EVALUATION OF WATER QUALITY MODELLING PARAMETERS TOWARDS THE EVOLVEMENT OF RE-AERATION COEFFICIENT FOR RIVERS IN THE NIGERIAN ENVIRONMENT

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ABSTRACT

This study was carried out on River Atuwara in Ota, Ogun State, Nigeria with the aim
of developing a coefficient of re-aeration model applicable to River Atuwara and
other rivers in the Nigerian environment. This was achieved by sourcing for data once
every month from 22 sampling locations of interest within a pre-selected segment of
the river over a period covering the dry and wet seasons. The data collected include
hydraulic data (depth, width, velocity and time of travel) and water quality data such
as Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD). Excel
Spreadsheet and MATLAB were used for data processing. Regression analysis was
carried out where stream velocity and depth were the regressors and the re-aeration
constant k2 (as a function of BOD, DO and Temperature) was the dependent variable.
A coefficient of re-aeration, k2, (Atuwara re-aeration model) was developed and
validated statistically. Its performance was also verified by comparing the model with
10 other internationally recognized models. It was found that even though Atuwara
model performed better than Agunwamba model and most of the other well cited
models, both Atuwara model and Agunwamba model could be safely adopted for
future water quality modelling researches in the Nigerian environment.
Results of detailed water analysis of samples from River Atuwara shows high level of pollution hence it is unfit for human consumption without adequate treatment. It is
recommended that River Atuwara and similar rivers in the country should be regularly
monitored for quality control.

CHAPTER ONE

INTRODUCTION

1.1 Background Information
Fresh water sources can be broadly categorized into groundwater and surface water
(Chapman, 1992). Surface water can again be sub-divided into ‘‘running’’ surface
water bodies and ‘‘stationary’’ surface water bodies. Examples of the former include
rivers, streams, and brooks while examples of the latter include lakes and ponds. The
most abused of all surface water bodies are the running surface water bodies because
people tend to believe that by disposing their wastes into these running water, they
have been rid of their waste disposal problems. In spite of its relative abundance,
water is still a very scarce resource when it is needed in its fresh form because 97.5%
of all available water is salt water (Krantz and Kifferstein, 2007; UNESCO, 2006). Of
the remaining 2.5%, 70% of it is frozen in the polar ice caps. The other 30% is mostly
present as soil moisture or is trapped in underground aquifers. In the end, only
0.007% of all water on earth is readily accessible as fresh water for direct human use
(UNESCO, 2006; Krantz and Kifferstein, 2007).
1.1.1 Water Sources Distribution in Nigeria
Record shows that 29% of Nigerians live in the rural areas, 33% reside in small towns
and 38% live in the urban areas (FGN, 2000). World Bank (2005) also revealed that
91% of Nigerians living in the rural areas (which translate to 37 million Nigerians,
using the 2006 census data) had no access whatsoever to treated water. Most
Nigerians derive their water from surface water (springs/stream/rivers), hand dug
wells, rain harvesting, pipe borne water, boreholes and vendors (FGN, 2000). It is
estimated that 48% (about 67 million) Nigerians harness surface water for their
domestic needs, 57% (79 million) use groundwater, 20% (27.8 million) harvest rain,
14% (19.5 million) have access to pipe borne water while 14% use boreholes (FGN,
2000). According to Ahianba et al., (2008) 33.82% (47.3 million) Nigerians depend
exclusively on surface water for their domestic water supply, 28.27% (39.3 million)
on hand dug well sources, 24.38% (33.9 million) on pipe borne water, 11.83% (16.4
million) on borehole water and 1.7% (2.4 million) on water vendors (Fig. 1.1).
Another interesting statistic suggests that 54.6% (75.9 million) Nigerians use pit
latrines exclusively, 13.71% (1.91 million) use water closet exclusively, 0.58% (806,
200) use the bucket system and 31.16% (43.3 million) Nigerians use other unsanitary
methods (Fig 1.2). Some of these unsanitary methods include defecating in open
fields and disposal into surface water bodies (Ahianba et al, 2008). When rain falls,
all the defecations disposed on land get washed down into the surface water bodies as
non-point source pollution. This is beside the pollution being discharged into surface
water bodies by industries. It can be inferred, therefore, that 47.3 million Nigerians
are potentially at risk of epidemic outbreak if our surface waters are not adequately
protected through legislations guided by scientific facts.
Source: Ahianba et al., 2008
Figure 1.1 – Nigerian Household distribution by source of water supply
Surface w ater
34%
Hand dug w ell
28%
Pipe borne
24%
Bore Hole
12%
Vendors
2%
Source: Ahianba et al., 2008
Figure 1.2 – Nigerian Household distribution by Toilet Facilities
It is therefore pertinent that the state of the available freshwater should be well
monitored and managed through governmental regulations and proper use. However,
proper legislation, monitoring and management cannot be achieved without scientific
studies to ascertain the state of pollution and the assimilative capacity of the rivers
and streams (Anyata and Nwaiwu, 2000). One of such areas of scientific study is
water quality modelling.
1.2 Water Quality Modelling
Aquatic systems are very dynamic in terms of constituents. These constituents have
direct impacts on water quality. By extension, these impacts on the water quality
affect aquatic and human lives. Water quality modelling describes a situation whereby
mathematical models are employed to explain, describe and predict the response of
aquatic ecosystems to changes imposed on them either by anthropogenic activities or
by other naturally induced conditions. Scores of water quality models have been
developed simply because no single model can be representative of all situations
(Chapman, 1992). While some models are situation or problem specific, others are
Pit Latrine
54%
Bucket system
1%
Closet system
14%
Other methods
31%
time specific and yet others are more general. Thus, modelling (development,
verification and validation) is a problem solving exercise that is going to be around
for a long time to come.
The Streeter-Phelps Dissolved Oxygen (DO) model is a very popular general model
put forward in 1925 by the scientists after whom the model was named (Villeneuve et
al., 1998). The model has since been modified and metamorphosed many times into
various forms and applications (Fair et al., 1971; Longe and Omole, 2008). A
prominent dependent variable present within most oxygen prediction models is the
self-purification factor, often symbolized by the letter, f, and is obtained by the
relationship expressed in equation
1
2
k
k
f =
1.1
Where k2 = coefficient of re-aeration and k1 = coefficient of de-oxygenation. k1 is a
function of the effluent (wastewater) discharged into the aquatic body. It can be fully
determined by testing the strength of the raw and diluted effluent after it had mixed
with the water body (Hammer, 1986). The determination of re-aeration coefficient
(k2) on the other hand is more difficult (Garg, 2006). Therefore, k2 is the critical term
in equation 1.1. This self-purification factor, f, describes the unique measure of the
ability of each surface water body to cleanse itself of whatever pollution that gets into
it. While flowing surface water bodies get self-purified faster than slow moving or
stagnant surface water bodies, a factor that contributes significantly to the rate of selfpurification
is temperature. Temperature is the distinguishing factor that differentiates
k2 in different geographical locations. Since temperature varies from place to place, it
is logical that k2 obtained from experiments performed in the temperate regions
cannot be representative of tropical environments. Unfortunately, however, the
available management policies and laws available in Nigeria have been based on the
adaptation of imported laws from countries where their own laws were formulated
based on their own local environmental conditions (Babalobi, 2005; AU, 2006).
Temperature is a very unpredictable and dynamic parameter. However, established
trends have been studied by scientists in the past who have published isothermal maps
that demarcate the entire world into different temperature regimes (Herbertson, 1912;
Parkins, 1926; Yongsiri et al., 2004; RWWF, 2007). These regimes can therefore be borrowed to form the basis for experimental work in Nigeria which falls into the
tropical region.
1.3 Description of the Study Location
River Atuwara (also known as River Iju) passes through Iju community in Ota, Ogun
State Nigeria. Ota is an urban and industrial centre. Ado/Odo Ota Local Government
Area (LGA) is the most populous LGA in Ogun State, with a population of 526, 565
(FRN, 2007). It is also the home to several other rivers like Balogun, Illo, Imojiba,
Ogun and Abesan. The town is located between Latitude 60 30’N-60 50’N and
longitude 30 02’E-30 25’E, with an elevation of 53 m above sea level (Iroham, 2005;
Omole, 2010). River Atuwara is located within the Owo catchment area. It is a
perennial river. Some rivers empty into it among which is River Balogun (Figure 1.3).
Figure 1.3: General Layout of the Study Area within Ado-Odo/Ota Local Government Area
1.4 Statement of the Problem
There is virtually no available literature on the subject of water quality modelling in
Nigeria (Agunwamba et al., 2007). A k2 model was proposed for the Nigerian context
by Agunwamba et al., (2007) following a sampling exercise that was carried out
during the rainy season only. In their recommendation, further work that would cut
across the two main climatic seasons was proposed. This study therefore is an attempt
to bridge this gap.
1.5 Aim of the Study
The aim of this study is to develop an appropriate re-aeration coefficient model that
adequately represents rivers in the Nigerian environment and to propose a
methodology that can be used in this pursuit.
1.6 Objectives of the Study

