Assessment of groundwater quality remains essential in water management; anthropogenic practices such as industrial and agricultural activities can have negative impacts on groundwater quality. The present study assessed heavy metal contamination of groundwater in Karu, Central Nigeria. Heavy metal was evaluated by the indices: degree of contamination, heavy metal pollution and evaluation. Groundwater was found to be significantly contaminated with heavy metal concentrations exceeding the WHO limit for drinking water, particularly around areas of poultry farming, dumpsite and dimension stone quarry and processing plant. Groundwater is therefore considered unsafe for not only drinking purposes but also poultry. Strong correlation was observed between the degree of contamination and heavy metal evaluation index; between Pb, Fe and all indices; between TDS and Pb; Pb and Fe, Fe and Zn, Zn and Cu, Ni and Cu/Zn. The elements Pb, Zn, Cu, Fe and Ni contribute significantly to the contamination observed relative to the others. Groundwater contamination is attributed to anthropogenic activities within the study area; therefore, waste disposal practices require modification. The study presents a case for environmental (both baseline and impact) assessment and continuous monitoring to control pollution of groundwater.

  • The first of its kind in the study area.

  • Provides baseline data on heavy metal load in groundwater of the study area.

  • Forms background for further analysis.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Background to the study

Groundwater remains an essential resource for sustainable development with respect to livelihood and food security. Groundwater resource all over the world is under threat due to contaminant load introduced into it through urbanization, industrialization, agriculture and exploitation of natural resources (Ravindra & Mor 2019). These are all basic activities that are associated with everyday sustenance in the world today but can introduce unwanted substances into the environment, especially toxic metals considered hazardous to human health (Nasrabadi 2015). Heavy metals, due to their stability and ability to accumulate in tissues of animals and plants, and the fact that they are not readily biodegradable, persist in the environment especially in groundwater. Heavy metals have been given important consideration globally (Adeyemi & Ojekunle 2021). They occur in trace but significant amounts in the environment and have adverse health effects even at such low concentrations (Hosseinpour et al. 2014).

Heavy metals such as Cr, Pb, Hg, Cd, As and Co have no useful effects in the body system; long-time exposure may cause more acute interruptions in the normal operations of the human organ systems where the metals are deposited (Mominul et al. 2018). Other metals, such as Cu, Zn and Fe for example, are considered micronutrients required for normal growth and functioning of the human body; but at higher concentrations, they become toxic (Wang et al. 2019). The key anthropogenic sources of trace metals in groundwater are natural matters leached into the soil or rocks, residue from agrochemicals, controlled release from the sewage treatment plant and industrial run-off, and unrestrained releases or escape from landfill spot and chemical accidents or calamities.

Heavy metals in groundwater are sourced from atmospheric precipitation, agricultural wastes, discharge of industrial wastewater, agro-pesticides leaching, and urban sewage, mineral mining, and infiltration of surface runoff. Groundwater is exposed to these pollutants due to it being a component of the water cycle, which includes the atmosphere, ground surface, rocks and surface water. More than 50% the world's population depends on groundwater for survival (Rajankar et al. 2009). As, Ag and Pb are toxic even in trace amounts and are released as effluents from industrial activities (Adekunle & Akinyemi 2004). Carcinogenic risks associated with consumption of groundwater from Ogun State in Nigeria contaminated by heavy metals was reported by Adekunle & Ojekunle (2021).

