact that the waters are acidic. From Table 21 it is observable that there were monthly variations in the pH values of the wells during this season. The pH values were higher (slightly acidic) in the well located at Asata
(HDW5),while theother wells in Abakpa (HDW1), Uwani(HDW2), Achara layout
(HDW3) and Ogui (HDW4) had lower pH values. Spatial variations were thus observed during this season (Fig 89). clxxi
3.2.2.2 Dry season period.
The dry season pH values have a range between 3.0 to 7.0 (Table 21).The well waters at this period are also acidic in nature except for the month of February, when the well in Abakpa (HDW1), Achara layout (HDW3) and Asata (HDW5) were alkaline in nature. This was also the month in which all the wells recorded high pH values except for the wells in Ogui (HDW4) (Fig 90).
Seasonal and spatial variations occurred during the rainy and the dry season.
8 7 6 5
pH 4 3 2 1
0 HDW1 A M J J A S HDW2 Months of the year HDW3 HDW4 FIG 89 : Rainy season pH variation pattern of the wells HDW5
Fig 89: Rainy season pH variation pattern of the wells
9 8 7 6 5
4 pH(units) 3 2 1 0 HDW1 O N D J F M HDW2 Months of the year HDW3 HW4 Fig 90:Dry season pH variation pattern of the wells HDW5
Fig 90: Dry season pH variation pattern of the wells clxxii
3.3.3 Turbidity variation pattern of the urban wells.
3.3.3.1 Rainy season period.
Turbidity levels of the wells range between 0 to 59 NTU (Table 22).The wells with the highest level of turbidity were those located at Abakpa (HDW1), while those located at Uwani (HDW5) had the lowest level during this season. From Fig. 91 it can be seen that monthly and spatial variations occurred in terms of the turbidity levels of wells in Enugu; with the well in Abakpa (HDW1) having the highest value.
3.3.3.2 Dry season period.
The turbidity level of the wells ranged between 1 to 27 NTU (Table 22).
Generally, the dry season turbidity values were higher than the rainy season values.
Figs. 91 and 92 show that there were monthly and spatial variations in the turbidity level of the wells during the rainy and dry seasons.
70
60
50
40
30
20 level(NTU) Turbidity 10
0 A M J J A S HDW1 Months of the year HDW2 HDW3 HDW4 Fig 91:Rainy season turbidity variation pattern of the wells HDW5
clxxiii
Fig 91: Rainy season turbidity variation pattern of the wells
30
25
20
15
10
level(NTU) Turbidity
5
0 HDW1 O N D J F M HDW2 Months of the year HDW3 HDW4 Fig 92:Dry season turbidity variation pattern of the wells HDW5
Fig 92: Dry season turbidity variation pattern of the wells
3.3.4 Total Dissolved Solids variation pattern of the urban wells.
3.3.4.1 Rainy season period.
The range of the rainy season total dissolved solids lies between 32 to 869 mg/l (Table 23). The highest total dissolved solid level was recorded during this season in the wells located at Abakpa (HDW1) in the month of August, while the lowest levels were also recorded during this season in two locations (Uwani (HDW2) and Asata (HDW5).
Figure 93 shows that monthly and spatial variations occurred in the turbidity levels of the wells in the rainy season.
3.3.4.2 Dry season period. clxxiv
The range of the dry season total dissolved solids lies within 33 to 2100 mg/l (Table 39). The highest value for the total dissolved solids during this season was recorded in the wells at Abakpa (HDW1) in the month of December. Monthly and spatial variations occurred in terms of the level of dissolved in the wells as depicted by Fig 94.
1000 900 800 700 600 500 400 300 200
Total dissolved solids(mg/l) dissolved Total 100 0 A M J J A S HDW1 HDW2 Months of the year HDW3 Fig 93:Rainy season total dissolved solids variation pattern of HDW4 the wells HDW5
Fig 93: Rainy season total dissolved solids variation pattern of the wells
2500
2000
1500
1000
solids(mg/l) dissolved Total 500
0 O N D J F M HDW1 Months of the year HDW2 HDW3 HDW4 Fig 94:Dry season total dissolved solid variation pattern of the wells HDW5
clxxv
Fig 94: Dry season total dissolved solids variation pattern of the wells
3.3.5 Conductivity variation pattern of the urban wells.
3.3.5.1 Rainy season period.
Conductivity of well water within this period ranged between 0.1 to
1.29µ SCM (Table 24). The rainy season had higher conductivity values, with he highest value for both seasons occurring within this season in the wells in Achara layout (HDW3) in the month of March (Fig 95).
3.3.5.2 Dry season period
The conductivity range was between 0 to 1.19µSCM (Table 24).
Conductivity was highest during this season in the wells in Abakpa (HDW1).And conductivity was generally very low in the months of November and December in all the wells (Fig 96).
Monthly and spatially variations are observed within this period.
1.4 1.2 1 0.8 0.6
0.4
Conductivity level( scm) level( Conductivity 0.2
0 HDW1 A M J J A S HDW2 Months of the year HDW3 HDW4 Fig 95:Rainy season conductivity variation pattern of the wells HDW5 Fig 95: Rainy season conductivity variation pattern of the wells
1.4
1.2
1
0.8
0.6
0.4
Conductivity level( scm) level( Conductivity 0.2
0 O N D J F M HDW1 Months of the year HDW2 HDW3 HDW4 Fig 96:Dry season conductivity variation pattern of the wells HDW5
clxxvi
Fig 96: Dry season conductivity variation pattern of the wells
3.3.6 Total hardness variation pattern of the urban wells.
3.2.6.1 Rainy season period.
Total hardness of the wells in the rainy season had a range between 0.2 to 3.26 mg/l (Table 25).Figure 97 shows that the highest values for all the wells in the five locations occurred in the month of September with the highest hardness value occurring in the wells in Asata (HDW5). The lower values were recorded in the months of April to August. Monthly and seasonal variations occurred during this period.
3.3.6.2 Dry season period.
The values range between 0.1to1.0 mg/l during this season (Table 25). The highest values occurred in all the wells in the month of October; with the highest value for this month occurring in the wells in Asata (HDW5) (Fig 98).
Very low values occurred in the month of November for all the wells. The wells located in Asata (HDW5) had the highest hardness values both in the rainy and the dry seasons.
3.5 3 2.5 2
1.5 1
Total hardness(mg/l) Total 0.5 0
A M J J A S HDW1 Months of the year HDW2 HDW3 Fig 97:Rainy season total hardness variation pattern of the HDW4 wells HDW5
clxxvii
Fig 97: Rainy season total hardness variation pattern of the wells
12
10
8
6
4
hardness(mg/l) Total
2
0 HDW1 O N D J F M HDW2 Months of the year HDW3 HDW4 Fig 98:Dry season total hardness variation pattern of the wells HDW5
Fig. 98: Dry season total hardness variation pattern of the wells clxxviii
3.3.7 Dissolved Oxygen variation pattern of the urban wells.
3.3.7.1 Rainy season period.
The rainy season dissolved oxygen for the wells had a range between 1.0 to 6.5 mg/l (Table 26). From Fig. 99 it can be seen that the lowest dissolved oxygen content was recorded in the month of April in the wells located in Achara layout
(HDW3). Dissolved oxygen of the wells during this season was highest in two different months in two different wells (i.e. wells located in Ogui (HDW4) and
Abakpa (HDW5)) in June and August respectively (Fig 99). Monthly and spatial variations thus exist between the wells in terms of their dissolved oxygen content.
3.3.7.2 Dry season period.
The dissolved oxygen content of the well waters range from 0.6 to 9.6 mg/l (Table 26). Fig. 100 shows that all the wells had high dissolved oxygen content during this season in the month of February. However the well waters in Ogui
(HDW4) had the highest level. The lowest dissolved oxygen content occurred in
January for all the wells. Monthly and season variations occurred in all the wells during this season.
The dissolved oxygen content for the wells were higher in the rainy season than the dry season (Figs 99 and 100).
7
6
5
4
3
2 Dissolved oxygen(mg/l) Dissolved 1
0 A M J J A S HDW1 Months of the year HDW2 HDW3 Fig 99:Rainy season dissolved oxygen variation pattrern of HDW4 the wells HDW5
clxxix
Fig 99: Rainy season dissolved oxygen variation pattern of the wells
12
10
8
6
4 Dissolved oxygen(mg/l) Dissolved 2
0 O N D J F M HDW1 Months of the year HDW2 HDW3 Fig 100:Dry season dissolved oxygen variation pattern of the HDW4 wells HDW5
Fig 100: Dry season dissolved oxygen variation pattern of the wells clxxx
3.8 Biochemical Oxygen Demand variation pattern of the urban wells.
3.3.8.1 Rainy season period.
The range is between 0.1 to 4.6 mg/l (Table 27). Figure 101 shows that monthly and spatial variations occurred within this season as the highest level of biochemical oxygen demand for all the wells during this season occurred in the month of April, with all the wells generally having high values in the month of May. The wells in Asata (HDW5) also had high biochemical oxygen demand in the month of
June, while the other stations had very low biochemical oxygen demand levels. The lowest values were recorded in the months of June and August in the wells in Abakpa
(HDW1).
3.3.8.2 Dry season period.
The dry season biochemical oxygen had a range of 0.3 to 4.9mg/l. From
Fig. 102 it can be seen that the lowest biochemical oxygen demand was recorded in the month of February for the wells in Ogui (HDW4); while the highest value occurred in January for the wells in Achara layout (HDW3). Monthly and spatial variations occurred in spite of the fact that some months had the same biochemical oxygen demand levels.
5 4.5 4 3.5 3 2.5 2 1.5 1
0.5 Biochemical oxygen demand(mg/l) oxygen Biochemical 0 A M J J A S HDW1 Months of the year HDW2 HDW3 Fig 101:Rainy season biochemical oxygen demand variation HDW4 pattern of the wells HDW5
clxxxi
Fig 101: Rainy season biochemical oxygen demand variation pattern of the
wells
6
5
4
3
2
1 Biochemical oxygen demand(mg/l) oxygen Biochemical 0 O N D J F M HDW1 Months of the year HDW3 HDW3 Fig 102: Dry season biochemical oxygen demand variation HDW4 pattern of the wells HDW5
Fig 102: Dry season biochemical oxygen demand variation pattern of the wells
clxxxii
3.3.9 Phosphate variation pattern of the urban wells.
3.3.9.1 Rainy season period.
The phosphate concentration for the rainy season varied between the values of 0 to 1.5 mg/l (Table 28). Fig. 103 shows that monthly and spatial variations occurred within this season as the highest level of phosphate for all the wells during this season occurred in the month of April, with the wells in Abakpa (HDW1) having the highest value. Very low values were generally recorded in all the wells in the months of May, June, July and August. The lowest values however occurred in the month of August.
3.3.9.2 Dry season period.
The range of the values obtained from the field work range between 0 to
7.4 mg/l (Table 28). Monthly and spatial variations occurred during this season as is shown by Fig 104. From Fig 104, it can be seen that the highest level of phosphate for all the wells during this season occurred in the month of March, with the wells in
Abakpa (HDW1) having the highest value. Very negligible phosphate levels occurred for all the wells in the months of November and December.
4.5 4.5 4 4 3.5 3.5 33 2.52.5 22 1.51.5
level(mg/l) Phosphate 1 Phosphate level(mg/l) Phosphate 1 0.5 0.5 0 0 HDW1 A M J J A S HDW1 A M J J A S HDW2 Months of the year HDW3 HDW2 Months of the year HDW4 HDW3 Fig 103:Rainy season phosphate variation pattern of the wells HDW5 HDW4 Fig 103:Rainy season phosphate variation pattern of the wells HDW5
clxxxiii
Fig 103: Rainy season phosphate variation pattern of the wells
8
7 6 5
4
3 Phosphate(mg/l) 2
1 0 O N D J F M HDW1 Moths of the year HDW2 HDW3 HDW4 Fig 104:Dry season phosphate variation pattern of the wells HDW5
Fig 104: Dry season phosphate variation pattern of the wells
3.3.10 Sodium variation pattern of the urban wells.
3.3.10.1 Rainy season period.
The sodium level of wells ranged between 1.8 to 62.1 mg/l (Table 29).
Fig 105 indicates that monthly and spatial variations occurred during this season as the sodium level of all the wells decreased from the month of April to the month of
September. The highest values for this season for all the wells occurred in the month of April; while the lowest values were recorded in September in all the wells.
3.3.10.2 Dry season period
The level of sodium in the well waters ranged between 0.06 to 61.5 mg/l. The highest values occurred in the month of March with the wells in Abakpa having the highest sodium level; while the lowest level occurred in the month of
October in the wells in Achara layout(HDW3)(Fig 106). Monthly and spatial variations thus occurred within this season. clxxxiv
70 60
50
40
30 sodium(mg/l) 20 10
0 HDW1 A M J J A S HDW2 Months HDW3 HDW4 FIG 105:Rainy season sodium variation pattern of the wells HDW5
Fig 105: Rainy season sodium variation pattern of the wells
70
60 50 40
30
sodium(mg/l) 20
10 HDW1 0 HDW2 O N D J F M HDW3 Months HDW4 FIG 106:Dry season sodium variation pattern of the wells HDW5
Fig 106: Dry season sodium variation pattern of the wells
3.3.11 Sulphate variation pattern of the urban wells.
3.3.11.1 Rainy season variation.
Rainy season sulphate values were between 0 to 13.3 mg/l (Table 30).
Monthly and spatial variations occurred as is depicted in Fig .107. It shows that higher clxxxv
sulphate levels occurred during this season with Abakpa having the highest level in
the month of April. The sulphate level in all the wells decreased from the month of
April to September.
3.3.11.2 Dry season period.
Sulphate values for the dry season range from 0 to 18.1 mg/l (table 30).
Generally, the sulphate levels were very low (Fig 108) except in the month in the
month of February, when the wells in Uwani (HDW2) had the highest level for the
season. Inspite of the low levels recorded during this season, variations occurred
monthly and spatially (Fig 108).
16 14 12
10
8
6 sulphate(mg/l) 4 2
0 HDW1 A M J J A S HDW2 months HDW3 HDW4 FIG 107:Rainy season sulphate variation pattern of the wells HDW5
Fig 107: Rainy season sulphate variation pattern of the wells
20 18 16 14 12 10 8
sulphate(mg/l) 6 4 2 0 O N D J F M HDW1 HDW2 Months of the year HDW3 HDW4 FIG 108:Dry Season sulphate variation pattern of the wells HDW5
clxxxvi
Fig 108: Dry season sulphate variation pattern of the wells
3.3.12 Ammonia variation pattern of the urban wells.
3.3.12.1 Rainy season period.
The rainy season ammonia values range between 0.09 to 8.7 mg/l
(Table 31). ). Figure 109 shows that monthly and spatial variations occurred within this season as the highest level of ammonia for all the wells during this season occurred in the month of April, with all the wells generally having high values in the month of June.
Very low values were recorded in all the wells in the month of July, August and September. Even within the low value months, variations still occurred among the wells with the wells in Achara layout having the highest ammonia levels.
3.3.12.2 Dry season period.
The ammonia range for the dry season was within the range of 0.01 to
4.2mg/l (Table 31). Ammonia levels in the wells were very low in the months of
November and December as is depicted in Fig 110. Four sites( Abakpa (HDW1), clxxxvii
Uwani (HDW2), Achara layout (HDW3) and Ogui (HDW4)) had high values in July with the wells in Abakpa(HDW1) and Uwani(HDW2) recording the highest values in this month of July (Fig 110). The dry season ammonia levels were generally higher in the months of January, February and March.
10 9 8 7 6 5 4
Ammonia(mg/l) 3 2 1 0 HDW1 A M J J A S HDW2 Months HDW3 HDW4 FIG 109:Rainy season ammonia variation pattern of the wells HDW5
Fig 109: Rainy season ammonia variation pattern of the wells
4.5 4 3.5 3 2.5 2
1.5 Ammonia(mg/l) 1 0.5 0 HDW1 O N D J F M HDW2 Months HDW3 FIG 110: Dry season ammonia variation pattern of the HDW4 wells HDW5
clxxxviii
Fig 110: Dry season ammonia variation pattern of the wells
3.3.13 Nitrate variation pattern of the urban wells.
3.3.13.1 Rainy season period.
The rainy season nitrate values were within the range of 0 to 7.0 mg/l
(Table 33). Generally, the nitrate levels were very low in all the wells and negligible in some wells the (Fig 111).The lowest values during this period were observed in the months of July and August. Inspite of the low values, the highest nitrate level was recorded in the month of June in Asata. Monthly and spatial variations occurred during this season.
3.3.13.2 Dry season period.
Nitrate values for the dry season range between 0 to 1.2 mg/l (Table 33).
Figure 112 shows that monthly and spatial variations occurred within this season as the highest level of nitrate for all the wells during this season occurred in the month of
October, with the highest value in the month of June. Very low values were recorded for all the wells in the month of December.
clxxxix
8 7 6 5 4
3 Nitrate(mg/l) 2 1 0 HDW1 A M J J A S HDW2 Months HDW3 HDW4 FIG 111:Rainy season nitrate variation pattern of the wells HDW5
Fig. 111: Rainy season nitrate variation pattern of the wells
1.6 1.4 1.2 1
0.8 0.6 Nitrate(mg/l) 0.4 0.2 0 O N D J F M HDW1 Months HDW2 HDW3 HDW4 FIG 112:Dry season nitrate variation pattern of the wells. HDW5
Fig. 112: Dry season nitrate variation pattern of the wells
3.3.14 Fecal coliform bacteria variation pattern of the urban wells.
3.3.14.1 Rainy season period.
The rainy season fecal coliform level range is from 1 to 18 cf/100ml
(Table 34). From Fig 113 the highest fecal coliform level occurred in the month of cxc
September in the well waters in Ogui (HDW4). The levels were generally high in the
month of June for all the wells and the coliform level was the same for all the wells in
July and August. Variations thus occurred monthly and spatially in terms of the fecal
coliform levels of the wells.
3.3.14.2 Dry season period.
The coliform level at this period ranged between 0 and 17. From figure
114 the highest fecal coliform level occurred in the month of October in the well
waters in Ogui (HDW4). The levels were generally consistent for all the wells in the
month of March. Monthly and spatial variations occurred in the coliform levels of the
wells.
20 18 16 14 12 10 8 6 4 2
0 Fecal coliform bacteria(cfu/100mls) coliform Fecal A M J J A S HDW1 HDW2 Months of the year HDW3 HDW4
FIG 113:Rainy season faecal coliform bacteria variation pattern of the wells HDW5
Fig. 113: Rainy season fecal coliform bacteria variation pattern of the wells
20 18 16 14 12 10 8 6 4 2
Fecal Fecal coliform bacteria(cfu/100mls) 0 HDW1 O N D J F M HDW2 Months of the year HDW3 HDW4 FIG 114:Dry season fecal coliform bacteria variation pattern of the wells HDW5 cxci
Fig. 114: Dry season fecal coliform bacteria variation pattern of the wells
3.4 Annual seasonal and spatial patterns of well water quality variations.
3.4.1 Annual temperature variation pattern of the urban wells.
3.4.1.1 Rainy and dry season period.
The temperature mean values for the rainy season shown on Table 38 shows that the wells in Abakpa (HDW1), Achara Layout (HDW3), Ogui (HDW4) and Asata (HDW5), had temperature mean values of 24ºC during this season, while the well in Achara Layout(HDW3) had the lowest value.
TABLE 38: Rainy season mean values of wells in Enugu urban area.
Parameters SAMPLE SITES
HDW1 HDW2 HDW3 HDW4 HDW5
Temperature 24 23 24 24 24
Ph 4.5 5.1 5.4 5.0 5.8
Turbidity 10.1 3.8 9.1 7 11.3
Total dissolved solid 397.5 134.1 279.5 246.6 182.6
Conductivity 0.7 0.2 0.6 0.3 0.3
Total Hardness 0.5 0.5 0.6 0.7 1.0
Dissolved oxygen 4.8 4.8 4.2 4.9 4.8 cxcii
Biochemical Oxygen demand 0.8 1.6 1.6 1.0 1.9
Phosphate 1.0 0.4 0.6 0.2 0.5
Sodium 20.0 17.2 22.9 16.7 15.7
Sulphate 3.9 0.8 2.0 0.6 0.9
Ammonia 1.7 1.8 3.3 1.9 1.8
Calcium 13.5 13.7 13.5 12.8 13.0
Nitrate 0.0 0.2 0.1 0.1 1.8
Fecal coliform 3.1 4 3.8 5.3 3
The dry season mean values for the wells(Table 39) also indicate that the wells in
Abakpa (HDW1), Achara layout (HDW3), Ogui (HDW4), and Asata (HDW5) had means of 25ºC, while Uwani (HDW2) had a mean of 22ºC(Table 39)
Fig. 115 further depicts that the temperatures were generally higher in the dry season than the rainy season.
TABLE 39: Dry season mean values of wells in Enugu urban area.
Parameters SAMPLE SITES
HDW1 HDW2 HDW3 HDW4 HDW5
Temperature 25 22 25 25 25
Ph 4.2 5.3 5.8 4.7 6.2
Turbidity 15.5 8.6 5.6 5.8 11.6
Total dissolved solid 768.3 323.5 212.1 647.8 146.1
Conductivity 0.5 0.2 0.4 0.4 0.3
Total Hardness 0.5 0.4 0.6 1.2 2.3
Dissolved oxygen 5.3 5.1 5.5 4.8 4.9
Biochemical Oxygen demand 1.3 1.9 1.8 1.2 1.9
Phosphate 2.2 1.3 1.6 1.5 2.1
Sodium 16.0 10.7 20.1 14.7 16.0
Sulphate 0.9 3.1 0.8 0.9 0.4
Ammonia 1.3 1.3 1.6 1.6 0.5
Calcium 9.5 9.8 10.2 9.4 9.4
Nitrate 0.2 0.19 0.43 0.47 0.5 cxciii
Fecal coliform 4 2.1 2.6 6.3 7.3
HDW5
HDW4
HDW3
Sample sites Sample HDW2
HDW1
20 21 22 23 24 25 26 Temperature level( C ) Temperature Level (°C) DRY FIG 115:Seasonal temperature pattern of the wells RAINY
Fig 115: Seasonal temperature pattern of the wells
3.4.2 Annual pH variation pattern of the urban wells.
3.4.2.1 Rainy and dry season period.
The rainy season mean pH values presented on Table 38 emphasizes the acidic nature of the well waters. The acidic levels of the well waters occur in the following decreasing order: Asata (HDW5), Achara layout (HDW3), Uwani (HDW2), Ogui
(HDW4), Abakpa (HDW1). cxciv
The dry season mean values (Table 39) indicate that the wells had acidity levels in the following decreasing order: Asata (HDW5), Achara layout (HDW3),
Uwani (HDW2), Ogui (HDW4) and Abakpa (HDW1).
