Abstract

Heavy metals are pollutants of river sediments, and their concentration varies depending on parental material and anthropogenic inputs, thus it is important to distinguish between the natural and anthropogenic inputs. The objective of this study is to use different types of indexes to assess the current pollution status in Ogbere River sediment and select the best index to describe the sediment quality. The indexes used in this study were enrichment factor (EF), geoaccumulation index (Igeo) and principal component analysis (PCA). The PCA has an advantage over other index analyses as it reduces the dimensionality of the data set and thus used to support multivariate cluster analysis. From the study, a total of 12 sediment samples were collected in both seasons across six sampling location and pollution indexes indicated three things: firstly, the metal distribution profile in the sediment showed that the heavy metals analysed were lower than the maximum allowable limits stipulated by Department of Petroleum Resources (DPR); secondly, minor to extremely severe significant levels of enrichment and thirdly, practically uncontaminated to a moderately contaminated degree of contamination in Ogbere River during the study period. The PCA is considered more sensitive in the analysis of benthic changes as well as sediment quality. However, the heavy metal assessment indices are not only used for sediment quality. Biological testing and ecological analysis of existing community related to sediment contamination are further recommended in River Ogbere.

Highlights

  • This study gives an assessment of the current pollution status in Ogbere River sediment in south-western Nigeria.

  • This study compares the sensitivity of three different indexes as tools for conducting the river assessment.

  • This study provides baseline data for the pollution level of Ogbere River in dry and wet seasons.

  • The study will inform policies that will mitigate the anthropogenic contributions to river pollution.

INTRODUCTION

Heavy metals are one of the important groups of pollutants in the aquatic environment that bind to sediments and the effects which ultimately linked to its transport and storage of materials (Horowitz 1991). However, Tamas et al. (2014) revealed that heavy metals are a group of elements that are toxic and persistent in the environment. These metals could remain and accumulate in the environment without breaking down, especially in sediments and lakes.

Figure 1

(a) Map of Ogbere River Basin showing the study location. (b) Ogbere River Basin and watershed.

Figure 1

(a) Map of Ogbere River Basin showing the study location. (b) Ogbere River Basin and watershed.

Figure 2

Component plot in rotated space across season.

Figure 2

Component plot in rotated space across season.

Figure 3

Graphical interpretation of heavy metal concentration in Ogbere River sediment, 2019.

Figure 3

Graphical interpretation of heavy metal concentration in Ogbere River sediment, 2019.

Sediments are a concern in water bodies because of their linkage with a wide range of water quality problems. The occurrence of increased levels of metals in sediments of water bodies can often be attributed to anthropogenic influences such as atmospheric deposition, agricultural activities and industrialization rather than natural enrichment of the sediment by geological weathering (Binning & Baird 2001). The analysis of heavy metals in sediments permits the detection of pollutants that may be either absent or in low concentration with its distribution providing a record of the spatial and temporal history of pollution in an ecosystem (USESE 2016). Sediment contamination has been reported to be the second leading cause of impairment to water bodies, and this is because suspended sediment can directly impact users of the water and increase water treatment cost (Ayandiran et al. 2014). Pekey et al. (2004) demonstrated that heavy metals tend to be trapped in an aquatic environment and accumulate in sediments. Once accumulated in sediments, the metals continue to pose a threat to aquatic life due to re-suspension into the water column from geochemical recycling, accumulation in benthic fauna that feed on sediments and through food chain transfer (Ahmet et al. 2005). Sediment quality values are useful to screen the potential for contaminants within sediment to induce biological effects and compare sediment contaminant concentration with the quality guideline (Speneer & Macleod 2002). In the last decade, several researchers (Abrahim & Parker 2008; Nobi et al. 2010; Ghrefat et al. 2011; Ladigbolu & Balogun 2011; Ajagbe et al. 2012; Ekaete et al. 2015) reported moderate to high concentrations of heavy metal contaminants in river sediments. Caeiro et al. (2005) also observed that different assessment indices for the aquatic environment have been developed and these are powerful tools for development, evaluation and conveying raw environmental information to decision makers, managers and water stakeholders. These authors described sediment as a pivotal influence in water contamination which involves analysis indexes to evaluate the degree to which the sediment-associated chemical status might adversely affect aquatic organisms and humans.

