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European Journal of Scientific ResearchISSN 1450-216X Vol.74 No.2 (2012), pp. 186-204
EuroJournals Publishing, Inc. 2012
http://www.europeanjournalofscientificresearch.com
Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria
Aina Olubukola Adeogun
Corresponding Author, Hydrobiology and Fisheries Research Unit
Department of Zoology, University of Ibadan, Ibadan, Nigeria E-mail: ainaadeogun@yahoo.com; aina.adeogun@.ui.edu.ng Tel:
+2348055261105
Taofeek Ademola Babatunde
Biology Department, Umaru Musa YarAdua University
Katsina, Nigeria
E-mail: babatundetaofeek@umyu.edu.ng
Tel: +2348037851688
Azubuike Victor Chukwuka
Ecology and Environmental Biology Unit, Department of Zoology
University of Ibadan, Ibadan Nigeria E-
mail: zubbydoo@gmail.comTel:
+2347030306066
Abstract
The worldwide deterioration of surface water quality has become a growing threat to human society
and natural ecosystems hence the need to better understand the spatial and temporal variabilities of pollutants
within aquatic systems. This study was aimed at evaluating water and sediment parameters of Ona river to
identify major pollutant sources using multivariate statistics. Accumulation Factor (AF) indicated that BOD
and COD significantly built up with an accumulation factor of 7.70 and 4.83 respectively. Heavy metals
Cu, Pb, Mn, Cd and Cr accumulated significantly in the sediment and water. The river recovered
significantly from a number of physicochemical parameters: EC (58%), TDS (64%), TS (61%), Chloride
(65%), COD (79%) and BOD had the highest recovery value of 87%. All the heavy metals measured showed a
high level of recovery in both water and sediment except for Zn (41% in both water and sediment) and
Fe (38%) in the sediment. Factor analysis (FA) showed that sediment factor accounted for 39.95% of the
total variance in water quality, while the second factor, labelled as seasonal factor accounted for a
complementary 21.94%. Discriminant analysis (DA) highlighted sediment factors as the major determinant of
water quality, with seasonal factors playing an augmenting role.
Keywords: Physicochemical parameters, sediment quality, water quality, Ona river, accumulationfactor, river recovery capacity,
http://www.europeanjournalofscientificresearch.com/mailto:ainaadeogun@yahoo.commailto:babatundetaofeek@umyu.edumailto:zubbydoo@gmail.commailto:zubbydoo@gmail.commailto:babatundetaofeek@umyu.edumailto:ainaadeogun@yahoo.comhttp://www.europeanjournalofscientificresearch.com/7/31/2019 Spatio temporal variation water quality
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 187
1. Introduction
The widespread deterioration in water quality of inland aquatic systems have been reported in the last
decade (Olajire et al., 2000; Heberer, 2002; Mara, 2003; Vi et al., 2009; Murray et al., 2010) and freshwater
systems which constitute a large number of inland water ways (Sigee, 2005) play a major role in the assimilation
and transportation of contaminants from a number of sources (Vutukuru, 2003; UN WWAP, 2003; Shell, 2004;
Vi et al., 2009, Adeogun et al.,2011). It is in view of the limited supply of freshwater worldwide (Chinhanga,
2010) and the role of anthropogenic activities in the decline of water quality that the protection of the integrity of
water resources have been given topmost priority in the 21st century (USEPA, 2007; Wei et al., 2008). This
however cannot be achieved without a spatio-temporal evaluation of water and sediment quality of the aquatic
systems of concern (Don Pedro et al., 2004).
Environmental data acquired by investigating the dynamics of water quality may present complex
patterns and variability which are dependent on a number of unpredictable factors. These factors may be
geogenic or lithological, hydrological, meteorological and anthropogenic (Bricker and Jones, 1995; Shrestha and
Kazama, 2007; Bu et al., 2010). The non-linear nature of environmental data makes spatial and temporal
variations of water quality often difficult to interpret therefore creating a critical need to provide a representative
and reliable estimation of the quality of surface waters (Dixon and Chiswell, 1996). Sequel to this challenge, the
application of different multivariate statistical techniques such as Discriminant Analysis (DA), Principal
Component Analysis (PCA) and Factor Analysis (FA) for the interpretation of complex data matrices andbetter understanding of the water quality variation have been developed (Vega et al., 1998; Lee et al., 2001;
Wunderlin et al., 2001; Reghunath et al., 2002). This study was aimed at examining the impact of all sources of
pollution on water and sediment quality of Ona River, and to identify the parameters responsible for spatial
and temporal variability in water and sediment quality.
2. Materials and Methods
2.1. Study Area
Ona river (7o
19-7o
42N; 3o
48- 3o
55E) is part of a dense network of inland watercourses that flows
through Ibadan city southwards into the Lagos lagoon and industrial facilities located adjacent to this riverinclude a food industry, beverage industry, agricultural production and processing industry. Effluents from these
industries are connected by a network of canals that empty into this river. Six sampling points were chosen
along the river course for this study. The coordinates of the sampling points were taken using GPS (Garmin
GPS 12 x L). The first sampling point located upstream (UPS) had no industrial or domestic inputs impacting
the water, thus suitable as a reference/ control point. The second sampling point was located at a site receiving
domestic discharge (DDP). Two sampling points were also taken along Oluyole industrial estate where effluents
from these industries are being discharged into the river respectively (EDP 1 and 2), while the last two sampling
points were located downstream after the point of discharge (DSR, 1 and 2).
