AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH
© 2010, Science Huβ, http://www.scihub.org/AJSIR
ISSN: 2153-649X
Interrelationships between physicochemical water pollution indicators: A
case study of River Yobe-Nigeria
M. Waziri. 1* and V.O. Ogugbuaja2
1
Department of Chemistry, Bukar Abba Ibrahim University, Damaturu-Nigeria.
2
Department of Chemistry, University of Maiduguri, Maiduguri-Nigeria.
ABSTRACT
Water samples were collected from river Yobe at eighteen sites where human and
animal
activities were high. A total of 162 samples were analyzed for dissolved oxygen DO, biochemical
oxygen demand BOD, chemical oxygen demand COD, total organic carbon TOC and total
dissolved solids TDS, during dry and wet seasons as well as the harmattan period. The total data
points were used to establish relationships between the parameters and data were also subjected
to statistical analysis and expressed as mean ± standard error of mean (SEM) at a level of
significance of p<0.05. The later was used as test data. Regression analysis was carried out using
Microsoft Excel to relate the parameters and relationships in form of scatter grams were obtained
between DO/BOD, COD/DO, BOD/TOC, COD/TOC, BOD/TDS and COD/TDS. The correlation
coefficient, r ranged from 0.3 to 1.0 indicating good correlations between these parameters. The
validity of the empirical equations obtained were tested with the test data and the relationships
were found to be similar, indicating that the equations can be used to predict the levels of these
pollution indicators when one variable is known especially for similar river waters.
Keywords: Regression analysis, harmattan period, scatter gram, variable.
INTRODUCTION
The availability of water determines the location and
activities of humans in an area and our growing
population is placing great demands upon natural
fresh water resources. Technological growth has also
put the ecosystem we depend on under stress and
the availability of water is at a very high risk
(Kulshreshtha, 1998; Osibanjo, 1996). Yobe state like
quite a number of states in Nigeria is faced with
increasing pressure on water resources and the
widespread, long-lasting water shortages in many
areas are as a result of rising demand, unequal
distribution and increasing pollution of existing water
supply. The by-product of agricultural activities,
urbanization and industrialization result in pollution
and degradation of the available water resource
(Waziri et al., 2009; Ajayi and Osibanjo, 1981).
It is important to analyse water to determine its
suitability for drinking, domestic use industrial use,
agricultural use etc. It is also important in water
quality studies to know the amount of organic matter
present in the system and the quantity of oxygen
required for stabilization of the water. The impact of
organic pollutants on water quality in this work is
expressed in terms of the Biochemical Oxygen
Demand, BOD and Chemical Oxygen Demand, COD
which all depend on the Dissolved Oxygen, DO. Total
organic Carbon, TOC and Total Dissolved Solids;
TDS on the other hand are used to define the organic
content of the water and the total ions in solution
respectively.
Several works on water quality have focused on the
physicochemical characteristics of waters (Waziri et
al., 2009; Hati et al., 2008; Izonfuo and Bareweni,
2001). Empirical relationships were also developed to
assess the quality of waste waters using testing and
calculation methods (Rene and Saidutta, 2008;
David, 1998) but not much was mentioned in
literature on the interrelationship between the
parameters as it affects river waters.
The fact that every problem in environmental studies
must be approached in a manner that defines the
problem necessitates the use of analytical techniques
in the field or laboratory to produce reliable results.
Once the problem is identified, samples are collected
and analysed. The procedure for statistical analysis
of results could be tedious, time consuming and
fraught with pitfalls especially when results are
needed urgently in cases like an outbreak of
contagious water bound disease. However, models
can be designed which will provide a simple,
economic and precise means of interpreting results
leading to satisfactory findings.
Am. J. Sci. Ind. Res., 2010, 1(1): 76-80
obtained were expressed as mean ± SEM and used
as test data. Regression analysis for the total data
points were carried out using Microsoft Excel and the
nature of correlations between parameters were
determined based on the correlation coefficient
obtained.
The aim of this study therefore, is to determine the
levels of some pollution indicators and to study the
statistical relationships between them. Regression
equations will also be established in a view to
providing an idea on the levels of pollution by the
parameters investigated and possibly proffering a
preventive measure prior to detailed investigation of
the Yobe River.
Data analysis: Results obtained were expressed as
mean ± SEM (standard error of mean). Student’s ttest was used to assess variations between seasons
at a level of significance of p< 0.05.
Experimental
Study area: Yobe State is situated in North Eastern
Nigeria, and the area of study is in the northern part
of the state, an area which is greatly threatened by
desertification. The River Yobe from which the state
derived its name, is located between latitude 100 N
and 1300 N and longitude 9.450 E and 12.300 E. The
major activities in the area are farming and fishing.
RESULTS AND DISCUSSION
The results of the analysis for all the parameters
used as test data are presented in Table 1 and
relationships between the parameters in form of
scatter gram are shown in Figures 1-6. The
regression analysis carried out to relate DO with
BOD, COD with DO, BOD with %TOC and COD with
%TOC gave correlation coefficient r=0.9, r=1.0, r=1.0
and r=0.7 respectively (Figures 1-4) indicating very
good correlation between the parameters. Good
correlation was also obtained for COD/TDS (Figure 6,
r=0.5), however, the correlation between BOD and
TDS (Figure 5, r=0.3), though within the acceptable
range but the deviations of some points are large,
indicating poor correlations between BOD and TDS.