The study area is a fast-growing semi-urban settlement, and has recorded increases in industrial and agricultural activities mainly poultry and fish farms, dimension stone quarry and processing plant. These activities can stress groundwater resources and impact negatively on its quality since wastes (solid and effluent) produced that contains heavy metals will eventually come in contact with groundwater. Studies linking heavy metal contamination of groundwater from anthropogenic activities in the area are lacking, the need to investigate groundwater quality in the area then arises. Assessment of the distribution of heavy metal load in groundwater can assist in linking this to the sources as well as design and implementation of prevention and mitigation measures. The objectives of this study were to (1) determine the distribution of heavy metal load in groundwater (2) evaluate groundwater contamination by these heavy metals using certain indices and (3) from the distribution pattern attempt to link heavy metal contamination to land use (anthropogenic activities) practices in the study area. Groundwater quality assessment based on heavy metal concentration will be achieved using the indices: contamination index; heavy metal pollution index and heavy metal evaluation index as described and used in: Backman et al. (1998); Prasad & Bose (2001) and Edet & Offiong (2002). These indices were also used to evaluate groundwater contamination: anomalous concentrations of heavy metals in groundwater (Panda et al. 2020); heavy metal contamination by hydrocarbons (Nnoli et al. (2021) and source of groundwater pollution by heavy metals (Vesali Naseh et al. 2018). The present study will evaluate heavy metal pollution (contamination) using the indices mentioned earlier to establish any risks posed to human health, plants or animals in the study area. Groundwater in the study area is essentially used for domestic purposes as well as providing the major source of water; heavy metal content thus affects its suitability for the intended use.

Study area description

The study area is centered on Tudun Wada town, Karu, Central Nigeria and lies within latitudes: 8 °53′N and 8 °56′N, and longitudes: 7 °38′E to 7 °44′E. The area is accessible through the Keffi–Abuja express road and has a network of other minor roads in between isolated settlements while footpaths provide accessibility to remote areas. The area has isolated hills mainly towards the northwestern part; it is drained by a major stream flowing generally towards the south with smaller streams originating from the northwestern and north eastern parts of the study area. The area is underlain by the Basement Complex rocks of North Central Nigeria; main rock units are gneiss (including banded gneiss) and schist.

Development has become an everyday norm as an increase in population demands increase in agricultural and industrial activities, i.e. land use. Land use activities are poultry and fish farming, which are predominant in the area, block industry, dimension stone quarry and processing plant and a waste dumpsite (Figure 1). In addition to this there are some irrigation farms adjacent the course of the major stream. Effluents or solid wastes from these anthropogenic activities can release heavy metals into groundwater sources, which if present in elevated concentration will significantly degrade water quality.
Figure 1

Location of the study area and sample collection points.

Figure 1

Location of the study area and sample collection points.

Close modal

Groundwater sampling and analysis

For the present study groundwater was sampled from 20 water points (Figure 1) consisting of 17 boreholes and 3 hand-dug wells. The hand-dug wells had depths in the range of 10 m–15 m while boreholes were deeper, reaching 120 m. Locations for the wells included households, dimension stone quarry and processing plant, irrigated farms but mostly boreholes from poultry and fish farms were sampled; Figure 1 shows the sampling stations within the study area. Sample collection points were centered on potential sources of heavy metals.

Groundwater samples were collected in 100 mL polyethylene bottles to prevent unpredictable changes in characteristics. The collected samples were treated with a few drops of HNO3 (to keep metals in solution) and kept at a temperature of 4 °C for further analysis. Prior to the sampling, physical parameters were measured using Sartorius potable meter (PT-10), i.e. pH, electrical conductivity (EC) and total dissolved solids (TDS). Elevation and coordinates values were taken for each sample location with the aid of a GPS. Concentrations of heavy metals (As, Zn, Pb, Ni, Fe and Cu) in water samples were determined using ICP-OES method.

Evaluation methods

To evaluate heavy metal pollution/contamination in the study area, the following indices were used: contamination index or degree of contamination (Cd; Backman et al. (1998), heavy metal pollution index (HPI; Prasad & Bose 2001) and heavy metal evaluation index (HEI; Edet & Offiong 2002). Panda et al. (2020) used the indices to evaluate trace metal anomalies in groundwater from a foothill aquifer in Tamil Nadu, India; anomalous values of the indices identified areas of groundwater contamination by the trace metals. Nnoli et al. (2021) used the indices to gain insights into contamination levels in the oil spill-ravaged Ogoni land in southern Nigeria; elevated levels of the indices were observed in water. Low levels of the indices, indicative of low contamination were observed by Vesali Naseh et al. (2018) while assessing groundwater pollution sources in Ghaen plain, Iran.