A comparison of the annual seasonal pH values of the wells (Fig 116) indicates that for the wells in Uwani(HDW2),Achara layout(HDW3) and Asata(HDW5)), the pH values were higher in the dry season than the rainy season; while for Abakpa
(HDW1)and Ogui(HDW4) the wells had higher pH values in the rainy season than the dry season.
HDW5
HDW4
HDW3 Sample sitesSample HDW2
HDW1
0 1 2 3 4 5 6 7 pH levelpH LEVEL DRY Fig 116:Seasonal pH pattern of the wells RAINY
Fig. 116: Seasonal pH pattern of the wells
3.4.3 Annual turbidity variation pattern of the urban wells.
3.4.3.1 Rainy and dry season period.
From the mean values obtained (Table 38), the wells with the highest level of turbidity were those located at Abakpa (HDW1), while those located at
Uwani (HDW2) had the lowest level during this season. The wells had turbidity levels cxcv in the following decreasing order: Abakpa (HDW1), Asata (HDW5), Achara layout
(HDW3), Ogui (HDW4) and Uwani (HDW2).
The highest level of dry season turbidity occurred in the wells in Abakpa
(HDW1) and the lowest value was recorded in the wells in Achara layout (HDW3)
(Table 39).
In decreasing order, the turbidity of the wells is as follows: Abakpa (HDW1), Asata
(HDW5), Uwani (HDW2), Ogui (HDW4) and Achara layout (HDW3).
Generally, Fig. 117 and shows that turbidity was higher in the dry season months in Abakpa (HDW1), Uwani (HDW2) and Asata (HDW5), than in the rainy season months. On the other hand, two wells (Achara layout (HDW3) and Ogui
(HDW4) had higher turbidity levels in the rainy season than the dry season. This also is evidence that there is spatial variation between the wells in terms of well water turbidity levels.
HDW5
HDW4
HDW3
sitesSample HDW2
HDW1
0 5 10 15 20 Turbidity level(NTU) DRY FIG 117:Seasonal turbidity pattern of the wells RAINY
Fig 117: Seasonal turbidity pattern of the wells
3.4.4 Annual total dissolved solids variation pattern of the urban wells. cxcvi
3.4.4.1 Rainy and dry season period.
The rainy season mean values (Table 38) indicate that Abakpa (HDW1) wells had the highest total dissolved solids. While Uwani (HDW2) wells had the least.
In decreasing order the levels of total dissolved solids in the wells was therefore as follows: Abakpa (HDW1), Achara layout (HDW3), Ogui (HDW4), Asata (HDW5) and Uwani (HDW2).
The dry season mean values (Table 39) indicate that the level of total dissolved solids in the wells occurred in the following decreasing order: Abakpa (HDW1), Ogui
(HDW4), Uwani (HDW2), Achara layout (HDW3). The total dissolved solids were higher in the wells in Abakpa (HDW1), Uwani (HDW2) and Ogui (HDW4) in the dry season than the rainy season (Fig 118).Those Achara layout (HDW3) and Asata
(HDW5) were higher in the rainy season period than the dry season.
HDW5
HDW4
HDW3
Sample sitesSample HDW2
HDW1
0 200 400 600 800 1000
Total Dissolved Solids(mg/l)
DRY FIG 118: Seasonal total dissolved solids pattern of the wells RAINY
Fig. 118: Seasonal total dissolved solids pattern of the wells
3.4.5 Annual conductivity variation pattern of the urban wells.
3.4.5.1 Rainy and dry season period. cxcvii
The rainy season mean values(Table 38) indicate that the wells in Abakpa
(HDW1) had the highest conductivity level with the lowest occurring in Uwani
(HDW2). The conductivity levels in decreasing order were as follows: Abakpa
(HDW1), Achara layout (HDW3), Ogui (HDW4) and Asata (HDW5), Uwani
(HDW2).
The dry season mean values (Table 39) show that in decreasing order, the conductivity levels were as follows: Abakpa (HDW1), Achara layout (HDW3), Ogui
(HDW4), Asata (HDW5) and Uwani (HDW2). Generally, conductivity was higher in the rainy season than in the dry season as is depicted in Figure 119.
HDW5
HDW4
HDW3
Sample sitesSample HDW2
HDW1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Conductivity level( SCM)
DRY FIG 119: Seasonal conductivity pattern of the wells RAINY
Fig 119: Seasonal conductivity pattern of the wells
3.4.6 Annual Total hardness variation pattern of the urban wells.
3.4.6.1 Rainy and dry season period.
The wells in Asata (HDW5) had the highest rainy season hardness mean value, while Abakpa wells (HDW1) had the lowest (Table 38). The total hardness of the wells in decreasing order was as follows: Asata (HDW1), Ogui (HDW4), Achara layout (HDW3), Uwani (HDW2) and Abakpa (HDW1). cxcviii
Asata (HDW5) wells had the highest dry season hardness mean value (Table
39), while Uwani (HDW2) had the lowest. In decreasing order, the levels of water hardness for this season were as follows: Asata (HDW5), Ogui (HDW4), Achara layout (HDW3), Abakpa (HDW1).
The seasonal pattern of water hardness depicted in Fig. 120, shows that the water hardness was higher in the wells in Ogui (HDW4) and Asata (HDW5) in the dry season. The other wells in Abakpa (HDW1), Uwani (HDW2) and Achara layout
(HDW3) had no variations for either season under consideration.
HDW5
HDW4
HDW3
sitesSample HDW2
HDW1
0 0.5 1 1.5 2 2.5 Hardness level (mg/l)
DRY FIG 120:Seasonal hardness pattern of the wells RAINY
Fig 120: Seasonal total hardness pattern of the wells
3.4.7 Annual dissolved oxygen variation pattern of the urban wells.
3.4.7.1 Rainy and dry season periods.
The rainy season mean values (Table 38); show that the wells in Ogui
(HDW4) had the highest dissolved oxygen level, while Achara layout (HDW3) had the lowest level. In decreasing order, the dissolved oxygen levels were as follows: cxcix
Ogui (HDW4), Asata (HDW5), Abakpa (HDW1), Uwani (HDW2) and Achara layout
(HDW3).
The dry season mean values indicate that the wells in Achara layout (HDW3) had the highest dissolved oxygen demand, while Asata (HDW5) wells had the lowest value. In decreasing order, the dissolved oxygen levels of the wells were as follows:
Achara layout (HDW3), Abakpa (HDW1), Uwani (HDW2), Asata (HDW5) and Ogui
(HDW4).
The dissolved oxygen content of the well waters were higher in the dry season than the rainy season for the wells in Abakpa (HDW1), Uwani (HDW2),
Achara layout (HDW3) and Asata (HDW5)(Fig 121 ). While the well waters in Ogui
(HDW4) had higher dissolved oxygen level in the rainy season than the dry season
(Fig 121).
HDW5
HDW4
HDW3
Sample sites HDW2
HDW1
0 1 2 3 4 5 6 Dissolved oxygen level(mg/l) DRY FIG 121:Seasonal dissolved oxygen pattern of the wells RAINY
Fig 121: Seasonal dissolves oxygen pattern of the wells
3.4.8 Annual biochemical oxygen demand variation pattern of the urban wells.
3.2.8.1 Rainy and dry season period cc
From the rainy season mean values obtained (Table 38), it is observable that the wells in Uwani (HDW2) had the highest value during this season, while the wells in Ogui had the lowest value. The biochemical oxygen demand levels of the wells in decreasing order were as follows: Asata (HDW5), Uwani (HDW2), Achara layout (HDW3), Ogui (HDW4) and Abakpa (HDW1).
The dry season mean values obtained (Table 39), indicate that the wells in Uwani
(HDW2) had the highest level, while those in Ogui (HDW4) had the least. The biochemical oxygen demand levels of the wells in decreasing order are as follows:
Uwani (HDW2), Asata (HDW5), Achara layout (HDW3), Abakpa (HDW1) and Ogui
(HDW4).
Generally, all the wells in four locations had higher biochemical oxygen demand levels in the dry season than the rainy season (Fig 122) with the exception of the wells in Asata (HDW5) that had higher value in the rainy season.
HDW5
HDW4
HDW3
Sample sitesSample HDW2
HDW1
0 0.5 1 1.5 2 2.5 Biochemical Oxygen Demand level(mg/l)
FIG 122:Seasonal pattern of Biochemical Oxygen Demand of DRY the wells RAINY
Fig 122: Seasonal biochemical oxygen demand pattern of the wells
3.4.9 Annual phosphate variation pattern of the urban wells.
3.2.9.1 Rainy and dry season period cci
The rainy season phosphate mean values (Table 38) indicate that the wells in Abakpa (HDW1) had the highest phosphate level, while those in Ogui (HDW4) had the least. The phosphate levels in decreasing order is as follows: Abakpa (HDW1),
Achara layout (HDW3), Asata (HDW5), Uwani (HDW2) and Ogui (HDW4).The dry season mean values for this season (Table 39); indicate that phosphate levels were highest in the wells in Asata (HDW5) and least in Uwani (HDW2).
The dry season phosphate levels of all the wells were all higher than the rainy season levels (Fig 123).
HDW5
HDW4
HDW3
Sample sites HDW2
HDW1
0 0.5 1 1.5 2 2.5 phosphate(mg/l) DRY Fig 123: Seasonal phosphate pattern of the wells RAINY
Fig 123: Seasonal phosphate pattern of the wells
3.4.10 Annual sodium variation pattern of the urban wells.
3.4.10.1 Rainy and dry season period
The means for the wells in the rainy season (Table 38) indicate that the wells in Achara layout (HDW3) had the highest sodium level, while the wells in Asata
(HDW5) had the lowest concentration. In decreasing order, the levels of sodium in the ccii
well waters are as follows: Achara layout (HDW3), Abakpa (HDW1), Uwani
(HDW2), Ogui (HDW4) and Asata (HDW5).
The dry season mean values (Table 39) show that Achara layout (HDW4)
wells had the highest values, while the wells in Uwani (HDW2) had the least. The
sodium level pattern in decreasing order is as follows: Achara layout (HDW3), Asata
(HDW5), Abakpa (HDW1), Ogui (HDW4) and Uwani (HDW2).
Figure 124 shows that the wells in four location( (Abakpa (HDW1), Uwani
(HDW2), Achara layout (HDW3) and Ogui (HDW4)) had higher sodium
concentrations in the rainy season than the dry season While the wells in Asata
(HDW5) had higher sodium concentration in the dry season; thus indicating spatial
variation in the levels of sodium in well waters in Enugu.
HDW5
HDW4
HDW3 Sample sitesSample HDW2
HDW1
0 5 10 15 20 25
sodium level(mg/l) DRY FIG 124: Seasonal sodium pattern of the wells RAINY
Fig 124: Seasonal sodium pattern of the wells
3.4.11 Annual sulphate variation pattern of the urban wells.
3.4.11.1 Rainy and dry season period
Rainy season mean values (Table 38) indicate that Abakpa (HDW1)
had the highest sulphate levels while; Ogui (HDW4) had the lowest. The sulphate cciii concentration in decreasing order is as follows: Abakpa (HDW1), Achara layout
(HDW5), Uwani (HDW2) and Ogui (HDW4).
The dry season mean values in Table 39 indicate that Uwani (HDW2) had the highest sulphate level during this period while Asata (HDW5) wells had the lowest value. The sulphate concentration for this period was as follows: Uwani
(HDW2), Ogui (HDW4), Abakpa (HDW1), Achara layout (HDW3) and Asata
(HDW5).
From Fig 125, it is observable that the wells in Uwani (HDW2), Ogui
(HDW4) had sulphate levels that were higher in the dry season than the rainy season, while those in Abakpa (HDW1), Achara layout (HDW3) and Asata (HDW5) had higher rainy season sulphate levels.
HDW5
HDW4
HDW3
Sample sitesSample HDW2
HDW1
0 1 2 3 4 5 Sulphate(mg/l)
DRY FIG 125: Seasonal sulphate pattern of the wells RAINY
Fig 125: Seasonal sulphate pattern of the wells
3.4.12 Annual ammonia variation pattern of the urban wells.
3.4.12.1 Rainy and dry season period
The rainy season mean values (Table 38) show that the highest ammonia values were recorded in wells in Achara layout (HDW3), while the lowest occurred in Abakpa (HDW1). The pattern exhibited in decreasing order is as follows: cciv
Achara layout (HDW3), Ogui (HDW4), Uwani (HDW2), Asata (HDW5), and Abakpa
(HDW1).
The mean values obtained for the dry season ammonia (Table 39) indicate that the wells in Achara layout (HDW3) had higher values, while the wells in Asata
(HDW5) had the least. The level of ammonia concentration in decreasing order is as follows: Achara layout (HDW3), Ogui (HDW4), Abakpa (HDW1), Uwani (HDW2) and Asata (HDW5).
From Fig 126, it is observable that the rainy season ammonia levels were higher than the dry season levels.
HDW5
HDW4
HDW3 Sample sitesSample HDW2
HDW1
0 0.5 1 1.5 2 2.5 3 3.5 Ammonia level(mg/l) DRY FIG 126: Seasonal ammonia pattern of the wells RAINY Fig 126: Seasonal ammonia pattern of the wells
3.4.13 Annual nitrate variation pattern of the urban wells.
3.4.13.1 Rainy and dry season periods.
The rainy season mean values (Table 38) show that the wells in Asata
(HDW5) had the highest values within this period, while the wells in Abakpa
(HDW1) had the lowest. The pattern arising from these mean values indicate that the ccv nitrate levels in decreasing order is as follows: Asata (HDW5), Uwani (HDW2),
Achara layout (HDW3), Ogui (HDW4) and Abakpa (HDW1).
The dry season mean values (Table 39) indicate that the wells in Ogui
(HDW4) had the highest nitrate level, while Asata (HDW5) wells had the lowest values. In decreasing order, the nitrate levels are as follows: Asata (HDW5), Ogui
(HDW4), Achara layout (HDW3), Abakpa (HDW1) and Uwani (HDW2).
Generally, the wells in Uwani (HDW2) and Asata (HDW5) had their nitrate levels being higher in the rainy season than the dry season; while others, Abakpa
(HDW1), Achara layout (HDW3) and Ogui (HDW4) had higher nitrate levels in the dry season (Fig 127).
HDW5
HDW4
HDW3
Sample sitesSample HDW2
HDW1
0 0.5 1 1.5 2 nitrate level(mg/l)
DRY FIG 127: Seasonal nitrate pattern of the wellsnitrate RAINY
Fig 127: Seasonal nitrate pattern of the wells
3.4.14 Annual fecal coliform bacteria variation pattern of the urban wells.
3.4.14.1 Rainy and dry season periods.
The rainy season mean values (Table 38) indicate that the wells in Ogui
(HDW4) had the highest level of coliform contamination, while the wells in Asata ccvi
(HDW5) had the lowest level. The pattern of coliform contamination in decreasing order is as follows: Ogui (HDW4), Uwani (HDW2), Achara layout (HDW3), Abakpa
(HDW1) and Asata (HDW5).
The dry season mean values (Table 39) show that the wells with the highest coliform level were the wells in Asata (HDW5), while those with the lowest level were those in Uwani (HDW2).The pattern of coliform concentration in the well waters in decreasing order is as follows: Asata (HDW5), Ogui (HDW4), Abakpa
(HDW1), Achara layout (HDW3) and Uwani (HDW2).
Fig. 128 shows that wells in Abakpa (HDW1), Ogui (HDW4) and Asata
(HDW5) had their coliform levels being higher in the dry season than the rainy season. On the other hand, wells in Uwani (HDW2) and Achara layout (HDW3) had coliform levels that were higher in rainy season than the dry season. This pattern shows that there is seasonal and spatial variation in terms of coliform levels in wells found in Enugu urban area.
HDW5
HDW4
HDW3 Sample sitesSample HDW2
HDW1
0 1 2 3 4 5 6 7 8 FecalFaecal coliform coliform bacteria bacteria level(cfU/100ml) level(cfU/100ml)
DRY FIG 128:Seasonal faecal coliform bacteria pattern of the wells RAINY
Fig 128: Seasonal fecal coliform bacteria pattern of the wells
ccvii
CHAPTER FOUR QUALITY INDICES OF RIVERS AND GROUNDWATER IN ENUGU URBAN AREA. 4.1 Water Quality Index (WQI) of Rivers in Enugu Urban. In line with the procedure of NSFWQI discussed in section 1.6 , the monthly WQI for the five rivers within Enugu Urban were calculated and the worksheets are presented in Appendices B to M. The results obtained were compared to the WQI categorization scale to determine the water quality rating (WQI) for the rivers and this is presented as Table 40. TABLE 40: Monthly Water Quality Index (WQI) of rivers in Enugu urban Months SWI SW2 SW3 SW4 SW5 January WQI 54 52 60 56 58 Scale Average Average Average Average Average February WQI 58 58 60 53 53 Scale Average Average Average Average Average March WQI 59 59 59 57 58 Scale Average Average Average Average Average April WQI 59 55 57 58 56 Scale Average Average Average Average Average May WQI 60 47 58 63 65 Scale Average Bad Average Average Average June WQI 63 59 60 63 62 Scale Average Average Average Average Average July WQI 66 65 66 66 63 Scale Average Average Average Average Average August WQI 65 66 65 67 66 Scale Average Average Average Average Average ccviii
September WQI 53 63 61 60 63 Scale Average Average Average Average Average October WQI 50 61 58 50 56 Scale Bad Average Average Bad Average November WQI 55 56 58 56 61 Scale Average Average Average Average Average December WQI 58 61 59 58 60 Scale Average Average Average Average Average
The WQI for Asata river (SW1) from January to December 2006 are presented in Table 40. The WQI obtained range from 50 to 66. From the table it is observable that Asata river (SW1) had a WQI of 54 in January and from February to August the WQI increased from 58 to 65. This is indicative of a better water quality level for seven months. A decrease in WQI was however noted from the month of September and this persisted till December. It is observable that the river had 11 months of average (medium) WQI, while only one month (October) had a WQI that was bad. Aria river (SW2) from January to December had WQI that range from 52 to 66. From Table 40, it is observable that the river (SW2) had a WQI of 52 in January and from February to April the WQI increased to 55 and these are indicative of higher water quality than that in the month of January. A decrease in WQI was however noted in the month of May after which an increase occurred till December. The river had 11 months of average WQI, while only one month (May) had a WQI that was bad. The noted increase and decrease in WQI is indicative of the fact that water quality within the river was not constant rather variation occurred monthly with poorer quality occurring between January and June. Ekulu river (SW3) from January to December had WQI that range from 58 to 66. From Table 40, it is observable that the river (SW3) had a WQI of 60 in January and February. From March to May the WQI decreased and from June to September the WQI increased. A decrease however occurred again from October to December. Inspite of the noted variation in the WQI the river had 12 months of average WQI. Ogbete river (SW4) from January to December had WQI that range from 50 to 67. From Table 40, it is observable that the river (SW4) had a WQI of 56 in January and then a decrease in February. From the month of March, the WQI increased till the month of September when a decrease in the WQI occurred. An ccix increase was however noted in the months of November and December. The river had 11 months of average WQI and one month of bad WQI. Immaculate river (SW5) from January to December had WQI that range from 53 to 66. From Table 40, it is observable that the river (SW5) had a WQI of 58 in January and then a decrease in February. In the month of March, the WQI increased, and then a decrease occurred in April. A consistent increase occurred from May to December. The river had 12 months of average WQI.
4.2 Comparative analysis of the monthly WQI of the rivers in Enugu urban. 4.2.1 Monthly rating of the rivers based on their WQI. The WQI for the rivers in the month of January was from 52 to 60. The river with the highest rating was Ekulu (SW3), while the river with the lowest rating was Aria river (SW2). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Ekulu river had the highest WQI, Immaculate river ranked second, Ogbete river ranked third, Asata river ranked fourth while Aria river had the lowest WQI. Ekulu river was thus healthier than the other rivers in January. Aria river had the lowest health level in this month. In the month of February, the WQI ranged between 53 and 60. The river with the highest rating was Ekulu (SW3), while the rivers with the lowest rating were Ogbete (SW4) and Immaculate river (SW5). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Ekulu river had the highest WQI, Asata (SW1) and Aria (SW2) rivers ranked second, while Ogbete (SW4) and Immaculate (SW5) rivers had the lowest WQI. Ekulu river was thus healthier than the other rivers in February. Ogbete (SW4) and Immaculate (SW5) rivers had the lowest health level in this month. The WQI for the rivers in March was from 57 to 59. Three rivers (Asata (SW1), Aria (SW2), and Ekulu (SW3) with the same WQI (59) had the highest rating, while the river with the lowest rating was Ogbete river (SW4). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Asata (SW1), Aria (SW2), and Ekulu (SW3) rivers had the highest WQI, Immaculate (SW5) river ranked second, while Ogbete river had the lowest WQI. Rivers Asata, Aria, and Ekulu were thus healthier than the other two rivers in the month of March. While Ogbete river had the lowest health level in March. ccx
In the month of April, the WQI ranged between 55 and 60. The river with the highest rating was Asata (SW1), while the river with the lowest rating was Aria (SW4). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Asata river had the highest WQI, Ogbete (SW4) river ranked second, Ekulu(SW3) ranked third, Immaculate river ranked fourth , while Aria(SW2) river had the lowest WQI. Asata river was thus healthier than the other rivers in April. Aria (SW2) river had the lowest health level in this month. The WQI ranged between 47 and 65 in May. The river with the highest rating was Immaculate (SW5), while the river with the lowest rating was Aria (SW2). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Immaculate river had the highest WQI, Ogbete (SW4) river ranked second, Asata (SW1) ranked third, Ekulu (SW3)river ranked fourth , while Aria(SW2) river had the lowest WQI. Immaculate (SW5) river was thus healthier than the other rivers. Aria (SW2) river had the lowest health level in May. The WQI for the rivers in June was from 59 to 63. Two rivers Asata (SW1) and Ogbete (SW4), with the same WQI of 63 had the highest rating, while the river with the lowest rating was Aria river (SW2). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Asata (SW1) and Ogbete (SW4) rivers had the highest WQI, Immaculate (SW5) river ranked third, Ekulu (SW3) river ranked fourth while Aria (SW2) had the lowest WQI. Rivers Asata and Ogbete were thus healthier than the other three rivers in the month of June. While Aria (SW2) river had the lowest health level in June. The WQI for the rivers in July was from 63 to 66. Three rivers (Asata (SW1), Ekulu (SW3) and Ogbete (SW4) with the same WQI (66) had the highest rating, while the river with the lowest rating was Immaculate river (SW5). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Asata (SW1), Ekulu (SW3) and Ogbete(SW4) rivers had the highest WQI, Aria (SW2) river ranked fourth, while Immaculate river had the lowest WQI. Rivers Asata, Ekulu and Ogbete were thus healthier than the other two rivers in the month of March. While Immaculate river had the lowest health level in July. The WQI ranged between 65 and 67 in August. The river with the highest rating was Ogbete (SW4), while the rivers with the lowest ratings were Asata (SW1) and Ekulu (SW3). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Ogbete river had the highest WQI, Aria (SW2) and Immaculate (SW5) ccxi rivers ranked second, while Asata (SW1) and Ekulu (SW3) rivers had the lowest WQI. Ogbete (SW4) river was thus healthier than the other rivers while Asata (SW1) and Ekulu (SW3) rivers had the lowest health level in August. The WQI for the rivers in September was from 53 to 63. Two rivers Aria (SW2) and Immaculate (SW5) with the same WQI (63) had the highest rating, while the river with the lowest rating was Asata river (SW1). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Aria (SW2) and Immaculate (SW5) rivers had the highest WQI, Ekulu (SW3) river ranked third, Ogbete (SW4) ranked fourth, while Asata (SW1) river had the lowest WQI. Rivers Aria and Immaculate were thus healthier than the other three rivers; while Asata river had the lowest health level in this month. In October, the WQI ranged between 50 and 61. The river with the highest rating was Aria (SW2), while the rivers with the lowest rating were Asata (SW1) and Ogbete (SW4). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Aria (SW2) river had the highest WQI, Ekulu (SW3) river ranked second, Immaculate (SW5) ranked third, while Asata (SW1) and Ogbete (SW4) rivers had the lowest WQI. Aria river was thus healthier than the other rivers in October, while Asata and Ogbete rivers had the lowest health level in this month. The WQI ranged between 55 and 61 in November. The river with the highest rating was Immaculate (SW5), while the river with the lowest rating was Asata (SW1). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Immaculate river had the highest WQI, Ekulu (SW3) river ranked second, Aria (SW2) and Ogbete (SW4) ranked third, while Asata (SW1) river had the lowest WQI. Immaculate (SW5) river was thus healthier than the other rivers, while Asata (SW1) river had the lowest health level in August. In December, the WQI ranged between 58 and 61. The river with the highest rating was Aria (SW2), while the rivers with the lowest rating were Asata (SW1) and Ogbete (SW4). The ranking of the rivers based on their WQI (Appendix Z) thus shows that Aria (SW2) river had the highest WQI, Immaculate (SW5) river ranked second, Ekulu river (SW3) ranked third, while Asata (SW1) and Ogbete (SW4) rivers had the lowest WQI. Aria river was thus healthier than the other rivers in December, while Asata and Ogbete rivers had the lowest health level in this month. From the ranking of the WQI, it is observable that rivers Asata (SW1), Aria (SW2) and Ekulu (SW3) all had four months in which they had the highest WQI. ccxii
River Aria (SW3) inspite of having four months of ranking highest, also had the highest(four months) of having the lowest WQI. This indicates that it was the river with the lowest health level as it had more months of low WQI than all the other rivers. For the whole year, with the exception of Ekulu river (SW3)( which recorded no month in which its WQI was lowest), Asata (SW1), Aria (SW2), Ogbete (SW4) and Immaculate (SW5) rivers all had different months in which their WQI were the lowest. It is also noteworthy that from January to April, October to November, at least four rivers had WQI in the 50’s with some bordering on the very low level of the average rating scale of 51-70. This highlights the deteriorating nature of the rivers and the urgent need to monitor and manage the river water qualities.
