Spatio temporal variation water quality

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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: [email protected]; [email protected] Tel:

+2348055261105

Taofeek Ademola Babatunde

Biology Department, Umaru Musa YarAdua University

Katsina, Nigeria

E-mail: [email protected]

Tel: +2348037851688

Azubuike Victor Chukwuka

Ecology and Environmental Biology Unit, Department of Zoology

University of Ibadan, Ibadan Nigeria E-

mail: [email protected]:

+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:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.europeanjournalofscientificresearch.com/


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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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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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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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Figure 2(a-l): Spatio-temporal variations of selected physicochemical parameters of Ona river. - continued


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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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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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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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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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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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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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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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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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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.

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