Contamination index

Using the contamination index, the quality of groundwater is evaluated by determining Cd. For each sample of water analyzed, Cd is computed using Equation (1) and represents the sum of contamination factors (Equation (2)) of individual components analyzed (heavy metal) that exceed the permissible value. That is the Cd gives a combined effect of contamination due to individual, considered harmful. The resultant Cd value identifies areas of varying contamination levels, which are grouped into three categories as follows: Cd<1 (low), Cd=1–3 (medium) and Cd>3 (high) (Edet & Offiong 2002).
formula
(1)
formula
(2)
where: is the contamination factor for each heavy metal for a particular groundwater sample; is the analytical concentration of the a particular heavy metal and is the permissible concentration limit for the heavy metal or the maximum allowed concentration (MAC). For this study, values are defined in Table 1.
Table 1

Standards used in computation of pollution indices (concentration in μg/L)

Heavy metalMACWiSiIi
As 50a 0.02 10 10 
Cu 1,000a 0.001 1,000 2,000 
Fe 200a 0.005 300 200 
Ni 70b 0.014 70 70 
Pb 1.5a 0.67 100 10 
Zn 5,000a 0.0002 5,000 3,000 
Heavy metalMACWiSiIi
As 50a 0.02 10 10 
Cu 1,000a 0.001 1,000 2,000 
Fe 200a 0.005 300 200 
Ni 70b 0.014 70 70 
Pb 1.5a 0.67 100 10 
Zn 5,000a 0.0002 5,000 3,000 

HPI

This index is based on a weighted average/arithmetic mean of variables in terms of quality. The index is computed in two steps; the first being establishment of a weighting scale for each parameter and then selecting based on the weighting, the parameters to be used in calculating the index. The weighting reflects the relative importance (and consequence as a result of its presence in water) of each heavy metal with respect to quality standard. The unit weight (Wi) as suggested by Prasad & Bose (2001) and used in Edet & Offiong (2002) can be taken as the inverse of the maximum allowable concentration (MAC) of the heavy metal. The HPI is given by Mohan et al. 1996:
formula
(3)
formula
(4)
where Qi is the sub-index of the ith parameter (heavy metal) and is given by Equation (4). Wi is the corresponding weighting while Mi, Ii, and Si are analytical, ideal and standard concentrations respectively of the heavy metal; and n is the number of heavy metals considered. This parameter combines the impact of n number of heavy metals and their individual impacts to assess water contamination. Heavy metals considered are: arsenic (As), copper (Cu), iron (Fe), nickel (Ni), lead (Pb) and zinc (Zn). WHO (2011) standard was taken as standard value Si; guide value for each element was used as the ideal value Ii; while the inverse of the MAC in each case was taken as the weighting (Table 1).

HEI

This index evaluates water quality with respect to heavy metal concentration just like the HPI. It is evaluated using Equation (5).
formula
(5)

In the equation, Hc is the concentration of each heavy metal as measured in water and Hmac is the maximum allowable concentration of the particular heavy metal in water.

Physical parameters of groundwater

Physical parameters measured in-situ at water points in the study area are: pH, EC and TDS (Table 2). PH ranged from 6.4 to 8 with a mean value of 7.11 ±0.09; the values fall within the permissible range for drinking water (6.5–8.5; WHO 2011). Given the values of pH measured, groundwater is slightly acidic to basic, the slight acidity will keep metals in solution. TDS values are as low as 30 mg/L to 620 mg/L, with a mean value of 183.50±32.68 mg/L; all values are within the permissible limit for drinking water (500 mg/L; WHO 2011) with the exception of two points, one adjacent a dumpsite and the other water point belonging to a dimension stone processing plant. The EC of water increases with concentration of ions and, therefore, dissolved solids.