4.3 Seasonal patterns of urban rivers . 4.3.1 Rainy season monthly WQI pattern of the urban rivers. In April, the rivers had WQI between 55 and 59(Table 41). The river with the highest WQI in this month was Asata (SW1) river, while the river with the lowest WQI was Aria (SW2) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Ogbete, Ekulu, Immaculate and Aria rivers. TABLE 41: Rainy season WQI of rivers in Enugu Urban.
Months SAMPLE STATIONS Highest Lowest SW1 SW2 SW3 SW4 SW5 WQI WQI per per Month Month WQI Score WQI Score WQI Score WQI Score WQI Score April 59 A 55 A 57 A 58 A 56 A 59 55 May 60 A 47 B 58 A 63 A 65 A 65 47 June 63 A 59 A 60 A 63 A 62 A 63 59 July 66 A 65 A 66 A 66 A 63 A 66 63 August 65 A 66 A 65 A 67 A 66 A 67 65 September 53 A 63 A 61 A 60 A 63 A 63 53
* A stands for average water quality * B stands for bad water quality
The month of May had WQI that ranged between 47 and 65. This was the rainy season month in which the lowest WQI was recorded. The river with the highest WQI in this month was Immaculate (SW5) river, while the river with the lowest WQI ccxiii was Aria (SW2) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Immaculate, Ogbete, Asata, Ekulu and Aria rivers. In June, the rivers had WQI between 59 and 63(Table 41). The rivers with the highest WQI in this month were Asata (SW1) and Ogbete (SW4) rivers, while the river with the lowest WQI was Aria (SW2) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Ogbete, Immaculate, Ekulu and Aria rivers. The month of July had WQI that ranged between 63 and 66. Three rivers (Asata (SW1), Ekulu (SW3), Ogbete (SW4), had the highest WQI in this month. The river with the lowest WQI was Immaculate (SW5) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Ekulu, Ogbete, Aria and Immaculate. In August, the rivers had WQI between 65 and 67(Table 41); all WQI were high in comparism with other WQI obtained. The river with the highest WQI in this month was Ogbete (SW4) river, while two river (Asata (SW1), and Ekulu (SW3) had the lowest WQI. In decreasing order, the rating of the rivers based on their WQI is as follows: Ogbete, Aria, Immaculate, Ekulu, and Asata rivers. The month of September had WQI that ranged between 53 and 63. Two rivers (Aria (SW2) and Immaculate (SW5), had the highest WQI in this month. The river with the lowest WQI was Asata (SW1) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Aria, Immaculate, Ekulu, Ogbete and Asata. From the ranking of the WQI for the rainy season(Appendix A1) ,it is observable that two of the rivers, Asata (SW1) and Ogbete (SW4),had three months in which they had the highest WQI, while Aria (SW3) had the highest number of months of low WQI. Ekulu (SW3) and Ogbete (SW4) did not have any months in which they had the lowest WQI. This indicates that Aria (SW2) river was the river with the lowest health level in the rainy season as it had more months of low WQI than all the other rivers. Asata (SW1) was the second and Immaculate (SW5) river was the third in terms of low WQI Ekulu (SW3) and Ogbete (SW4) rivers had no months in which their WQI was the lowest for all the rivers and thus they were the rivers with the better health levels in the rainy season.
ccxiv
4.4.2 Dry season monthly WQI pattern of the urban rivers. In October, the rivers had WQI between 50 and 61 (Table 42). This was the dry season month in which the lowest WQI were recorded. The river with the highest WQI in this month was Aria (SW2) river, while the rivers with the lowest WQI were Asata (SW1) and Ogbete rivers. In decreasing order, the rating of the rivers based on their WQI is as follows: Aria, Ekulu, Immaculate, Asata and Ogbete. The month of November had WQI that ranged between 55 and 61. The river with the highest WQI in this month was Immaculate (SW5) river, while the river with the lowest WQI was Asata river (SW1) . In decreasing order, the rating of the rivers based on their WQI is as follows: Immaculate, Ekulu, Aria and Ogbete (had the same WQI), Asata rivers.
TABLE 42: Dry season WQI of rivers in Enugu urban.
Months SAMPLE STATIONS Highest Lowest SW1 SW2 SW3 SW4 SW5 WQI WQI per per Month Month WQI Score WQI Score WQI Score WQI Score WQI Score
October 50 B 61 A 58 A 50 B 56 A 61 50 November 55 A 56 A 58 A 56 A 61 A 61 55 December 58 A 61 A 59 A 58 A 60 A 61 58 January 54 A 52 A 60 A 56 A 58 A 60 52 February 58 A 58 A 60 A 53 A 53 A 60 53 March 59 A 59 A 59 A 57 A 58 A 59 57
In December, the rivers had WQI between 58 and 61(table 42). The river with the highest WQI in this month was Aria (SW2), while the rivers with the lowest WQI were Asata (SW1) and Ogbete (SW4) rivers. In decreasing order, the rating of the rivers based on their WQI is as follows: Aria, Immaculate, Ekulu, Asata and Ogbete (had the same WQI) rivers. The month of January had WQI that ranged between 52 and 60. The river with the highest WQI in this month was Ekulu (SW3), while the river with the lowest WQI was Aria (SW2) river. In decreasing order, the rating of the rivers based on their WQI is as follows: Ekulu, Immaculate Ogbete, Asata and Aria. In February, the rivers had WQI between 53 and 60 (Table 42). The river with the highest WQI in this month was Ekulu (SW3) river, while two river (Ogbete (SW4), and ccxv
Immaculate (SW5) had the lowest WQI. In decreasing order, the rating of the rivers based on their WQI is as follows: Ekulu, Asata and Aria (had same WQI), Ogbete and Immaculate (had same WQI) rivers. The month of March had WQI that ranged between 57 and 59. Three rivers, Asata (SW1), Aria (SW2) and Ekulu (SW3), had the highest WQI in this month. The river with the lowest WQI was Ogbete river (SW4). In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Aria and Ekulu (all had the same WQI), Immaculate, and Ogbete. From the ranking of the WQI for the dry season (Appendix A2), it is observable that two of the rivers-Aria (SW2) and Ekulu (SW3), had three months in which they had the highest WQI, while three rivers namely Asata (SW1) Aria (SW2) Ogbete (SW4) had one month of low WQI each (this was also the highest number of low WQI pre well recorded during this season). Ekulu (SW3) and Immaculate (SW5) did not have any months in which they had the lowest WQI. Ekulu river (SW3) was the river with the highest health level in the dry season as it had more months of high WQI than all the other rivers. Aria (SW2) and Immaculate (SW5) were the second, while Asata (SW1) and Ogbete (SW5) rivers were the least healthy.
4.5 WQI Seasonal pattern for each urban river. 4.5.1 Rainy and Dry season WQI seasonal patterns per river. The rainy season WQI for Asata (SW1) river ranged between 53 and 66 (Table 41). The highest WQI was recorded in July (66). This was the period of mid- rainy season and the lowest WQI was recorded in the month of April, this was the beginning of the rainy season. The dry season WQI for Asata (SW1) river ranged between 50 and 59 (Table 42). The highest WQI was recorded in the month of March (59) this was the period of the end of dry season and the lowest WQI was recorded in the month of October, this was the beginning of the dry season. WQI for Aria (SW2) river in the rainy season ranged between 47 and 66 (Table 41).The highest WQI was recorded in the month of August (66) this was the period of the ending of the rainy season and the lowest WQI was recorded in the month of May (47), the beginning of the rainy season. ccxvi
The dry season WQI for Aria (SW2) river ranged between 52 and 61(Table 42). The highest WQI was recorded in two (October and December) of the dry season months these were the beginning of dry season. The lowest WQI was recorded in January, the mid dry season. River Ekulu (SW3) in the rainy season had WQI that ranged from 57 to 66(Table 41). The highest WQI was recorded in July (66) this was the period of mid rainy season and the lowest WQI was recorded in April (57), the beginning of the rainy season. The dry season WQI for Ekulu (SW3) river ranged from 58 to 60(Table 42). The highest WQI was recorded in two (January and February) of the dry season months; these were the ending of dry season. The lowest WQI was recorded in two months (October and November), the beginning of the dry season. The rainy season WQI for Ogbete (SW4) river ranged between 58 and 67 (Table 41). The highest WQI was recorded in August (67) and the lowest WQI was recorded in the month of April (58), this was the beginning of the rainy season. The dry season WQI for Ogbete (SW1) river ranged between 50 and 58(Table 42). The highest WQI was recorded in the month of December (58) this was the period of the mid dry season. The lowest WQI was recorded in the month of October; this was the beginning of the dry season. River Immaculate (SW5) in the rainy season had WQI that ranged from 56 to 66(Table 41). The highest WQI was recorded in August (66) this was the period of end of rainy season and the lowest WQI was recorded in the month of April (56), the beginning of the rainy season. The dry season WQI for Immaculate (SW5) river ranged from 53 to 61(Table 42). The highest WQI was recorded in the month of November (61) this was the period of the beginning of the dry season. The lowest WQI was recorded in the month of February; coinciding with the end of the dry season.
4.6 Water Quality Index (WQI) of the Urban Wells . ccxvii
The monthly WQI for wells within Enugu urban was calculated and the worksheet is presented as Appendix N to Y. The results obtained were compared to the WQI categorization scale to determine the water quality rating for the wells and this is presented as Table 43 TABLE 43: Monthly Water Quality Index(WQI) of wells in Enugu urban
Months HDW1 HDW2 HDW3 HDW4 HDW5 January WQI 41 52 53 35 54 Scale Bad Average Average Bad Average February WQI 61 56 46 53 59 Scale Average Average Bad Average Average March WQI 51 55 57 49 55 Scale Average Average Average Bad Average April WQI 51 58 54 58 55 Scale Average Average Average Average Average May WQI 55 61 57 61 59 Scale Average Average Average Average Average June WQI 57 60 58 57 52 Scale Average Average Average Average Average July WQI 59 56 57 59 60 Scale Average Average Average Average Average August WQI 58 63 61 60 65 Scale Average Average Average Average Average September WQI 55 57 55 57 56 Scale Average Average Average Average Average October WQI 55 62 54 51 53 Scale Average Average Average Average Average November WQI 52 60 59 60 54 Scale Average Average Average Average Average December WQI 56 59 62 54 63 Scale Average Average Average Average Average
The WQI for Abakpa (HDW1) wells from January to December 2006 are presented in Table 43. The WQI obtained range from 41 to 61. From Table 43 it is observable that Abakpa(HDW1) had a low WQI of 41 in January an improved WQI in February, and from March to December fluctuations were observed in the well’s WQI. It is observable that the well had 11 months of average WQI, while only one month (January) had a WQI that was bad. The wells in Uwani (HDW2) from January to December had WQI that range from 52 to 63. From Table 43, it is observable that the wells (HDW2) had a WQI of ccxviii
52 in January and from February to May the WQI increased to 61. A decrease in WQI was however noted from the month of June to September after which an increase occurred till December. The well had 12 months of average WQI. The wells in Achara layout (HDW3) from January to December had WQI that range from 46 to 62. From Table 43, it is observable that they (HDW3) had a WQI of 53 in January and a low WQI of 46 in February. From March to July, the WQI values fluctuated and a decrease occurred from September to November before an increase occurred again in the month of December. The wells had 11 months of average WQI, while only one month (February) had a WQI that was bad. The wells in Ogui (HDW4) from January to December had WQI that range from 35 to 61. It is also observable that the wells (HDW4) had a low WQI of 35 in January and then an increase in February (53). The WQI for March was a low WQI of 49, after which the WQI fluctuated between 54 and 61 till the month December. The well had 10 months of average WQI and 2 months of bad WQI. The wells in Asata (HDW5) from January to December had WQI that range from 52 to 65. From Table 43, it is observable that the well (HDW5) had a WQI of 54 in January and then an increase in February. In the months of March/ April, the WQI decreased and an increase occurred in May. The WQI fluctuated within the range of 52 to 63 from June to December. The river had 12 months of average WQI.
4.7 Comparative analyses of the monthly WQI of the wells. 4.7.1 Monthly rating of the wells based on their WQI. The WQI for the wells in January was from 35 to 54. The wells in Asata (HDW5) had the highest rating, while the wells with the lowest rating were the wells in Ogui (HDW4). The ranking of the wells based on their WQI (Appendix A3) thus shows that wells in Asata had the highest WQI, Achara layout wells ranked second, Uwani wells ranked third, Abakpa wells ranked fourth while Ogui wells had the lowest WQI. Asata wells were thus healthier than the other wells in January. Ogui wells had the lowest health level in this month. In February, the WQI of the wells ranged between 53 and 61. The wells with the highest rating were the wells in Abakpa (HDW1), while the wells with the lowest rating were Achara layout (HDW3) wells. The ranking of the wells based on their WQI (Appendix A3), shows that Abakpa (HDW1) wells had the highest WQI, Asata (HDW5) wells ranked second, Uwani (HDW2) wells ranked third, Ogui (HDW4) ccxix wells ranked fourth while Achara layout wells(HDW3) had the lowest WQI. The wells in Abakpa were thus healthier than the other wells in February. Achara layout wells had the lowest health level in this month. The WQI for the wells in March ranged between 49 and 57. The wells with the highest rating were the wells in Achara layout (HDW3), while the wells with the lowest rating were Ogui (HDW4) wells. The ranking of the wells based on their WQI (Appendix A3) shows that Achara layout (HDW3) wells, had the highest WQI, Uwani (HDW2) and Asata (HDW5) wells ranked second, Abakpa (HDW1) wells ranked fourth and Ogui (HDW4) wells had the lowest WQI. Achara layout (HDW3) wells were thus healthier than the other wells in the month of March. On the other hand, Ogui (HDW4) wells had the lowest health level. In April, the WQI for the wells ranged between 51 and 58. The wells with the highest rating were the wells in Uwani (HDW2) and Ogui (HDW4), while the wells with the lowest rating were Abakpa (HDW1) wells. The ranking of the rivers based on their WQI (Appendix A3) shows that Uwani (HDW2) and Ogui (HDW4) had the highest WQI, Asata (HDW5) wells ranked third, Achara layout wells ranked fourth, while Abakpa wells had the lowest WQI. Uwani (HDW2) and Ogui (HDW4) wells were thus healthier than the other wells in April. Abakpa (HDW1) wells had the lowest health level in this month. The WQI of the wells ranged between 55 and 61 in May. The wells with the highest rating were also the wells in Uwani (HDW2) and Ogui (HDW4), while the wells with the lowest rating were also Abakpa (HDW1) wells. The ranking of the rivers based on their WQI (Appendix A3) shows that Uwani (HDW2) and Ogui (HDW4) had the highest WQI, Asata (HDW5) wells ranked third, Achara layout (HDW3) wells ranked fourth, while Abakpa wells had the lowest WQI. Just as was experienced in April, Uwani (HDW2) and Ogui (HDW4) wells were thus healthier than the other wells in May. Abakpa (HDW1) wells had the lowest health level in this month. The WQI for the wells in June was from 52 to 60. The wells with the highest rating were also the wells in Uwani (HDW2), while the wells with the lowest rating were Asata (HDW5) wells. The ranking of the wells based on their WQI (Appendix A3) thus shows that the wells in Uwani (HDW2) had the highest WQI, Achara layout (HDW3) wells ranked second, Abakpa (HDW1) and Ogui (HDW4) wells ranked third, while Asata (HDW5) wells had the lowest WQI. Uwani (HDW2) ccxx wells were also healthier than the other wells in June just as they were in April and May. Asata (HDW1) wells had the lowest health level in this month. The WQI for the wells in July ranged from 56 to 60. The wells with the highest rating were the wells in Asata (HDW5), while the wells with the lowest rating were Uwani (HDW2) wells. The ranking of the rivers based on their WQI (Appendix A3) thus shows that the wells in Asata (HDW5) had the highest WQI, , Abakpa (HDW1) and Ogui (HDW4) wells ranked second, Achara layout (HDW3) wells ranked fourth, Uwani (HDW2) wells had the lowest WQI. Asata (HDW5) wells were healthier than the other wells in July. Uwani (HDW2) wells had the lowest health level in this month. For the month of August, the WQI ranged between 58 and 65. The wells with the highest rating were the wells in Asata (HDW5), while the wells with the lowest ratings were Abakpa (HDW1). The ranking of the rivers based on their WQI (Appendix A3) thus shows that Asata (HDW5), Uwani (HDW2) wells ranked second, Achara layout (HDW3) wells ranked third, Ogui (HDW4) wells ranked fourth, Abakpa (HDW1) wells had the lowest WQI. Asata (HDW5) wells were thus healthier than the other wells while Abakpa (HDW1) wells had the lowest health level in August. The WQI for the wells in September was from 55 to 57. The wells in Uwani (HDW2) and Ogui (HDW4) with the same WQI of 57 had the highest rating, while the wells with the lowest rating were the wells in Abakpa (HDW1) and Achara layout (HDW3). The ranking of the rivers based on their WQI (Appendix A3) thus shows that Uwani (HDW2) and Ogui (HDW4) wells had the highest WQI, Asata (HDW5) wells ranked third, Ogbete (HDW4) ranked fourth, while Abakpa (HDW1) and Achara layout (HDW3) wells had the lowest WQI.Uwani (HDW2) and Ogui (HDW4) wells were thus healthier than the other three wells; while Abakpa (HDW1) and Achara layout (HDW3) wells had the lowest health level in September. In October, the WQI ranged between 51 and 62. The wells with the highest rating were Uwani (HDW2) wells, while the wells with the lowest rating were Ogbete (HDW4). The ranking of the rivers based on their WQI (Appendix A3) thus shows that Uwani (HDW2) wells had the highest WQI, Abakpa (HDW1) wells ranked second, Achara layout (HDW3) wells ranked third, Asata (HDW5) ranked fourth, and were Ogbete (HDW4) had the lowest WQI. Uwani (HDW2) wells were thus healthier ccxxi
than the other wells in October, while Ogbete (HDW4) wells had the lowest health level in. The WQI ranged between 52 and 60 in November. The wells with the highest rating were Uwani (HDW2) and Ogbete (HDW4) wells, while the wells with the lowest rating were Abakpa (HDW1) wells. The ranking of the wells based on their WQI (Appendix A3) thus shows that Uwani (HDW2) and Ogbete (HDW4) wells had the highest WQI, Achara layout (HDW3) wells ranked third, Asata (HDW5) ranked fourth, while Abakpa (HDW1) wells had the lowest WQI. Uwani (HDW2) wells were thus healthier than the other wells, while Abakpa (HDW1) wells had the lowest health level in November. In December, the WQI ranged between 54 and 63. The wells with the highest rating were Asata (HDW5) wells, while the wells with the lowest rating were Ogbete (HDW4) wells. The ranking of the wells based on their WQI (Appendix A3) thus shows that Asata (HDW5) had the highest WQI, Achara layout (HDW3) wells ranked second, Uwani (HDW2) ranked third, Abakpa (HDW1) ranked fourth, while Ogbete (HDW4) wells had the lowest WQI. Asata (HDW5) wells were thus healthier than the other wells in December, while Ogbete (HDW4) had the lowest health level. From the ranking of the WQI (Appendix A3), it is observable that the wells in Uwani (HDW2) had six months in which they had the highest WQI. The wells in Asata (HDW5) had four months of having the highest WQI. The other wells Abakpa (HDW1), Achara layout (HDW3) and Ogui (HDW4) had only one month each of having the highest WQI. This indicates that the wells in Uwani had the highest health level as they had more months of high WQI. The wells in Ogui had the lowest health level as they had more months of low WQI.
