Post on 08-Jan-2023
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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
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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
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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
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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
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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
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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
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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
xxxvi
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.
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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,
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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
li
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).
liv
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.
lvii
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.
lix
FIG 6: MAP OF ENUGU SHOWING MAJOR WARDS Source: Fieldwork, 2006.
N
IGBO ETITI ISI UZO
0 1 2 3 4 5
LEGEND
Local Government Boundary
Ward Boundary
Rivers
Urban Boundary
INDEPENDENCE LAYOUT
ACHARA LAYOUT/ MARY LAND
COAL CAMP
UWANI
OGUI
ASATA
NEW HEAVEN
ABAKPA NIKE
IVA - VALLEY
GRA
lx
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).
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).
lxvi
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)
lxix
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.
lxx
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.
lxxi
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 Index
value)
Quality(Rank)
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
Solid(TDS)
Mg/l 500
Conductivity µ/SCM 1
Total Hardness Mg/l 500
Dissolved Oxygen Mg/l 3.0
Biochemical Oxygen
Demand
Mg/l 2
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)
lxxxiii
Fig 8: Comparison of River temperature to WHO’s MPL
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).
FIG 9:Comparism of river pH levels to WHO(MPL)
0
1
2
3
4
5
6
7
8
J F M A M J J A S O N D
Months of the year
pH
lev
el
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
Fig 8: Comparism of river temperatures to WHO(MPL)
21
22
23
24
25
26
27
28
J F M A M J J A S O N D
Months of the year
Te
mp
era
ture
( c
)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
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).
Fig 10: Comparison of rivers turbidity levels to WHO’s MPL
Fig 10: Comparism of rivers turbidity levels to WHO(MPL)
0
10
20
30
40
50
60
70
80
J F M A M J J A S O N D
Months of the year
Tu
rdid
ity(N
TU
)
SW1
SW2
SW3
SW4 SW5
SW5
WHO(MPL)WHO’s MPL
lxxxvi
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)
Fig 11: Comparism of rivers total dissolved solids to WHO(MPL)
0
50
100
150
200
250
300
J F M A M J J A S O N D
Months of the year
To
tal
Dis
so
lved
so
lid
s (
Mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5
WHO(MPL)500 WHO’s MPL
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
January 0.52 0.44 0.72 0.42 0.67
February 0.26 0.19 0.49 0.36 0.29
March 0.20 0.20 0.22 0.49 0.19
April 0.32 0.32 0.56 0.72 0.48
May 0.81 0.24 0.49 0.45 0.36
June 0.47 0.35 0.24 0.47 0.33
July 0.13 0.21 0.30 0.33 0.21
August 0.11 0.11 0.11 0.21 0.10
September 0.86 0.66 0.44 1.24 0.70
October 0.73 0.69 0.78 o.46 0.81
November 0.63 0.10 0.12 0.25 0.42
December 0.63 0.42 0.95 0.97 0.96
Source: Field work (2006).
Fig 12: Comparison of river conductivity levels to WHO’s MPL
Fig 12: Comparism of river conductivity levels to WHO(MPL)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
J F M A M J J A S O N D
Months of the year
co
nd
ucti
vit
y l
evel(
scm
)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
lxxxix
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
FIG 13:Comparism of total hardness of rivers to WHO(MPL)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
J F M A M J J A S O N D
Months of the year
Hard
ness(m
g/l
)
SW1
SW2
SW3
SW4
SW5
`
WHO’s MPL
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.
xcii
Fig 14: Comparison of river dissolved oxygen levels to WHO’s MPL
Fig 15: Comparison of river biochemical oxygen demand levels to WHO’s MPL
2.2.9 Phosphate of the urban rivers.
Fig 14: Comparism of river dissolved oxygen levels to
WHO(MPL)
0
2
4
6
8
10
12
J F M A M J J A S O N D
Months of the year
Dis
so
lved
oxyg
en
(mg
/l)
SWS 1
SWS2
SWS3
SWS4
SWS5
WHO(MPL)WHO’s MPL
Fig 15: Comparism of river Biochemical oxygen Demand levels to
WHO(MPL)
0
2
4
6
8
10
12
J F M A M J J A S O N D
Months of the year
Bio
ch
em
ical
oxyg
en
dem
an
d(m
g/l
)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
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
Fig 16: Comparison of river phosphate levels to WHO’s MPL
Fig 17: Comparison of river sodium levels to WHO’s MPL
Fig16: Comparism of river phosphate levels to WHO(MPL)
0
1
2
3
4
5
6
7
J F M A M J J A S O N D
Months of the year
Ph
osp
hate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
Fig 17: Comparism of river sodium levels to WHO(MPL)
0
5
10
15
20
25
30
35
40
J F M A M J J A S O N D
Months of the year
So
diu
m l
evel(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)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
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.
Fig 18: Comparism of river sulphate levels to WHO(MPL)
0
2
4
6
8
10
12
J F M A M J J A S O N D
Months of he year
Ph
osp
hate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
WHO’s MPL
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.
Fig 19: Comparison of river iron levels to WHO’s MPL
0
0.05
0.1
0.15
0.2
0.25
J F M A M J J A S O N D
Months of the year
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
WHO’s MPL
Iron level(m
g/l)
0
0.05
0.1
0.15
0.2
0.25
J F M A M J J A S O N D
Months of the year
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
WHO’s MPLWHO’s MPL
Iron level(m
g/l)
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
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)
Fig 20:Comparism of river ammonia levels to WHO(MPL)
0
1
2
3
4
5
6
7
8
9
10
J F M A M J J A S O N D
Months of the year
Am
mo
nia
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)WHO’s MPL
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
Fig 21: Comparison of river calcium levels to WHO’s MPL
Fig 22: Comparison of river nitrate levels to WHO’s MPL
Fig 22:Comparism of river nitrate levels to WHO(MPL)
0
0.5
1
1.5
2
2.5
3
3.5
J F M A M J J A S O N D
Months of the year
Nit
rate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
WHO’s MPL
Fig 21:Comparism of river calcium levels to WHO(MPL)
0
0.5
1
1.5
2
2.5
3
3.5
4
J F M A M J J A S O N D
Months of the year
Calc
ium
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
WHO’s MPL
civ
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.
Fig 23:Comparism of river faecal coliform bacteria levels to
WHO(MPL)
0
10
20
30
40
50
60
70
80
90
100
J F M A M J J A S O N D
Months of the year
Faecal
co
lifo
rm b
acte
ria
level(
cf/
100m
l)
SW1
SW2
SW3
SW4
SW5
WHO(MPL)
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).
Fig 24: Comparison of temperature levels of wells to WHO’s MPL
Fig 24:Comparism of temperature levels of wells to WHO(MPL)
0
5
10
15
20
25
30
J F M A M J J A S O N D
Months of the year
Tem
pera
ture
( C
)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)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).
Fig 25: Comparison of pH levels of wells to WHO’s MPL
Fig 25:Comparism of pH levels of wells to WHO(MPL)
0
1
2
3
4
5
6
7
8
9
J F M A M J J A S O N D
Months of the year
pH
level
HDW1
HDW2
HDW3
HW4
HDW5
WHO(MPL)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)
Fig 26: Comparison of turbidity levels of wells to WHO’s MPL
Fig 26:Comparism of turbidity levels of wells to WHO(MPL)
0
10
20
30
40
50
60
70
J F M A M J J A S O N D
Months of the year
Tu
rbid
ity l
evel(
NT
U)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)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.