To acquire data on the hydrographic and physico-chemical parameters of
River Atuwara in Ota, Ogun State, Nigeria that cut across the rainy and dry
seasons.

To model the reaeration coefficient (k2) based on the data obtained from the
study of River Atuwara and to validate the same statistically.

To consider the relative suitability of the newly developed model to the
existing models with respect to the Nigerian environment.
1.7 Significance of Study
At present, little research work has been carried out on water quality modelling in
Nigeria. The research is therefore an attempt to bridge this existing gap

A re-aeration coefficient model which reflected the existing local conditions was
developed.

Future legislations, regulations and researches can take their cue from the research
findings.The research findings have been made available to all the stakeholders. This
include: (a) the private citizens (so that they can be more alert to their responsibility
of protecting their environment and guarding their health).
(b) the polluters (so that they can know that their activities have a direct impact on
human lives and the environment) and
(c) the government (through their regulatory agencies, so that they can realise the
impact of defaulters of pollution standards on people and the environment).
1.8 Scope of Study
The study composed of three major aspects. The first aspect is the fieldwork for the
gathering of in-situ information on DO, the acquisition of raw water samples for BOD
analysis as well as information on other hydrodynamic factors such as stream velocity
and bathymetry. The sampled reach was limited to 1.3km. The second aspect of this
research work was the laboratory analyses of the raw water samples for physical,
chemical and bacteriological characteristics. The final aspect of this study was the
development of the k2 model based on the data collected. Data recording and handling
were carried out with the aid of Microsoft Excel while the modelling was done with
the use of using MATLAB software

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