Table 4
Mean ± SD of the Physicochemical Characteristics of Nworie Inland River
Stations
|
pH
|
Turb
|
CaCO3
|
COD
|
DO
|
Cl
|
Ca
|
Mg
|
K
|
Na
|
SO24
|
NO3
|
PO34
|
Upper River (UR)
|
6.157 + 0.361a
|
0.139 ± 0.010ab
|
168.3 ± 12.583b
|
5.800 ± 0.263b
|
0.137 ± 0.012b
|
48.03 ± 111.12a
|
63.10 ± 12.43b
|
10.81 ± 1.255b
|
2.333 ± 0.611ab
|
1.450 ± 0.180ab
|
1.167 ± 0.144b
|
0.360 ± 0.151c
|
0.500 ± 0.087a
|
Middle River (MR)
|
6.553 ± 0.314a
|
0.113 ± 0.013c
|
123.3 ± 10.408c
|
3.91 ± 0.142b
|
0.106 ± 0.015b
|
36.82.766bc
|
73.8 ± 7.718ab
|
17.23 ± 2.767a
|
1.333 ± 0.153a
|
1.032 ± 0.143c
|
1.827 ± 0.150a
|
1.040 ± 0.225a
|
0.360 ± 0.078b
|
Lower River (LR)
|
5.827 + 0.283c
|
0.169 ± 0.036a
|
226.7 ± 20.817a
|
39.79 ± 4.415a
|
0.836 ± 0.173a
|
31.74 ± 3.353c
|
172.4 ± 7.501a
|
18.70 ± 1.566a
|
3.333 ± 1.832c
|
1.650 ± 0.325a
|
1.660 ± 0.121a
|
0.627 ± 0.352ab
|
0.265 ± 0.077c
|
Mean
|
6.179
|
0.140
|
172.8
|
16.50
|
0.359
|
38.86
|
103.1
|
15.58
|
2.333
|
1.374
|
1.551
|
0.675
|
0.375
|
%Cv
|
5.2
|
16.1
|
8.8
|
15.8
|
28.0
|
17l7
|
9.2
|
12.7
|
25.8
|
16.8
|
9.0
|
38.0
|
21.5
|
Se
|
0.321
|
0.023
|
15.28
|
2.601
|
0.101
|
6.90
|
9.49
|
1.973
|
0.603
|
0.231
|
0.139
|
0.257
|
0.081
|
WHO STD
|
6.5–8.5
|
1.00
|
50
|
4
|
> 4
|
400
|
75
|
50
|
10
|
200
|
200
|
50
|
5
|
different Superscript letters are interpreted as significantly different while similar superscript letters are interpreted as not significantly different.
The physicochemical properties of Nworie Inland River
From Table 4, pH in the river ranged from 5.827–6.553, with the Lower River (LR) recording highest acidity that is significantly different from Upper River (UR) and middle River (MR) at P ≥ 0.05, with the mean pH value of 6.179. The mean pH of the present study is slightly acidic than the pH in the reports of Ayode and Nathaniel (2018), Madina, Omer and Esraa (2016) and Oluyemi et al. (2010), who reported pH of 6.95–7.88 in tropical man-made lake of south-western Nigeria, 6.6–7.8 in Dalanj area and 7.69 in Ife North L.G.A in Osun State, Nigeria where pH values ranged from 7.45 to 8.4 (WHO, 2011; Ayers and Westcot, 1994). The acidity observed in the lower course river could be from the medical waste discharge at the location (Ubuoh et al., 2017; Ubuoh et al., 2021). The near neutral acidity observed in the middle (6.553) river is suggested to be due to the washing down of organic residues into the water (Jain and Agarwal, 2012; Ubuoh et al., 2021). Similar ranges were reported for same river (5.70–7.20) by Ekhator et al. (2011) and elsewhere within Edo State (Imoobe and Koye, 2011; Anyanwu, 2012). WHO, (2007) stated that low pH levels is capable of facilitating corrosive characteristics, leading to drinking water contamination with adverse effect on its taste and appearance.
Turbidity ranged from 0.139–0.169 NTU. MR had the lowest value (0.139) while LR (0.169) had the highest value at P ≤ 0.05 significant difference, with a mean of 0.140 NTU lower than the WHO limit of 1.00 NTU (WHO, 2011). The mean turbidity of this study is at variant with result of Ayobahan et al. (2014) in Benin River, Nartey et al. (2012) in Accra and Chima et al. (2009) in Asata- Enugu whose findings were 4.33–6.11, 6.4–16.3 and 32.0-43.4NTU respectively. The turbidity levels in the middle and lower rivers may be attributed to the presence of household municipal wastes and medical wastes dumped in the river (Ubuoh et al., 2012; Ubuoh et al., 2021).
Hardness as CaCO3 recorded 226.7 mgL− 1 in the LR, while the lowest value of 123.3 mgL− 1 was observed in MR and were statistically different at P ≥ 0.05 across the courses of the river, with a mean of 172.8mg/l above the WHO standard of 100.0 mgL− 1 (WHO, 2011). Water hardness leads to rapid exhaustion of washing soap, and chiefly influenced by the presence of calcium and magnesium salts (Ca2+ and Mg2+) (DWAF, 1993).The value of the mean hardness in the present study exceeds that of the findings of Duru and Nwannekwu (2012) whose values were between 18.6 to 36.01mg/l. Adeosun et al. (2016) reported a lower value of 7.0- 50.4 in FUTA. The studies of Miyake et al. (2005), Arnedo-Penal et al. (2007) and Perkin et al. (2016) have correlated domestic hard water with increased eczema in children. Rivers with high value of hardness cannot lather with soap due to calcium stearate, with the chemical reaction according to Wikipedia (2022):
\({2 \mathbf{C}}_{17}\) \({\mathbf{H}}_{35}\)COO− \({\left(\mathbf{a}\mathbf{q}\right) +\mathbf{C}\mathbf{a}}^{2+}\left(\mathbf{a}\mathbf{q}\right)\to { ( \mathbf{C}}_{17}\) \({\mathbf{H}}_{35} \mathbf{C}\mathbf{O}{\mathbf{O} )}_{2}\mathbf{C}\mathbf{a} \left(\mathbf{s}\right)\) (4)
From the LR, the Chemical Oxygen Demand (COD) recorded the highest value of 39.79 mgL-1, MR recorded the lowest value of 3.91 mgL-1 while UR recorded 5.800mg/l with a mean of 16.50mg/l below the 40mg/l WHO/FME Standard ( 2003), and above the allowable (4 mg/L) limit of chemical oxygen demand for human drinking purpose in accordance with the set permissible limit of DoE (1997), WHO (2017a, 2017b), and DPHE (2019). The mean result of COD in this study is less than the observations of Ayobahan et al. (2016) who had reported values range of COD (19.14- 115.16 mgL-1) in Benin River, due to human activities within the catchments.