The first two points were 3000m and 1500m respectively from the effluent discharge point. Effluent
discharge points (EDP 1 and 2) were 600m apart within the effluent discharge area, while pointsdownstream (DSR, 1 and 2) were 1500m and 3000m respectively away from the effluent discharge points.
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188 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
Figure 1: Map of Ona River showing sampling points.
2.2. Collection of Samples
Bi- monthly samples of water and sediment were collected between February and July 2010. Water samples
for physicochemical and heavy metal analysis were collected in clean plastic bottles, prewashed, rinsed with
deionised water and dried. At each sampling point, surface water was collected at the middle of the river.
Samples were kept in the refrigerator prior to analysis. River sediment samples were collected with a Van Veen
grab and stored in clean polythene bags to avoid contamination. Sediment samples were later air-dried in thelaboratory, disaggregated and sieved to obtain clay fractions. The use of fine portions (clay fractions) was as
a result of their role as metal accumulators, due to their net negative charge and participation in sorption and
cation exchange processes (Ademoroti, 1996b).
2.3. Measurement of PhysicochemicalParameters
A total of 12 physicochemical parameters were measured namely Temperature, pH, Total Dissolved Solids
(TDS), Electrical Conductivity (EC), Total Solids (TS), Total Suspended Solids (TSS), Chloride ion, Chemical
Oxygen Demand (COD), Phosphate, Biochemical Oxygen Demand (BOD),Dissolved Oxygen (DO) and
Nitrate. Physicochemical analysis of water samples was carried out using standard methods of water analysis(USEPA, 1979, Trivedi and Goyal, 1986, APHA-AWWA-WPCF, 1995). Parameters such as pH, Temperature,
EC, DO and TDS were determined at the time of sampling in the field with a digital CS-C933T Multimeter
(TOPAC INSTRUMENTS INC).
COD was determined by the titrimetric method and BOD was determined using the incubation method
at 20o
C for five days (APHA-AWWA-WPCF, 1995). Total Solids (TS) was determined using gravimetric
procedures by evaporating a known volume of water to dryness in a pre-weighed crucible on a steam bath at105
oC. Phosphate wa s determined colorimetrically by the ascorbic acid- molybdenum blue method
(Eaton and Franson, 2005). Chloride (Cl-) was analyzed by titrating a known volume of water sample with
standardized 0.014N mercuric (II) nitrate solution. Nitrate was determined by the phenol disulphunic acid
method (Marczenko, 1986).
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 189
2.4. Analysis for Heavy Metals
Water samples for heavy metal analyses were filtered and digested with 10ml of concentrated analytical
grade nitric acid to 250ml of water sample. The solutions were evaporated in a crucible to approximately 5ml,
then filtered into 20ml standard flask and made up to the mark with distilled water (Ademoroti, 1996b). The
water extract was analyzed for metals (Fe, Pb, Zn, Cd, Cu, Mn, and Cd). Heavy metals in sediment were
extracted with concentrated nitric acid at the ratio of 2g of sediment sample to 5ml of acid. The mixture wasgently heated in a water bath temperature of (105
oC) until the sediment became bleached. The mixture was
diluted to 20ml with distilled water decanted and filtered for analysis. Analysis of heavy metal content of water
samples, sediment samples and effluent samples were carried out with a Perkin Elmer (Analyst 200 version 6.0)
Atomic Absorption Spectrometry (AAS).
2.5. Statisitcal Analysis
Data for physicochemical parameters and heavy metal concentrations of water and sediment samples were
presented as mean values and analyzed using descriptive analysis. Prism Graph pad software version 5.0 was
used for this analysis.The degree of contamination as a result of anthropogenic inputs was estimated by the Accumulation
Factor (AF). AF is the ratio of the average level of a given parameter downstream (after a source discharge) tothe corresponding average level upstream (before the source discharge) (Fakayode, 2005).
Accumulation factor (AF) was calculated by dividing the average parameters downstream with upstream
values (Fakayode, 2005).
The degree of River Recovery Capacity (RRC) for Ona river was calculated using the formula adapted
from Ernestova and Seminova (1994) as modified by Fakayode (2005) given as:
RRC= (S0-S1)/S0 100 (Expressed in %)
where:S0 is the level of a parameter downstream (i.e. immediately after the discharge point),
S1 is the corresponding average level upstream where the water is relatively unpolluted.
Multivariate analysis of variance (MANOVA) was used where several dependent variables
were measured for each sampling unit instead of one variable. The objective of MANOVA was to
investigate whether the mean vectors of water quality parameters across sampling points were the same or if thevariables differed significantly across sampling points (Rencher, 2002; Johnson and Wichern,
2002).Data were further subjected to Factor Analysis (FA). Factor analysis is designed to transform the
original variables into new uncorrelated variables called factors, which are linear combinations of the original
variables. The FA is a data reduction technique and suggests how many varieties are important to explain theobserved variances in the data. Principal components analysis (PCA) was used for extraction of the different
factors. The axis defined by PCA is rotated to reduce the contribution of the less significant variables (Johnson
and Wichern, 2002)Step-wise Discriminant Analysis (DA) which is also a multivariate statistical technique was used for
spatio-temporal analysis of water quality data. Linear discriminant functions were used to describe or
elucidate the differences between the sampling sites and the influence of season on water and sediment quality
of each sampling site. The relative contribution of all variables to the separation of groups was highlighted(Rencher, 2002).