Fig 27: Comparison of total dissolved solids levels of wells to WHO’s MPL
Fig 27:Comparism of total dissolved solids of wells to
WHO(MPL)
0
500
1000
1500
2000
2500
J F M A M J J A S O N D
Months of the year
To
tal
dis
so
lved
so
lid
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL) 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.
Fig 28: Comparison of conductivity of wells to WHO’s MPL
Fig 28:Comparism of conductivity of wells to WHO(MPL)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
J F M A M J J A S O N D
Months of the year
Co
nd
ucti
vit
y (
SC
M)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
Conductivity (
µSC
M)
Fig 28:Comparism of conductivity of wells to WHO(MPL)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
J F M A M J J A S O N D
Months of the year
Co
nd
ucti
vit
y (
SC
M)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
Fig 28:Comparism of conductivity of wells to WHO(MPL)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
J F M A M J J A S O N D
Months of the year
Co
nd
ucti
vit
y (
SC
M)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPLWHO’s MPL
Conductivity (
µSC
M)
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)
Fig 29: Comparison of total hardness levels of wells to WHO’s MPL
Fig 29: Comparism of total hardness levels of wells to
WHO(HDL)
0
2
4
6
8
10
12
J F M A M J J A S O N D
Months of the year
To
tal
hard
ness(m
g/l
)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)
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)
Fig 30: Comparison of dissolved oxygen of wells to WHO’s MPL
FIG 30: Comparism of dissolved oxygen of wells WHO (MPL)
0
2
4
6
8
10
12
J F M A M J J A S O N D
Months of the year
Dis
so
lved
Oxyg
en
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO (MPL)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.
Fig
31: Comparison of biochemical oxygen demand levels of wells to WHO’s MPL
Fig 31:Comparism of biochemical oxygen demand levels of
wells to WHO(MPL)
0
1
2
3
4
5
6
J F M A M J J A S O N D
Months of the year
Bio
ch
em
ica
l o
xy
ge
n d
em
an
d le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPLWHO’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)
Fig 32: Comparison of phosphate levels of wells to WHO’s MPL
2.3.10 Sodium of the urban wells.
FIG 32: Comparism of Phosphate levels of wells to WHO (MPL)
0
1
2
3
4
5
6
7
8
J F M A M J J A S O N D
Months of the year
Ph
os
ph
ate
le
ve
l (m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO (MPL)WHO’s MPL
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).
Fig 33: Comparison of sodium levels of wells to WHO’s MPL
2.3.11 Sulphate of the urban wells.
FIG 33: Comparism of Sodium levels of wells to WHO (MPL)
0
10
20
30
40
50
60
70
J F M A M J J A S O N DMonths of Year
So
diu
m l
evel
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MP)WHO’s MPL
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)
Fig. 34: Comparison of Sulphate levels of wells to WHO’s MPL
2.3.12 Ammonia of the urban wells.
FIG 34:Comparism of sulphate levels of wells to WHO(MPL)
0
1
2
3
4
5
6
7
8
J F M A M J J A S O N D
Months of the year
Su
lph
ate
le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
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)
Fig 35: Comparison of Ammonia levels of wells to WHO’s MPL
2.3.13 Calcium of the urban wells.
FIG 35: Comparism of Ammonia levels of the wells to WHO (MPL)
0
1
2
3
4
5
6
7
8
9
10
J F M A M J J A S O N D
Months of the year
Am
mo
nia
le
ve
l (m
g/l
)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
FIG 35: Comparism of Ammonia levels of the wells to WHO (MPL)
0
1
2
3
4
5
6
7
8
9
10
J F M A M J J A S O N D
Months of the year
Am
mo
nia
le
ve
l (m
g/l
)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPLWHO’s MPL
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).
Fig 36: Comparison of calcium levels of wells to WHO’s MPL
2.3.14 Nitrate of the urban wells.
Fig 36:Comparism of calcium levels of wells to WHO(MPL)
0
2
4
6
8
10
12
14
16
18
20
J F M A M J J A S O N D
Months of the year
Calc
ium
level(
mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
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).
Fig 37: Comparison of nitrate levels of wells to WHO’s MPL
2.3.15 Fecal coliform bacteria of the urban wells.
Fig 37:Comparism of nitrate levels of wells toWHO(MPL)
-1
0
1
2
3
4
5
6
7
8
J F M A M J J A S O N D
Months of the year
Nit
rate
level(
mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
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)
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.
Fig 38:Comparism of well faecal coliform bacteria levels to
WHO(MPL)
0
2
4
6
8
10
12
14
16
18
20
J F M A M J J A S O N D
Months of the year
Facal
co
lifo
rm b
acte
ria l
evel
HDW1
HDW2
HDW3
HDW4
HDW5
WHO(MPL)WHO’s MPL
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
Fig 39: Rainy season temperature variation pattern of the rivers
Fig 40: Dry season temperature variation pattern of the rivers
Fig 39:Rainy season temperature variation pattern of the
rivers
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
A M J J A S
Months of the year
Tem
pera
ture
level(
C)
SW1
SW2
SW3
SW4
SW5
Fig 40:Dry season temperature variation pattern of the rivers
21
22
23
24
25
26
27
28
O N D J F M
Months of the year
Tem
pera
ture
level(
C)
SW1
SW2
SW3
SW4
SW5
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
Fig 41: Rainy season pH variation pattern of the rivers
Fig 42: Dry season pH variation pattern of the rivers
Fig 41: Rainy season pH variation pattern of the rivers
0
1
2
3
4
5
6
7
8
A M J J A S
Months of the year
pH
level
SW1
SW2
SW3 SW4
SW4
SW5
Fig 42: Dry season pH variation pattern of the rivers
0
1
2
3
4
5
6
7
8
O N D J F M
Months of the year
pH
level
SW1
SW2
SW3
SW4
SW5
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
Fig 43: Rainy season turbidity variation pattern of the rivers
Fig 44: Dry season temperature variation pattern of the rivers
Fig 43: Rainy season turbidity variation pattern of the rivers
0
10
20
30
40
50
60
70
80
A M J J A S
Months of the year
Tu
rbid
ity l
evel(
NT
U)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 44:Dry season turbidity variation pattern of the rivers
0
10
20
30
40
50
60
O N D J F M
Months of the year
Tu
rbid
ity l
evel(
NT
U)
SW 1
SW 2
SW 3
SW 4
SW 5
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.