Flood incidence is a significant factor that contributes to the movement of sediment and metals with higher velocities of water. This issue of flooding has in recent years become a critical problem that plagues many communities in Nigeria most especially communities around River Ogbere in Ibadan, and has led to the accumulation of sediments and contamination of the river which is a major source of water supply to the community. The need to initiate public actions to address this occurrence is critical. Thus, quality of the river body is the utmost determinant in all aspect of the water exploitation, use and management in order to support human development around the watershed and ensure safe public health of the catchment area.

Hence, the objective of this study is to assess the heavy metal concentration in the river sediment and evaluate the sediment quality using two geochemical parameters such as enrichment factor (EF) and geoaccumulation index (Igeo). This study is restricted to the sampling area and not concerned with the study of the tributaries outside the river body. This study will provide the present quality status of River Ogbere sediment which will be useful to predict likely changes, effects and control measures to government and policymakers on how to exploit, conserve and appropriately allocate the water resource. Therefore, sediment quality data will serve as a launching pad for further researches into the potentials of the river to the catchment area.

MATERIALS AND METHOD

Description of study area

Ogbere River is located in the south-western part of Ogunpa drainage basin bordered in longitude 3°56′E and latitude 7°19′N with an estimated elevation of 174 m above sea level in Ibadan metropolis, Oyo State, Nigeria. The river is drained by Ogunpa River where it cut across farmlands, residential and commercial areas. Several farmlands and commercial activities are situated along its bank which discharges its effluent into it. The Ogbere River Basin is covered mainly by ferruginous tropical soil on basement complex rock. It is built up as a result of the urbanization process, which is fast replacing the natural vegetation in the basin with consequent effect on runoff production and sediment generation (Ajibade et al. 2010). The major climatic seasons are wet or rainy season, which begins in June and ends in July and the dry season, which begins in January and ends in March with rainfall figure from an average of 800 mm at the onset to 1,500 mm at its peak. The study area is predominantly an urban area (between 400 m) and a rural area with no industrial activity after the 400 m of the 1 km stretch of the river (Figure 1).

Sample collection and analysis

A total of 12 sediment samples were collected across six sampling point for analysis. Sediment samples were collected with the aid of a hand-operated auger during wet and dry season from six sampling points (200 m interval) into a polythene bag to avoid contamination and taken to the laboratory. In the laboratory, the sediment samples were air dried until there was no weight change. Thereafter, the samples were sieved with 1 mm mesh and stored tightly in glass bottles. Sediment samples of 1 g each were weighed using a Scientech Zeta series electronic balance manufactured in the year 2000. The samples were put into a 250 ml glass beaker and digested with 24 ml of aqua regia and then evaporated to near dryness. The sediment samples were then dissolved in 10 ml of 2% nitric acid, filtered and then diluted to 100 ml with distilled water. The digested results were then analysed with Atomic Absorption Spectrophotometer (AAS) to obtain the concentration of the heavy metals.

Sediment quality index assessment

The obtained results of heavy metals were then analysed using three pollution status indexes: EF, Igeo and principal component analysis (PCA) (Figure 2). The PCA explains the correlation between the observation in terms of the underlying factors, which are not directly observable (Yu et al. 2001). The results are expected to reveal the characteristics and extent of pollution for each cluster so that the spatial distribution of pollution in the watershed can be evaluated to reflect the real difference of sediment quality for monitoring stations, and to develop an evaluation model suitable for assessing the characteristics of river sediment pollution.

The PCA is an analytical method for multivariate data that reduces many variables to few variables without leaving aside important information. It is expressed as
formula
where z is the component score, a is the component loading, x is the measured value of the variable, i is the component number, j is the sample number, and m is the total number of variables. The factor coefficients generated with correlation greater than 0.50 are considered strong significant factor loading and 0.50 below are considered weak significant loading. The PCA explains the relationship between measured variables which can be used to infer the hydrogeochemical processes.