Dissolved oxygen (DO) ranged from 0.106 mgL− 1 in the MR to 0.836 mgL− 1 in the LR, having the mean value of 0.359mg/l below the ˃4.0 WHO/FME STANDARD (2003), and ≤ the findings of Duru and Nwanekwu (2012) who reported DO content of 1.21-3.6mg/L in a river and DO in River Tyśmienica ranging between 6.2 to 8.5 mg/L (Grzywna and Sender, 2021). Significant variations in COD and DO were observed at the three courses at P ≤ 0.05. The low DO could be attributed to nearness to human activity such as, bathing, washing of cars, as well as farming within the area (Adeosun et al., 2016). In addition, low DO is also tied to dumping of solid wastes, sewage discharge and high level of defecation in the river bank (Ubuoh, 2010). The observed mean value of DO in this study was < mean dissolved oxygen range (4.35–4.82 mg/l) of the Siluko River, southern Nigeria (Oboh and Agbala, 2017), < (4.5–6.4 mg/l) in Eleme River Chikere (Okpokwasili, 2001) <(3.18–3.27 mg/l) in Eruvbi stream (Imoobe and Koye, 2011) and < (1.84–5.22 mg/l) lower zone of Ikpoba river (Ogbeibu and Edutie, 2002). This could be as a result of discharge of increased levels of biodegradable and non-biodegradable matters into the river by the increased activities of humans (Oboh and Agbala, 2017).
The highest chloride content (48.03 mgL-1) was in upper river, while the lower river was having the lowest (31.74 mgL-1), with the mean value of 38.86 mg/l, all varying statistically at P ≤ 0.05. The mean of the chloride was not upto the 400 mg/l (WHO/FME Standard (mg/L) 2003). The findings were related to that of Agudozie et al. (2019) who reported 15–30 mgL-1 in surface water of Nworie River. Accordingly, the mean value of Chloride in the present study is higher than 1.70 ± 0.279, 4.68 ± 0.300, 2.20 ± 0.78, 4.79 ± 0.222 obtained in Otamiri River, Imo State, Nigeria (Adebayo et al., 2016), less than the observations of Oluyemi et al. (2010), and Ogwo et al. (2014) who observed high values of chloride to be from 82–92 and 3.64-184.04 mg/L-1 in rivers.
Calcium ranged from 63.10–172.40 mgL-1, and varied significantly at P ≤ 0.05, with a mean of 103.1 mg/l greater than the 75.00 mgL-1 WHO permissible limit (WHO, 2011). This finding is greater than the value of Ca obtained by Ayobahan et al. (2014) and Duru and Nwanekwu, (2012), who reported 0.10–1.49 mg/L-1 and 0.73–2.64 mgL-1. Again, Agudozie et al. (2019) reported a value of 5.1–7.57 mg/L from the same river. High level of calcium is capable of causing permanent hardness of the water and can impair its taste, resulting in gastrointestinal irritation (Agudozie et al., 2019).
Magnesium ranged from 10.81–18.70 mg/L, which were statistically the same across the sampling point at P ≤ 0.05. The mean of the magnesium being 15.58 mg/l was lower than the 50.00 mg/L WHO limit in water (WHO, 2011). The finding of Mg in this study is inconsistent with the findings of Duru and Nwanekwu (2012), and Agudozie et al. (2019) who recorded Mg content in rivers to range from 0.01–0.31 and 0,176- 0.268 mg/L respectively. Potassium concentration at the upper, middle and lower course river were 2.333, 1.333 and 3.333 mgL-1, respectively, and varied significantly at P ≤ 0.05. The mean value of the potassium concentration of the water was lower than the WHO limit (10 mgL-1) (WHO, 2011). In addition, the result of potassium of the study is in contrast with Agudozie et al. (2019) and Ayobohan et al. (2014), who recorded 3.87–5.53 and 2.94–49.95 mgL-1 in Nworie and Benin River respectively.
The sodium concentration in the lower river recorded the greatest number of 1.650, and lowest (1.023 mg/L) observed in middle river that varied differently at P ≤ 0.05. In this study, the mean (1.374mg/l) was below the finding of Agudozie et al. (2019), who reported 22.2-26.0mg/L of Na in a river, and below the 200.0 mgL-1 of WHO permissible limit in water (WHO, 2011).
Sulphate ranged from 1.167–1.827 mgL− 1, with UR having the lowest value and MR recording highest value, with a mean of 1.551 mgL− 1and varied significantly at P ≤ 0.05. This observation is disagreement with the reports of Ogwo et al. (2014) who noted 12.85-15.85mg/L of sulphate in a river, and the mean value of sulphate less than the 200 mg/L WHO limit in water (WHO, 2011).
Nitrate in the middle river recorded the highest nitrate concentration of 1.040 mg/L, while the lowest concentration of nitrate (0.360 mgL− 1), was observed in the upper river, with a mean of 0.675 mg/L, below the 50.00 mg/L WHO limit (WHO, 2011). This is within the findings of Duru and Nwanekwu (2012) and Ayobahan et al. (2016) who observed 0.10–0.40 and 0.93-1.18mg/l of nitrate in rivers respectively.
Accordingly, the contents of sulphate, nitrates and chloride were low and similar values have been observed elsewhere in Edo State (Ogbeibu and Anagboso, 2004; Imoobe and Koye 2011; Ekhator et al., 2011; Anyanwu, 2012). According to Beauchamp (1953), African Inland waters are generally deficient in sulphate, because of their low amounts in the non-sedimentary rocks within drainage areas.