Fig 45: Rainy season total dissolved solids variation pattern of the rivers
Fig 45:Rainy season total dissolved solids variation pattern of
the rivers
0
20
40
60
80
100
120
A M J J A S
Months of the year
To
tal
dis
so
lved
so
lid
s(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 45:Rainy season total dissolved solids variation pattern of
the rivers
0
20
40
60
80
100
120
A M J J A S
Months of the year
To
tal
dis
so
lved
so
lid
s(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
cxxx
Fig 46: Dry season total dissolved solids variation pattern of the rivers
Fig 47: Rainy season conductivity variation pattern of the rivers
Fig 46:Dry season total dissolved solids variation pattern of the
rivers
0
50
100
150
200
250
300
O N D J F M
Months of the year
To
tal
dis
so
lved
so
lid
s (
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 46:Dry season total dissolved solids variation pattern of the
rivers
0
50
100
150
200
250
300
O N D J F M
Months of the year
To
tal
dis
so
lved
so
lid
s (
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 47:Rainy season conductivity variation pattern of the rivers
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
A M J J A S
Months of the year
Co
nd
ucti
vit
y l
evel(
SC
M )
SW 1
SW 2
SW 3
SW 4
SW 5Fig 47:Rainy season conductivity variation pattern of the rivers
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
A M J J A S
Months of the year
Co
nd
ucti
vit
y l
evel(
SC
M )
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 48:Dry season conductivity variation pattern of the rivers
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
O N D J F M
Months of the year
Co
nd
ucti
vit
y l
evel(
SC
M )
SW 1
SW 2
SW 3
SW 4
SW 5
c
Fig 48:Dry season conductivity variation pattern of the rivers
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
O N D J F M
Months of the year
Co
nd
ucti
vit
y l
evel(
SC
M )
SW 1
SW 2
SW 3
SW 4
SW 5
c
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.
Fig 49: Rainy season hardness variation pattern of the rivers
Fig 50: Dry season hardness variation pattern of the rivers
3.1.7 Dissolved Oxygen Variation Pattern of the Rivers
Fig 49:Rainy season hardness variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
1.4
A M J J A S
Months of the year
Ha
rdn
es
s(m
g/l)
SW1
SW2
SW3
SW4
SW5Fig 49:Rainy season hardness variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
1.4
A M J J A S
Months of the year
Ha
rdn
es
s(m
g/l)
SW1
SW2
SW3
SW4
SW5
Fig 50:Dry season hardness variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
O N D J F M
Months of the year
Ha
rdn
es
s le
ve
ls(m
g/l)
SW1
SW2
SW3
SW4
SW5Fig 50:Dry season hardness variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
O N D J F M
Months of the year
Ha
rdn
es
s le
ve
ls(m
g/l)
SW1
SW2
SW3
SW4
SW5
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).
Fig 51: Rainy season temperature variation pattern of the rivers
Fig 51:Rainy season dissolved oxygen variation pattern of the
rivers
0
1
2
3
4
5
6
7
A M J J A S
Months of the year
Dis
so
lved
oxyg
en
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig 51:Rainy season dissolved oxygen variation pattern of the
rivers
0
1
2
3
4
5
6
7
A M J J A S
Months of the year
Dis
so
lved
oxyg
en
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig 52:Dry season dissolved oxygen variation pattern of the
rivers
0
2
4
6
8
10
12
O N D J F M
Months of the year
Dis
so
lved
oxyg
en
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig 52:Dry season dissolved oxygen variation pattern of the
rivers
0
2
4
6
8
10
12
O N D J F M
Months of the year
Dis
so
lved
oxyg
en
level(
mg
/l)
SW1
SW2
SW3
SW4
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.
Fig 53:Rainy season biochemical oxygen demand variation
pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the Year
Bio
ch
em
ical
oxyg
en
dem
an
d(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 53:Rainy season biochemical oxygen demand variation
pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the Year
Bio
ch
em
ical
oxyg
en
dem
an
d(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
cxxxvi
Fig 53: Rainy season biochemical oxygen demand variation pattern of the rivers
Fig 54: Dry season biochemical oxygen demand variation pattern of the
rivers
Fig 54:Dry season biochemical oxygen demand variation pattern
of the rivers
0
2
4
6
8
10
12
O N D J F M
Months of the year
Bio
ch
em
ical
oxyg
en
dem
an
d(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 54:Dry season biochemical oxygen demand variation pattern
of the rivers
0
2
4
6
8
10
12
O N D J F M
Months of the year
Bio
ch
em
ical
oxyg
en
dem
an
d(m
g/l
)
SW 1
SW 2
SW 3
SW 4
SW 5
Fig 55:Rainy season phosphate variation pattern of the rivers
0
1
2
3
4
5
6
A M J J A S
Months of the year
Ph
osp
hate
level(
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5Fig 55:Rainy season phosphate variation pattern of the rivers
0
1
2
3
4
5
6
A M J J A S
Months of the year
Ph
osp
hate
level(
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5
cxxxvii
Fig 55: Rainy season phosphate variation pattern of the rivers
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
Fig 56:Dry season phosphate variation pattern of the rivers
0
1
2
3
4
5
6
7
O N D J F M
Months of the year
Ph
osp
hate
level(
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5Fig 56:Dry season phosphate variation pattern of the rivers
0
1
2
3
4
5
6
7
O N D J F M
Months of the year
Ph
osp
hate
level(
mg
/l)
SW 1
SW 2
SW 3
SW 4
SW 5
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.
Fig 57: Rainy season sodium variation pattern of the rivers
Fig 57:Rainy season sodium variation pattern of the rivers
0
5
10
15
20
25
A M J J A S
Months of the year
So
diu
m l
evel(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 57:Rainy season sodium variation pattern of the rivers
0
5
10
15
20
25
A M J J A S
Months of the year
So
diu
m l
evel(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig 58:Dry season sodium variation pattern of the rivers
0
5
10
15
20
25
30
35
40
O N D J F M
Months of the year
So
diu
m l
evel(
mg
/l)
SWS 1
SWS2
SWS3
SWS4
SWS5Fig 58:Dry season sodium variation pattern of the rivers
0
5
10
15
20
25
30
35
40
O N D J F M
Months of the year
So
diu
m l
evel(
mg
/l)
SWS 1
SWS2
SWS3
SWS4
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.
Fig 59:Rainy season sulphate variation pattern of the rivers
0
1
2
3
4
5
6
7
8
A M J J A S
Months of the year
Su
lph
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 59:Rainy season sulphate variation pattern of the rivers
0
1
2
3
4
5
6
7
8
A M J J A S
Months of the year
Su
lph
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
cxl
Fig 59: Rainy season sulphate variation pattern of the rivers
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
Fig 60:Dry season sulphate variation pattern of the rivers
0
2
4
6
8
10
12
O N D J F M
Months pf the year
Su
lph
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 60:Dry season sulphate variation pattern of the rivers
0
2
4
6
8
10
12
O N D J F M
Months pf the year
Su
lph
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
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.
Fig 61:Rainy season iron variation pattern of the rivers
0
0.05
0.1
0.15
0.2
0.25
A M J J A S
Months of the year
Iro
n l
evel(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 61:Rainy season iron variation pattern of the rivers
0
0.05
0.1
0.15
0.2
0.25
A M J J A S
Months of the year
Iro
n l
evel(
mg
/l)
SW1
SW2
SW3
SW4
SW5
cxlii
Fig. 61: Rainy season iron variation pattern of the rivers
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
Fig 62:Dry season iron variation pattern of the rivers
0
0.05
0.1
0.15
0.2
0.25
O N D J F M
Months of the year
Iro
n l
evel
(mg
/l)
SW1
SW2
SW3
SW4
SW5
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
Fig 63:Rainy season ammonia variation pattern of the rivers
0
1
2
3
4
5
6
7
8
9
10
A M J J A S
Months of the year
Am
mo
nia
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 63:Rainy season ammonia variation pattern of the rivers
0
1
2
3
4
5
6
7
8
9
10
A M J J A S
Months of the year
Am
mo
nia
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig. 63: Rainy season ammonia variation pattern of the rivers
Fig. 64: Dry season ammonia variation pattern of the rivers
Fig 64:Dry season ammonia variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
O N D J F M
Months of the year
Am
mo
nia
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 64:Dry season ammonia variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
O N D J F M
Months of the year
Am
mo
nia
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
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.