Enrichment factor

The EF is used to assess the level of contamination and the possible anthropogenic impact on the sediments of the study area (Table 1). Al, Fe or Si could be used as the geochemical normalization. However, researchers have successfully used Fe for the geochemical normalization of metal contaminants (Baptista Neto et al. 2000; Mucha et al. 2003; Conrad & Chisholm-Brause 2004; Christophoridis et al. 2009; Meza-Figueroa et al. 2009; Esen et al. 2010; Bhuiyan et al. 2011). Therefore, Fe was used as a conservative tracer to differentiate natural from anthropogenic components in this study. The EF is defined in the equation below as recorded by Ergin et al. (1991):
formula
where (M/Fe)sample is the ratio of metal and Fe concentration of the sample and (M/Fe)background is the ratio of metal and Fe concentration of a background.
Table 1

Classes of EF by Sutherland (2000) 

EFImplication
<1 No enrichment 
1–3 Minor enrichment 
5–10 Moderately severe enrichment 
10–25 Severe enrichment 
25–50 Very severe enrichment 
>50 Extremely severe enrichment 
EFImplication
<1 No enrichment 
1–3 Minor enrichment 
5–10 Moderately severe enrichment 
10–25 Severe enrichment 
25–50 Very severe enrichment 
>50 Extremely severe enrichment 

Geoaccumulation index (Igeo)

The degrees of metal contamination in the sediment samples were determined using the Igeo classifications (Table 2). Igeo index was calculated using the crustal average values for the metals (Turekian & Wedepohl 1961). The geoaccumulation index used in quantifying the metal accumulation in soil or sediments is given in Equation (1) (Muller 1981)
formula
(1)
where C is the concentration of measured metal in the sample and B is the geochemical background of the element in the underlying parent material while 1.5 is used as correcting factor for the variation of the background data due to lithological variations. The average value of each element in the earth's crust proposed by Turekian & Wedepohl (1961) was used as background value in this study.
Table 2

Classes of geoaccumulation index by Muller (1981) 

Igeo rangeIgeo classSediment quality
<0 Practically uncontaminated 
0–1 Uncontaminated to moderately contaminated 
1–2 Moderately contaminated 
2–3 Moderately to heavily contaminated 
3–4 Heavily contaminated 
4–5 Heavy to extremely contaminated 
>6 Extremely contaminated 
Igeo rangeIgeo classSediment quality
<0 Practically uncontaminated 
0–1 Uncontaminated to moderately contaminated 
1–2 Moderately contaminated 
2–3 Moderately to heavily contaminated 
3–4 Heavily contaminated 
4–5 Heavy to extremely contaminated 
>6 Extremely contaminated 

RESULTS AND DISCUSSION

The results of heavy metals concentration in the sediment of Ogbere River are presented in the Supplementary Appendix, and the mean values are given in Table 3. The results obtained were below permissible limit value proposed by DPR except for lead (Pb) in S6 during dry season. This is far above the result obtained in sediments of Calabar River (Nwineewii et al. 2018). The average concentration of metals in the sediment for both seasons is lower than the freshwater sediment guideline (Burton 2002; Long et al. 2013). The low values obtained in sediments at various zones of the sampling point can be attributed to river current which moves most of the dissolved metals that should have settled within the river bed. Similar research was conducted on Ore and Okitipupa surface water (Ediagbonya & Ayedun 2018). It is to be noted that Mn, Fe, Cu, Zn, Pb, Cd, Ni and Cr have a higher mean concentration of 56.57 ± 17.11, 311.34 ± 74.56, 18.45 ± 2.49, 4.64 ± 1.02, 41.14 ± 37.76, 0.02 ± 0.03, 0.77 ± 0.25 and 3.74 ± 1.30 mg/kg, respectively, in dry season. While its mean concentration in that order is 39.42 ± 8.86, 184.32 ± 32.96, 11.34 ± 3.06, 1.84 ± 0.50, 17.93 ± 16.51, 0.008 ± 0.01, 0.11 ± 0.07 and 1.06 ± 0.14 mg/kg for wet season, respectively (Figure 3). The seasonal distribution of the mean concentration of metal in River Ogbere sediment has been shown in both seasons to be in the order of:
formula
Table 3