Phosphate values lies between 0.265–0.500 mgL− 1, with the upper, middle and lower course of the river recording 0.500, 0.360 and 0.265 mg/L respectively, and a mean of 0.375 mgL− 1. The mean value of Phosphate is lesser than 0.71–0.85 mg/l range of Phosphate in Ase, < 0.34-0.41mg/l in Agbarho,> 0.10-0.13mg/l in Ethiope, < 0.49–0.53 mg/lin Ekakpamre, and < 0.71–0.90 mg/l in Afiesere Rivers respectively (Kaizer and Osakwe,2010). The mean value of phosphate observed was above the 0.05 mg/L WHO limit in water (WHO, 2011). An increased phosphate level is an indication of pollution through eutrophication (Ubuoh et al., 2022), usually facilitated by an increase in nutrient content of water (Sobczyński and Joniak, 2013; Kowalik et al., 2014; Neverova-Dziopak and Preisner, 2015). The increase in phosphate amount is associated with indiscriminate waste disposal, agricultural drainage into rivers, application of pesticides and fertilizers with their corresponding capacities to pollute surface water (Mirzaei et al., 2016; Sun et al., 2016; Grzywna and Bronowicka-Mielniczuk, 2020).
Table 5: Correlation Matrix of physicochemical characteristics of Nworie Inland Surface Water
Variable
|
pH
|
Turb
|
CaCO3
|
COD
|
DO
|
Cl
|
Ca
|
Mg
|
K
|
Na
|
SO24
|
NO3
|
PO34
|
pH
|
1
|
|
|
|
|
|
|
|
|
|
|
|
|
Turb
|
-0.51*
|
1
|
|
|
|
|
|
|
|
|
|
|
|
CaCO3
|
-0.48
|
1.00
|
1
|
|
|
|
|
|
|
|
|
|
|
COD
|
-0.10
|
0.91**
|
0.92**
|
1
|
|
|
|
|
|
|
|
|
|
DO
|
-0.09
|
0.90**
|
0.92**
|
1.00
|
1
|
|
|
|
|
|
|
|
|
Cl
|
-0.63*
|
-0.34
|
-0.37
|
-0.71**
|
-0.71**
|
1
|
|
|
|
|
|
|
|
Ca
|
0.04
|
0.84**
|
0.86**
|
0.99**
|
0.99**
|
-0.80**
|
1
|
|
|
|
|
|
|
Mg
|
0.73**
|
0.22
|
0.25
|
0.61*
|
0.61*
|
-0.99**
|
0.71**
|
1
|
|
|
|
|
|
K
|
-0.54*
|
1.00**
|
1.00
|
0.89**
|
0.88**
|
-0.30
|
0.82**
|
0.18
|
1
|
|
|
|
|
Na
|
-0.70**
|
0.97**
|
0.96**
|
0.78**
|
0.77**
|
-0.11
|
0.69*
|
-0.02
|
0.98**
|
1
|
|
|
|
SO24
|
0.95**
|
-0.20
|
-0.17
|
0.23
|
0.24
|
-0.85**
|
0.36
|
0.91**
|
-0.24
|
-0.43
|
1
|
|
|
NO3
|
1.00**
|
-0.57*
|
-0.54*
|
-0.17
|
-0.16
|
-0.58*
|
-0.03
|
0.68*
|
-0.60*
|
-0.75**
|
0.92**
|
1
|
|
PO34
|
-0.55*
|
-0.44
|
-0.47
|
-0.78**
|
-0.78**
|
0.99**
|
-0.86**
|
-0.97**
|
-0.40
|
-0.21
|
-0.79**
|
-0.49
|
1
|
*means significant correlation at 0.05 level (2-tailed). while ** means significant correlation at 0.01 level (2-tailed).
Table 5 above shows a Pearson correlation matrix of closeness or relationship within and between physicochemical characteristics of Nworie river courses. Parameters showing r between (0.7) and 0.9 are taken to have a strong correlation, while parameters showing r between 0.5 and 0.7, are taken to be a moderately correlated (Helena et al. 2000). Again, when r is closer or nearer to + 1 or -1, it tells that there is perfect linear relationship existing between the two parameters (Saliu et al., 2020).
From Table 5, correlation coefficient (r) ranged from − 0.09 (negatively correlated) to + 1(strongly correlated). There were significant association among the physicochemical parameters of Nworie river. Most of the physicochemical parameters strongly correlated with each other (Table 5). Negative and positive correlations existed between the contaminants of pH and Turbidity as can be seen with r value between parameters in the brackets (− 0.51), Cl (-0.63), Mg (0.73), Na (-0.70), SO24(0.95), NO3 (1.00) and PO34(-0.55) respectively. Turbidity are positively correlated with CaCO3 (1.00), COD (0.91), DO (0.90), Ca (0.84), K (1.00), Na (0.97), at 0.01 significance level, and Turbidity/ NO3(r= -0.57) at the 0.05 level of significance. Total hardness (CaCO3) positively coexisted with COD (0.92), DO (0.92), Ca (0.86), K (1.00), Na (0.96) respectively at 0.01 level of significance, and negatively coexisted with NO3 (-0.54) at 0.05 level of significance. COD is positively coexisted with DO (1.00), leading to organic pollution of water due to wastes dumped in water (Ubuoh et al. 2022), negate Cl (-0.71), Ca (0.99), Mg (0.61), K (0.89), Na (0.78), and negate PO34 (-0.78) respectively. DO negatively coexisted with Cl (-0.71), and positive with Ca (0.99), Mg (0.61), K (0.88), Na (0.77), and negate PO34 (-0.78) respectively. Cl negatively correlated with Ca (r-0.80), Mg (r-0.99), SO24 (r=-0.85), and positively coexisted with PO34 (r= (0.99) respectively. Ca positively correlated with Mg (r = 0.71), K(r = 0.82), Na (r = 0.69) and negate PO34(r= -0.86). Mg positively coexisted with SO24, NO3 (r = 0.91; 0.68), and negate with PO34 (r= -0.97) respectively. K positively associated with Na (r = 0.98) and negatively correlated with NO3 (r=-0.60), Na negatively associated with NO3 (r= -0.75. Ultimately, SO24 positively associated with NO2(r = 0.92) resulting to acidity in water (Ubuoh et al., 2021), and negatively associated PO34(r= -0.79) respectively.