4.8 Seasonal patterns of the urban wells 4.8.1 Rainy season monthly WQI patterns of the urban wells. In April, the wells had WQI between 51 and 58(Table 44). The wells with the highest WQI in this month were Uwani (HDW2) wells, while the wells with the lowest WQI were Abakpa (HDW1) wells. In decreasing order, the rating of the wells based on their WQI is as follows: Uwani and Ogui wells, Asata, Achara layout and Abakpa. TABLE 44: Rainy season WQI of wells in Enugu Urban
Months SAMPLE STATIONS Highest Lowest WQI WQI per per Month Month ccxxii
HDW1 HDW2 HDW3 HDW4 HDW5
WQI Score WQI Score WQI Score WQI Score WQI Score April 51 A 58 A 54 A 58 A 55 A 58 51 May 55 A 61 A 57 A 61 A 59 A 61 55 June 57 A 60 A 58 A 57 A 52 A 60 52 July 59 A 56 A 57 A 59 A 60 A 60 57 August 58 A 63 A 61 A 60 A 65 A 65 58 September 55 A 57 A 55 A 57 A 56 A 57 55
For the month of May the wells had WQI that ranged between 55 and 61. The wells with the highest WQI in this month were Uwani (HDW2) wells and Ogui (HDW4), while the wells with the lowest WQI were Abakpa (HDW1) wells. In decreasing order, the rating of the wells based on their WQI is as follows: Uwani and Ogui, Asata, Achara layout and Abakpa wells. In June, the wells had WQI between 52 and 60(table 44). The wells with the highest WQI in this month were Uwani (HDW2) wells, while the wells with the lowest WQI were Asata (HDW5). In decreasing order, the rating of the wells based on their WQI is as follows: Uwani, Achara layout, Abakpa, Ogui, and Asata. The month of July had WQI that ranged between 57 and 60. Asata (HDW5) had the highest WQI in this month. The wells with the lowest WQI were Uwani (HDW2) wells. In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Ogui, Abakpa, Achara layout, Uwani. In August, the rivers had WQI between 58 and 65(Table 44). The wells with the highest WQI in this month were Asata (HDW4) wells, while Abakpa wells had the lowest WQI. In decreasing order, the rating of the wells based on their WQI is as follows: Asata, Uwani, Achara layout, Ogui, Abakpa. The month of September had WQI that ranged between 55 and 57. Wells in Uwani (HDW2) and Ogui (HDW4) had the highest WQI in this month. The wells with the lowest WQI were Abakpa (HDW1) wells. In decreasing order, the rating of the rivers based on their WQI is as follows: Uwani and Ogui, Asata, Abakpa, Achara layout. From the ranking of the WQI for the rainy season (Appendix A4), it is observable that wells in Uwani (HD2) had four months in which they had the highest WQI, while Abakpa (HDW1) wells had the highest number of months of low WQI. The wells in Achara layout (HDW3) and Ogui (HDW4) did not have any months in ccxxiii which they had the lowest WQI. This indicates that Abakpa wells were the wells with the lowest health level in the rainy season as it had more months of low WQI than all the other wells.
4.8.2 Dry season monthly WQI patterns of the urban wells. In October, the wells had WQI between 51 and 62 (Table 45). The wells with the highest WQI in this month were Uwani (HDW2) wells, while the wells with the lowest WQI were Ogui (HDW4). In decreasing order, the rating of the rivers based on their WQI is as follows: Uwani, Abakpa, Achara layout, Asata and Ogui. The month of November had WQI that ranged between 52 and 60. The wells with the highest WQI in this month were Uwani (HDW2) and Ogui (HDW4) wells, while the wells with the lowest WQI were Asata (HDW5) wells. In decreasing order, the rating of the wells based on their WQI is as follows: Uwani and Ogui (had the same values), Achara layout, Asata, Abakpa.
TABLE 45: Dry season WQI of wells in Enugu Urban Months SAMPLE STATIONS Highest Lowest HDW1 HDW2 HDW3 HDW4 HDW5 WQI WQI per per Month Month WQI Score WQI Score WQI Score WQI Score WQI Score
October 55 A 62 A 54 A 51 F 53 A 62 51 November 52 A 60 A 59 A 60 A 58 A 60 52 December 56 A 59 A 62 A 54 A 63 A 63 54 January 41 B 52 A 53 A 35 B 54 A 54 35 February 61 A 56 A 46 B 53 A 59 A 61 46
March 51 A 55 A 57 A 49 A 55 A 57 49
ccxxiv
In December, the wells had WQI between 54 and 63(Table 45). The wells with the highest WQI in this month were Asata (HDW5) wells, while the wells with the lowest WQI were Ogui (HDW4) wells. In decreasing order, the rating of the rivers based on their WQI is as follows: Asata, Achara layout, Uwani, Abakpa, and Ogui. The month of January had WQI that ranged between 35 and 54. This was also the month in which the lowest WQI was recorded. The wells with the highest WQI in this month were Asata (HDW5) wells, while the wells with the lowest WQI were Ogui (HDW4) wells. This pattern was also obtainable in the month of December. In decreasing order, the rating of the wells based on their WQI is as follows: Asata, Achara layout, Uwani, Abakpa, and Ogui. In February, the wells had WQI between 46 and 61 (Table 45). The wells with the highest WQI in this month were Abakpa (HDW1) wells, while Achara layout (HDW3) wells, had the lowest WQI. In decreasing order, the rating of the wells based on their WQI is as follows: Abakpa, Asata, Uwani, Ogui, Achara layout. The month of March had WQI for the wells that ranged between 49 and 57. The wells with the highest WQI in this month were Achara layout (HDW3) wells, while Ogui (HDW4) had the lowest WQI in this month. In decreasing order, the rating of the rivers based on their WQI is as follows: Achara layout, Uwani and Asata (had the same WQI), Abakpa and Ogui. From the ranking of the WQI of wells for the dry season (Appendix A5), it is observable that the wells in Uwani (HDW2) and Asata (HDW5) had two months in which they had the highest WQI, while the wells in Achara layout (HDW3) and Ogui (HDW4) had only one month of having the highest WQI. On the other hand, Ogui (HDW4) had four months of low WQI; Abakpa (HDW1) and Achara layout (HDW3) had only one month of having the lowest WQI. Uwani (HDW2) and Asata (HDW5) wells did not have any month in which they had the lowest WQI. Based on the fact that for the dry season, Uwani (HDW2) and Asata (HDW5) wells had the highest number of months (two) in which they had the highest health level in the dry season as they had more months of high WQI than all the other wells. Ogui (HDW4) wells which had more months (four) of low WQI than the other wells were the least healthy in terms of low WQI in the dry season.
4.9 Seasonal pattern of WQI of the urban wells. 4.9.1 Rainy and dry season WQI patterns. ccxxv
The rainy season WQI for the wells in Abakpa (HDWI) ranged between 51 and 59(Table 44). The highest WQI was recorded in the month of July (59) this was the period of mid rainy season and the lowest WQI was recorded in the month of April (51), this was the beginning of the rainy season. The dry season WQI for the wells in Abakpa (HDW1) ranged between 41 and 61(Table 45). The highest WQI was recorded in the month of February (61) this was the period of the end of dry season and the lowest WQI was recorded in the month of January (41), this was the beginning of the dry season. WQI for Uwani (HDW2) wells in the rainy season ranged between 57 and 63 (Table 44). The highest WQI was recorded in the month of August (63) this was the period of the ending of the rainy season and the lowest WQI was recorded in the month of September (57), also the end of the rainy season. The dry season WQI for Uwani (HDW2) wells ranged between 52 and 62(Table 45). The highest WQI was recorded in October (62), this was the beginning of dry season. The lowest WQI was recorded in January (52), the mid dry season. Achara layout (HDW3) wells in the rainy season had WQI that ranged from 57 to 61(Table 44). The highest WQI was recorded in September (61) this was the period of the end of the rainy season and the lowest WQI was recorded in the month of April (54), the beginning of the rainy season. The dry season WQI for Achara layout (HDW3) wells ranged from 46 to 59(Table 45). The highest WQI was recorded in November the beginning of dry season. The lowest WQI was recorded in February, the end of the dry season. The rainy season WQI for Ogui (HDW4) wells ranged between 57 and 61 (Table 44). The highest WQI was recorded in May (61) this was beginning of rainy season and the lowest WQI was recorded in June and September (57), these occurred mid and end of the rainy season. The dry season WQI for Ogui (HDW4) wells ranged between 35 and 60(Table 45). The highest WQI was recorded in November (60) this was at the beginning of the dry season. The lowest WQI was recorded in October; this also was the beginning of the dry season. Asata (HDW5) wells in the rainy season had WQI that ranged from 52 to 65(Table 44). The highest WQI was recorded in August (65) this was the period of end of rainy season and the lowest WQI was recorded in June (52), the mid rainy season. ccxxvi
The dry season WQI for Asata (HDW5) wells ranged from 53 to 63(Table 45). The highest WQI was recorded in the month of December (63) this was mid dry season. The lowest WQI was recorded in October; this was the beginning of the dry season.
4.10 Comparative analysis of the monthly Water Quality Indices(WQI) of rivers and wells in Enugu urban area. A comparism of the WQI of the rivers and wells in Enugu urban presented in Appendix A6 shows that in the month of January, the WQI range for both were between 35 and 58. The highest WQI for this month was recorded by a river, while the lowest was recorded by a well. The rivers had WQI that ranged between 54 and 58 only, while the wells had a WQI range of 35 to54. The WQI of four rivers(Asata(SW1), Ekulu(SW3), Ogbete(sw4) and Immaculate(SW5) exceeded the WQI for all the wells, while only one river(Aria(SW2)) had a WQI that was either the same with the wells(Uwani(HDW2)) but the other wells had WQI that exceeded that of Aria(SW2) river. Generally, the WQI for the rivers exceeded the WQI of the wells in January thus indicating that the rivers had a better quality than the wells. In February, the WQI ranges for both were between 46 and 61. In this month, wells in Achara layout had WQI that were below average (medium). However, the highest WQI for this month was recorded by a well, while the lowest was recorded also by a well. The entire river WQI exceeded the WQI of the wells. This reveals that for February, the water quality of the rivers were better than those of the wells. The rivers and wells in March had WQI that range between 49 and 59. The highest WQI for this month was recorded by a river, while the lowest was recorded by a well. The rivers had WQI that ranged between 57 and 59 only, while the wells had a WQI range of 49 to 57. In this month, wells in Ogui had WQI that was below average. The entire river WQI exceeded the WQI of the wells. This shows that for March, the water quality of the rivers were better than those of the wells. In April, the WQI ranges for both were between 51 and 59. The highest WQI for this month was recorded by a river, while the lowest was recorded also by a well. The rivers all had WQI that exceeded the WQI of the wells. This reveals that the rivers were healthier in April than wells. ccxxvii
The rivers and wells in May had WQI that range between 47 and 65. The highest WQI for this month was recorded by a river, while the lowest was recorded also by a well. The rivers had WQI that ranged between 47and 65, while the wells had a WQI range of 55 to 61. In this month, Aria (SW2) river had a WQI that was below average (medium). The other river WQI exceeded the WQI of the wells. This shows that for May, the water quality of the rivers were better than those of the wells with the exception of Aria river. In June, the WQI range for both were between 52 and 63. The highest WQI for this month was recorded by rivers, while the lowest was recorded also by a well. The rivers all had WQI that exceeded the WQI of the wells.This reveals that the month of June had healthier rivers than wells. The rivers and wells in July had WQI that range between 57 and 66. The highest WQI for this month were recorded by rivers, while the lowest was recorded by a well. The rivers had WQI that ranged between 63 and 66, while the wells had a WQI range of 56 to 60. The rivers all had WQI that exceeded the WQI of the wells. This shows that for July, the water quality of the rivers were better than those of the wells. In August, the WQI range for both was between 58 and 67. The highest WQI for this month was recorded by rivers, while the lowest was recorded also by a well. The rivers all had WQI that exceeded the WQI of the wells. This reveals that the month of August had healthier rivers than wells. The rivers and wells in September had WQI that range between 53 and 63. The highest WQI for this month was recorded by a river, and the lowest was also recorded by a river. The rivers had WQI that ranged between 53 and 63, while the wells had a WQI range of 55 to 57. In this month, Asata (SW1) river had a WQI that was below those of the wells. However, the other rivers had WQI that exceeded the WQI of the wells. This shows that for the month of September, the water quality of the rivers were better than those of the wells with the exception of Asata river. In October, the WQI range for both was between 50 and 62. The highest WQI for this month was recorded by a well, and the lowest was also recorded by rivers. The rivers had WQI that ranged between 50 and 61, while the wells had a WQI range of 51 to 62. In this month, two rivers (Asata (SW1) and Ogbete (SW4)) had WQI that was below those of the wells. However, the other rivers had WQI that exceeded the WQI of the wells. This shows that for the month of October, the water ccxxviii
quality of the rivers were better than those of the wells with the exception of Asata and Ogbete rivers. The rivers and wells in November had WQI that range between 52 and 61. The highest WQI for this month was recorded by a river, and the lowest was recorded by a well. The rivers had WQI that ranged between 55 and 61, while the wells had a WQI range of 52 to 60. In this month, the rivers on average had WQI that exceeded the WQI of the wells. This shows that for the month of November, the water quality of the rivers were better than those of the wells. In December, the WQI range for both was between 54 and 63. The highest WQI for this month was recorded by a well, and the lowest was also recorded by a well also. The rivers had WQI that ranged between 58 and 61, while the wells had a WQI range of 54 to 63. In this month, the rivers on average had WQI that exceeded the WQI of the wells. This shows that for the month of December, the water quality of the rivers were better than those of the wells.
CHAPTER FIVE PREVALENCE PATTERN AND THE SPATIAL DIMENSIONS OF WATER- RELATED DISEASES IN ENUGU URBAN AREA. 5.1 Introduction Water quality has a vital impact on people's health. A large percentage of the infectious and parasitic diseases that plague the developing world are associated with inadequate water quality and sanitation level. In some countries like Nigeria, rivers and lakes have become receptacles for a vile assortment of wastes (including untreated or partially treated municipal sewage, toxic industrial effluents, and harmful chemicals). Based on this practice, the supply of freshwater available is shrinking, as many freshwater resources become increasingly reduced in quality. Caught between finite and increasingly polluted water supplies on one hand and rapidly rising demand from population growth and development on the other, very major human health problems have arisen. These problems are heightened by the failure to provide even the most basic water services for the populace. ccxxix
Gleick (1998) is of the opinion that this failure to provide safe drinking water and adequate sanitation services to all people is perhaps the greatest development and health failure of the 21st century; while the most serious consequence of this failure is widespread water- related diseases and death. Water-related diseases are a human tragedy as about 2.3 billion people in the world suffer from diseases that are linked to water (Kristof, 1997; Vanderslice et al, 1996). It also prevents millions of people from leading healthy lives, and undermines development efforts (Nash, 1993; Olshansky et al 1997). This problem of water-related diseases is one of the most serious public health crisis facing developing countries today as water-related diseases exact a terrible toll on human health. One-quarter of all people attending hospitals are ill from water-related diseases and in some urban centers, water-related diseases make up a high proportion of all illnesses among adults and children. As the number of people living with HIV and AIDS increases the problems become more obvious as people living with AIDS are more likely to be infected with water-related diseases. In this chapter the aim is to identify the water-related diseases prevalent in the urban area and the seasonality pattern of the four major water-related diseases.
5.2 Monthly prevalence pattern of water-related diseases in the wards of Enugu Urban area.
The World Health Organization (1996) defines water-related disease as any significant or widespread adverse effects on human health, such as death, disability, illness or disorders, caused directly or indirectly by the condition, or changes in the quality and quantity of any waters. The causes of water-related disease include micro-organisms, parasites, toxins and chemical contamination of water. According to Hunter (1997), prevalence refers to the number of cases of a disease in a defined population at a particular point in time. A survey was carried out to determine the pattern of monthly prevalence of water-related diseases in the wards in Enugu urban area for the year 2006. The result obtained is presented in table 46.
TABLE 46: Monthly total of water-related disease prevalence in Enugu urban Month Number of patients Percentage treated value ccxxx
January 425 11 February 431 11 March 304 8 April 244 6 May 271 7 June 438 12 July 311 8 August 224 6 September 280 7 October 290 8 November 263 7 December 332 9 Grand Total 3813 100% Source: Field work, 2006. Table 46 indicates that in January, 425(11 %) people were treated for water- related diseases. In this month as is depicted in Fig 129, the ward that had the highest number of cases of water-related diseases was Coal Camp with a percentage value of 16.7 %, while the lowest number of patients was from three wards- New Haven, Independence Layout and Ogui (6 % respectively).
7% 6% 6% 8%
6%
16% 7%
Ogui Achara layout 16% Asata Abakpa 16% Iva valley Coal camp 12% Uwani New haven Independence layout G.R.A
Fig 129: January water-related diseases prevalence pattern in Enugu urban
ccxxxi
In February (Fig. 130) a total of 433 patients were treated for water-related diseases and this number constituted 11 % of the total number treated for the whole year (table 46). Of the 433 patients in February, the ward with the highest number of residents that were treated for water-related diseases was Abakpa and Asata (14% respectively) (Fig 130), while the least number of patients was recorded in two wards namely Independence layout and Government Reserved Area (G.R.A); each having a percentage contribution of 5 %. For the 304 patients (8 %) (Table 46) that were treated for water-related diseases in the month of March, Fig 131 shows that Achara Layout with a percentage value of 14 % had the highest number of patients while Independence Layout that had 12 patients (4 %) had the lowest.
5% 5% 9% 9% 13%
10% n
14% Ogui Achara layout Asata 13% Abakpa Iva valley Coal camp 8% 14% Uwani New haven Independence layout G.R.A
Fig. 130: February water-related diseases prevalence pattern in Enugu urban
ccxxxii
5% 4% 13% 10%
14%
11% Ogui Achara layout 7% Asata Abakpa Iva valley 13% Coal camp 13% Uwani 10% New haven Independence layout G.R.A
Fig. 131: March water-related diseases prevalence pattern in Enugu urban
In the month of April, 244 people (constituting 6 %) who attended the hospitals were treated for water-related diseases (Table 46). Fig 132 shows that the wards with the highest number of patients were Iva valley and Coal Camp each contributing 13%.The ward with the lowest number of patients on the other hand was Ogui contributing 6 % for the month. ccxxxiii
7% 6%
8% 10%
12% 13%
7% Ogui 13% Achara layout Asata Abakpa 12% Iva valley 12% Coal camp Uwani New haven Independence layout G.R.A
Fig. 132: April water-related diseases prevalence pattern in Enugu urban
From Table 46, it can be seen that during the month of May, 271 patients attended the hospitals for water-related diseases. This number represents 7 % of the total. While Figure 133 showing the percentage contribution of each ward in May indicates that the wards that had the highest number of patients were Uwani and New haven. Each of these wards contributed 13 %, while Achara Layout contributing 6 % was the ward with the lowest number of patients for this month. ccxxxiv
7% 9% 10% 6%
9%
13%
9% Ogui Achara layout Asata 13% 12% Abakpa Iva valley 12% Coal camp Uwani New haven Independence layout G.R.A
Fig. 133: May water-related diseases prevalence pattern in Enugu urban
In June, 438 patients, representing 12 %, were treated for water-related diseases. This was the month in which the hospitals recorded the highest number of patients that were treated for water-related diseases. During this period as is shown in Figure 134, the ward with the highest number of patients was Abakpa with a percentage contribution of 15 %, while two wards- Independence Layout and the Government Reserved Area (G.R.A) had the lowest number of patients contributing 5 % respectively. A total of 311 patients representing 8 % were treated of a water-related disease in July. From Fig 135, it is observable that within this month, there were two wards (Coal Camp and Uwani) that recorded the highest incidents of water-related diseases. Each of them had a percentage contribution of 15 %, while the lowest contributing ward was Independence Layout (5 %).
ccxxxv
5% 5% 9%
12% 11%
7% 12% Ogui Achara layout Asata 15% Abakpa Iva valley 11% Coal camp Uwani 13% New haven Independence layout G.R.A
Fig. 134: June water-related diseases prevalence pattern in Enugu urban
8% 8% 5% 8%
9% 10%
Ogui 15% Achara layout 12% Asata Abakpa Iva valley Coal camp 15% 10% Uwani New haven Independence layout G.R.A
Fig. 135: July water-related diseases prevalence pattern in Enugu urban
For the month of August, 224 patients representing 6 %( Table 46) reported ill with various types of water-related diseases. Within this month as is depicted in Fig. 136, the ward with the highest number of patients that presented with water- related diseases was Uwani representing 18 %, while the ward with the lowest number was New Haven with a percentage contribution of 2 %. ccxxxvi
6% 5% 7% 2% 10%
18% 11%
Ogui Achara layout 13% Asata 13% Abakpa Iva valley Coal camp 15% Uwani New haven Independence layout G.R.A
Fig 136: August water-related diseases prevalence pattern in Enugu urban
In September, 280 patients representing 7 % (Table 46) were treated for water-related diseases. From Fig 137, the wards that had the highest number of patients suffering from water-related diseases were Achara Layout and Iva Valley each with a percentage contribution of 15 %; while the G.R.A. had the lowest number of cases ; contributing only 3% during this month. For the month of October, 290 patients presented with water-related diseases and this represents 8 % of the annual total (Table 46). Fig. 138 showing the hospital visitation pattern for October indicates that the ward contributing the highest number of patients was Coal camp, while the lowest contributing ward was Independence Layout.
ccxxxvii
4%3% 4% 14%
13%
15%
14% Ogui 5% Achara layout Asata Abakpa 13% Iva valley 15% Coal camp Uwani New haven Independence layout G.R.A
Fig 137: September water-related diseases prevalence pattern in Enugu urban
5% 5% 4% 10% 5%
11% 16%
Ogui Achara layout Asata 21% 13% Abakpa Iva valley Coal camp 10% Uwani New haven Independence layout G.R.A
Fig. 138: October water-related diseases prevalence pattern in Enugu urban
ccxxxviii
Table 46 shows that 263 patients were treated for water-related diseases November and this represents 7 %. During this month, the wards with the highest number of hospital visitations were Ogui and Achara layout both with a percentage value of 15.The ward with the lowest number of patients was the G.R.A. with a 5 % contribution (Fig 139).