Multivariate analysis was carried out with the computer program SPSS 18.0 for windows
(SPSS Inc. USA).
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190 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
3. Results
3.1. Physicochemical Analysis
The man values of physicochemical parameters across sampling points are presented in Fig 2.
Temperature, pH and DO demonstrated a seasonal cycle during the study period. High temperature values were
recorded (29-30oC) between February and April (transition of dry to rainy season) and low values (26-28
oC)
were recorded between May and July (rainy season). Water pH ranged from 6.0-8.0 with the lowest valueslightly below NESREA (2011) acceptable limits for surface waters. The pH values at the point of discharge
showed an increasing tendency towards acidity from dry season into the rainy season while upstream anddownstream values tended towards neutral pH during the rainy season. DO values were generally higher
upstream and downstream, and lowest at the points of effluent discharge. There was however a progressive
increase in DO values at all sampling points with transition into the rainy season.
The highest values of TDS, EC, TS and TSS were recorded at the effluent discharge points in both dry
and wet seasons (Fig 2). TS and TSS showed the highest values at the transitional period (March and April)
between the dry season and wet season. There were considerably lower levels of chloride ions upstream
compared to the points of effluent discharge points and downstream sites respectively (Fig 2) and lower valueswere recorded in the rainy season. COD and BOD values were markedly increased at the points of effluent
discharge throughout the sampling period thus showing the contribution of effluent discharge to the increased
values (Fig 2). Phosphate concentrations were significantly elevated at the effluent discharge points but didnot show any significant difference in trend across seasons (Fig 2). Nitrate levels increased in water samples
during the rainy season.
Figure 2(a-l): Spatio-temporal variations of selected physicochemical parameters of Ona river.
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 191
Figure 2(a-l): Spatio-temporal variations of selected physicochemical parameters of Ona river. - continued
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192 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
3.2. Heavy Metals of Water Samples
The heavy metal profile of water samples are presented in Fig. 3. Elevated levels of Cu were recorded at
effluent discharge points but there was a marked increase in the rainy season compared to the dry season at all
sampling points (Fig 3). Zn although elevated at the points of effluent discharge showed wider differences in
values in the rainy season between the upstream value and point of domestic waste discharge (Fig 3). AlthoughPb showed higher levels in the rainy season compared to the dry season, the highest levels of Pb was detected
at the onset of the rainy season (Fig 3). Mn, Cr and Fe showed a downward trend from the dry season into the
rainy season while Cd showed its highest values in the dry season and onset of the rainy season (Fig 3).
Figure 3(a-g): Spatio-temporal variations of some heavy metals in water samples of Ona river
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 193
Figure 3(a-g): Spatio-temporal variations of some heavy metals in water samples of Ona river - continued
3.3. Heavy Metals in Sediment Samples
The concentration of Cu and Cr in sediments was higher in the rainy season compared to the dry season.On the other hand the concentration of Cu upstream showed a decreasing trend with progression into
the rainy season (Fig 4), while the concentration of Zn and Fe showed a general decrease from the dry to
the rainy season with the higher values in the dry season (Fig 4). Although the concentration of Pb, Mn and Cd
showed a fair trend across seasons, there was a significant increase (p
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194 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
Figure 4 (a-g): Spatio-temporal variations of some heavy metal samples of Ona river. - continued
3.4. Index of Geoaccumulation(Igeo)
The quantitative estimation of aquatic sediment contamination by metal was proposed by Mller (1979) in
terms of the Igeo ( Index of Geoaccumulation), that is, the enrichment on a geological substrate.Geoaccumulation index (Igeo) is used to calculate metal concentrations in sediment by comparing observed
concentrations with undisturbed or crustal sediment (control) levels using the expression:
Igeo = ln (Cn/1.5Bn),
where Cn is the measured content of element n,
Bn is the geochemical background value of the same element (metal) n and
Factor 1.5 is the possible variations of background data due to lithogenic impacts.
The results were interpreted according to the geoaccumulation index classification table of Muller
(1979) (Table 1) and the geoaccumulation index (Igeo) analysis for Ona river sediment is given in Table 2.