Table 2

Physical parameters measured in-situ in the study area

Sample IDpHEC (μS/cm)TDS (mg/L)
6.8 300 140 
7.4 250 120 
6.5 310 150 
7.5 560 270 
7.2 310 150 
7.6 430 210 
6.6 250 110 
6.4 190 80 
19 530 250 
10 310 150 
11 7.3 400 190 
12 7.4 170 70 
13 260 120 
14 6.9 260 120 
15 7.1 300 140 
16 6.8 1,080a 530a 
17 7.6 350 110 
18 7.5 80 30 
19 6.7 1,250a 620a 
20 240 110 
Mean±1se 7.11±0.09 391.50±64.54 183.50±32.68 
Sample IDpHEC (μS/cm)TDS (mg/L)
6.8 300 140 
7.4 250 120 
6.5 310 150 
7.5 560 270 
7.2 310 150 
7.6 430 210 
6.6 250 110 
6.4 190 80 
19 530 250 
10 310 150 
11 7.3 400 190 
12 7.4 170 70 
13 260 120 
14 6.9 260 120 
15 7.1 300 140 
16 6.8 1,080a 530a 
17 7.6 350 110 
18 7.5 80 30 
19 6.7 1,250a 620a 
20 240 110 
Mean±1se 7.11±0.09 391.50±64.54 183.50±32.68 

aTDs/EC values higher than WHO (2011) limits.

EC values range from 80 μS/cm to 1,250 μS/cm with an average value of 391.50 ±64.54 μS/cm; all values apart from the two locations that had high TDS values, had EC values within permissible limits for drinking water (1,000 μS/cm; WHO 2011). EC and TDS values measured in the study area show a linear relationship, this is a demonstration of the linear relationship between specific conductance and ionic strength as is the case for conductance in the range of 10–10,000 μS/cm (Lind 1970). The constant of proportionality in this case is 0.48 (Figure 2) and is close to the reported values between 0.55 and 0.85 (Walton 1989; Atekwana et al. 2004).
Figure 2

Linear relationship between EC and TDS (Lind 1970) as observed in the study area.

Figure 2

Linear relationship between EC and TDS (Lind 1970) as observed in the study area.

Close modal

Trace elements in groundwater samples

Concentrations of the trace metals in groundwater from the study area are presented in Table 3. The elements: Pb, As, Fe, Ni, Zn and Cu had mean concentrations of 81.10±11.54; 854.50±82.02; 860.15±152.72; 62.65±20.24; 2,537.20±335.13 and 1,527.40±175.29 μg/L respectively. The concentrations in most water points exceed the limits set by the World Health Organization (WHO 2011). Concentrations of Pb and As in all samples exceed the WHO limit of 10 μg/L. Concentration of Fe in all but two locations exceed the WHO limit of 300 μg/L. Concentration of Ni in the samples are within the WHO limits except for five locations which exceed the limit of 70 μg/L. For Zn and Cu, water samples from 6 points had concentrations above the set limit of 3,000 and 2,000 μg/L respectively.

Table 3

Concentration of Pb, As, Fe, Ni, Zn and Cu in groundwater samples collected from the study area