5% 6% 15%
10%
15% 7%
8%
Ogui 10% Achara layout Asata 11% Abakpa 13% Iva valley Coal camp Uwani New haven Independence layout G.R.A
Fig. 139: November water-related diseases prevalence pattern in Enugu urban
In December, 332 patients representing 9 % presented with water-related diseases. The ward with the highest number of patients was Abakpa (17 %), while the G.R.A contributed the lowest (5 %) (Fig 140). ccxxxix
5% 6% 12% 7%
7% 15%
11% 8% Ogui Achara layout Asata Abakpa 12% Iva valley 17% Coal camp Uwani New haven Independence layout G.R.A
Fig. 140: December water-related diseases prevalence pattern in Enugu urban
A ranking of the prevalence dimensions for the year (Table 46) and (Fig 141) indicates that the month of June ranked first, showing that this was the period the highest number of patients were treated for water-related diseases. February ranked second, January ranked third, December ranked fourth, July ranked fifth, March ranked sixth, October ranked seventh, September ranked eighth, May ranked ninth, November ranked tenth, April ranked eleventh, while August ranked twelfth(the lowest number of patients were treated during this month). ccxl
9% 11% 7%
11% 8%
7% 8% Jan Feb Mar 6% 6% Apr May Jun 8% 7% Jul Aug 12% Sep Oct Nov Dec
Fig. 141: Monthly percentages of water-related diseases prevalence pattern in Enugu urban
5.2 Seasonal Dimensions of Water-related Diseases in Enugu Urban. Water-related diseases recognized in this study are categorized into four distinct types. They are as follows:
5.2.1 Water-borne diseases: These diseases are "dirty-water" diseases—those caused by water that has been contaminated by human, animal, or chemical wastes (Alberin et al, 1996). They are illnesses caused by drinking water contaminated by human or animal faeces which contain pathogenic micro organisms (i.e. they are fecal-oral in nature) (Alam, 1989). Water-borne diseases thus spread through water containing human or animal faeces and urine, either when you drink such water directly or you eat food that has been cleaned with it (Basch, 1990). The fecal-oral route of disease transmission is depicted in Fig 143.
ccxli
Faeces
Water Flies Hands
Food
Mouth
Fig 142: Transmission routes of water-borne diseases Source: Brandley, 1994
Contaminated water thus causes a range of diseases that can be life-threatening. Lack of clean water for domestic activities and lack of proper sanitation facilities are to be blamed for most of the water-borne diseases. The spread of these diseases is very rapid.
The major water-borne diseases are - cholera and other diarrhea diseases; dysentery, typhoid fever; polio; roundworm; whipworm; shigella; meningitis and hepatitis A and E. Human beings and animals can act as hosts to the bacterial, viral, or protozoa organisms that cause these diseases.
5.2.1.1 Seasonal dimensions of water-borne diseases in Ogui. A total of 140 patients from Ogui were treated for water-borne diseases in the rainy and dry season as is shown in table 47.
ccxlii
Table 47: Prevalence Pattern of water-borne diseases in Enugu Urban.
Months Wards of the urban area Ogui Achara Asata Abakpa Iva valley Coal Uwani New Independence G.R.A Total layout camp Haven layout (all wards )
January 12 17 28 16 11 30 9 8 2 4 137 February 17 20 30 37 14 22 15 6 3 2 166 March 22 20 8 32 30 32 18 8 3 0 173 April 10 5 12 9 18 14 6 7 3 12 96 May 12 7 15 10 21 7 12 11 1 8 104 June 25 20 10 12 20 22 15 4 5 7 140 July 10 20 17 9 10 15 12 10 5 3 111 August 5 6 4 11 13 10 5 0 0 0 54 September 3 10 9 5 23 12 18 7 5 3 95 October 9 20 20 20 22 23 17 6 3 7 140 November 3 17 15 15 8 0 7 12 7 0 91 December 12 27 8 12 14 9 12 3 0 3 100 Total for each 140 189 176 188 204 196 146 82 37 49 1407 ward Source: Field work 2006 Of these 140 patients, the seasonal prevalence pattern shown as Tables 48 and 49 reveal that 65(46.4 %) were treated in the rainy season, while 75(53.6 %) were treated in the dry season. In the rainy season, patients were highest in the month of June and lowest in September. Table 48: Rainy season prevalence of water-borne diseases in Ogui,
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 3 1 5 1 0 10 15.4 May 4 0 5 1 2 12 18.5 June 2 2 15 2 4 25 38.5 July 1 0 7 1 1 10 15.4 August 2 0 3 0 0 5 7.6 September 0 0 3 0 0 3 4.6 Total 12 3 38 5 7 65 Disease % 18.4 4.6 58.5 7.7 10.8 100% Source: Field work, 2006. ccxliii
Table 49: Dry season prevalence of water-borne diseases in Ogui
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 5 1 3 0 0 9 12 November 0 0 3 0 0 3 4 December 2 0 6 0 4 12 16 January 2 0 8 2 0 12 16 February 3 0 11 0 3 17 22.7 March 10 2 6 2 2 22 29.3 Total 22 3 37 4 9 75 Disease % 29.3 4 49.4 5.3 12 100% Source: Field work, 2006 The dry season patients were highest in March and lowest in November. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 58.5 % for the rainy season and 49.4 % in the dry season.
5.2.1.2 Seasonal dimensions of water-borne diseases in Achara layout. 189 patients from Achara layout were treated for water-borne diseases for the year 2006 in the rainy and dry seasons. Seasonal prevalence patterns of this are shown in Tables 50 and 51. Table 50: Rainy season prevalence of water borne diseases in Achara layout.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 5 0 0 0 0 5 7.4 May 0 0 7 0 0 7 10.3 June 3 2 14 0 1 20 29.4 July 5 1 10 1 3 20 29.4 August 3 0 3 0 0 6 8.8 September 1 1 5 1 2 10 14.7 Total 17 4 39 2 6 68 Disease % 25 4 57.4 2.9 8.8 100% Source: Field work, 2006.
Table 51: Dry season prevalence of water-borne diseases in Achara layout. ccxliv
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 12 0 6 0 2 20 16.5 November 2 0 12 0 3 17 14.1 December 10 0 16 0 1 27 22.3 January 3 0 12 0 2 17 14.1 February 1 0 16 2 1 20 16.5 March 3 3 12 1 1 20 16.5 Total 31 3 74 3 10 121 Disease % 25.6 2.4 61.2 2.5 8.3 100% Source: Field work, 2006. From Table 50, it can be seen that the number of cases in the rainy season was highest in June and July, while it was lowest in April. The dry season patients (Table 51) were highest in the month of December and lowest in November and January. In both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 57.4 % for the rainy season and 61.2 % in the dry season while cholera contributing 5.9 % in the rainy season and 2.4 % in the dry season had the lowest incidents for both seasons. The number of people that were treated for water-borne diseases in Achara Layout therefore were more in the dry season (64 %) than in the rainy season (36 %). 5.2.1.3 Seasonal dimensions of water-borne diseases in Asata. A total of 176 patients from Asata were treated for water borne diseases in the rainy and dry season as is shown in Table 47. The seasonal patterns shown as tables 52 and 53 reveal that of the 176 patients, 67(38 %) were treated in the rainy season, while 109(62 %) were treated in the dry season. Table 52: Rainy season prevalence of water-borne diseases in Asata
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 7 0 3 0 2 12 17.9 May 3 0 7 4 1 15 22.4 June 3 0 3 2 2 10 14.9 July 3 2 10 0 2 17 25.4 August 0 0 4 0 0 4 6 September 5 0 2 1 1 9 13.4 Total 21 2 29 7 8 67 Disease % 31.3 3 43.3 10.5 11.9 100% Source: Field work, 2006.
Table 53: Dry season prevalence of water borne diseases in Asata. ccxlv
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % contribution October 6 0 11 2 1 20 18.3 November 3 3 7 2 0 15 13.8 December 5 1 2 0 0 8 7.3 January 10 0 15 0 3 28 25.7 February 9 0 18 2 1 30 27.6 March 3 0 5 0 0 8 7.3 Total 36 4 58 6 5 109 Disease % 33 3.7 53.2 5.5 4.6 100% Source: Field work, 2006. From Tables 52 and 53, it is observable that in the rainy season, the patients were highest in July and lowest in August. The dry season patients were highest in February and lowest in December. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 43.3 % for the rainy season and53.2 % in the dry season.
5.2.1.4 Seasonal dimensions of water-borne diseases in Abakpa. Of the 188 patients from this ward(Table 47) who were treated for water-borne diseases in the rainy and dry season, 56(30%) were treated in the rainy season while 132(70%) were treated in the dry season. The seasonal prevalence patterns shown as Tables 54 and 55 indicate that the patients were highest in June, while being lowest in September. The dry season patients were highest in February and lowest in December. Table 54: Rainy season prevalence of water-borne diseases in Abakpa.
Months Water- borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 5 0 4 0 0 9 16.1 May 3 0 3 1 3 10 17.8 June 3 3 4 0 2 12 21.4 July 3 0 6 0 0 9 16.1 August 3 1 5 1 1 11 19.6 September 2 0 2 1 0 5 9 Total 19 4 24 3 6 56 Disease % 33.9 7.1 42.9 5.4 10.7 100% Source: Field work, 2006.
Table 55: Dry season prevalence of water-borne diseases in Abakpa.
Months Water-borne Diseases ccxlvi
Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 6 0 11 2 1 20 15.2 November 3 3 7 0 2 15 11.4 December 7 0 3 0 2 12 9.1 January 6 0 10 0 0 16 12.1 February 7 2 18 5 5 37 28 March 32 0 0 0 0 32 24.4 Total 61 5 49 7 10 132 Disease % 46.2 3.8 37.1 5.3 7.6 100% Source: Field work, 2006. In both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 42.9 % for the rainy season and 37.1 % for the dry season. Cholera contributing 5.9 % in the rainy season and 2.4 % in the dry season had the lowest incidents for both seasons.
5.2.1.5 Seasonal dimensions of water-borne diseases in Iva Valley. 204 patients from Iva valley were treated for water borne diseases in the rainy and dry season as is shown in Table 47. The rainy and dry season patterns (Tables 56 and 57) indicate that of the 204 patients, 105(51.5 %) were treated in the rainy season, while 99(48.5%) were treated in the dry season. Table 56: Rainy season prevalence of water-borne diseases in Iva valley.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 3 3 10 1 1 18 17.1 May 5 1 10 3 2 21 20 June 6 0 11 2 1 20 19.0 July 1 1 6 0 2 10 9.5 August 4 0 4 2 3 13 12.4 September 6 1 12 4 0 23 22 Total 25 6 53 12 9 105 Disease % 23.8 5.7 50.5 11.4 8.6 100% Source: Field work, 2006.
Table 57: Dry season prevalence of water-borne diseases in Iva valley.
Months Water-borne Diseases ccxlvii
Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 7 0 9 4 2 22 22.2 November 6 0 2 0 0 8 8.1 December 3 1 9 1 0 14 14.1 January 3 0 7 0 1 11 11.2 February 5 0 4 5 0 14 14.1 March 12 0 9 2 7 30 30.3 Total 36 1 40 12 10 99 Disease % 36 1 41 12 10 100% Source: Field work, 2006. In the rainy season, the patients were highest in September and lowest in July. The dry season patients were highest in March and lowest in November. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage of 50.5 % for the rainy season and 41 % in the dry season. The disease with the lowest number of patients was cholera for both seasons
5.2.1.6 Seasonal dimensions of water-borne diseases in Coal camp. Of the 196 patients from Coal camp who were treated for water borne diseases in the rainy and dry season table 48 , 80(41 %) were treated in the rainy season while 116(59 %) were treated in the dry season. The seasonal prevalence patterns shown as Tables 58 and 59 indicate that the rainy season patients were highest in June, while being lowest in May. The dry season patients were highest in March and lowest in November as no patients reported for the treatment of any of the water borne diseases. Table 58: Rainy season prevalence of water-borne diseases in Coal Camp.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 4 1 5 4 0 14 17.5 May 1 1 3 2 0 7 8.8 June 9 0 7 2 4 22 27.5 July 4 0 6 5 0 15 18.7 August 3 0 6 0 1 10 12.5 September 2 0 7 3 0 12 15 Total 23 2 34 16 5 80 Disease % 28.8 2.5 42.5 20 6.2 100% Source: Field work, 2006.
Table 59: Dry season prevalence of wate- borne diseases in Coal Camp.
Months Water Borne Diseases ccxlviii
Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 5 2 12 0 4 23 19.8 November 0 0 0 0 0 0 0 December 7 0 1 1 0 9 7.8 January 10 0 16 2 2 30 25.8 February 5 1 12 2 2 22 19 March 12 0 11 2 7 32 27.6 Total 39 3 52 7 15 116 Disease % 33.6 2.6 44.8 6.1 12.9 100% Source: Field work, 2006. In both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 42.5 % for the rainy season and 44.8 % in the dry season. Cholera contributing 2.5 % in the rainy season and 2.6 % in the dry season had the lowest incidents for both seasons.
5.2.1.7 Seasonal dimensions of water-borne diseases in Uwani. A total of 146 patients from this ward were treated for water-borne diseases in the rainy and dry season as is shown in Table 47. The seasonal patterns shown as Tables 60 and 61 indicate that of the 146 patients, 68(46.6 %) were treated in the rainy season, while 78(53.4 %) were treated in the dry season. Table 60: Rainy season prevalence of water-borne diseases in Uwani.
Months Water -Borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 2 1 2 1 0 6 8.8 May 1 0 8 1 2 12 17.6 June 3 0 9 3 0 15 22.1 July 3 1 6 1 1 12 17.6 August 0 0 5 0 0 5 7.4 September 6 1 8 2 1 18 26.5 Total 15 3 38 8 4 68 Disease % 22 4.4 55.9 11.8 5.9 100% Source: Field work, 2006.
Table 61: Dry season prevalence of water-borne diseases in Uwani.
Months Water -Borne Diseases ccxlix
Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 4 1 10 1 1 17 21.8 November 2 0 4 0 1 7 9 December 2 1 6 1 2 12 15.4 January 6 0 3 0 0 9 11.5 February 3 0 12 0 0 15 19.2 March 5 0 10 2 1 18 23.1 Total 22 2 45 4 5 78 Disease % 28.3 2.6 57.6 5.1 6.4 100% Source: Field work, 2006. In the rainy season, the patients were highest in September and lowest in August. The dry season patients were highest in March and lowest in November. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 55.9 % for the rainy season and 57.6 % in the dry season. Cholera was the disease that contributed the least (4.4% in the rainy season and 2.6% in the dry season). The dry season typhoid fever patients were however more than the rainy season patients, while the rainy season cholera patients were more than the dry season.
5.2.1.8 Seasonal dimensions of water-borne diseases in New Haven. Of the 82 patients from New Haven who were treated for water- borne diseases in the rainy and dry season (Tables 47), 39(47.6 %) were treated in the rainy season while 43(52.4 %) were treated in the dry season. From the rainy season pattern shown as Table 62, it is observable that the patients were highest in March, and lowest in August. The dry season pattern (Table 63) patients were highest in November and lowest in December. Table 62: Rainy season prevalence of water-borne diseases in New Haven.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 0 0 5 1 1 7 17.9 May 8 0 2 0 1 11 28.2 June 2 0 0 0 2 4 10.3 July 3 0 5 1 1 10 25.7 August 0 0 0 0 0 0 0 September 2 0 2 0 3 7 17.9 Total 15 0 14 2 8 39 Disease % 38.5 0 35.9 5.1 20.5 100% Source: Field work, 2006.
ccl
Table 63: Dry season prevalence of water-borne diseases in New Haven.
Months Water-borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % October 3 0 3 0 0 6 14 November 2 1 7 1 1 12 27.9 December 3 0 0 0 0 3 6.9 January 2 0 6 0 0 8 18.6 February 4 0 1 1 0 6 14 March 2 0 2 2 2 8 18.6 Total 16 1 19 4 3 43 Disease % 37.2 2.3 44.2 9.3 7 100% Source: Field work, 2006. In both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 35.9 % for the rainy season and 44.2 % in the dry season. Cholera which contributed nothing in the rainy season and only 2.6 % in the dry season had the lowest incidents for both seasons. The typhoid fever patients were more in the dry than the rainy season and the cholera patients were also more in the dry than the rainy season.
5.2.1.9 Seasonal dimensions of water-borne diseases in Independence Layout. 37 patients from Independence layout were treated for water-borne diseases in the rainy and dry seasons as is shown in Table 47. The rainy and dry season patterns (Tables 64 and 65) indicate that of the 37 patients, 19(51.4 %) were treated in the rainy season, while 18(48.6%) were treated in the dry season. Table 64: Rainy season prevalence of water-borne diseases in Independence Layout. Months Water- borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 1 0 2 0 0 3 15.8 May 0 0 0 0 1 1 5.3 June 3 0 0 0 2 5 26.3 July 2 0 2 1 0 5 26.3 August 0 0 0 0 0 0 0 September 2 0 1 0 2 5 26.3 Total 8 0 5 1 5 19 Disease % 42.1 0 26.3 5.3 26.3 100% Source: Field work, 2006.
Table 65: Dry season prevalence of water-borne diseases in Independence layout.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % ccli
October 0 0 3 0 0 3 16.7 November 1 1 4 0 1 7 38.9 December 0 0 0 0 0 0 0 January 2 0 0 0 0 2 11 February 1 0 1 0 1 3 16.7 March 0 0 2 1 0 3 16.7 Total 4 1 10 1 2 18 Disease % 22.2 5.6 55.6 5.6 11 100% Source: Field work, 2006. In the rainy season, the patients were highest in June, July and September and lowest in August. The dry season patients were highest in November and lowest in December. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 26.3 % for the rainy season and 55.6 % in the dry season. Cholera with a percentage of 0 in rainy and 5.6 in dry season contributed the least. The typhoid fever patients were more in the dry than the rainy season and the cholera patients were also more in the dry than the rainy season.
5.3.1.10 Seasonal dimensions of water-borne diseases in Government Reserved Area (G.R.A). A total of 49 patients from this ward were treated for water-borne diseases in the rainy and dry season as is shown in Table 47. The seasonal patterns shown as Tables 66 and 67 indicate that of the 49 patients, 33(67.4 %) were treated in the rainy season, while 16(32.6 %) were treated in the dry season. Table 66: Rainy season prevalence of water-borne diseases in G.R.A.
Months Water -borne Diseases Diarrhea Cholera Typhoid Dysentery Hepatitis Total % April 2 0 7 1 2 12 36.4 May 6 0 2 0 0 8 24.2 June 2 0 3 1 1 7 21.2 July 1 1 0 1 0 3 9.1 August 0 0 0 0 0 0 0 September 1 0 2 0 0 3 9.1 Total 12 1 14 3 3 33 Disease % 36.4 3.0 42.4 9.1 9.1 100% Source: Field work, 2006.
Table 67: Dry season prevalence of water-borne diseases in G.R.A.
Months Water -borne Diseases Diarrhoea Cholera Typhoid Dysentery Hepatitis Total % cclii
October 0 0 0 0 0 0 0 November 1 0 4 0 2 7 43.8 December 2 0 1 0 0 3 18.7 January 0 0 2 0 2 4 25 February 1 0 1 0 0 2 12.5 March 0 0 0 0 0 0 0 Total 4 0 8 0 4 16 Disease % 25 0 50 0 25 100% Source: Field work, 2006. In the rainy season, patients were highest in April and lowest in August. The dry season patients were highest in November and lowest in March. For both seasons, the disease with the highest number of patients was typhoid fever which had a percentage value of 42.4% for the rainy season and 50% in the dry season. Cholera was the disease that contributed the least ((3 %) in the rainy season and in the dry season cholera and dysentery contributed nothing). The foregone discussion shows that water-borne diseases do occur in Enugu and higher incidents were recorded in the dry season than in the rainy season. Spatially, seven wards (namely Ogui, Achara layout, Asata, Abakpa, Coal camp, Uwani and New Haven) had more dry season patients than the rainy season; while only three wards (namely Iva valley, Independence layout and Government Reserved Area (G.R.A) had higher incidents in the rainy season (Fig 143).Typhoid fever was the most frequently occurring illness for both seasons, while cholera was the least frequent illness for both seasons.
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ISI UZO IGBO ETITI
ABAKPA NIKE
GRA
IVA - VALLEY NEW HEAVEN
ASATA
INDEPENDENCE LAYOUT OGUI
COAL CAMP UWANI
ACHARA LAYOUT/ MARY LAND
N LEGEND
Local Government Boundary
Urban Boundary
0 1 2 3 4 5 Ward Boundary
Areas of Dry Season Prevalence
Areas of rainy season prevalence
FIG 143 SEASONAL PATTERN OF WATER BORNE DISEASES IN ENUGU URBAN Source: Fieldwork, 2006.
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5.2.2 Water -washed diseases: These diseases consist of diseases that develop where clean freshwater is scarce (Lankinen et al, 1994). They are called ‘water-washed’ or ‘water scarce’ because they are caused by lack of proper sanitation and hygiene. Thus water washed diseases thrive whenever there is scarcity of fresh water, insufficient good quality water for washing and personal hygiene, or where there is skin or eye contact with contaminated water(Dwight et al, 2005). These diseases include scabies, trachoma and flea, lice and tick-borne disease. Many other diseases include leprosy, tuberculosis, whooping cough, tetanus, and diphtheria (Fechem et al, 1977; Feuerstein, 1997).
5.2.2.1 Water-washed seasonal dimensions in Ogui. A total of 12 patients from Ogui were treated for water-washed diseases in the rainy and dry season as shown in Table 68.
Table 68: Number of Patients treated for water-washed diseases in Enugu urban
Months Wards of the urban area Ogui Achara Asata Abakpa Iva Coal Uwani New Independence G.R.A Total (all layout valley camp Haven layout wards)
January 5 4 10 10 9 5 2 0 0 0 45 February 3 5 3 7 1 6 1 4 0 0 30 March 1 0 0 2 0 3 0 0 0 0 6 April 0 0 7 5 2 0 0 1 0 0 15 May 0 2 3 0 0 0 2 0 0 0 7 June 1 0 5 6 10 0 0 0 0 0 22 July 2 0 1 3 0 0 0 4 0 0 10 August 0 1 0 3 4 0 0 0 0 0 8 September 0 0 1 0 5 0 0 0 0 0 6 October 0 0 3 0 0 3 3 0 0 0 9 November 0 0 0 0 0 0 0 0 0 0 0 December 0 0 0 8 5 0 0 0 0 0 13 Total per ward 12 12 33 44 36 17 8 9 0 0 171 Source: Field work, 2006. The seasonal patterns shown as Tables 69 and 70, indicate that of the 12 patients, 3(25 %) were treated in the rainy season, while 9(75%) were treated in the dry season. In the rainy season, the patients for water-washed diseases were only in the months of June and July and July with the latter month having the higher morbidity. There were no cases of water-washed diseases noted in the hospitals in four of the rainy months (April, May, August and September) (Table 69).
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Table 69: Rainy season prevalence of water-washed diseases in Ogui.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 0 0 0 0 0 0 0 0 May 0 0 0 0 0 0 0 0 0 June 0 1 0 0 0 0 0 1 33.3 July 0 1 0 1 0 0 0 2 66.7 August 0 0 0 0 0 0 0 0 0 September 0 0 0 0 0 0 0 0 0
Total 0 2 0 1 0 0 0 3 % per 0 66.7 0 33.3 0 0 0 100% disease Source: Field work, 2006.