Heavy metal concentration in sediment from Ogbere River

Mn
Fe
Cu
Zn
Pb
Cd
Ni
Cr
mg/kg
S1 Dry 54.73 270.73 17.53 3.85 27.26 0.00 0.92 2.83 
 Wet 49.20 168.87 11.47 1.67 10.07 0.01 0.25 1.27 
S2 Dry 63.63 253.50 19.43 3.83 22.50 0.00 0.77 1.83 
 Wet 41.41 162.13 11.93 1.72 10.03 0.00 0.07 1.00 
S3 Dry 73.67 330.83 16.07 6.47 25.47 0.00 0.92 3.87 
 Wet 41.87 211.00 11.80 2.67 10.30 0.00 0.07 1.17 
S4 Dry 71.67 453.83 18.77 4.82 24.50 0.00 1.08 5.63 
 Wet 46.30 240.40 16.47 1.87 10.13 0.00 0.07 0.97 
S5 Dry 28.67 272.67 16.17 4.92 29.03 0.00 0.53 4.23 
 Wet 25.90 164.87 8.07 1.97 51.33 0.00 0.07 0.90 
S6 Dry 46.03 286.50 22.73 3.95 118.10 0.10 0.42 4.07 
 Wet 31.87 162.67 8.30 1.13 15.70 0.00 0.09 1.03 
Average Crustal value  850 47,000 45 95 20 0.30 68 90 
DPR Standard  850 38,000 36 140 85 0.8 3.5 100 
Mn
Fe
Cu
Zn
Pb
Cd
Ni
Cr
mg/kg
S1 Dry 54.73 270.73 17.53 3.85 27.26 0.00 0.92 2.83 
 Wet 49.20 168.87 11.47 1.67 10.07 0.01 0.25 1.27 
S2 Dry 63.63 253.50 19.43 3.83 22.50 0.00 0.77 1.83 
 Wet 41.41 162.13 11.93 1.72 10.03 0.00 0.07 1.00 
S3 Dry 73.67 330.83 16.07 6.47 25.47 0.00 0.92 3.87 
 Wet 41.87 211.00 11.80 2.67 10.30 0.00 0.07 1.17 
S4 Dry 71.67 453.83 18.77 4.82 24.50 0.00 1.08 5.63 
 Wet 46.30 240.40 16.47 1.87 10.13 0.00 0.07 0.97 
S5 Dry 28.67 272.67 16.17 4.92 29.03 0.00 0.53 4.23 
 Wet 25.90 164.87 8.07 1.97 51.33 0.00 0.07 0.90 
S6 Dry 46.03 286.50 22.73 3.95 118.10 0.10 0.42 4.07 
 Wet 31.87 162.67 8.30 1.13 15.70 0.00 0.09 1.03 
Average Crustal value  850 47,000 45 95 20 0.30 68 90 
DPR Standard  850 38,000 36 140 85 0.8 3.5 100 

The result of the sediment study indicated a general absence of serious heavy metal pollution in the Ogbere River, whereas the concentrations of elements found could mainly be attributed to geological and industrial sources.

The mean EF and geoaccumulation index values across the sampling points for both seasons are presented in Tables 4 and 5. The geochemical background values or reference values in the area of study for these metals are not available in the literature for now. Therefore, the background concentration of Pb, Zn, Cd, Cr, Ni, Cu, Fe and Mn were obtained from Turekian & Wedepohl (1961). These values have been used by several authors to determine the extent and degree of pollution of metals in sediments (Nobi et al. 2010; Ghrefat et al. 2011). Zhang & Liu (2002) stated that EF values greater than 1.5 could imply that their origins are probably from anthropogenic processes while less than 1.5 are mostly from crustal materials or are formed from natural processes. The values of EF imply that Ni could be from a natural process while Mn, Fe, Zn, Cu, Pb, Cd and Cr have anthropogenic origin. The distribution of EF values according to Chen et al. (2007) shows that Zn and Cr are moderately severely enriched in dry season and with minor enrichment in wet season. The EF values of nickel are minor enriched in dry season and not enriched in the wet season of the study area sediment. The EF value of Cu and Pb are extremely severe enriched in the sediment of River Ogbere for both seasons. The variation in EF values for the different metals in the sediment could result from the difference in the extent of input for each metal in the sediment in the rate at which each metal is being removed from the sample. Metals could be released with the water phase when parameters such as pH, ionic strength and complexing agent change (Ghrefat et al. 2011). The result obtained for EF is an indication of anthropogenic influence in the presence of some of the metals in the environment (Han et al. 2006).