Muller (1981) further classified Igeo as it relates to contaminant levels into 7 classes i.e. Class 0, Igeo
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 195Table 2: Geoaccumulation index (Igeo) values of surface sediments of Ona river
Heavy metals Igeo Sediment quality description
CuZn
Pb
Mn
Cr
CdFe
1.90.3
3.02.2
2.5
3.51.2
Moderately contaminatedUncontaminated to moderately contaminated
Strongly contaminatedModerately to strongly contaminated
Moderately to strongly contaminated
Strongly contaminatedModerately contaminated
The Accumulation Factor (AF) and River Recovery Capacity (RRC) of the physicochemical
parameters during the sampling period are presented in Table 3. The trend in accumulation factor of the
parameters revealed that the COD and BOD of water downstream was about 5 and 8 times respectively more
than values observed upstream. Other parameters showed an average accumulation factor of 2. COD and BOD
showed the highest percentage recoveries of 79% and 87% while phosphate and nitrate showed the lowest
recoveries of 47% and 42% respectively in water downstream. In other words there was a drop in COD and
BOD values by 79% and 87% respectively in waters downstream compared to values at the discharge points orpoints of impact, while less than 50% of phosphate and nitrate introduced at discharge point was removed by
self-purification downstream.
Table 3: Accumulation Factor and River Recovery Capacity for physicochemical parameters of Ona river
EC
( - Icm- 1) TDS(mg/l) TS(mg/l) TSS(mg/l) Cl-
(mg/l)
P0-
(mg/l )
COD
(mg/l)
BOD
(mg/l)
DO
(mg/l)
Nitrate
(mg/l)
A FRRC%
2.3958
2.6664
2.5961
2.8064
3.0065
1.947
4.8379
7.5087
0.39-156
1.742
AF: Accumulation factor, RRC: River recovery capacity.
The Accumulation Factor and River Recovery Capacity for heavy metals in water and sediment are
presented in Table 4. Pb was the highest accumulated metal in water and sediment downstream and was 21 and
17 times more than the average levels upstream whereas Cu, Mn, Cr and Cd showed an accumulation of about
10 times and 6 times more than average levels upstream for water and sediment respectively. All the metals,except Zn and Fe showed a recovery of >90% and >80% downstream for water and sediment respectively.
Recovery values for Zn and Fe were 41% and 65% in water and 41% and 38% respectively in sediment
indicating that there was little or no change in values downstream compared to values upstream especially forZinc.
Table 4: Accumulation Factor and River Recovery Capacity for heavy metals in water and sediment samples
of Ona river.
Cu Zn Pb Mn Cr Cd Fe
Water column (mg/l)
Sediment (g/g)
AFRRC
%AF
11.491
6.785
1.641
1.741
21.595
17.994
10.690
7.787
1593
6.685
1291.6
5.481
2.865
1.638AF: Accumulation factor, RRC: River recoverycapacity
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196 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
3.5. Multivariate Analysis
All studied variables were subjected to multivariate statistical techniques to explore temporal and spatial
variations in parameters. The results of the multivariate analysis of variance (MANOVA) for water andsediment parameters are presented in Table 5. It can be inferred from this data that there was a significant
difference in water and sediment parameters across the six sampling sites and across seasons (p
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 197
Table 6: The varimax rotated factor loadings for water and sediment parameters of Ona river
Factor
1 2 3 4
Pb .947
Cr .931
Zn .904Cr_sed .878
Fe .866
Mn .863
Cu .834
Cd .794
Pb_sed .781
Mn_sed .727
Cu_sed .682
Cd_sed .609
BOD .824
Zn_sed -.809
pH -.808
TSS .772
Fe_sed -.769
Season .750DO .635
COD .596
Temp -.560
Nitrate
Chloride .968
TDS .711
Phosphate
TS .928
EC .878
% Variance Explained 39.94 21.95 10.94 5.68% Cumulative Variance 39.94 61.89 72.83 78.51
Rotation Method: Varimax and Kaiser Normalization.sed - sediment
Note: Groups of values circled along the same column represent clusters of parameters with high significant loadings foreach factor. The clusters were created by data rotation.
Spatial variations in water and sediment parameters were evaluated through step-wise Discriminant
Analysis (DA). Five discriminant functions (DFs) were found to discriminate the quality of the six samplingsites used in this study (Table 7). Wilks Lambda test showed that all five functions were statistically significant
(p
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198 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor ChukwukaTable 7: Discriminant function coefficients for spatial variation in water and sediment parameters of Ona
river
ParameterFunction
1 2 3 4 5
Pb
Cu_sed
Pb_sed
Cr_sed
Cd_sedFe_sed
% Variance
% Cumulative Variance
-1.703.153
3.119
7.435
.162
.325
83.0
83.0
3.126-2.488
-3.187
-2.153
8.154.783
10.8
93.8
2.404-1.984
.571
-1.261
-9.824.871
3.5
97.30
1.3511.477
-.956
-4.440
2.636.291
2.3
99.60
.118
.478
-4.257
2.577
.905
.477
0.4
100
sed - sediment
From the canonical discriminant plot (Fig 5), the industrial effluent discharge points (EDP 1 and 2)
showed high positive loadings for factors represented by the first discriminant function (DF1) indicating that theeffluent discharge points were highly characterized by elevated levels of Pb in water and sediment, and Cr in
sediment. The sites upstream from the discharge points i.e. UPS and DDP depicted negative associations with
these factors indicating that these sites were characterized by minimal levels of these metal contaminants. On
the other hand, the domestic discharge point (DDP) showed high positive loadings for the factors represented
by the second discriminant function (DF2) indicating that the DDP was characterized by elevated levels of Cdcompared to EDP 1 and 2.