Table 70: Dry season prevalence of water-washed diseases in Ogui.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per Infectio cough month n
April 0 0 0 0 0 0 0 0 0 May 0 0 0 0 0 0 0 0 0 June 0 0 0 0 0 0 0 0 0 July 0 0 0 5 0 0 0 5 55.6 August 0 0 0 2 0 1 0 3 33.3 September 0 0 0 1 0 0 0 1 11.1
Total 0 0 0 8 0 1 0 9 % per 0 0 0 88.9 0 11.1 0 100% disease Source: Field work, 2006. For the dry season as indicated by Table 70, patients were treated for water- washed diseases in only three dry season months and they were highest in the month of January while in three months October, November, and December no patients were treated for water-washed disease. For both seasons, there was a variation with regard to the disease with the highest number of patients. Skin infection was the most prevalent with a percentage contribution of 66.7 % for the rainy season while leprosy was the most prevalent (88.9 %) in the dry season. cclvi
5.2.2.2 Seasonal dimensions of water-washed diseases in Achara Layout. Table 68 shows that 12 patients from Achara Layout were treated for water- washed diseases. Of the 12 patients, 3(25%) were treated in the rainy season, while 9(75%) were treated in the dry season (Tables 71 and 72).
Table 71: Rainy season prevalence of water-washed diseases in Achara Layout .
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 0 0 0 0 0 0 0 0 May 0 2 0 0 0 0 0 2 66.7 June 0 0 0 0 0 0 0 0 0 July 0 0 0 0 0 0 0 0 0 August 0 1 0 0 0 0 0 1 33.3 September 0 0 0 0 0 0 0 0 0
Total 0 3 0 0 0 0 0 3 % per 0 100 0 0 0 0 0 100% disease
Source: Field work, 2006.
Table 72: Dry season prevalence of water-washed diseases in Achara Layout.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
October 0 0 0 0 0 0 0 0 0 November 0 0 0 0 0 0 0 0 0 December 0 0 0 0 0 0 0 0 0 January 0 0 0 3 0 1 0 4 44.4 February 2 0 0 2 0 1 0 5 55.6 March 0 0 0 0 0 0 0 0 0
Total 0 0 0 5 0 2 0 9 % per 22.2 0 0 55.6 0 22.2 0 100% disease Source: Field work, 2006.
In the rainy season( Table 71) , water-washed disease patients reported only in two of the months( May and August )and the higher number was recorded in May, while no patient reported for the treatment of any of the water-washed diseases in the other four months. The dry season patients (Table 72) were treated only in January cclvii
and February. In the other dry season months (October, November, December and March) patients were not treated for water-washed diseases. In the rainy season, the disease with the highest number of patients was skin infection which had a percentage value of 100 %. It was thus the only significant water-washed disease in this ward during this period. On the other hand, tuberculosis contributing 55.6 % was the most significant water-washed disease in the dry season.
5.2.2.3 Seasonal dimension of water-washed diseases in Asata. A total of 33 patients from Asata were treated for water washed diseases in the rainy and dry season as is shown in Table 68. The rainy and dry season patterns presented as Tables further indicate that of the 33 patients, 17(51.5 %) were treated in the rainy season, while 16(48.5 %) were treated in the dry season(Table 73 and 74). Table 73: Rainy season prevalence of water-washed diseases in Asata.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 4 0 3 0 0 0 7 41.2 May 0 1 0 2 0 0 0 3 17.7 June 1 3 0 1 0 0 0 5 29.5 July 0 0 0 1 0 0 0 1 5.8 August 0 0 0 0 0 0 0 0 0 September 0 0 0 1 0 0 0 1 5.8
Total 1 8 0 8 0 0 0 17 % per 5.8 47.1 0 47.1 0 0 0 100% disease Source: Field work, 2006.
Table 74: Dry season prevalence of water-washed diseases in Asata.
Month Water -washed Diseases cclviii
Trachoma Skin infection Leprosy Tuberculosis Whopping cough Tetanus Diphtheria Total % per month
October 0 1 0 2 0 0 0 3 18.7 November 0 0 0 0 0 0 0 0 0 December 0 0 0 0 0 0 0 0 0 January 2 6 0 2 0 0 0 10 62.6 February 0 0 0 3 0 0 0 3 18.7 March 0 0 0 0 0 0 0 0 0
Total 2 7 0 7 0 0 0 16 % per disease 12.6 43.7 0 43.7 0 0 0 100% Source: Field work, 2006.
From Table 73, it is observable that in the rainy season, patients were highest in the month of April and lowest in August. The dry season patients were highest in the month of January and lowest in three months- November, December and March (Table 74). For both seasons, the diseases with the highest number of patients were skin infection and tuberculosis. While skin infection had a percentage value of 47.1 % for the rainy season, tuberculosis had a value of 43.7 % in the dry season.
5.2.2.4 Seasonal dimensions of water-washed diseases in Abakpa. Of the 44 patients from Abakpa that were treated for water-washed diseases in the rainy and dry season Table 68, 17(38.6%) were treated in the rainy season while 27(61.4%) were treated in the dry season (Tables 75 and 76).
Table 75: Rainy season prevalence of water-washed diseases in Abakpa.
Month Water -washed Diseases cclix
Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 1 0 4 0 0 0 5 29.5 May 0 0 0 0 0 0 0 0 0 June 0 4 0 1 0 1 0 6 35.3 July 2 0 0 1 0 0 0 3 17.6 August 0 0 0 3 0 0 0 3 17.6 September 0 0 0 0 0 0 0 0 0
Total 2 5 0 9 0 1 0 17 % per 11.8 29.4 0 52.9 0 5.9 0 100% disease Source: Field work, 2006.
Table 76: Dry season prevalence of water-washed diseases in Abakpa.
Month Water- washed Diseases Trachoma Skin infection Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per Cough month
October 0 0 0 0 0 0 0 0 0 November 0 0 0 0 0 0 0 0 0 December 0 2 0 6 0 0 0 8 29.6 January 1 7 0 2 0 0 0 10 37 February 0 2 0 5 0 0 0 7 26 March 0 0 0 0 0 2 0 2 7.4
Total 1 11 0 13 0 2 0 27 % per disease 3.7 40.8 0 48.1 0 7.4 0 100% Source: Field work, 2006. The seasonal prevalence patterns shown as Tables 75 and 76 indicate that in the rainy season water-washed disease patients were highest in June, while no patients reported in May and September for treatment. The dry season patients were highest in the month of January and in October and November no patient reported for treatment. For both seasons, the disease with the highest number of patients was tuberculosis (47.1%) in rainy season and 43.7% in dry season. Leprosy, whopping cough and diphtheria were the water-washed diseases that had no patients in the dry and rainy seasons. In Abakpa, the patients treated for water-washed diseases in the dry season (61.4 %) were more than in the rainy season (38.6 %).
5.2.2.5 Seasonal dimensions of water-washed diseases in Iva Valley. cclx
Thirty-six patients from this ward were treated for water-washed diseases in the rainy and dry seasons as shown in table 68. The rainy and dry season patterns indicate that 21 representing 58.3 % were treated in the rainy season, while 99(41.7%) were treated in the dry season (Tables 77 and 78). Table 77: Rainy season prevalence of water-washed diseases in Iva Valley.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 1 0 1 0 0 0 2 9.5 May 0 0 0 0 0 0 0 0 0 June 2 3 0 4 0 1 0 10 47.6 July 0 0 0 0 0 0 0 0 0 August 0 0 0 4 0 0 0 4 19.1 September 0 1 0 4 0 0 0 5 23.8
Total 2 5 0 13 0 1 0 21 %per disease 9.5 23.8 0 61.9 0 4.8 0 100% Source: Field work, 2006.
Table 78: Dry season prevalence of water-washed diseases in Iva Valley.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
October 0 0 0 0 0 0 0 0 0 November 0 0 0 0 0 0 0 0 0 December 0 3 0 2 0 0 0 5 33.3 January 1 2 0 5 0 1 0 9 60 February 0 0 0 1 0 0 0 1 6.7 March 0 0 0 0 0 0 0 0 0
Total 1 5 0 8 0 1 0 15 %per disease 6.7 33.3 0 53.3 0 6.7 0 100% Source: Field work, 2006.
In the rainy season, patients were highest in June while in May and July no patients were treated for water-washed diseases. The dry season patients were highest in the month of January and October, November and March no patients were treated for any of the water-washed diseases. For both seasons, the disease with the highest cclxi
number of patients was tuberculosis which had a percentage value of 61.9% for the rainy season and 53.3% for the dry season. Leprosy, whopping cough and diphtheria had no reported incidence in both seasons.
5.2.2.6 Seasonal dimensions of water-washed diseases in Coal Camp. Of the17 patients from Coal Camp that were treated for water- washed diseases in the rainy and dry seasons (Table 68), no patient was treated in the rainy season. All those treated for the water-washed diseases were treated in the dry season only (Table 79). Table 79: Dry season prevalence pattern of water-washed diseases in Coal camp. Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total %per infection cough month
April 0 3 0 0 0 0 0 3 17.6 May 0 0 0 0 0 0 0 0 0 June 0 0 0 0 0 0 0 10 0 July 0 2 0 3 0 0 0 0 29.4 August 0 0 0 6 0 0 0 4 35.4 September 0 0 0 2 0 0 0 5 17.6
Total 0 5 0 11 0 1 0 17 % per 0 29.4 0 64.7 0 5.9 0 100% disease Source: Field work, 2006
It is observable therefore, that in the rainy season no patient reported for the treatment of no water-washed diseases. The dry season patients (Table 79) were highest in February and lowest in November and December. In the rainy season, no water-washed disease was significant as no patient reported for the treatment of these diseases. However in the dry season, the disease with the highest number of patients was tuberculosis which had a percentage value of 64.7%. Diseases such as trachoma, leprosy, whopping cough and diphtheria had no patients reporting for them. These diseases are thus not commonly associated with this area. The number of people that were treated for water washed diseases in Coal Camp were more in the dry season (100%) than the rainy season (0%).
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5.2.2.7 Seasonal dimensions of water-washed diseases in Uwani. A total of 8 patients from Uwani were treated for water washed diseases in the rainy and dry seasons as shown in Table 68. The rainy and dry season patterns presented as Tables 80 and 81 indicate that of the 8 patients, 2(25 %) were treated in the rainy season, while 6(75 %) were treated in the dry season.
Table 80: Rainy season prevalence of water-washed diseases in Uwani.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
April 0 0 0 0 0 0 0 0 9.5 May 0 1 0 0 1 0 0 2 0 June 0 0 0 0 0 0 0 0 47.6 July 0 0 0 0 0 0 0 0 0 August 0 0 0 0 0 0 0 0 19.1 September 0 0 0 0 0 0 0 0 23.8
Total 0 1 0 0 1 0 0 2 %per disease 0 50 0 0 50 0 0 100% Source: Field work, 2006.
Table 81: Dry season prevalence of water-washed diseases in Uwani.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % per infection cough month
October 0 2 0 1 0 0 0 3 50 November 0 0 0 0 0 0 0 0 0 December 0 0 0 0 0 0 0 0 0 January 0 0 0 1 1 0 0 2 33.3 February 0 1 0 0 0 0 0 1 16.7 March 0 0 0 0 0 0 0 0 0
Total 0 3 0 2 1 0 0 6 % per 0 50 0 33.3 16.7 0 0 100% Disease Source: Field work, 2006. cclxiii
In the rainy season the prevalence rate of water-washed diseases in Uwani was low. The two patients that were treated in this season were treated in May. The dry season patients were highest in October and lowest in November, December and March. For both seasons, the disease with the highest number of patients was skin infection which had a percentage value of 50 % for the rainy season and 50 % for the dry season. Trachoma, leprosy, tetanus and diphtheria were the diseases that contributed the least (0 % each) in the rainy and dry seasons.
5.2.2.8 Seasonal dimensions of water-washed diseases in New Haven. Of the 9 patients from this ward who were treated for water-washed diseases in the rainy and dry seasons (Table 68), 5(55.6 %) were treated in the rainy season while 4(44.4 %) were treated in the dry season. The seasonal prevalence patterns shown as Tables 82 and 83 indicate that in the rainy season the patients were highest in the months of July, while in the other four months no patient was treated for water-washed diseases. The dry season patients reported only in the month of February, while no water-washed disease was recorded in the other five dry season months.
Table 82: Rainy season prevalence of water-washed diseases in New Haven.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % infection cough per month
April 0 0 0 1 0 0 0 1 20 May 0 0 0 0 0 0 0 0 0 June 0 0 0 0 0 0 0 0 0 July 0 4 0 0 0 0 0 4 80 August 0 0 0 0 0 0 0 0 0 September 0 0 0 0 0 0 0 0 0
Total 0 4 0 1 0 0 0 5 % 0 80 0 20 0 0 0 100% per disease Source: Field work, 2006.
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Table 83: Dry season prevalence of water-washed diseases in New haven.
Month Water -washed Diseases Trachoma Skin Leprosy Tuberculosis Whopping Tetanus Diphtheria Total % infection cough per month
October 0 0 0 0 0 0 0 0 0 November 0 0 0 0 0 0 0 0 0 December 0 0 0 0 0 0 0 0 0 January 0 0 0 0 0 0 0 0 0 February 0 4 0 0 0 0 0 4 100 March 0 0 0 0 0 0 0 0 0
Total 0 4 0 0 0 0 0 4 % 0 100 0 0 0 0 0 100% per disease Source: Field work, 2006 In both seasons, the disease with the highest number of patients was skin infection which had a percentage value of 80 % for the rainy season and 100 % in the dry season.
5.2.2.9 Seasonal dimensions of water-washed diseases in Independence Layout. As shown in Table 68 no patient from Independence layout was treated for any of the water-washed diseases throughout the period of study. Basically this shows that water-washed diseases were not frequent diseases in Independence Layout.
. 5.2.2.10 Seasonal dimensions water-washed diseases in Government Reserved Area G.R.A). No patient from this ward was treated for any of the water washed diseases throughout the period of study as is shown by Table 68. Water washed diseases are not frequent diseases in the G.R.A. From the water-washed seasonal patterns observed in Enugu Urban, it can be seen that the wards recorded more incidents of water-washed diseases in the dry season than the rainy season (Fig 144). This assertion is based on the fact that the research finding indicates that water-washed diseases occurred more in dry than the rainy season in five wards(Ogui, Achara layout, Abakpa, Coal Camp and Uwani). cclxv
On the other hand, three wards (Asata, Iva Valley and New Haven) had more water-washed disease incidence in the rainy than the dry season. Two wards (Independence Layout and Government Reserved Area recorded no incidence of water-washed diseases for both seasons (Fig144). The very low levels recorded for Independence layout and the Government Reserved Area (G.R.A) is an indication of the fact that these are low population residential areas where poverty level is also low. The residents of the areas are also able to purchase water from reliable sources during the periods of water scarcity.
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ISI UZO IGBO ETITI
ABAKPA NIKE
GRA
IVA - VALLEY NEW HEAVEN
ASATA
INDEPENDENCE LAYOUT OGUI
COAL CAMP UWANI
ACHARA LAYOUT/ MARY LAND
N LEGEND
Local Government Boundary
Urban Boundary
0 1 2 3 4 5 Ward Boundary Areas of Dry Season Prevalence
Areas of rainy season prevalence
Areas without Water washed Diseases
FIG 144: SEASONAL PATTERN OF WATER-WASHED DISEASES IN ENUGU URBAN
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5.2.3 Water-based diseases: A water-based disease is one whose pathogen spends a part of its life- cycle in a water snail or other aquatic animal. In other words,water-based diseases are due to infection by parasitic worms (helminths) which depend on aquatic intermediate hosts to complete their life cycles(Lankinen et al, 1994). They come from hosts that live in water or require water for part of their life cycle and are caused by parasites found in intermediate organisms living in contaminated water. Water-based diseases spread through water that is contaminated with parasites (worms), either when humans drink it, use it for washing or when it penetrates the skin, usually through an open wound. These organisms can thrive in either polluted or unpolluted water (Brandley, 1994). They include: schistosomiasis, which can stunt growth and development; guinea worm, which causes a disfiguring and disabling disease.
5.2.3.1 Seasonal incidence of water-based diseases in Enugu Urban A total of three patients were treated for water bases diseases as is shown by table 84. In the rainy season, no patient from Asata reported for the treatment of any water based disease. The three patients that were treated for water based disease from this ward were all treated in January during the dry season. This is indicative of the fact that these diseases have a very low occurrence rate in this ward. Guinea worm infection was the prevalent water based disease in Asata with occurrence being only in the dry season. A total of six patients were treated for water bases diseases in Abakpa as is shown by Table 84. Seasonally the illness pattern indicates that in the rainy season only one patient from Abakpa was treated for water-based disease. The other five patients that were treated for water-based disease were all treated in the dry season month of January. The water-based disease with highest prevalence rate was guinea worm infection which contributed 100% in the dry season. A total of 5 patients were treated for water-based diseases as is shown by Table 84. The seasonal pattern of the illness shows that in the rainy season, five patients from Coal Camp were treated for only one type of water based disease (guinea worm); in June. No patient however was treated for water based disease in the dry season months. This indicates that the water based disease was more in the rainy than the dry season. cclxviii
Table 84: Number of Patients treated for water-based diseases in Enugu urban Months Wards of the urban area Ogui Achara Asata Abakpa Iva Coal Uwani New Independenc G.R.A Total layout valley camp Haven e layout for all wards
January 0 0 3 5 0 0 0 0 0 0 8 February 0 0 0 0 0 0 0 0 0 0 0 March 0 0 0 0 0 0 0 0 0 0 0 April 0 0 0 1 0 0 0 0 0 0 1 May 0 0 0 0 0 0 0 0 0 0 0 June 0 0 0 0 0 5 0 0 0 0 5 July 0 0 0 0 0 0 0 0 0 0 0 August 0 0 0 0 0 0 0 0 0 0 0 September 0 0 0 0 0 0 0 0 0 0 0 October 0 0 0 0 0 0 0 0 0 0 0 November 0 0 0 0 0 0 0 0 0 0 0 December 0 0 0 0 0 0 0 0 0 0 0 Total for 0 0 3 6 0 5 0 0 0 0 14 each ward Source: Field work, 2006.
5.2.3.2 Non-incidence of water-based diseases in Enugu Urban Table 84 shows that no patients were treated for water-based disease in the following wards : Ogui, Achara Layout, Iva Valley, Uwani, New Haven, Independence Layout and G.R.A. Water-based diseases were not found in these wards. From the discussion of the seasonal patterns of water-based diseases, it can be seen that seven wards (Ogui, Achara Layout, Iva Valley, Uwani, New Haven, Independence Layout and G.R.A) had no incidence of water-based diseases for the year under study (Fig 145). The low incidents underscore the fact that water-based diseases were not common in Enugu Urban area. Asata ward had all the patients for water-based diseases reporting for the illness only in the dry season’ while Coal Camp had all the patients for water-based diseases reporting for the illness only in the rainy season. Abakpa ward was the only ward that recorded more incidents in the dry season than the rainy season (Fig150). Generally, in Enugu Urban however, the prevalence of water-based diseases is very low. cclxix
ISI UZO IGBO ETITI
ABAKPA NIKE
GRA
IVA - VALLEY NEW HEAVEN
ASATA
INDEPENDENCE LAYOUT OGUI
COAL CAMP UWANI
ACHARA LAYOUT/ MARY LAND
N LEGEND
Local Government Boundary
Urban Boundary
0 1 2 3 4 5 Ward Boundary
No water borne disease prevalence Dry Season Prevalence only Rainy Season Prevalence only Dry season more than rainy season FIG 145: SEASONAL PATTERN OF WATER-BASED DISEASES IN ENUGU URBAN Source: Fieldwork, 2006.
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5.2.4 Water-related vector diseases
These diseases are water-related insect carrier diseases spread through insects that feed or breed (live) in or near dirty water. People can be bitten at the river sides or the waterholes by these insects. They include: malaria; Japanese encephalitis; sleeping sickness; river blindness; yellow fever; break bone fever (dengue), filariasis, onochoceriasis and trypanosomiasis (Bradley, 1994).
5.2.4.1 Seasonal dimensions of water-related vector diseases in Ogui.
A total of 209 patients from this ward were treated for water-related
vector diseases as is shown in Tables 85.
Table 85: Number of Patients treated for Water-related vector diseases in Enugu urban
Months Wards of the urban area Ogui Achara Asata Abakpa Iva Coal camp Uwani New Independence G.R.A Total for layout valley Haven layout all wards
January 8 11 25 39 30 36 20 17 23 26 235 February 20 30 27 18 19 30 26 30 17 18 235 March 17 23 12 7 0 5 15 22 9 15 125 April 5 20 10 14 10 16 12 22 17 6 132 May 13 6 5 14 10 27 22 25 25 12 160 June 14 30 15 48 28 22 36 50 15 13 271 July 14 6 14 25 20 30 33 15 10 23 190 August 10 15 21 16 17 20 35 5 13 10 162 September 37 36 4 30 14 28 18 3 5 4 179 October 6 8 22 19 8 32 13 9 9 15 141 November 37 23 11 20 22 20 11 13 10 5 172 December 28 24 18 40 21 26 10 20 20 12 219 Total for each 209 232 184 291 199 292 251 231 173 159 2221 ward Source: Field work, 2006. The seasonal pattern shown as Tables 86 and 87 indicate that of the 209 patients, 93(44.5 %) were treated in the rainy season, while 116 (55.5 %) were treated in the dry season.
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Table 86: Rainy season prevalence of water-related vector diseases in Ogui
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 5 0 0 0 0 0 5 5.4 May 13 0 0 0 0 0 13 14 June 14 0 0 0 0 0 14 15 July 14 0 0 0 0 0 14 15 August 10 0 0 0 0 0 10 10.8 September 37 0 0 0 0 0 37 39.8 Total 93 0 0 0 0 0 93 % per 100 0 0 0 0 0 100 disease Source: Field work, 2006.
Table 87: Dry season prevalence of water-related vector diseases in Ogui
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 6 0 0 0 0 0 6 5.2 November 37 0 0 0 0 0 37 31.9 December 28 0 0 0 0 0 28 24.1 January 8 0 0 0 0 0 8 6.9 February 19 0 0 0 0 0 20 17.2 March 17 0 0 0 1 0 17 14.7 Total 115 0 0 0 0 0 116 % per 99 0 0 0 1 0 100 disease Source: Field work, 2006. In the rainy season, the patients were highest in September and lowest in April. The dry season patients were highest in November and lowest in October. For both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100% for the rainy season and 99% in the dry season.
5.2.4.2 Seasonal dimensions of water-related vector diseases in Achara cclxxii
Layout A total of 232 patients from Achara Layout were treated for water-related diseases (Tables 85). 113(48.7%) were treated in the rainy season while 119(51.3%) were treated in the dry season. In the rainy season (Table 88), the patients were highest in the month of September, while being lowest in May. In the dry season (Table 89), the patients were highest in February and lowest in October.