Fig 65: Rainy season calcium variation pattern of the rivers
Fig 65:Rainy season calcium variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
4
A M J J A S
Months of the year
Cali
um
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 65:Rainy season calcium variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
4
A M J J A S
Months of the year
Cali
um
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
cxlvi
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
Fig 66:Dry season calium variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
O N D J F M
Months of the year
Calc
ium
level(
mg
/l)
SW1
SW2
SW3 SW4
SW4
SW5Fig 66:Dry season calium variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
O N D J F M
Months of the year
Calc
ium
level(
mg
/l)
SW1
SW2
SW3 SW4
SW4
SW5
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).
Fig 67: Rainy season nitrate variation pattern of the rivers
Fig 67:Rainy season nitrate variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
A M J J A S
Months of the year
Nit
rate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 67:Rainy season nitrate variation pattern of the rivers
0
0.5
1
1.5
2
2.5
3
3.5
A M J J A S
Months of the year
Nit
rate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5
Fig 68:Dry season nitrate variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
O N D J F M
Months of the year
Nit
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
SW5Fig 68:Dry season nitrate variation pattern of the rivers
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
O N D J F M
Months of the year
Nit
ate
level(
mg
/l)
SW1
SW2
SW3
SW4
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
Fig 69: Rainy season fecal coliform bacteria variation pattern of the rivers
Fig 70: Dry season fecal coliform bacteria variation pattern of rivers
Fig 70:Dry season faecal coliform bacteria variation pattern of
the rivers
0
10
20
30
40
50
60
70
80
90
100
O N D J F M
Months of the year
F
eacal
co
lifo
m b
acte
ria
level(
c/1
00m
g)
SW1
SW2
SW3
SW4
SW5
Fig 70:Dry season faecal coliform bacteria variation pattern of
the rivers
0
10
20
30
40
50
60
70
80
90
100
O N D J F M
Months of the year
F
eacal
co
lifo
m b
acte
ria
level(
c/1
00m
g)
SW1
SW2
SW3
SW4
SW5
Fig 69:Rainy season faecal coliform bacteria variation pattern
of the rivers
0
2
4
6
8
10
12
14
A M J J A S
Months of the year
Faecal
co
lifo
rm b
acte
ria l
evel
(cf/
100m
g)
SW1
SW2
SW3
SW4
SW5
Fig 69:Rainy season faecal coliform bacteria variation pattern
of the rivers
0
2
4
6
8
10
12
14
A M J J A S
Months of the year
Faecal
co
lifo
rm b
acte
ria l
evel
(cf/
100m
g)
SW1
SW2
SW3
SW4
SW5
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
Fig 71:Seasonal temperature pattern of the rivers
23.4 23.6 23.8 24 24.2 24.4 24.6 24.8 25 25.2
SW1
SW2
SW3
SW4
SW5
Riv
ers
Temperature C
DRY
RAIN
Temperature °C
Fig 71:Seasonal temperature pattern of the rivers
23.4 23.6 23.8 24 24.2 24.4 24.6 24.8 25 25.2
SW1
SW2
SW3
SW4
SW5
Riv
ers
Temperature C
DRY
RAIN
Temperature °C
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).
Fig 72: Seasonal pH pattern of the rivers
Fig 72:Seasonal pH pattern of the rivers
5.8 5.9 6 6.1 6.2 6.3 6.4
SW1
SW2
SW3
SW4
SW5
Riv
ers
pH level
DRY
RAINY
Fig 72:Seasonal pH pattern of the rivers
5.8 5.9 6 6.1 6.2 6.3 6.4
SW1
SW2
SW3
SW4
SW5
Riv
ers
pH level
DRY
RAINY
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).
Fig 73: Seasonal turbidity pattern of the rivers
0 5 10 15 20 25 30
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Turbidity level(mg/l)
DRY
RAINYFig 73: Seasonal turbidity pattern of the rivers
0 5 10 15 20 25 30
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Turbidity level(mg/l)
DRY
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.
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
Fig 74: Seasonal total dissolved solids pattern of the rivers
0 20 40 60 80 100 120 140
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Total dissolved solids(mg/l)
DRY
RAINYFig 74: Seasonal total dissolved solids pattern of the rivers
0 20 40 60 80 100 120 140
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Total dissolved solids(mg/l)
DRY
RAINY
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
Fig 75: Seasonal conductivity pattern of the rivers
Fig 76: Seasonal total hardness pattern of the rivers
Fig 75: Seasonal conductivity pattern of the rivers
0 0.05 0.1 0.15 0.2 0.25 0.3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Conductivity level(SCM)
DRY
RAINYFig 75: Seasonal conductivity pattern of the rivers
0 0.05 0.1 0.15 0.2 0.25 0.3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Conductivity level(SCM)
DRY
RAINY
Fig 76: Seasonal total hardness pattern of the rivers
0 0.1 0.2 0.3 0.4 0.5 0.6
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Toatal hardness level(mg/l)
DRY
RAINYFig 76: Seasonal total hardness pattern of the rivers
0 0.1 0.2 0.3 0.4 0.5 0.6
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Toatal hardness level(mg/l)
DRY
RAINY
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.
Fig 77:Seasonal dissolved oxygen pattern of the rivers
0 1 2 3 4 5 6 7
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Dissolved oxygen(mg/l)
DRY
RAINYFig 77:Seasonal dissolved oxygen pattern of the rivers
0 1 2 3 4 5 6 7
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Dissolved oxygen(mg/l)
DRY
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.
Fig 78:Seasonal biochemical oxygen demand pattern of the
rivers
0 0.5 1 1.5 2 2.5 3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sta
tio
ns
Biochemical oxygen demand level(mg/l)
DRY
RAINY
Fig 78:Seasonal biochemical oxygen demand pattern of the
rivers
0 0.5 1 1.5 2 2.5 3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sta
tio
ns
Biochemical oxygen demand level(mg/l)
DRY
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.
Fig 79:Seasonal phosphate pattern of the rivers
0 0.5 1 1.5 2 2.5 3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Phosphate level(mg/l)
DRY
RAINYFig 79:Seasonal phosphate pattern of the rivers
0 0.5 1 1.5 2 2.5 3
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Phosphate level(mg/l)
DRY
RAINY
clxii
Fig 79: Seasonal phosphate pattern of the rivers
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
Fig 80:Seasonal sodium pattern of the rivers
0 2 4 6 8 10 12
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Sodium level(mg/l)
DRY
RAINYFig 80:Seasonal sodium pattern of the rivers
0 2 4 6 8 10 12
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Sodium level(mg/l)
DRY
RAINY
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.