Sample IDPb (μg/L)As (μg/L)Fe (μg/L)Ni (μg/L)Zn (μg/L)Cu (μg/L)
76 765 750 25 2,960 1,102 
40 1,080 603 387 2,660 1,098 
72 721 980 107 4,034 2,169 
28 621 476 43 2,870 2,106 
16 841 507 198 3,901 2,000 
69 1,303 681 70 2,600 1,043 
78 587 1,000 76 2,400 2,899 
67 486 512 106 1,980 1,032 
19 81 816 524 2,098 1,781 
10 74 799 660 18 1,187 823 
11 58 1,203 451 784 654 
12 61 518 890 65 898 1,043 
13 95 453 386 1,081 543 
14 66 992 229 20 2,660 2,000 
15 100 1,900 1,830 20 3,711 2,879 
16 87 337 402 342 865 
17 85 546 298 600 300 
18 92 887 705 30 3,045 1,453 
19 279 1,145 2,609 46 6,041 2,350 
20 98 1,090 2,710 30 4,892 2,408 
Mean±1se 81.10±11.54 854.50±82.02 860.15±152.72 62.65±20.24 2,537.20±335.13 1,527.40±175.29 
Sample IDPb (μg/L)As (μg/L)Fe (μg/L)Ni (μg/L)Zn (μg/L)Cu (μg/L)
76 765 750 25 2,960 1,102 
40 1,080 603 387 2,660 1,098 
72 721 980 107 4,034 2,169 
28 621 476 43 2,870 2,106 
16 841 507 198 3,901 2,000 
69 1,303 681 70 2,600 1,043 
78 587 1,000 76 2,400 2,899 
67 486 512 106 1,980 1,032 
19 81 816 524 2,098 1,781 
10 74 799 660 18 1,187 823 
11 58 1,203 451 784 654 
12 61 518 890 65 898 1,043 
13 95 453 386 1,081 543 
14 66 992 229 20 2,660 2,000 
15 100 1,900 1,830 20 3,711 2,879 
16 87 337 402 342 865 
17 85 546 298 600 300 
18 92 887 705 30 3,045 1,453 
19 279 1,145 2,609 46 6,041 2,350 
20 98 1,090 2,710 30 4,892 2,408 
Mean±1se 81.10±11.54 854.50±82.02 860.15±152.72 62.65±20.24 2,537.20±335.13 1,527.40±175.29 

Groundwater was classified using the scheme proposed by Ficklin et al. (1992) and modified by Caboi et al. (1999). The scheme uses the pH of groundwater and the combined metal load computed as the sum of the individual concentrations of the heavy metals for each sample point. Based on this scheme, all samples plot in the field for near-neutral high metal (Figure 3).

Cd

Computed values for Cd provide insights into the level of contamination by these trace elements. According to the classification scheme presented in Edet & Offiong (2002), Cd can be grouped into three categories as follows: Cd<10 (low), Cd=10–20 (medium) and Cd>20 (high). For the study area, all samples had Cd values much greater than 20 (Table 4) indicating that there is a high degree of contamination by trace elements in groundwater of the study area.

Table 4

Groundwater quality evaluation indices for the study area

Sample IDCdHPIHEI
65.77 126.53 71.77 
52.44 124.21 58.44 
65.82 123.96 71.82 
30.76 64.72 36.76 
29.63 73.99 35.63 
72.03 158.36 78.03 
67.20 119.61 73.20 
53.89 98.97 59.89 
19 69.24 132.95 75.24 
10 63.93 125.93 69.93 
11 59.82 135.52 65.82 
12 51.63 95.96 57.63 
13 69.10 121.03 75.10 
14 61.80 128.04 67.80 
15 111.72 238.76 117.72 
16 61.70 104.53 67.70 
17 63.51 116.50 69.51 
18 79.09 151.72 85.09 
19 220.16 379.86 226.16 
20 98.50 186.31 104.50 
Mean±1se 72.39±8.81 140.37±15.15 78.39±8.81 
Sample IDCdHPIHEI
65.77 126.53 71.77 
52.44 124.21 58.44 
65.82 123.96 71.82 
30.76 64.72 36.76 
29.63 73.99 35.63 
72.03 158.36 78.03 
67.20 119.61 73.20 
53.89 98.97 59.89 
19 69.24 132.95 75.24 
10 63.93 125.93 69.93 
11 59.82 135.52 65.82 
12 51.63 95.96 57.63 
13 69.10 121.03 75.10 
14 61.80 128.04 67.80 
15 111.72 238.76 117.72 
16 61.70 104.53 67.70 
17 63.51 116.50 69.51 
18 79.09 151.72 85.09 
19 220.16 379.86 226.16 
20 98.50 186.31 104.50 
Mean±1se 72.39±8.81 140.37±15.15 78.39±8.81 

HPI

Using the classification for the HPI: HPI<100 – low; HPI=100 medium; HPI>100 – high (Edet & Offiong 2002); four locations had HPI less than 100 and are thus classified as having a low level of contamination (Table 4). On the other hand all other samples had HPI greater than 100 indicating a high level of contamination. The high HPI may be due to wastewater from industrial and agricultural activities and domestic sewage; land use in the study area is mainly poultry farming and dimension stone processing, while domestic regions are also prevalent. These anthropogenic activities may be contributing to the trace element load observed.