Table 88: Rainy season prevalence of water-related vector diseases in Achara layout. Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 20 0 0 0 0 0 20 17.7 May 6 0 0 0 0 0 6 5.3 June 30 0 0 0 0 0 30 26.5 July 6 0 0 0 0 0 6 5.3 August 15 0 0 0 0 0 15 13.2 September 36 0 0 0 0 0 36 32 Total 113 0 0 0 0 0 113 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006.
Table 89: Dry season prevalence of water-related vector diseases in Achara layout. Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 8 0 0 0 0 0 8 6.7 November 23 0 0 0 0 0 23 19.3 December 24 0 0 0 0 0 24 20.2 January 11 0 0 0 0 0 11 9.3 February 30 0 0 0 0 0 30 25.2 March 23 0 0 0 1 0 23 19.3 Total 119 0 0 0 0 0 119 % per 100 0 0 0 1 0 100% disease Source: Field work, 2006. In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100% for the rainy season and 100% in the dry season. The other water-related vector diseases were not reported in the ward.
5.2.4.3 Seasonal dimensions of water-related vector diseases in Asata . cclxxiii
A total of 184 patients were treated for water-related vector diseases in Asata as is shown by table 85. The seasonal prevalence pattern shown as Tables 90 and 91 indicate that in the rainy 69(37.5%) people from this ward were treated in the sampled hospitals for water-related vector diseases, while 115(62.5%) were treated in the dry season.
Table 90: Rainy season prevalence of Water-related vector diseases in Asata
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 10 0 0 0 0 0 10 5.4 May 5 0 0 0 0 0 5 14 June 15 0 0 0 0 0 15 15 July 13 0 0 0 1 0 14 15 August 21 0 0 0 0 0 21 10.8 September 4 0 0 0 0 0 4 39.8 Total 68 0 0 0 1 0 69 % per 98.6 0 0 0 1.4 0 100% disease Source: Field work, 2006.
Table 91: Dry season prevalence of water-related vector diseases in Asata
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 22 0 0 0 0 0 22 19.1 November 11 0 0 0 0 0 11 9.6 December 18 0 0 0 0 0 18 15.7 January 20 0 0 0 3 2 25 21.7 February 27 0 0 0 0 0 27 23.5 March 12 0 0 0 0 0 12 10.4 Total 110 0 0 0 3 2 115 % per 95.7 0 0 0 2.6 1.7 100% disease Source: Field work, 2006. In the rainy season, the patients were highest in the month of August (30.4%), while being lowest in September (Table 90). The dry season patients were highest in the months of February lowest in November (Table 91). In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 98.6% occurrence in the rainy season and 95.7% in cclxxiv the dry season; while some of the other diseases were either not experienced or it occurred with a very low percentage.
5.2.4.4 Seasonal dimensions of water-related vector diseases in Abakpa
Of the 291 patients from Abakpa who were treated for water vectored diseases (Table 85), 148(50.9%) were treated in the rainy season while 143(49.1%) were treated in the dry season (Tables 92 and 93).
Table 92: Rainy season prevalence of water-related vector diseases in Abakpa Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 14 0 0 0 0 0 14 9.5 May 15 0 0 0 0 0 15 10.1 June 48 0 0 0 0 0 48 32.4 July 25 0 0 0 0 0 25 16.9 August 16 0 0 0 0 0 16 10.8 September 26 0 0 0 0 4 26 20.3 Total 144 0 0 0 0 4 148 % per 97.3 0 0 0 0 2.7 100% disease Source: Field work, 2006.
Table 93: Dry season prevalence of water-related vector diseases in Abakpa Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 19 0 0 0 0 0 19 13.4 November 20 0 0 0 0 0 20 14 December 40 0 0 0 0 0 40 28 January 36 0 0 3 0 0 39 27.3 February 18 0 0 0 0 0 18 12.5 March 7 0 0 0 0 0 7 4.8 Total 140 0 0 3 0 0 143 % per 97 0 0 3 0 0 100% disease Source: Field work, 2006.
In the rainy season (Table 92), the patients were highest in the month of June, while being lowest in April. The dry season patients were highest in the month of December and lowest in March (table 93). In both seasons, the disease with the cclxxv highest number of patients was malaria which had a percentage value of 97.3% for the rainy season and 97% for the dry season.
5.2.4.5 Seasonal dimensions of water-related vector diseases in Iva Valley
A total of 199 patients were treated for water vectored diseases in Iva valley as is shown by Table 85. The seasonal pattern shown as Tables 94 and 95 indicate that of the 199 patients, 99 (49.7 %) were treated in the rainy season, while 100(50.3%) were treated in the dry season.
Table 94: Rainy season prevalence of Water-related vector diseases in Iva valley
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 10 0 0 0 0 0 10 10.1 May 9 0 0 0 1 0 10 10.1 June 24 0 0 0 4 0 28 28.2 July 20 0 0 0 0 0 20 20.2 August 17 0 0 0 0 0 17 17.2 September 14 0 0 0 0 0 14 14.2 Total 94 0 0 0 5 0 99 % per 94.9 0 0 0 5.1 0 100% disease Source: Field work, 2006 Table 95: Dry season prevalence of Water-related vector diseases in Iva valley Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 8 0 0 0 0 0 8 8 November 22 0 0 0 0 0 22 22 December 21 0 0 0 0 0 21 21 January 30 0 0 0 0 0 30 30 February 19 0 0 0 0 0 19 19 March 0 0 0 0 0 0 0 0 Total 100 0 0 0 0 0 100 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006. In the rainy season, the patients were highest in June, while being lowest in May (Table 94). The dry season patients were highest in January and lowest in March (Table 95).In both seasons, the disease with the highest number of patients was cclxxvi malaria which had a percentage value of 95 % occurrence in the rainy season and 100% in the dry season.
5.2.4.5 Seasonal dimensions of water-related vector diseases in Coal Camp.
A total of 292 patients from Coal Camp were treated for water-related vector diseases in the rainy and dry seasons as is shown in table 85.The rainy and dry season patterns indicate that of the 292 patients, 143 patients representing 49 % were treated in the rainy season, while 145(51%) were treated in the dry season. In the rainy season (Table 96), the patients were highest in the month of July, and lowest in April. The dry season patients were highest in January and lowest in March (Table 97).
Table 96: Rainy season prevalence of water-related vector diseases in Coal camp.
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 16 0 0 0 0 0 16 11.2 May 27 0 0 0 0 0 27 18.9 June 22 0 0 0 0 0 22 15.4 July 30 0 0 0 0 0 30 21 August 20 0 0 0 0 0 20 14 September 28 0 0 0 0 0 28 19.5 Total 143 0 0 0 0 0 143 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006.
Table 97: Dry season prevalence of water-related vector diseases in Coal camp. cclxxvii
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 32 0 0 0 0 0 32 24.2 November 20 0 0 0 0 0 20 13.4 December 26 0 0 0 0 0 26 17.4 January 32 0 0 0 4 0 36 24.2 February 30 0 0 0 0 0 30 20.1 March 5 0 0 0 0 0 5 3.4 Total 145 0 0 0 4 0 149 % per 97.3 0 0 0 2.7 0 100 100% disease Source: Field work, 2006.
In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100 % in the rainy season and 97.3% in the dry season. The number of people that were treated for Water-related vector diseases in Coal camp were more in the dry season (51%) than the rainy season (49 %).
5.2.4.7 Seasonal dimensions of water-related vector diseases in Uwani.
A total of 251 patients were treated for water-related vector diseases in Uwani as shown by Table 85. Of the 251 patients, 156 were treated for water- related vector diseases in the rainy season, while 95 were treated in the dry season. In the rainy season (Table 98), the patients were highest in June, while being lowest in April. The dry season patients were highest in February (27.4%) and lowest in December (10.5 %) (Table 99).
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Table 98: Rainy season prevalence of Water-related vector diseases in Uwani
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 12 0 0 0 0 0 12 7.7 May 22 0 0 0 0 0 22 14.1 June 36 0 0 0 0 0 36 23.1 July 33 0 0 0 0 0 33 21.2 August 35 0 0 0 0 0 35 22.4 September 18 0 0 0 0 0 18 11.5 Total 156 0 0 0 0 0 156 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006. Table 99: Dry season prevalence of Water-related vector diseases in Uwani Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 11 0 0 0 0 2 13 13.7 November 11 0 0 0 0 0 11 11.6 December 10 0 0 0 0 0 10 10.5 January 20 0 0 0 0 0 20 21.1 February 23 0 0 0 1 0 26 27.4 March 15 0 0 0 0 2 15 15.7 Total 90 0 0 0 1 4 95 % per 94.7 0 0 0 1.1 4.2 100% disease Source: Field work, 2006.
In both seasons, the disease with the highest number of patients was malaria which had a percentage of 100% in the rainy season and 94.7% in the dry season.
5.2.4.8 Seasonal dimensions of water-related vector diseases in New Haven
A total of 231 patients from New Haven were treated for Water-related vector diseases in the rainy and dry season as is shown in Table 85.The rainy and dry season patterns indicate that of the 231 patients, 120 patients representing 52 % were treated in the rainy season, while 111(48%) were treated in the dry season. In the rainy season (Table 100), the patients were highest in the month of June, and lowest in September. The dry season patients were highest in the month of February and lowest in October (Table 101). cclxxix
Table 100: Rainy season prevalence of Water-related vector diseases in New Haven
Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 22 0 0 0 0 0 22 18.3 May 25 0 0 0 0 0 25 20.8 June 50 0 0 0 0 0 50 41.7 July 15 0 0 0 0 0 15 12.5 August 5 0 0 0 0 0 5 4.2 September 3 0 0 0 0 0 3 2.5 Total 120 0 0 0 0 0 120 % per 100 0 0 0 0 0 100% disease
Source: Field work, 2006.
Table 101: Dry season prevalence pattern of Water-related vector diseases in New Haven Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 9 0 0 0 0 0 9 8.2 November 13 0 0 0 0 0 13 11.7 December 20 0 0 0 0 0 20 18.0 January 17 0 0 0 0 0 17 15.3 February 30 0 0 0 0 0 30 27.0 March 22 0 0 0 1 0 22 19.8 Total 111 0 0 0 1 0 112 % per 100 0 0 0 1 0 100% disease
Source: Field work, 2006. In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100 % in the rainy season and 99% in the dry season.
5.2.4.9 Seasonal dimensions of water-related vector diseases in Independence Layout
A total of 173 patients were treated for water-related vector diseases in Independence Layout as shown by Table 85. The seasonal patterns shown as Tables 102 and 103 indicate that in the rainy season 85(49%) patients were treated for water-related vector diseases, while 88(51%) were treated in the dry season. cclxxx
Table 102: Rainy season prevalence of water-related vector diseases in Independence Layout.
Water-related vector diseases Months Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 17 0 0 0 0 0 17 20 May 25 0 0 0 0 0 25 29.4 June 15 0 0 0 0 0 15 17.6 July 10 0 0 0 0 0 10 11.8 August 13 0 0 0 0 0 13 15.3 September 5 0 0 0 0 0 5 5.9 Total 85 0 0 0 0 0 85 % per 100 0 0 0 0 0 100% disease .
Table 103: Dry season prevalence pattern of water-related vector diseases in Independence Layout. Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 9 0 0 0 0 0 9 10.2 November 10 0 0 0 0 0 10 11.4 December 20 0 0 0 0 0 20 22.7 January 23 0 0 0 0 0 23 26.1 February 17 0 0 0 0 0 17 19.4 March 9 0 0 0 0 0 9 10.2 Total 88 0 0 0 0 0 88 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006. In the rainy season, the patients were highest in May, and lowest in September. The dry season patients were highest in February and lowest in March and October; 10.2 each).In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100 % in the rainy season and 100% in the dry season.
cclxxxi
5.2.4.10 Seasonal dimensions water-related vector diseases in Government Reserved Area (G.R.A)
Table 65 shows that 159 patients were treated for water-related vector diseases. The rainy and dry season patterns indicate that of the 159 patients, 68 representing 43 % were treated in the rainy season, while 91(57%) were treated in the dry season. In the rainy season (Table 104), patients were highest in the month of July, and lowest in September. The dry season patients were highest in the month of January and lowest in November (5.4 %) (Table 105).
Table 104: Rainy season prevalence pattern of water-related vector diseases in G.R.A Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month April 6 0 0 0 0 0 6 9 May 12 0 0 0 0 0 12 17.6 June 13 0 0 0 0 0 13 19.1 July 23 0 0 0 0 0 23 33.8 August 10 0 0 0 0 0 10 14.7 September 4 0 0 0 0 0 4 5.8 Total 68 0 0 0 0 0 68 % per 100 0 0 0 0 0 100 disease Source: Field work, 2006.
Table 105: Dry season prevalence pattern of water-related vector diseases in G.R.A Months Water-related vector diseases Malaria Japanese Sleeping River Yellow Dengue Total % per fever sickness blindness fever fever month October 15 0 0 0 0 0 15 16.5 November 5 0 0 0 0 0 5 5.4 December 12 0 0 0 0 0 12 13.2 January 26 0 0 0 0 0 26 28.6 February 18 0 0 0 0 0 18 19.8 March 15 0 0 0 0 0 15 16.5 Total 91 0 0 0 0 0 91 % per 100 0 0 0 0 0 100% disease Source: Field work, 2006. cclxxxii
In both seasons, the disease with the highest number of patients was malaria which had a percentage value of 100 % in the rainy season and 100% in the dry season. The other diseases were not experienced in this ward. From the foregone discussion, it is discernible that water-related vector diseases were more in the dry season than the rainy season in seven of the ten wards (namely Ogui, Achara layout, Asata, Iva valley, Coal camp, Independence layout and G.R.A). Three wards (Abakpa, Uwani and New Haven) had more incidents in the rainy than dry season (Fig 146). cclxxxiii
ISI UZO IGBO ETITI
ABAKPA NIKE
GRA
IVA - VALLEY NEW HEAVEN
ASATA
INDEPENDENCE LAYOUT OGUI
COAL CAMP UWANI
ACHARA LAYOUT/ MARY LAND
N LEGEND
Local Government Boundary
Urban Boundary
0 1 2 3 4 5 Ward Boundary
Dry season prevalence
Rainy Season prevalence
FIG 146: SEASONAL PATTERN OF WATER-RELATED VECTOR DISEASES IN ENUGU URBAN
Source: Fieldwork, 2006.
cclxxxiv
5.3 Annual pattern of water-related diseases in Enugu Urban.
5.3.1 Rainy season pattern of water-related diseases in Enugu urban .
For the year under study, the total number of patients treated for water- related diseases was 3813 (Table 46). Of this total number 1768 patients were treated in the rainy season (Table 106).
TABLE 106: Incidence of water–related diseases during rainy season in Enugu Urban.
Month Number of Percentage(%) patients contribution treated
April 244 14
May 271 15
June 438 24
July 311 18
August 224 13
September 280 18
Total 1768 100%
Source: Field work 2006
From Table 106, it can be seen that during the rainy season, the month that recorded the highest incidents of water-related diseases was the month of June with (438(24%) patients. The lowest number of patients occurred in August when 224 patients representing 13% were treated.
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5.3.2 Dry season pattern of water-related diseases in Enugu urban area.
The total number of patients treated for water-related diseases during the dry season was 2045(Table107).
Table 107: Incidence of water–related diseases during dry season in Enugu Urban.
Month Number of patients Percentage % treated contribution
October 290 14
November 263 13
December 332 16
January 425 21
February 431 21
March 304 15
Total 2045 100%
Source: Field work, 2006
Table 107 shows that during the dry season, the month that had the highest number of patients was the month of February when 431 patients (21%) were treated for water-related diseases in the urban area. The lowest number of patients occurred in the month of November, when 263 patients (13%) were treated. A comparison of the rainy and dry season patterns indicates that the dry season period with a total of 2045 patients (53.6%), had more patients reporting for the treatment of water-related diseases than the rainy season period with a total of 1768 patients (46.4%).
This seasonal trend noted in Enugu urban area is explainable by the fact that the dry season period is the period of extreme water scarcity in the urban area when residents utilize water from compromised and uncompromised sources. At this period also the residents visit water sources where they are further exposed to water-related insect vector diseases.
cclxxxvi
5.4 Prevalence pattern of the four major water-related diseases.
The water-borne diseases prevalence in Enugu shown as Table 108 indicates that of the 1407 recorded cases of water-borne diseases in all the wards of Enugu , 680 patients representing 48.3% reported for typhoid thus making typhoid the most prevalent water-borne disease in the urban area.
Table 108: Water-borne diseases (Patient Number/ Percentage in Enugu urban)
Water borne Total No of Percentage (%) Ranking diseases patients contribution
Diarrhoea 438 31.2 2nd
Cholera 48 3.4 5TH
Typhoid 680 48.3 1st
Dysentery 107 7.6 4th
Hepatitis 134 9.5 3rd
TOTAL 1407 100%
Source: Field work, 2006.
Diarrhoea with 438 patients ranked second (2nd), hepatitis with a contribution of 9.5% was the third most frequent illness. The fourth most prevalent disease was dysentery with 107 patients (7.6). Cholera which had only forty eight (48) patients was the least frequently occurring of all the water-borne diseases. Thus, for the water- borne diseases typhoid was the most prevalent, while cholera was the least prevalent.
The water-washed disease pattern shown as Table 109 indicates that of the one hundred and seventy one (171) patients that visited the hospitals, tuberculosis had the highest number 86 patients (50.2%).
Table 109: Water-washed Diseases (Patient Number/ Percentage in Enugu Urban) cclxxxvii
Water washed Total no of Percentage contribution Ranking diseases patients
Trachoma 11 6.4 3rd
Skin infection 63 36.9 2nd
Leprosy 0 0 6th
Tuberculosis 86 50.2 1st
Whopping cough 2 1.2 5th
Tetanus 9 5.3 4th
Diphtheria 0 0 6th
TOTAL 171 100%
Source: Field work, 2006.
Skin infection with 65 patients contributed 36.9% and ranked second (2nd) while trachoma contributing 11 patients (6.4%) ranked third (3rd). Diphtheria and leprosy were the least prevalent as no patients reported for these illnesses from any of the wards in the urban area.
The water-based disease pattern shown as Table 110 reveals that the most prevalent disease was the guinea worm infection which contributed 100%. No patient reported for the treatment of schistosomaisis.
Table 110: Water-based Diseases (Patient Number/ Percentage in Enugu Urban)
Water based Diseases Total no of patients Percentage contribution Ranking
Guinea worm 14 100 1st
Schistosomiasis 0 0 2nd
Total 14 100
Source: Field work, 2006.
The water-related vector disease pattern shown as Table 111 indicates that of the 2221 patients, malaria with 2193(98.8) patients had the highest number of patients. Yellow fever and Dengue fever were the second most frequently occurring illnesses as they both had a contribution of 0.6% each; while other diseases such as Japanese encephalitis, sleeping sickness and river blindness were not identified in Enugu urban. cclxxxviii
Table 111: Water-related vector diseases (Patient Number/ Percentage in Enugu Urban)
Water vector diseases Total number of % contribution Rank patients Malaria 2193 98.8 1st Japanese 0 0 3rd Sleeping sickness 0 0 3rd River blindness 0 0 3rd Yellow fever 14 0.6 2nd Dengue fever 14 0.6 2nd Grand Total 2221 100
An overview of the four major diseases thus indicates that the water-related vector disease with a percentage contribution of 58.2 had the highest number of patients and is thus the most prevalent water-related disease in Enugu urban while water-borne disease (36.9%) ranked second (Table 112).
TABLE 112: PATIENT NUMBER/PERCENTAGE FOR THE WATER- RELATED DISEASES
Water-related Total number of Percentage % contribution Disease rating disease group patients
Water borne 1407 36.9 2nd
Water washed 171 4.5 3rd
Water based 14 0.4 4th
Water-related 2221 58.2 1st insect vector
Grand total 3813 100%
Source: Field work, 2006.
The water-washed disease with a percentage value of 4.5% ranked third (3rd) while water-based disease contributing 0.4% only is the least prevalent.
CHAPTER SIX
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ENVIRONMENTAL AND POLICY IMPLICATIONS OF THE WATER QUALITY IN ENUGU URBAN.
Low water quality has the potential for becoming a threat if not properly managed. The World Health Organization (WHO) guideline for drinking water stipulates specific acceptable and safety limits for drinking water. Deviations from these limits usually have environmental and health implications due to either elevations or reductions in the level of the physical, chemical and biological parameters. These environmental and health implications vary with the nature of the parameters.
6.1 Implications of the physical parameters. The water temperature of a river/well is very important for water quality. This is especially so as it determines the physical nature of water and influences the water chemistry. The rate of chemical reaction increases at higher temperature which in turn affects biological activities. From our water analysis it was observed that the temperature of the urban rivers and wells were generally within the WHO’s MPL. This indicates that the rivers and wells are not of low quality thermally. This parameter is not yet of environmental concern because they lie within the expected temperature for tropical water and the temperature of tropical waters do not deviate significantly. There is however a need to monitor and maintain the observed temperature especially as it was observed from field investigation that continued urban expansion has necessitated cutting down trees that help shade the rivers thus exposing the water bodies to the effect of direct sunlight. The increase in more tarred streets, sidewalks and parking lots generate storm water that runs off warmed urban surfaces contributing to the river and well temperatures. Other various human activities contribute suspended solids carried by the rivers making the water turbid. Turbid water absorbs the sun’s rays, causing water temperatures to rise. Temperature also affects the palatability of water. Its increase stimulates growth of taste and odour, producing organisms which later affect man by causing intestinal irritation. If the temperatures of the urban rivers are unchecked the highlighted effects would become issues of environmental concern in no distant time. The turbidity of the rivers and wells exceeded the WHO’s MPL. This indicates that the clarity of water was affected for all the months of the year. The river ccxc and well waters had low turbidity quality reducing the aesthetic value. Continued deterioration of the water turbidity will affect water transparency and this also has impact on light penetration. The reduced light penetration in rivers causes photosynthesis and dissolved oxygen reduction in rivers. With continued reduction in aesthetic value of the water, usage will be restricted and this will consequently lead to the rejection of the well and river waters. Water scarcity in the urban area will become not only an issue of supply, but also that of not being of beneficial use. The high turbidity was discovered to have relationship with colouration of the well and river waters such that from field investigation, residents in areas such as Asata, Ogui, and Uwani complained about colouration of their cloths by well water used for laundry especially in the dry season. According to Taggart et al (1991), turbid water due to the presence of protozoa, diatoms and excess solids in water also give rise to irritation of bowel especially when consumed. As seen from Table 108, people were treated for various kinds of intestinal irritation, with typhoid fever being the most frequent of the water- borne diseases. To reduce and ensure that the hospital beds are not over- taken by patients suffering from water-borne diseases, the turbidity levels of the urban waters have to be closely monitored as development continues. The total dissolved solids of the urban rivers and wells located in Asata were within the WHO’s MPL thus indicating that dissolved substances both organic and inorganic are not yet of environmental concern. However wells found in four locations (Abakpa, Uwani, Achara layout and Ogui) recorded values that exceeded the WHO’s MPL. Excessive total dissolved solids in these wells indicate that the wells have excessive amounts of solids suspended in water, whether mineral (soil particles) or organic (algae). It provides attachment places for other pollutants and high total dissolved solids readings are used as “indicator” of other potential pollutants that would reduce the quality of the water body. The high concentration of particulate matter in wells can cause sedimentation and siltation of the wells necessitating constant clearing of the wells. This usually adds to the cost of maintenance of the wells as observed in the study area.