Table 6
Comparison of Heavy metals concentration of Nworie surface water to global regulatory standards limits
Station
|
Cd (mgL− 1)
|
Cr (mgL− 1)
|
Cu (mgL− 1)
|
Fe (mgL− 1)
|
Ni (mgL− 1)
|
Pb (mgL− 1)
|
Zn (mgL− 1)
|
Upper River (UR)
|
0.078 ± 0.011b
|
0.014 ± 0.006b
|
0.020 ± 0.004b
|
0.152 ± 0.00b
|
0.037 ± 0.006a
|
0.995 ± 0.179a
|
0.024 ± 0.006b
|
Middle River (MR)
|
0.062 ± 0.002b
|
0.019 ± 0.015b
|
0.078 ± 0.021b
|
0.232 ± 0.064b
|
0.033 ± 0.013a
|
0.925 ± 0.099a
|
0.024 ± 0.015b
|
Lower River (LR)
|
0.178 ± 0.63a
|
0.115 ± 0.074a
|
0.805 ± 0.167a
|
0.947 ± 0.153a
|
0.003 ± 0.001b
|
0.443 ± 0.073b
|
0.129 ± 0.025a
|
Mean
|
0.106
|
0.049
|
0.301
|
0.443
|
0.024
|
0.787
|
0.059
|
%Cv
|
12.7
|
86.7
|
37.1
|
21.5
|
33.5
|
16.0
|
31.2
|
Se
|
0.007
|
0.043
|
0.112
|
0.096
|
0.008
|
0.126
|
0.018
|
WHO LIMIT
|
0.003
|
0.10
|
1.00
|
0.03
|
0.02
|
0.01
|
3.0
|
SON
|
0.003
|
0.050
|
1.00
|
0.300
|
0.020
|
0.010
|
3.000
|
WPCL
|
0.003
|
0.020
|
1.00
|
0.450
|
0.020
|
0.010
|
4.250
|
EPA
|
0.010
|
0.050
|
1.00
|
0.500
|
0.020
|
0.050
|
5.000
|
EC
|
5.000
|
50.00
|
-
|
-
|
20.00
|
10.00
|
-
|
WHO,2003/2011; SON, 2007 ; WPCL, 2004; EPA, 2002; EC, 1998
|
WHO- World Health Organization, SON- Standard Organisation of Nigeria, WPCL- Water Pollution Control Legislation, EPA- Environmental Protection Agency (US), EC- European Commission.
Heavy metal level in Nworie Inland River
The cadmium (Cd) concentration in Nworie River at the course streams ranges from 0.031–0.089 mgL− 1, with the MR having the lowest value and LR having the highest value, with a mean of 0.053 mgL− 1. (Table 6). The mean values of Cd concentration in the water was above the 0.003 mgL− 1WHO limit in water (WHO, 2008), due to heterogeneous dumpsites (Ubuoh et al., 2017; Ubuoh et al., 2021). The result of Cd in the present study is at variant with the observations of Edori et al. (2019) who recorded very low Cd in Elelenwo River in Rivers State, Nigeria. Accordingly, Edori and Iyama (2020), reported 0.0– 0.0008 ± 0.00 mg/L in Edagberi Creek, Engenni, Rivers State. Ogwo et al. (2014) reported 0-0.011mg/L of cadmium in Igwi stream. High content of Cd in rivers can cause toxicity that is inimical to human health and threat to growth and development of environmental flora and fauna (Bennet-Chambers et al.,1999; Olmedo et al., 2013). cumulative Cd poisoning is capable of damaging the human kidney, liver, testes and prostate (Adelekan and Alawode, 2011; Abdullahi et al. 2020). Excess Cd in the body can also result to loss of bone and muscle calcification atrophy, circulation problems, anaemia, and high blood pressure (Kabata-Pendias and Pendias 1993; Ubuoh et al., 2019). Associated diseases with Cd intake at high content are hematology and kidney dysfunction, cardiac and vascular neurology, procreative disorder, damage to hepatocytes and other important body parts (Tirkey et al., 2012).
The chromium (Cr) concentration values at the upper, middle and lower river were 0.014, 0.019 and 0.115 mgL− 1, respectively, and varied significantly at P ≤ 0.05. Cr values recorded in Nworie river when compared with reports from related environments, showed higher values of Cr when compared to levels recorded in few rivers in the Niger Delta, Nigeria (Marcus and Edori, 2016; Nwineewii et al., 2019; Ekpete et al., 2019 ; Edori and Iyama, 2020). The result of Cd in this study is in tandem with the observations of Ogwo et al. (2014) who recorded chromium values ranging from 0.011-0.013mg/Lin the river. The mean value of the chromium (0.049mg/L) was less than Cr 0.096 mg/ L, in river, Taiwan (Lin et al., 2017), and lower than the 0.1 mgL− 1 WHO limit in water (WHO, 2008).
Copper (Cu) ranged from 0.020–0.805 mgL-1 and the values at the upper, middle and lower river were 0.020, 0.078 and 0.805 mgL-1 respectively. These findings were similar to 0.18 − 0.14 of copper in water as reported by Oluyemi et al. (2010). The mean values of Cu in the present study: 0.301mg/l is lower than the 1.000 mgL-1 WHO limit in water (WHO, 2008), and less than Cu in the Houjing River water ( Lin et al., 2017), Buriganga River (Bhuiyan et al., 2015), and Hindon River (Suthar et al., 2009), greater than the concentrations of Cu reported in Bomu and Oginigba Rivers located in Rivers State, Nigeria (Marcus and Edori, 2016), and Asonye et al.( 2007), who recorded no traces of Cu in the water sample sources examined.