Table 4

Pollution level of River Ogbere sediment in dry season, 2019

Heavy metalsEFDegree of enrichmentIgeoDegree of Igeo
Mn 10.06 Severe enrichment −4.51 Practically uncontaminated 
Fe –  −7.82 Practically uncontaminated 
Cu 61.46 Extremely severe enrichment −1.87 Practically uncontaminated 
Zn 7.50 Moderately severe enrichment −4.92 Practically uncontaminated 
Pb 258.14 Extremely severe enrichment 0.21 Uncontaminated to moderately contaminated 
Cd 8.59 Moderately severe enrichment −4.72 Practically uncontaminated 
Ni 1.72 Minor enrichment −7.04 Practically uncontaminated 
Cr 6.32 Moderately severe enrichment −5.17 Practically uncontaminated 
Heavy metalsEFDegree of enrichmentIgeoDegree of Igeo
Mn 10.06 Severe enrichment −4.51 Practically uncontaminated 
Fe –  −7.82 Practically uncontaminated 
Cu 61.46 Extremely severe enrichment −1.87 Practically uncontaminated 
Zn 7.50 Moderately severe enrichment −4.92 Practically uncontaminated 
Pb 258.14 Extremely severe enrichment 0.21 Uncontaminated to moderately contaminated 
Cd 8.59 Moderately severe enrichment −4.72 Practically uncontaminated 
Ni 1.72 Minor enrichment −7.04 Practically uncontaminated 
Cr 6.32 Moderately severe enrichment −5.17 Practically uncontaminated 
Table 5

Pollution level of River Ogbere sediment in wet season, 2019

Heavy metalsEFDegree of enrichmentIgeoDegree of Igeo
Mn 11.82 Severe enrichment −5.01 Practically uncontaminated 
Fe –  −8.59 Practically uncontaminated 
Cu 64.58 Extremely severe enrichment −2.57 Practically uncontaminated 
Zn 4.95 Moderately severe enrichment −6.28 Practically uncontaminated 
Pb 225.58 Extremely severe enrichment −0.74 Practically uncontaminated 
Cd 8.44 Moderately severe enrichment −8.83 Practically uncontaminated 
Ni 0.40 No enrichment −9.91 Practically uncontaminated 
Cr 3.05 Minor enrichment −7.00 Practically uncontaminated 
Heavy metalsEFDegree of enrichmentIgeoDegree of Igeo
Mn 11.82 Severe enrichment −5.01 Practically uncontaminated 
Fe –  −8.59 Practically uncontaminated 
Cu 64.58 Extremely severe enrichment −2.57 Practically uncontaminated 
Zn 4.95 Moderately severe enrichment −6.28 Practically uncontaminated 
Pb 225.58 Extremely severe enrichment −0.74 Practically uncontaminated 
Cd 8.44 Moderately severe enrichment −8.83 Practically uncontaminated 
Ni 0.40 No enrichment −9.91 Practically uncontaminated 
Cr 3.05 Minor enrichment −7.00 Practically uncontaminated 

The Igeo has seven grades with the highest grade being referred to as class 6, which means a 100-fold enrichment above background values. Igeo values of the sediment samples were interpreted with support of the classification of Muller (1981). The analysis revealed that the sediment is practically uncontaminated with respect to Mn, Fe, Cu, Zn, Cd, Cr and Pb and exception of Pb in dry season that indicated uncontaminated to moderately contaminated. The numerical result indicates pollution level as practically uncontaminated which implies the metal concentration comes from the natural weathering process.

Results of the PCA for both seasons as shown in Table 6 have three component factors and two component factors in dry and wet season, respectively, with initial eigen value greater than 1. Hence, these main factors are selected for illustrating the major factors that affect the water quality of the watershed. Furthermore, a Kaizer–Mayer–Olkin (KMO) measure of the sampling adequacy suggested by Kaiser (1974) is assigned to a data set in both seasons.