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
Fig 81:Seasonal sulphate pattern of the rivers
0 0.5 1 1.5 2 2.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Sulphate(mg/l)
DRY
RAINYFig 81:Seasonal sulphate pattern of the rivers
0 0.5 1 1.5 2 2.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Sulphate(mg/l)
DRY
RAINY
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).
Fig 82:Seasonal iron pattern of the rivers
0 0.05 0.1 0.15 0.2
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Iron(mg/l)
DRY
RAINYFig 82:Seasonal iron pattern of the rivers
0 0.05 0.1 0.15 0.2
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Iron(mg/l)
DRY
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.
clxvi
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
Fig 83:Seasonal ammonia pattern of the rivers
0 0.5 1 1.5 2 2.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Ammonia(mg/l)
DRY
RAINYFig 83:Seasonal ammonia pattern of the rivers
0 0.5 1 1.5 2 2.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Ammonia(mg/l)
DRY
RAINY
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.
Fig 84: Seasonal calcium pattern of the rivers
Fig 84:Seasonal calcium pattern of the rivers
0 0.5 1 1.5 2 2.5 3 3.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Calcium level(mg/l)
DRY
RAINYFig 84:Seasonal calcium pattern of the rivers
0 0.5 1 1.5 2 2.5 3 3.5
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Calcium level(mg/l)
DRY
RAINY
Fig 85:Seasonal nitrate pattern of the rivers
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Nitrate level(mg/l)
DRY
RAINYFig 85:Seasonal nitrate pattern of the rivers
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
SW1
SW2
SW3
SW4
SW5
Sam
ple
sit
es
Nitrate level(mg/l)
DRY
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.
Fig 86:Seasonal feacal pattern colifom bacteria level
0 5 10 15 20 25 30 35
SW1
SW2
SW3
SW4
SW5
Sa
mp
le s
ite
s
Feacal coliform bacteria level( cfu/l)DRY
RAINYFig 86:Seasonal feacal pattern colifom bacteria level
0 5 10 15 20 25 30 35
SW1
SW2
SW3
SW4
SW5
Sa
mp
le s
ite
s
Feacal coliform bacteria level( cfu/l)DRY
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.
Fig 87:Rainy season temperature variation pattern of the wells
0
5
10
15
20
25
30
A M J J A S
Months of the year
Te
mp
era
ture
( C
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 87:Rainy season temperature variation pattern of the wells
0
5
10
15
20
25
30
A M J J A S
Months of the year
Te
mp
era
ture
( C
)
HDW1
HDW2
HDW3
HDW4
HDW5
clxx
Fig 87: Rainy season temperature variation pattern of the wells
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).
Fig 88:Dry season temperature variation pattern of the wells
24
24.5
25
25.5
26
26.5
27
27.5
O N D J F M
Months of the year
Te
mp
era
ture
( C
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 88:Dry season temperature variation pattern of the wells
24
24.5
25
25.5
26
26.5
27
27.5
O N D J F M
Months of the year
Te
mp
era
ture
( C
)
HDW1
HDW2
HDW3
HDW4
HDW5
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.
Fig 89: Rainy season pH variation pattern of the wells
Fig 90: Dry season pH variation pattern of the wells
FIG 89 : Rainy season pH variation pattern of the wells
0
1
2
3
4
5
6
7
8
A M J J A S
Months of the year
pH
HDW1
HDW2
HDW3
HDW4
HDW5FIG 89 : Rainy season pH variation pattern of the wells
0
1
2
3
4
5
6
7
8
A M J J A S
Months of the year
pH
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 90:Dry season pH variation pattern of the wells
0
1
2
3
4
5
6
7
8
9
O N D J F M
Months of the year
pH
(un
its
)
HDW1
HDW2
HDW3
HW4
HDW5Fig 90:Dry season pH variation pattern of the wells
0
1
2
3
4
5
6
7
8
9
O N D J F M
Months of the year
pH
(un
its
)
HDW1
HDW2
HDW3
HW4
HDW5
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.
Fig 91:Rainy season turbidity variation pattern of the wells
0
10
20
30
40
50
60
70
A M J J A S
Months of the year
Tu
rbid
ity
le
ve
l(N
TU
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 91:Rainy season turbidity variation pattern of the wells
0
10
20
30
40
50
60
70
A M J J A S
Months of the year
Tu
rbid
ity
le
ve
l(N
TU
)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxiii
Fig 91: Rainy season turbidity variation pattern of the wells
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.
Fig 92:Dry season turbidity variation pattern of the wells
0
5
10
15
20
25
30
O N D J F M
Months of the year
Tu
rbid
ity
le
ve
l(N
TU
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 92:Dry season turbidity variation pattern of the wells
0
5
10
15
20
25
30
O N D J F M
Months of the year
Tu
rbid
ity
le
ve
l(N
TU
)
HDW1
HDW2
HDW3
HDW4
HDW5
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.
Fig 93: Rainy season total dissolved solids variation pattern of the wells
Fig 93:Rainy season total dissolved solids variation pattern of
the wells
0
100
200
300
400
500
600
700
800
900
1000
A M J J A S
Months of the year
To
tal d
iss
olv
ed
so
lid
s(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 93:Rainy season total dissolved solids variation pattern of
the wells
0
100
200
300
400
500
600
700
800
900
1000
A M J J A S
Months of the year
To
tal d
iss
olv
ed
so
lid
s(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 94:Dry season total dissolved solid variation pattern of
the wells
0
500
1000
1500
2000
2500
O N D J F M
Months of the year
To
tal d
iss
olv
ed
so
lid
s(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 94:Dry season total dissolved solid variation pattern of
the wells
0
500
1000
1500
2000
2500
O N D J F M
Months of the year
To
tal d
iss
olv
ed
so
lid
s(m
g/l)
HDW1
HDW2
HDW3
HDW4
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.
Fig 95: Rainy season conductivity variation pattern of the wells
Fig 95:Rainy season conductivity variation pattern of the wells
0
0.2
0.4
0.6
0.8
1
1.2
1.4
A M J J A S
Months of the year
Co
nd
uc
tiv
ity
le
ve
l( s
cm
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 95:Rainy season conductivity variation pattern of the wells
0
0.2
0.4
0.6
0.8
1
1.2
1.4
A M J J A S
Months of the year
Co
nd
uc
tiv
ity
le
ve
l( s
cm
)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 96:Dry season conductivity variation pattern of the wells
0
0.2
0.4
0.6
0.8
1
1.2
1.4
O N D J F M
Months of the year
Co
nd
uc
tiv
ity
le
ve
l( s
cm
)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 96:Dry season conductivity variation pattern of the wells
0
0.2
0.4
0.6
0.8
1
1.2
1.4
O N D J F M
Months of the year
Co
nd
uc
tiv
ity
le
ve
l( s
cm
)
HDW1
HDW2
HDW3
HDW4
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.
Fig 97:Rainy season total hardness variation pattern of the
wells
0
0.5
1
1.5
2
2.5
3
3.5
A M J J A S
Months of the year
To
tal h
ard
ne
ss
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 97:Rainy season total hardness variation pattern of the
wells
0
0.5
1
1.5
2
2.5
3
3.5
A M J J A S
Months of the year
To
tal h
ard
ne
ss
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxvii
Fig 97: Rainy season total hardness variation pattern of the wells
Fig. 98: Dry season total hardness variation pattern of the wells
Fig 98:Dry season total hardness variation pattern of the wells
0
2
4
6
8
10
12
O N D J F M
Months of the year
To
tal h
ard
ne
ss
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 98:Dry season total hardness variation pattern of the wells
0
2
4
6
8
10
12
O N D J F M
Months of the year
To
tal h
ard
ne
ss
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
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).