6.2 Implications of the chemical parameters. Excessive amounts of most chemicals in domestic water (either as rivers or wells) have detrimental effects on man and his environment (Bath, 1990; Bartram, ccxci
1996). However, some chemicals such as calcium, iron, phosphate and fluoride are needed in the body as supply of nutritional minerals for human body needs although they tend to be undesirable when in excess. The pH determines the acidity or alkalinity of water. The pH of rivers and wells in the study area were found to be within the WHO’s MPL. The values obtained in most months showed that the urban rivers and wells are acidic in nature. This conforms to the nature of the tropical waters (Egboge et al, 1986). The effect of the acidic nature of the waters is confirmed by the corroded nature of the metal pipes used to distribute water in the various houses. Conductivity values of the urban rivers from the study were found to be within WHO’s MPL indicating that ionic components of the water bodies are within expected levels as tropical fresh waters bodies usually have very low conductivity (Egboge, 1971b). The conductivity of the wells however exceeded the WHO’s MPL in most months of the year. This indicates changes in mineral composition of in the well and a reflection of a measure of low freshness of the water. The dissolved oxygen values obtained from the laboratory analysis for this study indicated that all the rivers and the wells had values that exceeded the WHO’s MPL throughout the year. This shows that the rivers and wells have high organic polluting materials added to the water or there is absorption of oxygen during the corrosion of metals, breathing of aquatic organisms and production of oxygen during the process of photosynthesis. The consistence of this parameter in exceeding the WHO’s MPL is an indication of the poor nature of the rivers and wells, the biological state and the corrosiveness of the waters. Biochemical oxygen demand of the rivers and wells were found from our laboratory analysis to have been within the WHO’s MPL in more months in the year of study. However, the fact that there were some months when the biochemical oxygen demand was exceeded and can not be overlooked because it is a measure of the amount of oxygen used by microorganisms in the aerobic oxidation of organic matter. It is a reflection of oxygen demand for the decomposition of organic matter as aerobic bacteria may decompose organic matter at such a fast rate that dissolved oxygen decreases causing a biochemical oxygen demand. A continued increase in biochemical oxygen demand will thus increase the depletion of dissolved oxygen which has both ecological and environmental implications to the water bodies. Ecologically, microorganisms die, increased need for ccxcii their decomposition is then reflected on the dissolved oxygen, which in turn increases the biochemical oxygen demand. This will lead to the fouling of the water by introduction of odour. All these generally lead to the reduction of aesthetic value of the rivers and wells. The nutrients to be classified here as micronutrients (i.e. phosphate, nitrate, ammonia, iron and sulphate) and macronutrients (calcium and sodium) of the rivers and wells were found to be within WHO’s MPL. Only phosphate exceeded the WHO’s MPL. In Enugu urban the micro/macro nutrients do not yet pose environmental problems. There is however a great need in view of our finding to ensure that these nutrients, at the worst, remain at the ascertained levels. This is because excess calcium in domestic water produces scales, taste and contributes to hardening of water. It also gives rise to laxative effect on man. Sodium in excess impacts taste to river and well waters. It also enhances corrosion of metal utensils and containers; poses great problems to people suffering from heart disease, hypertension, renal and liver diseases. Excessive iron impacts on laundry a red to brown stain and nitrate when converted within the body to nitrite causes methemoglobinemia especially in children. Phosphate levels of both the rivers and the wells in the urban area exceeded the WHO’s MPL. This is an indication of the wells and rivers having low quality in terms of their phosphate levels. In groundwater, phosphate concentration is usually minute such that an increase in its concentration indicates the presence of pollutants. The increased phosphate concentration in water is usually an indication of pollution from municipal waste and industrial discharges; overland flow from garden fertilizers and urban lawns. From field work it was discovered that in the urban area, various agricultural activities were being carried out along the bank of the rivers (Plates 1, 2) and fertilizer use was indicated by the farmers. The need to monitor the chemical parameters of both rivers and wells in Enugu urban can not be over emphasized.
6. 3 Implications of the biological parameter. Fecal coliform bacteria are microscopic animals that live in the warm blooded animals (intestinal tract of man and animals), waste material or feaces excreted from the intestinal tract. Bacteria are the most numerous organisms in water and when present in high numbers in water sample, it means that the water has ccxciii received fecal matter from one source or another. In the course of our field work, it was observed that intestine of slaughtered chickens from artisan market were usually cleaned out for sale at the Asata river (Plate 1). Ensuring none pollution of water by fecal coliform bacteria is very important because they do not mix with water and float downstream. Instead they multiply quickly when conditions are favourable for growth or die when unfavourable. The primary source of fecal coliform bacteria to rivers and wells are waste water discharges, failing septic systems, animal waste from abattoirs, leaking sanitary sewers, old disintegrating storm sewers and storm water runoff in urbanized areas. The presence of fecal coliform bacteria in domestic water gives rise to widespread water-related diseases as is applicable to the study area (table 108). Table 112 also shows that all different types of diseases are evident in Enugu urban area with malaria and typhoid fever ranking very high. When these water-related illnesses occur, the patients spend large sums of money in the treatment of the illnesses. What is spent usually depends on what illness is being treated and the duration of the illness. These illnesses lead to the inability of patients to carry out their various economic activities.
6.4 Implications of the obtained Water Quality Index (WQI) of the rivers and the wells in Enugu urban. The WQI generates a score that describes water quality status and evaluates water quality trends. It is very useful in communicating information to the lay public and to legislative decision makers.By the computation of the WQI of the rivers and streams, we have meaningfully integrated the data sets and converted them into information that can be disseminated to the lay public. From our study, the computed WQI of the rivers had WQI that ranged between 47 and 67. These WQI indicate that generally the rivers had average health levels except for some months when the WQI obtained were bad. This is indicative of the fact that the Enugu urban rivers are at the marginal level where any additional physical, chemical, and biological pollutants to the rivers will greatly reduce the ccxciv quality of the urban rivers and therefore the WQI. The fact that some of the rivers had WQI that were bad shows that the health of some of the rivers are already low. These rivers do not meet expectations and there should be very high concern about regulating the use of the rivers especially as dump sites. The wells had WQI that range between 35 and 63. These WQI indicate that generally the wells had average health levels except for some months when the WQI obtained was just bad. This shows that the health of the wells is marginal and in some months they do not even attain the expected levels. This emphasizes the fact that the water quality status of the wells should be of great concern to the government and the populace. The obtained WQI underscore the fact that the urban waters are at the level where any additional pollutant into the water bodies will reduce the level to where they would be considered to be of very low quality and beneficial use (especially as regards their usage for any activity that involves human consumption). Since low water quality can injuriously affect human life, industrial processes and living conditions, there is a need to institute measures that will curtail further deterioration of the rivers and wells.
6.5 Social Implications of low water quality. From the obtained WQI for Enugu urban rivers and wells it is obvious that the WQI range between average (medium) and bad (poor) water quality. These water sources serve as water sources for human consumption such that the observed qualities are not encouraging as they have social implications. Most of these water sources serve as source of water-related diseases that affect the health of the urban populace. This therefore leaves them unhealthy. The amount spent in treating these water-related diseases when they occur reducing their ability to perform other social functions. The ill health creates such chronic problems that the life expectancy is affected also as some of these water-related diseases result to deaths. The number of hours spent not being able to attend to social activities usually increases. The inability of the unhealthy members of the family to take care of themselves increases also. ccxcv
6.6 Economic Implications of the low water quality. In situations where the health and general well-being of the urban dwellers are affected, the effects on the economy of such a population are usually unquantifiable as man hours are lost thus leading to reduced productivity. Also more money is spent on the treatment of the illness. Where the families are aware of the quality level of water being obtained, more money is spent making conscious effort to purchase water (e.g. bottled) considered to be of a better quality. More capital that would have been utilized for other economic ventures are then ploughed into purchasing water and remedying the already dilapidated health of the people. Based on the various added economic loss, saving of money becomes difficult to achieve. The reduced productivity generally leads to economic stagnation. A continued reduction in the urban water quality would also adversely affect the industries operating in the urban area especially those that utilize ground water resources.
6.7 Policy Implications of the low water quality. At the Federal level in Nigeria, three ministries have the responsibility for water supply quality management. These are i. The Federal Ministry of Water Resources(FMWR) which responsible for formulating policies, regulating the water sector and providing technical and financial support to state governments in the planning, implementation and monitoring of water supply projects. ii. The Federal Ministry of Environment which has the responsibility of protecting, restoring and preserving the ecosystem of the Nigerian environment including its water resources. iii. The Federal Ministry of Health (FMOH) which has the mandate of guarding water quality as it affects public health. All these Ministries exist at the state level with the same mandates and the overall responsibility for public water supply quality management is shared ccxcvi between these agencies. Two main water policies and regulatory instruments for the management of the water quality in Nigeria exist. These are the National Water Supply and Sanitation Policy by the Federal Ministry of Water Resources and The National Guideline and Standards for Water quality in Nigeria by the Federal Ministry of Environment. This shows that policies do exist nationally and it is binding on the state governments to utilize these policies in their water administration. The field investigation showed that these ministries do exist in Enugu state but there are indications that the published National Guidelines and standards do not have the full support of some regulating parastatals and state governments. Thus inter- ministerial differences and bottle necks do exist. Even though these policies do exist no effort is being made by the state government to ensure compliance by the residents or companies. There are also no clear records of monitoring of compliance to these National Standards by the state government. On the bases of our obtained water analysis which indicates that at least four parameters exceed the WHO’s MPL and the urban water resources are of very low bacteriological quality, there is an urgent need to review our water policies and to ensure compliance in each State. The Enugu State government needs to make the policy functional by ensuring the monitoring of compliance. Where the Enugu State Government decides to disregard monitoring of water quality and ensuring compliance to stipulated water quality standards, the water quality will deteriorate very fast as urbanization continues. A further reduced water quality will create WQI that will range from 0 to 25. This is usually classified as very bad water that has very little beneficial use. The urban area will experience intensified water scarcity especially as the alternative sources in times of scarcity will be the most affected.
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CHAPTER SEVEN.
CONCLUSION AND RECOMMENDATIONS
7.1 Summary of findings.
The results of this study established values for selected physical, chemical and biological parameters for Enugu urban area. The parameters tested yielded results that were compared to the Maximum Permissible Levels of World Health Organization’s (WHO) guideline for drinking water. It was established that for the rivers, twelve parameters (temperature, pH, total dissolved solids, hardness, conductivity, phosphate, sodium, sulphate, ammonia, calcium, nitrate and iron conformed to the WHO(MPL). Four parameters (turbidity, dissolved oxygen, biochemical oxygen demand and fecal coliform bacteria) exceeded the WHO’s MPL. ccxcviii
In the case of the wells, eleven parameters (temperature, pH, total dissolved solids, conductivity, hardness, phosphate, sodium, sulphate, ammonia, calcium and nitrate) conformed to the WHO’s MPL, while four parameters (turbidity, dissolved oxygen, biochemical oxygen demand and fecal coliform bacteria) exceeded the WHO’s MPL. An analysis of the variation pattern of each river and each well indicated that the values varied per month. Variations also occurred in terms of the seasons of high or low values per parameter. A comparison of the variation patterns of the river parameters yielded two discernible patterns as follows: Situation where all the rivers had higher physico-chemical and biological values in one season (temperature, pH, phosphate, ammonia, and fecal coliform bacteria in the dry season; calcium and nitrate in the rainy season.) Situation where some rivers had higher physico-chemical values in both seasons for one or more rivers for parameters such as turbidity, conductivity, hardness, dissolved oxygen, biochemical oxygen demand, sodium, sulphate and iron. And the wells depicted patterns as follows: Situation where all the wells had higher physico-chemical values in one season (temperature, dissolved oxygen, biochemical oxygen demand, and phosphate) in the dry season. Situation where some wells had higher physico-chemical and biological values in one season (dry season) for some of the parameters such pH, turbidity, sulphur, nitrate and fecal coliform bacteria. The Water Quality Index(WQI) which integrates a series of key water quality parameters into a single number that can be used to compare different sampling locations over time, was calculated for the rivers and wells of Enugu urban and yielded values from 35 t0 67. This indicates that the sample sites scoring between 50 and 80 indicate that their water qualities are of “moderate concern”, while those below 50 are of high concern as they are already impaired. Their remaining unprotected thus has serious water quality concerns. The WQI further revealed that from January to December 2006, two rivers(Asata and Aria each had eleven months of average WQI and one month of bad WQI while three rivers (Ekulu, Ogbete and Immaculate each had twelve months of average WQI. The WQI of well waters showed that two locations(Uwani and Asata each had twelve months of average WQI; two locations (Abakpa and Achara layout each had eleven ccxcix months of average and one month of bad WQI. One location (Ogui (HDW4) had ten months of average and two months of bad WQI. From the WQI obtained for the rivers and wells, the research finding shows also that all the rivers had various months in which they had the highest or lowest WQI. However, three rivers (Ekulu), Ogbete and Immaculate had the best health levels, while Asata had the lowest. The wells located in Uwani had the best health level, while Abakpa wells had the lowest. The seasonal pattern of the WQI revealed that Abakpa wells had the lowest health level in the rainy and dry seasons, while Uwani wells had the highest rainy and dry season health levels. For the rivers, two rivers (Ogbete and Immaculate had better health level in the rainy season, while Ekulu river had the lowest level. For the dry season three rivers (Aria, Ekulu and Immaculate had the best health levels, while two rivers (Asata and Ogbete) had the lowest health levels. A comparison of the river and well WQI showed that on average, the monthly WQI of the rivers were higher than those of the wells. This very essential finding of this research shows that the well waters being utilized in Enugu urban a major safe water supply are generally of a lower quality than the rivers. The study indicated that both the rivers and wells are fecally contaminated as they all exceeded the WHO (MPL) greatly. To appreciate the impact of this on the health of the residents of the urban area, the prevalence was determined. The result indicated that all the four major water-related (associated) diseases are identifiable in Enugu urban area. Field investigation revealed that there are variations in the monthly prevalence of water-related diseases and in each month there was variation in the ward that had the highest and lowest number of patients. The water-related diseases had the highest and lowest number of patients in the rainy season months of June and August respectively. The seasonal dimensions of the disease patterns showed that the number of patients that were treated for water-borne diseases were more in the dry season in seven wards, while three wards had more patients in the rainy season. For the water-washed diseases five wards had dry season prevalence, three had rainy season prevalence while two wards (G.R.A and independence Layout) had no water-washed disease prevalence thus confirming the fact that the disease is associated with poor sanitation and poverty. ccc
Water-based disease prevalence was very low in all the wards and only three wards had patients reporting to the hospital, while eight wards were noted as water- based disease free. The periods of prevalence of water based diseases varied with rainy and dry season prevalence. Water-related vector diseases in Enugu were prevalent in all the wards while seven wards had dry season prevalence, three had rainy season prevalence. The predominant diseases for each of the four major water-related diseases were as follows: Water-borne diseases: Typhoid. Water-washed diseases: Tuberculosis. Water-based diseases: Guinea worm. Water-related vector diseases: Malaria. The least predominant diseases for each of the four major water-related diseases were as follows: Water-borne diseases: Cholera. Water-washed diseases: Diphthera Water-based diseases: Schistosomiasis. Water-related vector diseases: Japanese fever, sleeping sickness and river blindness. It was revealed that of the four major water-related diseases, water-related insect vector disease was the most significant, water-borne diseases ranked second, water-washed diseases ranked third while the least prevalent was the water-based diseases. On the bases of these findings some recommendations regarding the improvement of water quality and reduction of water-related diseases were made.
7.2 Recommendations. As the water quality of an area reduces, the water changes will impact on the residents, industries, and the government. The impact may lead to search for more and new water sources to be exploited and water crisis where other alternative sources are not available. Based on the findings of the research and their subsequent implications, some recommendations were made.
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7.2.1. Review and Reduction of inter-ministerial problems. The overall responsibility of monitoring and regulating water quality issues is shared by three different agencies namely- National Agency for Food and Drug Administration and Control (NAFDAC), Ministry of Environment and Federal Ministry of Water Resources at the three levels of government in Nigeria. All these ministries responsible for public water supply quality management exist in Enugu State and are charged with the same responsibility. There is need however for the State government to integrate their activities in the state and reduce inter-ministerial problems inherent in overlap of job descriptions.
7.2.2 Review of the National Water Policy The central objective of the National Water Supply and Sanitation Policy provides six strategies for achieving it. These are as follows: a) The WHO drinking water quality standards shall be the base for the drinking water quality. b) All waterworks serving 5000 citizens and above to be equipped with functional water quality laboratory of appropriate capacity. c) Maintain a national water quality reference laboratory network. d) Monitor and protect the quality of raw water sources for drinking. e) Monitor the output of water supply undertakings for conformity with drinking water quality standards. f) Tradition water supply sources shall be protected and traditional water quality practices promoted. These stipulations need to be reviewed Nationally with a view to ensuring that strategies b, c, d, e, f are enforced within the States. The modalities for achieving these needs to be articulated and the State Governments are to ensure that they are enforced. There is a need for each State Government to concentrate on the policies that will help address the water quality issue being faced by the State as depending on a single policy with fixed parameters has failed as a “cure-all” approach. A National Drinking Water Policy that emphasizes not just assurance of safe drinking water but also water quality management to help reduce water quality deterioration is urgently needed. The specific provisions of this policy should be a) Water quality monitoring. cccii b) Protection and restoration of the water resources. c) Strengthening of the regulations concerning agrochemicals and industrial effluent monitoring. Enugu State can work with the reviewed National Policy but must ensure that the set objectives are effectively implemented in an articulated manner.
7.2.3 Regular Monitoring of Water Quality. The State Government needs to institute viable ways of monitoring both the surface and ground water bodies in order to build up data base for the planning of her water resources. Water quality monitoring principally should incorporate the following functions: data acquisition, data management and storage and information generation and dissemination. They are first to set clear objectives for monitoring and also create network of monitoring and surveillance centers. This effective water quality management will require substantial financial investment and at the moment, the government remains the main source of funding. To decentralize the burden, the Enugu State Government needs to encourage the residents to create cells that would contribute money for monitoring the water sources at the community level. Money generated by the community cells can be utilized in buying kits for water analysis or paying for laboratory tests. The community cells which would comprise of people with various educational backgrounds can also utilize the expertise of their members in monitoring the water sources and interpreting the data generated. The community cells can publicize information regarding the water quality of their urban area by creating bulletins and websites. The State Government should help in developing and sustaining these community cells and should also ensure that data generated by the cells are forwarded to them for documentation on regular bases. This will encourage the existence of water quality data at different levels.
7.2.4 Improved Data Management. Poor data management is a key problem in water sector of the State. The main focus of the Government is the provision of water with no focus on data ccciii requirements for water quality management. There is a need to create facilities to handle the storage and networking of generated data. The obtained technical information is to be translated into WQI that can be understood and appreciated by the populace.
7.2.5 Strengthening of Institutional Mechanism. There is urgent need to establish a regulatory body to monitor the water quality of water resources of Enugu state. This need of the State highlights also the fact that monitoring of the water bodies should be extended to cover all parts of Nigeria. The monitoring networks to be created should be handled at the national level by ensuring that all the states of the Federation have monitoring units not just at the urban areas. Data generated from the different monitoring units should be coordinated both at the State and the National levels. This would enhance the ability of the government and populace to plan and manage her water resources.
7.2.6 Strengthening of Regulatory Mechanism. A very weak institutional mechanism was detected in the State. This is to be corrected by enforcing regulation compliance. Compliance can be achieved by enforcing polluter-pays-principle (pollution levy system); Industrial permits policy; close down policy. This can de accomplished by engaging the services of the newly created parastatal of the Federal Ministry of Environment, Housing and Urban Development known as National Environmental Standards and Regulations Enforcement Agency. On the alternative the various State Water Boards should be empowered to handle all affairs related to water quality management.
7.2.7 Creation of Public Awareness There is a need to emphasise formal and informal programmes that promise prevention by early intervention. This new paradigm is to be emphasized ccciv
through education and creation of awareness. This can be achieved by targeting school children and the women as they are the major groups that carry out the household water and waste dumping functions. Health educations in relation to resultant water-related diseases are to be inculcated in the programme. This can be facilitated by creating websites for information dissemination. Utilizing consumers’ forum of various forms will make this functional.
7.2.9 Capacity Building The State Government is to support active research in issues related to water quality and intensify its efforts on capacity building for water quality management. Training of personnel to monitor the networks to be created is urgently needed. To successfully achieve monitoring of the water resources the State Government needs to train the needed personnel, pull together already existing personnel. The staff need to be dedicated to the course and to be computer literate. Necessary legal changes need to be made and water quality parameters for different uses need to be continuously reviewed to effect improvement in water quality. For groundwater safety the government should recommend the distance the wells are to be from toilet soakaways as no policy exists (30 meters is recommended).
7.3 Suggestions for further research. The emphasis of this work is on the water quality of Enugu urban area and the prevalence pattern of the water-related diseases. We are aware that for an improved water quality and efficient monitoring of the water resources being advocated for, the understanding of the contributory sources is very essential. To this end, we suggest that further research be directed towards identifying the point and non-point pollution sources and relating this to the water quality index obtained at the time of study. Secondly, we computed the water quality index which made it possible to reduce the earlier technical discussions on parameters that exceed the WHO’s MPL to a form understandable and useable for planning. Further work can be done on computing WQI for at least three years (from data pooled from the monitoring units) and observing the trend of the water quality. The WQI can be cccv mapped to create the base data needed for further work. There is also need for studies related to determining the WQI for low and high flows and comparing obtained results. Finally research on water quality management options available to developing countries can be carried out.
7.4 CONCLUSION. The manner in which the water resources in Enugu urban area are being used and misused and the lack of information on the quality and health conditions of the urban waters necessitated this study. The result of the study has expanded our understanding of the physical, chemical and biological parameters that are either within or exceed the WHO’s MPL. It also identified the seasonal variations and the monthly water quality index reflecting the health status and quality level of the urban water resources. The prevalent and spatial patterns of the four major water-related diseases were highlighted. Implications of the findings were discussed and recommendations have been made on how to prevent further reduction of the quality of surface and groundwater resources. It is hoped that when these recommendations are properly implemented, it will improve the monitoring, surveillance and yield better WQI. This improved water quality will result in improved alternative water supply, reduced incidents of water-related diseases and higher productivity.