HEI

HEI focuses on heavy metals in water samples for estimating the water quality (Edet et al. 2003). The water quality index is classified into the categories: HEI<10 (low), HEI=10–20 (medium) and HEI>20 (high). All samples had HEI much greater than 20 and are thus categorized as having high level of contamination.

Correlation assessment

To assess relative contribution of the trace elements to contamination, Pearson's univariate correlation coefficient between the elements and the computed indices was calculated (Table 5). Very strong correlation was observed between the indices; especially so between the degree of contamination and HEI.

Table 5

Pearson's bivariate correlation matrix for physical parameters, heavy metal concentration and the indices of pollution

pHEC (μS/cm)TDS (μg/L)Pb (μg/L)As (μg/L)Fe (μg/L)Ni (μg/L)Zn (μg/L)Cu (μg/L)CdHPIHEI
pH 1.00            
EC (μS/cm) −0.23 1.00           
TDS (μg/L) −0.25 1.00 1.00          
Pb (μg/L) −0.23 0.63 0.62 1.00         
As (μg/L) 0.27 −0.02 0.00 0.21 1.00        
Fe (μg/L) 0.12 0.27 0.28 0.67 0.48 1.00       
Ni (μg/L) 0.03 −0.20 −0.17 −0.30 0.08 −0.07 1.00      
Zn (μg/L) −0.01 0.17 0.10 0.46 0.49 0.75 0.22 1.00     
Cu (μg/L) −0.15 0.07 0.57 0.22 0.41 0.59 0.74 0.74 1.00    
Cd −0.14 0.56 0.51 0.97 0.43 0.78 −0.21 0.59 0.35 1.00   
HPI −0.07 0.50 0.51 0.91 0.59 0.79 −0.16 0.62 0.38 0.98 1.00  
HEI −0.14 0.56 0.57 0.97 0.43 0.78 −0.21 0.59 0.35 1.00 0.98 1.00 
pHEC (μS/cm)TDS (μg/L)Pb (μg/L)As (μg/L)Fe (μg/L)Ni (μg/L)Zn (μg/L)Cu (μg/L)CdHPIHEI
pH 1.00            
EC (μS/cm) −0.23 1.00           
TDS (μg/L) −0.25 1.00 1.00          
Pb (μg/L) −0.23 0.63 0.62 1.00         
As (μg/L) 0.27 −0.02 0.00 0.21 1.00        
Fe (μg/L) 0.12 0.27 0.28 0.67 0.48 1.00       
Ni (μg/L) 0.03 −0.20 −0.17 −0.30 0.08 −0.07 1.00      
Zn (μg/L) −0.01 0.17 0.10 0.46 0.49 0.75 0.22 1.00     
Cu (μg/L) −0.15 0.07 0.57 0.22 0.41 0.59 0.74 0.74 1.00    
Cd −0.14 0.56 0.51 0.97 0.43 0.78 −0.21 0.59 0.35 1.00   
HPI −0.07 0.50 0.51 0.91 0.59 0.79 −0.16 0.62 0.38 0.98 1.00  
HEI −0.14 0.56 0.57 0.97 0.43 0.78 −0.21 0.59 0.35 1.00 0.98 1.00 

Bold indicates strong positive correlation, while the underlined fonts depict strong correlation.