Fig 99:Rainy season dissolved oxygen variation pattrern of
the wells
0
1
2
3
4
5
6
7
A M J J A S
Months of the year
Dis
so
lve
d o
xy
ge
n(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 99:Rainy season dissolved oxygen variation pattrern of
the wells
0
1
2
3
4
5
6
7
A M J J A S
Months of the year
Dis
so
lve
d o
xy
ge
n(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxix
Fig 99: Rainy season dissolved oxygen variation pattern of the wells
Fig 100: Dry season dissolved oxygen variation pattern of the wells
Fig 100:Dry season dissolved oxygen variation pattern of the
wells
0
2
4
6
8
10
12
O N D J F M
Months of the year
Dis
so
lve
d o
xy
ge
n(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 100:Dry season dissolved oxygen variation pattern of the
wells
0
2
4
6
8
10
12
O N D J F M
Months of the year
Dis
so
lve
d o
xy
ge
n(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
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.
Fig 101:Rainy season biochemical oxygen demand variation
pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
A M J J A S
Months of the year
Bio
ch
em
ica
l o
xy
ge
n d
em
an
d(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
Fig 101:Rainy season biochemical oxygen demand variation
pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
A M J J A S
Months of the year
Bio
ch
em
ica
l o
xy
ge
n d
em
an
d(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxxi
Fig 101: Rainy season biochemical oxygen demand variation pattern of the
wells
Fig 102: Dry season biochemical oxygen demand variation pattern of the wells
Fig 102: Dry season biochemical oxygen demand variation
pattern of the wells
0
1
2
3
4
5
6
O N D J F M
Months of the year
Bio
ch
em
ica
l o
xy
ge
n d
em
an
d(m
g/l)
HDW1
HDW3
HDW3
HDW4
HDW5
Fig 102: Dry season biochemical oxygen demand variation
pattern of the wells
0
1
2
3
4
5
6
O N D J F M
Months of the year
Bio
ch
em
ica
l o
xy
ge
n d
em
an
d(m
g/l)
HDW1
HDW3
HDW3
HDW4
HDW5
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.
Fig 103:Rainy season phosphate variation pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the year
Ph
os
ph
ate
le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 103:Rainy season phosphate variation pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the year
Ph
os
ph
ate
le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5 Fig 103:Rainy season phosphate variation pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the year
Ph
os
ph
ate
le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 103:Rainy season phosphate variation pattern of the wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A M J J A S
Months of the year
Ph
os
ph
ate
le
ve
l(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxxiii
Fig 103: Rainy season phosphate variation pattern of the wells
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.
Fig 104:Dry season phosphate variation pattern of the wells
0
1
2
3
4
5
6
7
8
O N D J F M
Moths of the year
Ph
os
ph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5Fig 104:Dry season phosphate variation pattern of the wells
0
1
2
3
4
5
6
7
8
O N D J F M
Moths of the year
Ph
os
ph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
clxxxiv
Fig 105: Rainy season sodium variation pattern of the wells
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
FIG 105:Rainy season sodium variation pattern of the wells
0
10
20
30
40
50
60
70
A M J J A S
Months
so
diu
m(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 105:Rainy season sodium variation pattern of the wells
0
10
20
30
40
50
60
70
A M J J A S
Months
so
diu
m(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 106:Dry season sodium variation pattern of the wells
0
10
20
30
40
50
60
70
O N D J F M
Months
so
diu
m(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 106:Dry season sodium variation pattern of the wells
0
10
20
30
40
50
60
70
O N D J F M
Months
so
diu
m(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
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).
Fig 107: Rainy season sulphate variation pattern of the wells
FIG 107:Rainy season sulphate variation pattern of the wells
0
2
4
6
8
10
12
14
16
A M J J A S
months
su
lph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 107:Rainy season sulphate variation pattern of the wells
0
2
4
6
8
10
12
14
16
A M J J A S
months
su
lph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 108:Dry Season sulphate variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
O N D J F M
Months of the year
su
lph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 108:Dry Season sulphate variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
O N D J F M
Months of the year
su
lph
ate
(mg
/l)
HDW1
HDW2
HDW3
HDW4
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.
Fig 109: Rainy season ammonia variation pattern of the wells
FIG 109:Rainy season ammonia variation pattern of the wells
0
1
2
3
4
5
6
7
8
9
10
A M J J A S
Months
Am
mo
nia
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 109:Rainy season ammonia variation pattern of the wells
0
1
2
3
4
5
6
7
8
9
10
A M J J A S
Months
Am
mo
nia
(mg
/l)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 110: Dry season ammonia variation pattern of the
wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
O N D J F M
Months
Am
monia
(mg/l)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 110: Dry season ammonia variation pattern of the
wells
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
O N D J F M
Months
Am
monia
(mg/l)
HDW1
HDW2
HDW3
HDW4
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
Fig. 111: Rainy season nitrate variation pattern of the wells
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
FIG 111:Rainy season nitrate variation pattern of the wells
0
1
2
3
4
5
6
7
8
A M J J A S
Months
Nitra
te(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 111:Rainy season nitrate variation pattern of the wells
0
1
2
3
4
5
6
7
8
A M J J A S
Months
Nitra
te(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 112:Dry season nitrate variation pattern of the wells.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
O N D J F M
Months
Nitra
te(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 112:Dry season nitrate variation pattern of the wells.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
O N D J F M
Months
Nitra
te(m
g/l)
HDW1
HDW2
HDW3
HDW4
HDW5
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.
Fig. 113: Rainy season fecal coliform bacteria variation pattern of the wells
FIG 113:Rainy season faecal coliform bacteria variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
A M J J A S
Months of the year
Fe
ca
l c
olifo
rm b
ac
teri
a(c
fu/1
00
mls
)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 113:Rainy season faecal coliform bacteria variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
A M J J A S
Months of the year
Fe
ca
l c
olifo
rm b
ac
teri
a(c
fu/1
00
mls
)
HDW1
HDW2
HDW3
HDW4
HDW5
FIG 114:Dry season fecal coliform bacteria variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
O N D J F M
Months of the year
Fecal
co
lifo
rm b
acte
ria(c
fu/1
00m
ls)
HDW1
HDW2
HDW3
HDW4
HDW5FIG 114:Dry season fecal coliform bacteria variation pattern of the wells
0
2
4
6
8
10
12
14
16
18
20
O N D J F M
Months of the year
Fecal
co
lifo
rm b
acte
ria(c
fu/1
00m
ls)
HDW1
HDW2
HDW3
HDW4
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
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).
FIG 115:Seasonal temperature pattern of the wells
20 21 22 23 24 25 26
HDW1
HDW2
HDW3
HDW4
HDW5
Sam
ple
sit
es
Temperature level( C )
DRY
RAINY
Temperature Level (°C)
FIG 115:Seasonal temperature pattern of the we lls
20 21 22 23 24 25 26
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Tempera ture leve l( C )
DRY
RAINY
Temperature Level (°C)
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.