Figure 5: Discriminant plot for spatial variation in water and sediment parameters of Ona river
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 199
Temporal variations in water and sediment parameters were also evaluated through Discriminant
Analysis (DA). Data was subjected to DA after data was sorted into two seasonal groups, i.e. dry season and wet
season. DA was conducted to assess whether there was a significant difference in seasonal trends and which
parameters exhibited a seasonal trend or had strong associations with change in season. DF1 accounted for 100%
of the total variance between seasons. Wilks Lambda test (p< 0.05) showed that this DF and its constituent
variables with the highest contributions were able to significantly discriminate water and sediment parameters
influenced by seasonal changes. Table 8 presents the standardized function coefficients, which highlight that Cr in
sediment, Zn in water, and DO in were parameters that exhibited the highest change across seasons.
Table 8: Discriminant Function Coefficients for temporal variation in water and sediment parameters of Onariver
ParametersFunction
1
Temp
DO
Zn
Cr_sed
% Variance
-.591
1.041
1.869
-1.818
100%sed sediment
Discriminant plot for temporal variations (Fig 6) showed that the dry season was strongly characterized
by increased temperatures and elevated levels of Cr in sediment while DO availability and temperature had
strong positive associations with the wet season. Although Zn showed negative associations with both seasons,there was an increase in Zn from dry season to the wet season.
Figure 6: Discriminant plot for temporal variations (Fischers linear discriminant functions) for water and
sediment parameters of Ona river
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200 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka4. Discussion
The descriptive analysis indicated that the change in each water and sediment parameter across
sampling sites and seasons, showed elevated values at points of effluent discharge (EDP 1 and 2). This pattern
clearly implicates unnatural sources and reports have it that although potentially harmful substances may have
either natural or human-made sources or both, the contribution of some compounds of human origin to thenatural environment far overshadows natural sources (Charkabi and Sakizadeh, 2006; Shrestha and Kazama,
2007; Najafpour et al., 2008; Bu et al., 2009).
The accumulation factor of the physicochemical parameters (Table 3) clearly indicated elevated valuesdownstream compared to the reference point upstream and is a clear pointer of anthropogenic impact. The River
Recovery Capacity (RRC) values for these parameters indicated that the level to which the self-purification
mechanism of the river was able to refresh water in the presence of pollutants was not very high. The
observed values showed that about 60% of most of the physicochemical changes due to domestic and industrial
input upstream was alleviated about 3000m downstream of the discharge points. This suggests that a longer
stretch of un-impacted river will be required for higher recovery values. The low recovery values observed for
phosphate and nitrate suggests that these substances are being released into the river in quantities that cannot be
single- handedly removed by aquatic-plants and facultative algae which constitute part of the rivers self-
purification mechanism (Fakayode, 2005; Adeogun et al, 2011). The high levels of these nutrients clearly
overwhelm the river recovery capacity thus indicating poor water quality. The high recovery of BOD, COD and
Cl
-
downstream may also be a pointer to the efficiency of the rivers aeration mechanism which is anothermeans of self-purification (Ernestova and Semenova, 1994).
The ability of river sediments to store metal contaminants of human origin has been reported
(Salomons and Stigliani, 1995, Netpae & Phalaraksh, 2009) even in levels that sometimes outweigh the content of
metal in water and fish (Klavin et al., 2000; Demark et al., 2006). The increased values of heavy metals
downstream compared to average values upstream strongly implicate anthropogenic activity and this is
confirmed by the accumulation factor reported for each metal downstream compared to upstream values
(Table 4). Although all the heavy metals showed a level of accumulation downstream, Zn and Fe showed the
least accumulation. This suggests a high abundance of these metals within this stretch of river studied hence there
was no significant change in availability downstream compared to upstream values.
The geoaccumulation index (Igeo), which is an index of metal enrichment in the sediment revealed the
heavy metal concentration as Cd>Pb>Cr>Mn>Cu>Fe>Zn (Table 2). Metals such as Cd, Cr and Pb have higherpossibilities of interacting with organic matter in the aqueous phase and settling, thus reflecting in the high
concentration of these metals in the sediment (EC, 2002). This ability of sediments to store pollutants such
as metals is a clear pointer of the role of sediment quality in assessing and evaluating metal
contamination in natural waters (Wardas et al., 1996). The high recovery observed for most of the heavy
metals i.e. >90 for water, and >80 for sediment may be attributed to the natural heavy metal removal
mechanisms of river systems one of which entails redistribution and partitioning of the metals between water
and sediment (Fakayode, 2005; Naji and Ismail, 2011). The low recovery values observed for Zn and Fe
downstream compared to upstream values may be a reflection of their mobility within the river system
(Thornton, 1983).
Results of the factor analysis (FA) indicated that Factor 1 accounted for 39.94% of the total
variations in water quality. This factor was labelled sediment factor because of the nature of positive loadings
within the factor (Table 6). The second factor i.e. Factor 2 which was termed seasonal factor accounted for
21.95% of the total variations in water and sediment parameters. By comparing these two factors the FA
depicts that the highest variations in water and sediment quality was as a result of non-seasonal factors, more
specifically human factors. In other words, the all year round discharge of effluents impacts the sediment
quality, which in-turn ensures a replenishment of contaminant levels within the water column. Sediments are
an integral and inseparable part of the aquatic environment and play a very important role in the
physicochemical and ecological dynamics of any aquatic system. Therefore, there is a higher tendency for
metal deposits to be retained in sediments than for it to be released into the water column. This replenishing
effect for most metals is to a large extent not dependent on seasonal changes. This is consistent with reports that
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Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 201
anthropogenic discharges constitute a constant polluting source for aquatic systems (Singh et al., 2004; Karbassi
et al., 2007; Najafpour et al., 2008).