Iron (Fe) ranged from 0.152–0.947 mg/L, with LR greater than UR and MR (which are statistically the same at P ≥ 0.05) with a mean of 0.443mg/L above the 0.03mgL− 1 (WHO, 2008). The mean value is in contrast with the findings of Ayobahan et al., (2014) whose values were 0.52-2.62mg/L of iron. The mean Fe of 0.443 in this study is greater than Iron (Fe) values (0.028–0.075mg/L) in the Edagberi Creek (Edori and Iyama, 2020). Authors like Asonye et al. (2007), Haxhibeqiri et al. (2015) and Nwineewii et al. (2019) reported lower values of Fe in river than the present study. Although Iron (Fe) is invaluable in natural biochemical processes in humans (Edori and Iyama, 2020), it is known to have a toxicological effect on human tissues and organ when in excessive amounts (Abbaspour et al., 2014). The presence of iron in form of Fe3+ oxide in H2O could damage the gills of fishes, reduce the oxygen intake amount (Ogaga et al., 2018), and distort the normal respirational pattern of fishes in the river (Edori and Iyama, 2020). More so, excessive iron levels could result to organ failure in humans such as liver cancer, heart diseases, diabetes, liver cirrhosis, as well as infertility issues etc (Kumar et al., 2017). In addition, higher amount of Fe in water leads to decoloration in water, water taste and odour, as well as cloth stains and corrosion of water pipe lines (Behera et al., 2012; Kumar et al., 2017). The symptoms of iron poisoning in water are fatigue, weakness, joint pain and abdominal pain (Edwards, 2019). The sources of iron pollution in the river are suspected to come from refuse dumps, runoff from metal scrape and medical wastes dumpsites.
Nickel statistically varied at P ≤ 0.05 among the river courses, and ranging from 0.003–0.037 mg/L with a mean of 0.024mgl slightly above the 0.02mg/l WHO permissible limit of potable water (WHO, 2011), and less than Ni concentrations that ranged from 17 to 455 mg/L on surface water chemistry of Werii catchment, Tigray, Ethiopia (Haftu and Estifanos, 2020). Nickel comes from discharges of industrial and municipal wastes into the river (Ubuoh et al., 2010; Zaigham et al., 2012; EFSA, 2015; Ubuoh et al., 2021). High dose of Nickel in drinking water has been reported to cause liver, lung and kidney diseases in animals like dogs, mice and rats, affecting their stomach and blood (Ishimatsu et al.,1995), as well as reducing their immune system, and inhibiting their reproduction and development (Chashschin et al., 1994; ATSDR, 2005).
Lead (Pb) can find its way into the environment through either natural or anthropogenic sources, but chiefly from human induced sources (Adebanjo and Adedeji, 2019). Some of the human induced sources of lead include batteries, particle emission inhibition equipment, dyes and coating paints, elastic tools, amalgams, polyvinyl chloride conduits, fossil fuels, cable concealments and insecticides etc (Bytyç et al., 2018). Lead (Pb) levels in this study fell within 0.443-0.995mg/L with LR less than UR and a mean value of 0.787mg/l, greater than 0.02–0.19 of lead reported by Ayobahan et al.(2014), greater than the concentrations of Pb from Edagberi Creek (0.0006–0.003) (Edori and Iyama, 2020), and above the 0.010 mgL− 1 WHO limit in water(WHO, 2008). The inimical effect of high levels of Lead (Pb) in higher animals include acute damage to the brain, especially in adult and children, but most especially, obstruct the proper mental development of infants (Saheed and Abimbola 2021; Foster et al., 2020a). Pb has also been reported to cause heart-related diseases (Kopper et al., 1988).
The LR recorded the highest zinc concentration (0.129 mgL− 1) while the least concentration of zinc (0.024 mg/L) was recorded in both upper and middle rivers, with a mean value of 0.059 mg/l. This finding was similar to the findings of Ayobahan et al. (2014), Ogwo et al. (2014) and Oluyemi et al. (2014) who recorded zinc values in the range between 0.17–1.49, 0.011–0.021 and 0.03–0.22 respectively. Zn concentrations of this study were less than those in Wiser and Elbe Rivers (Pache et al., 2008), and the recent researches like the Buriganga river (Bhuiyan et al., 2015), the Nakdong River (Chung et al., 2016), Yellow River (Yan et al., 2016), and Bortala River (Zhang et al., 2016c).
In a bid to determine the level of heavy metal chemistry in Nworie inland aquatic ecosystem, a standard comparison of data results with other global water standards was done. The mean concentration of Cd 0.053mg/L was above WHO, SON, EPA, EC, and WPCL standard in Table 6. Cr was below SON, WHO, EPA, and EC but higher than 0.020 stipulated by WPCl (2004). Zn was lower than all the guidelines in Table 6. Pb was also above all the water guidelines except 10.00mg/l given by EC (1998), Ni was slightly raised above SON, WPCL, WHO (0.020mg/L) but below 20mg/l by EC in the table above. While Fe in water sample were above the SON and WHO guidelines but below 0.500 and 0.450mg/l given by EPA and WPCL respectively. Above all, the excessive concentration of heavy metals above the environmental standards in the study river could be connected to anthropogenic sources, which is in tandem with the findings of Bhuiyan et al. (2011) in lagoon and canal water in the Dhaka, Bangladesh Fu et al. (2014) in Jialu River, China.
The elemental concentrations in Nworie surface water therefore occurred in descending abundance: Pb2+ ≥ Fe2+ ≥ Cu2+ ≥ Zn2+ ≥ Cd 2+ ≥ Cr2+≥ Ni2+
Table 7 A comparative global heavy metals concentrations of rivers to Nworie river
Rivers
|
Cu
|
Cd
|
As
|
Cr
|
Ni
|
Zn
|
Mn
|
Pb
|
Fe
|
Ref
|
Nworie River, Nigeria.
|
0.301
|
0.053
|
-
|
0.049
|
0.024
|
0.059
|
-
|
0.787
|
0.443
|
This Study
|
Uruan River, Nigeria.
|
(0.001–0.003)
|
-
|
-
|
-
|
-
|
(0.014–0.55)
|
-
|
(0.004–0.06)
|
(.37-1.23)
|
Denise E. M. and John (2004)
|
Weihe River, Xian, China.
|
(18.23–69.34)
|
-
|
(18.43–39.93)
|
(60.54-142.93)
|
(15.43–62.38)
|
(71.32–143.64)
|
(519.25–1212.79)
|
(15.62–36.39)
|
-
|
Ahamad et al. (2020)
|
Zijiang River, Hunan, China.