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
Fig 116:Seasonal pH pattern of the wells
0 1 2 3 4 5 6 7
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
pH LEVEL
DRY
RAINY
pH level
Fig 116:Seasonal pH pattern of the wells
0 1 2 3 4 5 6 7
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
pH LEVEL
DRY
RAINY
pH level
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.
Fig 117: Seasonal turbidity pattern of the wells
3.4.4 Annual total dissolved solids variation pattern of the urban wells.
FIG 117:Seasonal turbidity pattern of the wells
0 5 10 15 20
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Turbidity level(NTU)
DRY
RAINYFIG 117:Seasonal turbidity pattern of the wells
0 5 10 15 20
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Turbidity level(NTU)
DRY
RAINY
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.
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.
FIG 118: Seasonal total dissolved solids pattern of the wells
0 200 400 600 800 1000
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Total Dissolved Solids(mg/l)
DRY
RAINY
FIG 118: Seasonal total dissolved solids pattern of the wells
0 200 400 600 800 1000
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Total Dissolved Solids(mg/l)
DRY
RAINY
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.
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).
FIG 119: Seasonal conductivity pattern of the wells
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Conductivity level( SCM)
DRY
RAINYFIG 119: Seasonal conductivity pattern of the wells
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Conductivity level( SCM)
DRY
RAINY
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.
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:
FIG 120:Seasonal hardness pattern of the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Hardness level (mg/l)
DRY
RAINYFIG 120:Seasonal hardness pattern of the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Hardness level (mg/l)
DRY
RAINY
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).
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
FIG 121:Seasonal dissolved oxygen pattern of the wells
0 1 2 3 4 5 6
HDW1
HDW2
HDW3
HDW4
HDW5
Sam
ple
sit
es
Dissolved oxygen level(mg/l)
DRY
RAINYFIG 121:Seasonal dissolved oxygen pattern of the wells
0 1 2 3 4 5 6
HDW1
HDW2
HDW3
HDW4
HDW5
Sam
ple
sit
es
Dissolved oxygen level(mg/l)
DRY
RAINY
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.
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
FIG 122:Seasonal pattern of Biochemical Oxygen Demand of
the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Biochemical Oxygen Demand level(mg/l)
DRY
RAINY
FIG 122:Seasonal pattern of Biochemical Oxygen Demand of
the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Biochemical Oxygen Demand level(mg/l)
DRY
RAINY
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).
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
Fig 123: Seasonal phosphate pattern of the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sam
ple
sit
es
phosphate(mg/l)
DRY
RAINYFig 123: Seasonal phosphate pattern of the wells
0 0.5 1 1.5 2 2.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sam
ple
sit
es
phosphate(mg/l)
DRY
RAINY
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.
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
FIG 124: Seasonal sodium pattern of the wells
0 5 10 15 20 25
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
sodium level(mg/l)
DRY
RAINYFIG 124: Seasonal sodium pattern of the wells
0 5 10 15 20 25
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
sodium level(mg/l)
DRY
RAINY
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.
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:
FIG 125: Seasonal sulphate pattern of the wells
0 1 2 3 4 5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Sulphate(mg/l)
DRY
RAINYFIG 125: Seasonal sulphate pattern of the wells
0 1 2 3 4 5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Sulphate(mg/l)
DRY
RAINY
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.
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
FIG 126: Seasonal ammonia pattern of the wells
0 0.5 1 1.5 2 2.5 3 3.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Ammonia level(mg/l)DRY
RAINYFIG 126: Seasonal ammonia pattern of the wells
0 0.5 1 1.5 2 2.5 3 3.5
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Ammonia level(mg/l)DRY
RAINY
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).
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
FIG 127: Seasonal nitrate pattern of the wellsnitrate
0 0.5 1 1.5 2
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
nitrate level(mg/l)
DRY
RAINYFIG 127: Seasonal nitrate pattern of the wellsnitrate
0 0.5 1 1.5 2
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
nitrate level(mg/l)
DRY
RAINY
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.
Fig 128: Seasonal fecal coliform bacteria pattern of the wells
FIG 128:Seasonal faecal coliform bacteria pattern of the wells
0 1 2 3 4 5 6 7 8
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Faecal coliform bacteria level(cfU/100ml)
DRY
RAINY
Fecal coliform bacteria level(cfU/100ml)
FIG 128:Seasonal faecal coliform bacteria pattern of the wells
0 1 2 3 4 5 6 7 8
HDW1
HDW2
HDW3
HDW4
HDW5
Sa
mp
le s
ite
s
Faecal coliform bacteria level(cfU/100ml)
DRY
RAINY
Fecal coliform bacteria level(cfU/100ml)
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.
* 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
Months SAMPLE STATIONS Highest
WQI
per
Month
Lowest
WQI
per
Month
SW1 SW2 SW3 SW4 SW5
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
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.
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
Months SAMPLE STATIONS Highest
WQI
per
Month
Lowest
WQI
per
Month
SW1 SW2 SW3 SW4 SW5
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
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.
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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 .
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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
WQI
per
Month
Lowest
WQI
per
Month
ccxxii
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
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
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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
WQI
per
Month
Lowest
WQI
per
Month
HDW1 HDW2 HDW3 HDW4 HDW5
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
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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
treated
Percentage
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).
Fig 129: January water-related diseases prevalence pattern in Enugu urban
6% 8%
16%
16%
12%
16%
7%
6%
6% 7%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
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.
Fig. 130: February water-related diseases prevalence pattern in Enugu urban
9%
13%
14%
14% 8%
13%
10%
9%
5% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
n
ccxxxii
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.
13%
14%
7%
13% 10%
13%
11%
10%
4% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxiii
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.
6%
10%
13%
13%
12% 12%
7%
12%
8%
7%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxiv
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 %).
9%
6%
9%
9%
12%
12%
13%
13%
10%
7%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxv
Fig. 134: June water-related diseases prevalence pattern in Enugu urban
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 %.
9%
11%
7%
15%
13%
11%
12%
12%
5% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
8%
8%
10%
12%
10% 15%
15%
9%
5% 8%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxvi
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.
7%
10%
11%
13%
15%
13%
18%
2%
6% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxvii
Fig 137: September water-related diseases prevalence pattern in Enugu urban
Fig. 138: October water-related diseases prevalence pattern in Enugu
urban
14%
15%
5%
13% 15%
14%
13%
4% 4% 3%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
5%
10%
16%
13%
10%
21%
11%
5%
4% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
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).
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).
15%
15%
10%
13% 11%
8%
7%
10%
6% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxxxix
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).
12%
15%
8%
17% 12%
11%
7%
7%
6% 5%
Ogui Achara layout Asata Abakpa Iva valley Coal camp Uwani New haven Independence layout G.R.A
ccxl
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.
11%
11%
8%
6%
7%
12%
8%
6%
7%
8%
7%
9%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
ccxli
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.
Faeces
Water Flies Hands
Food
Mouth
Faeces
Water Flies Hands
Food
Mouth
ccxlii
Table 47: Prevalence Pattern of water-borne diseases in Enugu Urban.