Table 6

Summary of communalities for factor analysis with rotated component matrix across season, 2019

Dry season
Wet season
Heavy metal parametersFactor 1Factor 2Factor 3Factor 1Factor 2
Mn −0.083 0.923 0.087 0.698 0.690 
Fe 0.023 0.605 0.763 −0.161 0.911 
Cu 0.975 0.068 −0.160 0.137 0.923 
Zn − 0.522 0.034 0.645 −0.174 0.530 
Pb 0.899 −0.366 0.095 − 0.535 − 0.609 
Cd 0.917 −0.323 0.078 0.940 −0.164 
Ni −0.452 0.857 0.143 0.945 −0.217 
Cr 0.125 0.037 0.955 0.882 0.144 
Initial eigenvalue 3.10 2.19 90.98 3.40 2.90 
% of total variance 38.75 27.47 24.75 42.54 36.30 
Cumulative % of variance 38.75 66.23 90.98 42.54 78.84 
Dry season
Wet season
Heavy metal parametersFactor 1Factor 2Factor 3Factor 1Factor 2
Mn −0.083 0.923 0.087 0.698 0.690 
Fe 0.023 0.605 0.763 −0.161 0.911 
Cu 0.975 0.068 −0.160 0.137 0.923 
Zn − 0.522 0.034 0.645 −0.174 0.530 
Pb 0.899 −0.366 0.095 − 0.535 − 0.609 
Cd 0.917 −0.323 0.078 0.940 −0.164 
Ni −0.452 0.857 0.143 0.945 −0.217 
Cr 0.125 0.037 0.955 0.882 0.144 
Initial eigenvalue 3.10 2.19 90.98 3.40 2.90 
% of total variance 38.75 27.47 24.75 42.54 36.30 
Cumulative % of variance 38.75 66.23 90.98 42.54 78.84 

Note: Extraction method: PCA.

Rotation method: Varimax with Keiser Normalization.

Rotation converged in 5 iterations.

Bold values are values greater than 0.5 and are considered to be significant. Any value above +0.50 is a significant pollution due to anthropogenic sources. Any value above −0.50 is significant and due to natural causes.

The varimax rotated analysis for the dry season revealed three factors. The percentage of variability of data set ranges between 24.75 and 38.75%; while 90.98% of the total variance of the data set is explained by the three factors leaving 9.02% unidentified. Factor 1 showed significant positive loadings for Cu, Pb and Cd with negative loading for Zn. Cu has the highest positive loading of 0.975 which could be sourced from anthropogenic sources from agricultural runoff, sewage which causes anaemia and kidney failure. This agrees with Aliyu et al. (2018) on River Lavun, Bida. Factor 2 revealed high positive loading for Mn, Fe and Ni with relatively no negative loading. This could be best put as an anthropogenic source from domestic wastes runoff and urban wastes in whose high concentration causes siderosis in liver. Similar results were observed by Alani et al. (2014) and Nwineewii et al. (2018). Although, corrosive materials as a result of waste deposits on river bank may wear off and contribute to the positive loading of Ni (Oketola et al. 2013). Likewise, factor 3 showed positive loading for Fe, Zn and Cr with no negative loading. The high positive loading of Fe and Zn may be as a result of commercial activities from metal filings and domestic wastes. This was also observed by Aliyu et al. (2018).

The varimax rotated analysis result for wet season revealed two factors. The percentage of variability of data set ranges between 36.30 and 42.54% with 78.84% of the total variance explained, leaving 21.16% unidentified. Factor 1 has positive loadings for Mn, Cd, Ni and Cr with negative loading for only Pb. Factor 2 revealed positive loading for Mn, Fe, Cu and Zn with negative loading for Pb. The negative loading of Pb could be as a result of mining and natural processes such as weathering and precipitation. This was similar to lead concentration reported by Dike et al. (2004) in River Jakara, Kano State.

CONCLUSION

The present study shows that the geochemical distribution of elements in sediment samples from the study area varies widely. The metal distribution profile in the river sediment revealed a lower concentration to the DPR standard of allowable limits. The sediments were suffering from ‘no to practically uncontaminated’ pollutant with the studied heavy metals according to Igeo values and moderately to extremely severe enrichment pollutants according to EF. The highest enrichment value and geoaccumulation value were recorded in Pb and thus the activities in this area including the incessant flooding of the area should be checked in order to forestall the vulnerability of biotic life to the metal contamination. The PCA is considered more sensitive in the analysis of benthic changes as well as sediment quality. In order to improve the PCA loading value, large sample size should be included for adequate scientific information. Furthermore, the application of all these indexes at present cannot provide information on the effect of the combination of pollutant, but it can provide the public some understanding about the quality of Ogbere River sediment.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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