|
(18.37–59.01)
|
-
|
(6.90–74.34)
|
(48.47–95.32)
|
(21.50–52.29)
|
(42.41–251.61)
|
(570.75–2106.73)
|
(12.70–104.32)
|
-
|
Zhang et al. (2018)
|
Jialu River, China.
|
(8.82–107.61)
|
-
|
(2.39–14.57)
|
(40.04–96.39)
|
(19.75–80.26)
|
(42.39–210.00)
|
NA
|
(14.79–51.17)
|
-
|
Fu et al. (2011)
|
Korotoa River, Bangladesh.
|
76
|
-
|
25
|
109
|
95
|
NA
|
NA
|
58
|
-
|
Islam, et al. (2015)
|
Axios River, Greece.
|
93
|
-
|
40
|
180
|
188
|
271
|
NA
|
140
|
-
|
Karageorgis et al. (2003)
|
River Po, Italy.
|
90.1
|
-
|
NA
|
NA
|
16198.5
|
645
|
NA
|
98.5
|
-
|
Farkas et al.(2007)
|
Gomti River, India.
|
245.33
|
-
|
NA
|
88.7
|
76.08
|
343.47
|
834.7
|
156.2
|
-
|
Singh et al. (2005)
|
Chenab River, Pakistan.
|
(5.80–9.40)
|
-
|
NA
|
NA
|
NA
|
(11.7–50.5)
|
(245–851)
|
(2.4–32.4)
|
-
|
Hanif et al. (2016)
|
Almendares River, Cuba.
|
420.8
|
-
|
NA
|
23.4
|
NA
|
708.8
|
NA
|
189
|
-
|
Olivares-Rieumont et al. (2005)
|
Nile River Egypt.
|
81
|
-
|
NA
|
274
|
NA
|
221
|
2810
|
23.2
|
-
|
Rifaat (2005)
|
South Platte River, USA.
|
480
|
-
|
31
|
71
|
NA
|
3700
|
6700
|
270
|
-
|
Heiny, and Tate (1997)
|
Tees River, UK.
|
76.9
|
-
|
NA
|
NA
|
NA
|
1920
|
5240
|
6880
|
-
|
Hudson-Edwards et al (1999)
|
Table 7 shows that Nworie river recorded relatively low level of selected heavy metals when compared to other rivers of the world. Although cadmium content was recorded in Nworie river but not in other rivers like wise As and Mn that were not indicated in the studied Nworie inland water.
Table 8
Correlation Matrix of Heavy Metals In Surface Water of Nworie Micro Watershed
Element
|
Cd2+
|
Cr2+
|
Cu2+
|
Fe2+
|
Ni2+
|
Pb2+
|
Zn2+
|
Cd2+
|
1.000
|
|
|
|
|
|
|
Cr2+
|
-0.309
|
1.000
|
|
|
|
|
|
Cu2+
|
-0.515*
|
0.013
|
1.000
|
|
|
|
|
Fe2+
|
0.816**
|
-0.162
|
-0.856**
|
1.000
|
|
|
|
Ni2+
|
0.381
|
0.302
|
-0.918**
|
0.700**
|
1.000
|
|
|
Pb2+
|
0.603**
|
0.005
|
0.185
|
0.319
|
-0.239
|
1.000
|
|
Zn2+
|
0.681**
|
-0.236
|
-0.916**
|
0.940**
|
0.712**
|
0.062
|
1.000
|
* Significant correlation at 0.05 p level ** Significant correlation at 0.01 plevel |
Correlation matrix of heavy metals in inland surface water
Table 8 shows a correlation matrix of the heavy metals. A correlational matrix can help identify the source and transport of metals among heavy metals. (Suresh, et al., 2011, Wang et al., 2012), The correlation between the main metals indicated the presence of anthropogenic sources (Fu et al., 2014; Maanan et al., 2015). The inter-relationship between the heavy metal levels in Nworie inland aquatic ecosystem is presented in Table 8 and the closer the elements lines lay together; the stronger is the mutual correlation (Ter Braak and Smilauer, 2002). The correlation relationship showed close positive association between Cd2+ and Fe2+ (r = 0.816), Cd2+ /Pb2+ (r = 0.603), Cd2+/ Zn2+ (r = 0.681), Fe2+ /Ni2+ (r = 0.700) and Fe2+ /Zn2+ (r: 0.940) which were significant. While copper have negative relationship with Fe2+ (r= -0.856) and Zn2+ (r: = -0.916) respectively. Iron (Fe2+ ) shows high positive association with Nickel (Ni2+ ) (r = 0.700), Zinc (Zn2+ ) (r = 0.940) respectively. Nickel (Ni) shows positive correlation with Zn2+ (r = 0.712). Others showed a relatively weak correlation among themselves in the surface water. The significant positive correlations found between metals in the river may not have similar behaviors in other aspects, and this is in tandem with the findings of Nguyen et al. (2005) of Lake Balaton, Zhang et al. (2018) in Zhanjiang Bay, China. Elements that correlated significantly did not necessarily originate from common source, or neither are the elements source and pathway dependently correlated (Jørgensen et al., 2005; Ma et al., 2016). The result is consistent with the finding of Li et al. (2017) who with the use of multivariate statistical tools that includes correlation, reported, Cd, Cu, Pb and Zn levels in soils to emanate from anthropogenic sources, while Cr and Ni chiefly come from natural processes such as from parent materials (Lian et al., 2019).