Months Wards of the urban area
Ogui Achara
layout
Asata Abakpa Iva valley Coal
camp
Uwani New
Haven
Independence
layout
G.R.A Total
(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
ward
140 189 176 188 204 196 146 82 37 49 1407
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
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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
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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.
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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 %
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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 %
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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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FIG 143 SEASONAL PATTERN OF WATER BORNE DISEASES IN ENUGU URBAN
Source: Fieldwork, 2006.
N
IGBO ETITI ISI UZO
IVA - VALLEY
GRA
ABAKPA NIKE
ASATA
INDEPENDENCE LAYOUT
NEW HEAVEN
ACHARA LAYOUT/ MARY LAND
COAL CAMP
OGUI
UWANI
0 1 2 3 4 5
LEGEND
Local Government Boundary
Ward Boundary
Urban Boundary
Areas of Dry Season Prevalence
Areas of rainy season prevalence
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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
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).
Months Wards of the urban area
Ogui Achara
layout
Asata Abakpa Iva
valley
Coal
camp
Uwani New
Haven
Independence
layout
G.R.A Total (all
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
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Table 69: Rainy season prevalence of water-washed diseases in Ogui.
Month Water -washed Diseases
Trachoma Skin
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
disease
0 66.7 0 33.3 0 0 0 100%
Source: Field work, 2006.
Table 70: Dry season prevalence of water-washed diseases in Ogui.
Month Water -washed Diseases
Trachoma Skin
Infectio
n
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
month
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
disease
0 0 0 88.9 0 11.1 0 100%
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.
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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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
disease
0 100 0 0 0 0 0 100%
Source: Field work, 2006.
Table 72: Dry season prevalence of water-washed diseases in Achara Layout.
Month Water -washed Diseases
Trachoma Skin
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % 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 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
disease
22.2 0 0 55.6 0 22.2 0 100%
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
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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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
disease
5.8 47.1 0 47.1 0 0 0 100%
Source: Field work, 2006.
Table 74: Dry season prevalence of water-washed diseases in Asata.
Month Water -washed Diseases
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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
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Trachoma Skin
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
disease
11.8 29.4 0 52.9 0 5.9 0 100%
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
Cough
Tetanus Diphtheria Total % per
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.
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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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
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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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total %per
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
disease
0 29.4 0 64.7 0 5.9 0 100%
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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total % per
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
Disease
0 50 0 33.3 16.7 0 0 100%
Source: Field work, 2006.
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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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total %
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
%
per disease
0 80 0 20 0 0 0 100%
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
infection
Leprosy Tuberculosis Whopping
cough
Tetanus Diphtheria Total %
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
%
per
disease
0 100 0 0 0 0 0 100%
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).
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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.
cclxvi
FIG 144: SEASONAL PATTERN OF WATER-WASHED DISEASES IN ENUGU URBAN
N
IGBO ETITI ISI UZO
IVA - VALLEY
GRA
ABAKPA NIKE
ASATA
INDEPENDENCE LAYOUT
NEW HEAVEN
ACHARA LAYOUT/ MARY LAND
COAL CAMP
OGUI
UWANI
0 1 2 3 4 5
LEGEND
Local Government Boundary
Ward Boundary
Urban Boundary
Areas of Dry Season Prevalence
Areas without Water washed Diseases
Areas of rainy season prevalence
cclxvii
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.
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Table 84: Number of Patients treated for water-based diseases in Enugu urban
Months Wards of the urban area
Ogui Achara
layout
Asata Abakpa Iva
valley
Coal
camp
Uwani New
Haven
Independenc
e layout
G.R.A Total
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
each ward
0 0 3 6 0 5 0 0 0 0 14
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
FIG 145: SEASONAL PATTERN OF WATER-BASED DISEASES IN ENUGU URBAN
Source: Fieldwork, 2006.
N
IGBO ETITI ISI UZO
IVA - VALLEY
GRA
ABAKPA NIKE
ASATA
INDEPENDENCE LAYOUT
NEW HEAVEN
ACHARA LAYOUT/ MARY LAND
COAL CAMP
OGUI
UWANI
0 1 2 3 4 5
LEGEND
Local Government Boundary
Ward Boundary
Urban Boundary
Dry Season Prevalence only
No water borne disease prevalence
Rainy Season Prevalence only
Dry season more than rainy season
cclxx
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
layout
Asata Abakpa Iva
valley
Coal camp Uwani New
Haven
Independence
layout
G.R.A Total for
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
ward
209 232 184 291 199 292 251 231 173 159 2221
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100
Source: Field work, 2006.
Table 87: Dry season prevalence of water-related vector diseases in Ogui
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
99 0 0 0 1 0 100
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
Source: Field work, 2006.
Table 89: Dry season prevalence of water-related vector diseases in Achara layout.
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 1 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
98.6 0 0 0 1.4 0 100%
Source: Field work, 2006.
Table 91: Dry season prevalence of water-related vector diseases in Asata
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
95.7 0 0 0 2.6 1.7 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
97.3 0 0 0 0 2.7 100%
Source: Field work, 2006.
Table 93: Dry season prevalence of water-related vector diseases in Abakpa
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
97 0 0 3 0 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
94.9 0 0 0 5.1 0 100%
Source: Field work, 2006
Table 95: Dry season prevalence of Water-related vector diseases in Iva valley
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
97.3 0 0 0 2.7 0 100 100%
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).
cclxxviii
Table 98: Rainy season prevalence of Water-related vector diseases in Uwani
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
Source: Field work, 2006.
Table 99: Dry season prevalence of Water-related vector diseases in Uwani
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
94.7 0 0 0 1.1 4.2 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 1 0 100%
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.
Months
Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
.
Table 103: Dry season prevalence pattern of water-related vector diseases in
Independence Layout.
Months Water-related vector diseases
Malaria Japanese
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100
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
fever
Sleeping
sickness
River
blindness
Yellow
fever
Dengue
fever
Total % per
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
disease
100 0 0 0 0 0 100%
Source: Field work, 2006.
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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
FIG 146: SEASONAL PATTERN OF WATER-RELATED VECTOR DISEASES IN ENUGU URBAN
Source: Fieldwork, 2006.
N
IGBO ETITI ISI UZO
IVA - VALLEY
GRA
ABAKPA NIKE
ASATA
INDEPENDENCE LAYOUT
NEW HEAVEN
ACHARA LAYOUT/ MARY LAND
COAL CAMP
OGUI
UWANI
0 1 2 3 4 5
LEGEND
Local Government Boundary
Ward Boundary
Urban Boundary
Dry season prevalence
Rainy Season prevalence
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
patients
treated
Percentage(%)
contribution
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
treated
Percentage %
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.
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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
diseases
Total No of
patients
Percentage (%)
contribution
Ranking
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)
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Water washed
diseases
Total no of
patients
Percentage contribution Ranking
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.
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Table 111: Water-related vector diseases (Patient Number/ Percentage in Enugu Urban)
Water vector diseases Total number of
patients
% contribution Rank
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
disease group
Total number of
patients
Percentage % contribution Disease rating
Water borne 1407 36.9 2nd
Water washed 171 4.5 3rd
Water based 14 0.4 4th
Water-related
insect vector
2221 58.2 1st
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
cclxxxix
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
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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,
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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
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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
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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
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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.
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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.
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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
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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.
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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.
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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
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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
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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
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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.