Factor 2, designated as seasonal factors, showed positive loadings for TSS, Season, DO, COD and BOD
and negative loadings for Zn_sediment, pH, Fe_sediment and Temperature. The loadings of this factor suggest
that the transition in seasons affects the water temperature and pH which in turn regulate the COD and BOD
content (Jain, 2002; Bu et al., 2010).This factor also depicts that the levels of Zn and Fe in sediment had a strong
negative relationship with seasonal changes. In other words, in transition from dry to wet season, these metalsare mobilized from sediments into the water column thus resulting in a relative decrease in sediment
concentrations compared to the initial concentrations. It has been reported that seasonal variations in
precipitation, surface run-off, ground water flow, interception and abstraction strongly affect river discharge
and, consequently the concentrations of pollutants in river water (Vega et al., 1998; Khadka and Khanal, 2008;
Mtethiwa et al., 2008).
Factor 3, named the mineral factor was highly correlated with TDS and Chloride levels. This factor is
an essential feature of any water-body which represents mineral content resulting from the balance between
dissolution and precipitation. It accounted for 10.94% of the total variance in water and sediment parameters.
Factor 4 i.e. conductivity factor showed high positive loadings for TS (0.913) and EC (0.897) accounting for
5.68% of the total variance in parameters. This suggests that there is a strong relationship between TS and EC,
and the relationship is not dependent on seasonal changes. Throughout the sampling period the EC at theeffluent discharge points was above 1,200 S cm
-1(Fig 2). According to UNESCO/WHO/UNEP (1996) the
conductivity of most freshwaters ranges from 10 to 1,000 S cm-1
but may exceed 1,000 S cm-1
, especially in
polluted waters, or those receiving large quantities of land run-off. Conductivity can be measured to establish a
pollution zone, e.g. around an effluent discharge, or the extent of influence of run-off waters. The presence of
heavy metals and other conducting species such as nitrate, phosphate and chloride could be responsible for
elevated levels of EC downstream (UNESCO/WHO/UNEP, 1996).
From the spatial discriminant analysis (DA), the first discriminant function (DF) accounted for 83.0% of
the total spatial variance across the sampling sites (Table 7). The spatial DA using the first DF suggests that Pb
in water, Pb in sediment and Cr in sediment were the most significant parameters that discriminated between the
six sampling sites. The negative correlation of Pb in water (-1.703) and Pb in sediment (3.119) with the DF also
brings out the inverse relationship between sediment contaminant and contaminant in the overlying water column.In other words there is a higher tendency for Pb to be retained in sediment than released into the water column.
This agrees with reports that sediments hold a very large percentage of the total quantity of a metal present in an
aquatic system (Netpae & Phalaraksh, 2009). Pb content in water might be due to terrestrial run off from
sewage effluent, through seepage from waste sites (Fatoki et al., 2002) or runoff from agricultural fields where
phosphorus fertilizers with lead impurities have been applied (Ramachandra, 2006). The positive association ofthe domestic discharge point (DDP) with Cd in sediment implicates inputs of Cd from domestic waste discharge
into the river. Nodeberg et al., (2005) reported sewage from industrial and municipal areas as major sources of Cd
into freshwater systems.
From the temporal discriminant analysis (DA), only one DF accounted for 100% of the temporal
variations in water quality across sampling sites. Zinc showed increased mobilization in water in the rainy season
than in the dry season. On the other hand, Cr showed higher availability in the sediment in the dry seasonand lower availability in the rainy season. This is a depiction of the same trend by variables from different
media. The increased mobilization of Zn in water means that there will be reduced levels in sediment, while
the increased values of Cr in sediment indicate that there will be reduced values in the water column; although
from the discriminant plot (Fig. 6) Zn had a stronger relationship with the rainy season than Cr. Some authors
(Thornton, 1983; Alloway, 1990a; Kumar et al., 2010) have reported that Zn represents a group of metals that
are easily mobilized from sediment deposits in the presence of seasonal factors, whereas Cr represent a group of
metals that will require more rigorous geochemical factors for mobilization (EC, 2002; Nodberg et al., 2005).
The presence of Cr in sediment as a significant variable in both spatial and temporal DA is an indication that Cr
availability is affected by both anthropogenic and seasonal factors, but the results from the FA implies that
anthropogeniceffects outweighs the seasonal effects on metal availability in Ona river.