Table 9
Principal Component Analysis (PCA) for surface water samples
|
PC 1
|
PC 2
|
PC 3
|
pH
|
-0.157
|
0.763
|
-0.223
|
Turbidity
|
0.765
|
-0.41
|
-0.006
|
Hardness as (CaCO3)
|
0.923
|
-0.334
|
0.046
|
COD
|
0.969
|
0.09
|
0.107
|
DO
|
0.987
|
0.022
|
-0.02
|
Chloride
|
-0.593
|
-0.383
|
0.641
|
Calcium
|
0.973
|
0.181
|
-0.041
|
Magnesium
|
0.521
|
0.74
|
-0.016
|
Potassium
|
0.835
|
-0.385
|
0.006
|
Sodium
|
0.542
|
-0.18
|
0.796
|
Sulphate
|
0.187
|
0.842
|
-0.314
|
Nitrate
|
-0.24
|
0.932
|
0.075
|
Phosphate
|
-0.53
|
-0.738
|
-0.195
|
Cadmium
|
0.944
|
0.039
|
0.265
|
Chromium
|
0.874
|
-0.012
|
-0.289
|
Copper
|
0.93
|
0.33
|
0.022
|
Iron
|
0.964
|
0.208
|
0.093
|
Lead
|
-0.878
|
-0.244
|
0.062
|
Zinc
|
0.952
|
0.149
|
0.159
|
Nickel
|
-0.869
|
-0.225
|
-0.249
|
Total Eigenvector
|
12.191
|
4.184
|
1.506
|
% of Variance
|
60.953
|
20.922
|
7.532
|
Cumulative %
|
60.953
|
81.875
|
89.407
|
Principal component analysis (PCA) for Nworie inland surface water samples
Table 9x-rays a varimax rotated PCA of the chemistry of Nworie river performed with SPSS 19.0. The PCA was used chiefly to show the relationship existing among the major ions, identify the sources of the ions as well as the validity of the elements of concern, according to the Bartlett’s test (Ahamad et al., 2020). Principal Components, alongside their respective plots were harnessed and rotated in space (Tables 9, Figs. 2 and 3). Three factors in the study area with high eigen-values > 1 were harnessed, which totals 89.41% of the cumulative. Accordingly, Liu et al. (2003), Çiçek et al. (2019) classified “strong” (> 0.75), “moderate” (0.75 − 0.50), and “weak” (0.50 − 0.30) in order of factor loadings values. The first principal component (PC1) shows upto 61% of the total variance and is characterized by highly positive loading values of turbidity (0.785), hardness (0.923), COD (0.969), DO (0.987) calcium (0.973), potassium (0.835), Cd (0.944), Cr (0,874), copper (0.93), iron(0.964), and zinc (0.952). Accordingly, high COD and low DO may
suggest increased level of organic matter content and nutrients load in water (El-Gamel and Shafik,1985; EL-Naggar et al., 1998), while lead (-0.878) and nickel (-0.869) were negatively loaded. The loading in PC1 may be as a result of the use of agricultural chemicals like pesticides, manure, and fertilizers, in the soil within the watershed leading to Cd, Cr, Cu, Ni, Pb, Zn, K, Fe, Ni etc (Duzgoren-Aydin, and Weiss, 2008; Cai et al., 2012 ; Xue et al., 2014; Dai et al.,2015; Ubuoh et al., 2022.). A number of the metals are essential nutrients in crop production though in trace amounts, and are provided chiefly by inorganic and commercial fertilizers so as to enhance soil quality and fertility. A few metals like Pb, Cd, and Cr, are not needed by plant for growth and development and thus, not intentionally introduced (Lian et al., 2019), but are suggested to have come from vehicular flow and workshops during sand mining. The result is in tandem with the previous reports by researchers, implicating industrial and vehicular processes to be responsible for the release of Zn, Cu, and Pb, into the soils and the environment (Imperato et al., 2003), with Cu arising primarily from machine production plants, Zn (a hardness additive) emanating from tire dust, while Pb comes from automobile exhaust emissions and coal combustion (Duzgoren-Aydin, and Weiss, 2008; Cai et al., 2012). Zinc pigment, is also used in production of plastic (Gakwisiri et al., 2012; Chi et al., 2017), and is found in solid wastes (Ubuoh et al., 2013). The break through on the removal of leaded petrol has greatly reduced the amount of Pb in the environment, although, the wearing and tearing of vehicle brake pads and tires still release Pb into the environment, therefore implicating vehicular source as a source of pollution (Imperato et al., 2003). The second principal component (PC2) shows upto 20.9% of the total variance, indicating high loading of pH (0.763), Sulphate (0.842) and nitrate (0.932). Only phosphate is negatively loaded (-0.738). This suggested to have originated from agriculture (Ubuoh et al., 2022), resulting in nutrients enrichment and eutrophication in water. The third principal component (PC3) explains 7.53% of the total variance, and was equally loaded with chloride (0.641) and Sodium (0.796) positively.
Table 10
Risk Assesment of Heavy Metals In Nworie Inland Aquatic Ecosystem
Stations
|
Contamination factor (Cf),
|
|
|
Cd
|
PL1
|
Cd2+
|
Cr2+
|
Cu2+
|
Fe2+
|
Ni2+
|
Pb2+
|
Zn2+
|
Mn2+
|
Hg2+
|
UR
|
0.796
|
4x10− 4
|
8x10− 4
|
3.8x10− 3
|
1.9x10− 4
|
0.050
|
3.3x10− 4
|
0.1–9.72
|
0.1-0.067
|
0.845
|
1.6x10− 4
|
MR
|
0.632
|
5.4x10− 4
|
3.1x10− 3
|
5.8x10− 3
|
1.9x10− 3
|
0.046
|
3.3x10− 3
|
-
|
-
|
0.689
|
2.2x10− 5
|
LR
|
1.816
|
3.3x10− 3
|
0.032
|
0.024
|
1.5x10− 4
|
0.022
|
1.8x10− 3
|
-
|
-
|
1.899
|
1.3x10− 4
|
Mean
|
1.081
|
1.4 x 10− 4
|
0.012
|
0.011
|
1.3x10− 3
|
0.039
|
1.4x10− 3
|
-
|
-
|
1.14
|
1.7x10− 4
|
Edori and Iyama (2020)
|
0.167–0.267
|
0.06–0.30
|
0.003–0.029
|
0.093–0.25
|
2.514–3.13
|
0.06–0.30
|
0.0067–0.025
|
-
|
-
|
3.422–13.162
|
0.0996–0.194
|
Remarks
|
LC:
cf > 1
|
LC:
cf < 1
|
LC: cf < 1
|
LC :cf < 1
|
LC:cf < 1
|
LC: cf < 1
|
LC:
cf < 1
|
|
|
LCd:
< 8
|
NP > 1
|
Heavy metals with cf < 1 points to low contamination (LC), heavy metals with < 8 indicates low degree of contamination (LCd). While pollution load index (PLI) < 1 shows no pollution (Hakason, 1980).