Experimental: Water samples were collected in precleaned plastic containers from 18 sampling locations
spread across the River Yobe from areas where
human, animal and agricultural activities were high.
The duration of sampling were categorized into three:
dry season, wet season and harmattan period for
2007. Samples were collected on weekly basis
throughout the seasons. The samples were analysed
for DO, BOD, COD, TOC and TDS using standard
analytical techniques (Ademoroti, 1996; Radojevic
and Bashkin, 1999; Sawyer et al., 1994). Results
Table 1: Annual Projected levels (mean±SEM) of DO, BOD, COD, TDS (mg/l) and %TOC at different
locations of the River.
Location
DO
BOD
COD
%TOC
TDS
A
5.87±0.43
3.34±0.28
189.89±13.77
0.78±0.12
83.20±11.60
B
7.09±0.33
2.82±0.20
174.44±7.54
0.45±0.11
70.61±8.37
C
7.20±0.45
2.48±0.17
167.11±12.38
0.50±0.08
56.65±6.78
D
7.38±0.43
2.43±0.18
146.83±10.63
0.43±0.09
47.75±5.62
E
7.09±0.30
2.60±0.35
163.47±11.87
0.57±0.32
57.73±4.40
F
6.65±0.44
2.92±0.22
171.36±11.41
0.46±0.09
60.88±9.75
77
Am. J. Sci. Ind. Res., 2010, 1(1): 76-80
Fig. 1 : The scatter gram of DO against BOD for River
Yobe-Nigeria
Fig. 4 : The scatter gram of COD(mg/l) against %TOC
for River Yobe-Nigeria
Fig. 2 : The scatter gram of COD(mg/l) against DO(mg/l)
for River Yobe-Nigeria
Fig. 5 : The scatter gram of BOD(mg/l) against
TDS(mg/l) for River Yobe-Nigeria
Fig. 6 : The scatter gram of COD(mg/l) against
TDS(mg/l) for River Yobe-Nigeria
Fig. 3 : The scatter gram of BOD(mg/l) against %TOC
for River Yobe-Nigeria
78
Am. J. Sci. Ind. Res., 2010, 1(1): 76-80
established using regression analysis. The validity of
the equations were tested with the test data results
analysed in this work and results obtained from
similar Rivers but in different environment and the
relationships were found to be similar. This indicates
the reliability of the relationships which suggests that
it can be used to predict the levels of pollution by the
parameters investigated and possibly proffering a
preventive measure prior to detailed investigation of
the Yobe River or in pollution monitoring. However,
the authors suggest the application of a different
model to relate TDS with other pollution indicators for
a better correlation.
BOD tests only measures biodegradable fraction of
the total potential DO consumption of a water
sample, while COD tests measures the oxygen
demand created by toxic organic and inorganic
compounds as well as by biodegradable substances
[Sawyer et al., 1994; Donaldson, 1977). High BOD
levels indicates decline in DO (Fig. 1) because the
oxygen that is available in the water is being
consumed by the bacteria leading to the inability of
fish and other aquatic organisms to survive in the
river. Since DO can be measured in-situ the
regression equations y=1.37x + 10.663 and 25.652x
+ 333.52 can be used to estimate the values of BOD
and COD respectively. This will also ease the
calculations of BOD/COD ratios in order to predict the
biodegradability of the water since high BOD/COD
ratios indicates that water is polluted and is relatively
biodegradable. Studies (Waziri, 2006) have shown
that the River Yobe contains high concentrations of
nitrates and phosphates which led to the quick
growth as well as death of plants and algae. The
result is accumulation and decomposition of organic
wastes leading to high BOD values.
TOC is used to define the organic content of an
aquatic system. Typically, anthropogenic organic
pollutants occur at low concentrations (parts per
billion) in an aquatic environment where as the TOC
of water may reach several concentrations (parts per
million) (Kavanaught, 1978). Since there is good
correlation between BOD and %TOC as well as
between COD and %TOC, the regression equations,
y= 2339x + 1.8884 and y=55.675x +122.36 can be
used to estimate TOC. TDS on the other hand is
equally important in water quality studies, though
there was no serious health effect associated with
TDS ingestion in water but some regulatory agencies
(EPA, 1980; FEPA, 1991; FME, 2001; NAFDAC,
2001) recommended a maximum TDS value of
500mg/l in drinking water supplies. From the
regression analysis obtained in this work poor
correlations were obtained in relation to TDS which
suggests that other models must be used to correlate
TDS to the other parameters investigated.
The regression equations obtained in this work were
tested with the test data and similar relationships
were observed. The equations were also tested with
results obtained from similar analysis carried out at
rivers Gongola and Benue in northern Nigeria
(Ogugbuaja and Kinjir, 2001) and the relationships
were also found to be similar.
Conclusion: Interrelationships were established
between some physicochemical water pollution
indicators
where
reliable
correlations
were
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