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202 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
5. Conclusions
This study has shown that the highest sources of variation in Ona river water and sediment quality are non-
seasonal factors particularly anthropogenic factors. The level of pollution increased from the domestic discharge
point to the industrial effluent discharge points. All sampled parameters indicated significant spatial variability
and partial temporal differences. Multivariate analysis indicated that Ona river water quality is primarily
influenced by industrial waste water discharge. Anthropogenic activities involving the discharge of
industrial effluents into aquatic systems directly impact the sediments which in turn replenish contaminantlevels in the water column. Moreso, the addition of excessive levels of naturally occurring compounds
through domestic runoffs further degrades water quality which in turn erodes the availability of good quality
water for humans and ecosystems. This may result in increased financial costs for human users (water
purification process), and may lead to decreased species diversity and abundance in affected communities.
Remediation activities should therefore be focused on reduction of river pollution through enforcement of
regulations concerning treatment of industrial wastewater, although runoff from domestic activities should also
be closely monitored to reduce their probable enhancement of heavy metal pollution.
Acknowledgement
The authors hereby acknowledge the MacArthur Foundation Startup Research Grant for female faculty for
providing funds for this research.
References
[1] Ademoroti, C.M.A. 1996b. Environmental Chemistry and Toxicology. Fodulex Press Limited Ibadan.
pp 121.
[2] Adeogun, A. O., A. V. Chukwuka and O. R. Ibor 2011. Impact of Abbatoir and Saw mill
effluents on Water Quality of Upper Ogun River (Abeokuta). American Journal of Environmental
Sciences 7(6):525-530.
[3] Alloway, B. J. 1990a. Soil processes and the behaviour of metals. In: Alloway, B.J. (Eds.) Heavy
Metals in Soils, New York, Blackie, John Wiley and Sons, Inc., pp.7-28.
[4] APHA -AWWA-WPCF. 1995. Standard methods for the examination of water and waste water.American Public Health Association, American Water Works Association and Water Environment
Federation, Washington, D.C.[5] Bricker, O. P., and B. F., Jones 1995. Main factors affecting the composition of natural waters.
In: Salbu, B., Steinnes, E. (Eds.), Trace Elements in Natural Waters. CRC Press, Boca Raton, FL, 1-5.
[6] Bu, H., X., Tang, S., Li and Q., Zhang 2010. Temporal and spatial variations of water quality in the
Jinshui River of the South Qinling Mts.,
[7] Chinhanga, J.R. 2010. Impact of industrial effluent from an iron and steel company on the
physicochemical quality of Kwekwe River water in Zimbabwe. International Journal of Engineering,
Science and Technology 2 (7): 29-40
[8] Demark, A., F. Yilmaz, A.L. Tuna and N. Ozdemir, 2006. Heavy metals in water, sediment and tissues of
Leuciscus cephalus from a stream in southwestern Turkey. Chemosphere, 63: 1451
1458[9] Dixon, W. and B., Chiswell 1996. Review of aquatic monitoring program design. Water
Resources 30(9): 1935-1948.[10] Don-Pedro K.N., E. O., Oyewo and A. A. Otitoloju 2004. Trend of heavy metal concentration in Lagos
Lagoon ecosystem, Nigeria. West Afri. J. of Appl. Eco. 5:103-114.
[11] European Commission (EC) 2002. Heavy Metals in Waste. Final Report DG ENV. E3. 86pp. [12]
Ernestova, L. S., and I. V. Semenova 1994. The Self-purification Capacity of Natural Water as
an Indicator of the Ecological State of a Water Body. Water Resources 21(2): 146-150.
[13] Fakayode, S.O. 2005. Impact Assessment of Industrial Effluent on Water Quality of the
Receiving Alaro River in Ibadan, Nigeria.Ajeam-Ragee 10:1-13.
[14] Heberer, T. 2002. Occurrence, fate, and removal of pharmaceutical residues in the aquatic
environment: a review of recent research data. Toxicol Lett., 131: 517
7/31/2019 Spatio temporal variation water quality
18/19
Spatial and Temporal Variations in Water and Sediment
Quality of Ona River, Ibadan, Southwest Nigeria 203
[15] Jain, C.K., 2002. A hydro-chemical study of a mountainous watershed: The Ganga, India.
Water Resources 36: 12621274.
[16] Karbassi, A. R.; J., Nouri, and G. O. Ayaz 2007. Flocculation of Cu, Zn, Pb, Ni and Mn during mixing
of Talar River water with Caspian Seawater.Int. J. Environ. Res.1(1): 66-73[17] Khadka, R. B. and A. B. Khanal 2008. Environmental Management Plan (EMP) for Melamchi water
supply project, Nepal.Environ. Monitor. Assess. 146 (1-3): 225-234
[18] Kumar, R., C. S., Joseph, M. M., Gireesh, T. R. Kumar, K. R. Renjith, M. N., Manju, and N.Chandramohanakumar 2010. Spatial Variability and Contamination of Heavy Metals in the
Inter-tidal Systems of a Tropical Environment.Int. J. Environ. Res. 4(4):691-700
[19] Lee, J. Y.; Cheon, J. Y.; Lee, K. K.; Lee, S. Y.; Lee, M. H., (2001). Statistical evaluation of
geochemical parameter distribution in a ground water system contaminated with petroleum
hydrocarbons.J. Environ. Qual., 30: 1548-1563.