Contamination factor (C f )
From Table 10, Cf for Cd2+ fell within 0.316–0.908, with a mean of 1.08, Cr2+ ranged from 3.3x10-3 -5.4x10-4, with a mean value of 1.4 10− 3, Cu2+ ranged from 0.032–8x10-4, with a mean value 0.012, Fe2+ ranged from 0.024 -5.8x10-3, with a mean value 0.011, Ni2+ ranged from 1.5x10-4-1.9x10-4, with a mean value of 1.3x10-3, Pb2+ ranged from 0.022–0.050, with a mean of 0.039, and Zn2+ ranged from 1.8x10-3- 3.3x10-4, with a mean value of 1.4x10-3. The implication is that the heavy metals recorded a low contamination factors in the river. (Hakason, 1980). Based on the mean of heavy metals in surface water of Nworie inland river contamination factor is in order of: Cd2+≥ Pb2+≥Cu2+ ≥ Fe2+≥ Zn2+ ≥ Ni2+≥Cr2+. with Cadmium as the major contamination factor and at variance with the finding of Leghouchi et al. (2009) who observed highest content of Chromium from the tannery of Jijel in the Mouttas river (Algeria).
Degree of contaminant (Cdegree) and the pollution load (PLI)
Table 10 showed that the LR recorded the highest degree of contaminant with a value of 1.899 while the middle river had the lowest (0.689), with a mean value of 1.14 and degree of contaminant ≤ 8 indicating low. This is in slight disagreement with the findings of Edori and Iyama (2020) who obtained low to moderate contamination degree of heavy metals in Edagberi Creek. The order of contamination degree of each sampling point was in order of: LR ≥ UR ≥ MR, with (LR) having highest degree of contaminants, suspected to be due to human activities along the river course, which is consistent with the finding of Zhang et al. (2013), Zhang et al. (2015), Gooddy et al. (2016), and Kong et al. (2018) who reported that human activities such as rapid agricultural and economic development are the leading causes of collective pollution of surface water of the rivers.
The values of pollution index (PLI) ranged from 1.3x10− 4 -2.2x10− 5, with a mean value of 1.7x10− 4, which is less than 1 (0 < PLI ≤ 1), and signifying no heavy pollution status of the river, greater than the PLI values that ranged from 0.0996–0.194, and falling within the bracket of which goes to show that the river is not heavily polluted but moderately polluted with heavy metals most especially cadmium. This is also in tandem with the findings in Edagberi Creek (Edori and Iyama, 2020)
The pollution load ranged from 1.6 × 10− 4 in the upper river to 2.2 × 10− 5 in the middle river, in decreasing order of UR ≥ MR ≥ LR, with a mean PL of 1.7x10− 4. The values obtained of the pollution load were less than one (1) which implies no pollution. This means that the water at the different course streams were not polluted by the heavy metals. The result of the study is in contrast with the finding of Abdullah et al. (2016), who reported the high values of PLI index of the river due to metals at the Balok in Kuantan, Malaysia, and the pollution load in a river was also observed by Panda et al.(2020) in Salandi River – Downstream, Bhadrak, Odish.
Table 11
Ecosystem risk factor (Er) and index (Ir) of heavy metals In Nworie Inland aquatic ecosystem Inand
Stations
|
(Er)Cd2+
|
(Er)Cr2+
|
(Er)Cu2+
|
(Er)Ni2+
|
(Er)Pb2+
|
(Er)Zn2+
|
Ir
|
UR
|
11.940
|
8x10
|
4x10− 3
|
9.5x10− 3
|
0.249
|
3.3x10−
|
12.204
|
MR
|
9.480
|
1.1x10− 3
|
0.016
|
8.5x10− 3
|
0.232
|
3.3x10− 4
|
9.738
|
LR
|
27.24
|
6.6x10− 3
|
0.160
|
7.5x10− 4
|
0.111
|
1.8x 10− 3
|
27.520
|
Mean
Remarks
|
16.22
LPER
∑ir≤ 40
|
2.8x10− 3
LPER
∑ir≤ 40
|
0.053
LPER
∑ir≤ 40
|
6.3x10− 3
LPER
∑ir≤ 40
|
0.197
LPER
∑ir≤ 40
|
8.2x10− 3
LPER
∑ir≤ 40
|
16.48
LER
Ir<150
|
Table 11: shows that ecological risk factor of heavy metals in water is between ∑ir≤ 40 which indicates low potential risk (LPER) and the ecological risk index is below 150 which indicates low ecological risk. (LER).
Table 11 above summarizes the ecosystem risk assessment results of the heavy metals in Nworie Inland river. From the summary, the ecosystem heavy metal risk indicating markers were ranked in the following order: Cd2+ ≥ Pb2+ ≥ Cu2+ ≥ Zn2+ ≥ Cr2+ ≥ Ni2+, and were below 40, indicating low ecological risk factor. The result is in tandem with the findings of El-Amier et al. (2018), who observed low potential ecosytem risk (< 40) of all metals in Idku Lake. Accordingly, the result of Er of the study differs with the finding of El-Amier et al (2022), who observed very high ecosytem risk (> 320) in water body. Going forward to quantifying the overall potential ecosystem risk associated with heavy metals in Nworie water, the value of the risk index (Ir) ranged from 9.738 in the middle river to 27.520 in the lower river, and the range was below 150 indicating low ecological risk index (Hakason, 1980). The result of Ir also differs from the finding of El-Amier et al. (2022) who observed that the risk index values in the lake showed very high ecosystem risk (> 600) in Lake Qarun Wetland, Egypt, with Cd2+ accounting for most of the total risk factor, but below ≤ 40 of Er, which is consistent with the finding of Yuguda et al. (2020) in river water and sediment in Gashua Towa, Yobe, Nigeria. The result is against the finding of Zang et al. (2018) who observed Cd with high risk that associated with public health concern worldwide. The risk index numbers obtained in this present study are less significant compared with other studies conducted in wetland in Iran (Yavar Ashayeri and Keshavarzi, 2019) as well as the studies conducted on the coast of the Persian Gulf respectively.