[20] Mara, D.D (2003). Domestic wastewater treatment in developing countries. 75pp
[21] Mtethiwa, A. H.; A., Munyenyembe, W., Jere, and E., Nyali 2008. Efficiency of oxidation ponds in
wastewater treatment.Int. J. Environ. Res. 2 (2):149-152
[22] Mller, G., 1979. Schwermetalle in den sedimenten des Rheins - Vernderungenseit
1971.Umschau 79:778-783.
[23] Mller, G., 1981. Die Schwermetallbelastung der sedimente des Neckars und seiner
Nebenflusse: eine Bestandsaufnahme.Chemical Zeitung. 105:157-164
[24] Murray, K.E., S. M., Thomas and A. A. Bodour 2010. Prioritizing research for trace pollutants and
emerging contaminants in the freshwater environment: Environmental Pollution. 158:3462-3471.
[25] Najafpour, S., A. F. M., Alkarkhi, M. O. A., Kadir and G. D. Najafpour 2008. Evaluation of spatial
and temporal variation in river water quality.Int. J. Environ. Res., 2(4): 349-358.[26] Netpae, T. and C. Phalaraksh 2009. Bioaccumulation of copper and lead in Asian Clam tissues from
Bung Boraphet Reservoir, Thailand.Int. J.Agric. Biol., 11: 783786
[27] Nordberg, G. F., B. A. Fowler, M. Nordberg 2005. Handbook on Toxicology of Metals. 3rd edition.
969pp
[28] OECD SIDS 2002. Potassium Hydroxide: UNEP Publications. ID: 1310-58-3
[29] Olajire. A. A. and F. F. Imeokparia 2001. Water Quality Assessment of Osun River. Studies on
Inorganic Nutrients.Environmental Monitoring and Assessment69: 17-28.
[30] Reghunath, R., T. R. S. Murthy and B. R. Raghavan 2002. The utility of multivariate statistical
techniques in hydrogeochemical studies: An example from Karnataka, India. Water Res. 36 (10):2437-2442
[31] Salomons, W. and W. M. Stigliani 1995. Biogeodynamics of pollutants in soils and sediments.
Springer Verlag.
[32] Shell 2004. A Journal of effluent monitoring for the Shell Petroleum Development Company of
Nig. Ltd. East Vol. 23. 28pp
[33] Shrestha, S. and F. Kazama 2007. Assessment of surface water quality using multivariate
statistical techniques: A case study of the Fuji river basin, Japan. Environ. Model. Software22(4): 464-475
[34] Singh, K. P., A. Malik, D., Mohan and S. Sinha 2004. Multivariate statistical techniques for the
evaluation of spatial and temporal variations in water quality of Gomti River (India): A case study.
Water Res., 38 (18): 3980-3992.
[35] Thornton, I. (1983). Applied Environmental Geochemistry. Academic Press, London.[36] Travedi, R. R. and P. K. Goyal 1986. Chemical and Biochemical Methods for Water pollution studies.
Env.Pub.
[37] UNESCO-WHO-UNEP. 1996. Water Quality Assessments - A Guide to Use of Biota,Sediments and Water in Environmental Monitoring - Second Edition. Deborah Chapman. Eds.
609pp
[38] UN WWAP. 2003. United Nations World Water Assessment Programme. The World Water
Development Report 1: Water for People, Water for Life. UNESCO: Paris, France.
[39] USEPA, 2007. Recent recommended water quality criteria. United States Environmental
ProtectionAgency,http://www.epa.gov/waterscience/criteria/wqcriteria.html
http://www.epa.gov/waterscience/criteria/wqcriteria.htmlhttp://www.epa.gov/waterscience/criteria/wqcriteria.html7/31/2019 Spatio temporal variation water quality
19/19
204 Aina Olubukola Adeogun, Taofeek Ademola Babatunde and Azubuike Victor Chukwuka
[40] Vega, M., R. Pardo, E. Barrado and L., Deban 1998. Assessment of seasonal and polluting effects
on the quality of river water by exploratory data analysis. Water Res. 32 (12): 3581-
3592
[41] Vi, J.C., C. Hilton-Taylor and S. N. Stuart (Eds.) 2009. Wildlife in a Changing World An Analysis
of the 2008 IUCN Red List of Threatened Species. Gland, Switzerland: IUCN. 180 pp.[42] Vutukuru, S. S. 2003. Chromium induced alterations in some biochemical profiles of the Indian major
carpsLabeo rohita (Hamilton).Bull Environ. Contam. Toxicol, 70:118-123.
[43] Wardas M, L. Budek and E. H. Rybicka 1996. Variability of heavy metals content in bottomsediments of the Wilga River, a tributary of the Vistula River (Krakow area, Poland). Applied
Geochemistry 11:197-202.[44] Wei, S., Y., J. C. W., Wang, G. J., Lam, M. K. Zheng, L. W. Y. So, Y. Yeung, L. Horii, H.
Chen, N. Yu, N. Yamashita and P. K. S. Lam 2008. Historical trends of organic pollutants in sediment
cores from Hong Kong.Mar. Pollut. Bull. 57: 758766.[45] Wunderlin, D. A., M. P. Diaz, M. V. Ame, S. F. Pesce, A. C., Hued and M. A. Bistoni 2001.
Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality.
A case study: Suquia river basin (Cordoba, Argentina). Water Res. 35(12): 2881-2894.
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