A very strong correlation is also observed between Pb and all indices implying that Pb contributes significantly to contamination in the study area. A strong correlation is also observed between Fe and all indices; to a lesser degree, correlation is also observed between EC/TDS and Zn with the indices. For the trace elements, correlation is observed between TDS and Pb; Pb and Fe, Fe and Zn, Zn and Cu, Ni and Cu/Zn. This suggests that the elements Pb, Zn, Cu, Fe and Ni contribute significantly to the contamination observed relative to the other trace elements. Secondly, for the study area, TDS/EC is a reflection of contamination by the trace elements, while the pH measured does not reflect the state of contamination. In addition to Pearson's correlation, regression plots (Figure 4) between the indices indicate a linear relationship with r2 values close to 1.
Figure 3

Classification based on heavy metal load and pH (Edet & Offiong 2002).

Figure 3

Classification based on heavy metal load and pH (Edet & Offiong 2002).

Close modal
Figure 4

Regression plots of the contamination indices showing r2 values.

Figure 4

Regression plots of the contamination indices showing r2 values.

Close modal

Spatial distribution of trace metals and contamination indices in relation to land use in the study area

Surface plots of heavy metal concentration in groundwater and the assessment indices were created using Matlab (Rb2011) software and compared with land use activities in the study area.

Lead is a naturally occurring element found in rocks and used in products such as pipes, car batteries, paint and gasoline. Lead enters groundwater due to natural or anthropogenic sources; it can leach into groundwater through contact with lead bearing rocks or even lead containing materials like pipes. The distribution of lead in the study area (Figure 5(a) is such that the concentration is highest in the northeastern region; coinciding with the area around a waste dumpsite. Leachate from this dumpsite may have polluted groundwater within its vicinity.
Figure 5

Spatial distribution of concentration of (a) lead and (b) iron in the study area. High concentrations are observed in the region of the waste dumpsites for both Fe and Pb while high concentration is recorded for Fe in the region of poultry farms.

Figure 5

Spatial distribution of concentration of (a) lead and (b) iron in the study area. High concentrations are observed in the region of the waste dumpsites for both Fe and Pb while high concentration is recorded for Fe in the region of poultry farms.

Close modal

Iron is the second-most abundant metallic element in the Earth's crust; in spite of this, its concentration in water is small (Ngah & Nwankwoala 2013). Iron in groundwater originates from dissolution from the aquifer framework and subsequent percolation into the saturated zone. It is an essential element in the metabolism of animals and plants, for nutrition and in the formation of mammalian haemoglobin; but if present at high concentration, it forms a red oxy-hydroxide precipitate that stains laundry and plumbing fixtures, dish wares and glasses owing to its very reactive nature. Iron actually presents no health hazards even in excess concentration except for imparting a metallic taste to water if the concentration is above 1,800 μg/L (Ngah & Nwankwoala 2013).

The distribution of iron in the study area (Figure 5(b)) is such that the concentration is highest in the north eastern and southern regions, coinciding with the area around a waste dumpsite and poultry farming respectively. Leachate from this dumpsite and poultry waste may have polluted groundwater within these regions.

The primary sources of zinc and copper are rock-forming minerals, also found in soils, the air and water. These metals are introduced into the groundwater system by artificial pathways such as by-products of industrial activities, e.g. steel production or coal-fired power stations, or from waste materials. Drinking water containing high levels of zinc can lead to stomach cramps, nausea and vomiting (SaskH20 2008). Long-term exposure to copper can cause irritation of the nose, mouth and eyes and has negative impact on the digestive system – stomachaches, vomiting and diarrhea. Both zinc and copper have high concentrations in the regions of waste dumpsites and the poultry farms (Figure 6). This implies that leachate from the dumpsite as well as effluent from poultry farming contribute to the copper and zinc load recorded in groundwater samples.
Figure 6

Spatial distribution of concentration of (a) zinc and (b) copper in the study area. High concentrations are observed in the regions of the waste dumpsites and poultry farms for both Zn and Cu.

Figure 6

Spatial distribution of concentration of (a) zinc and (b) copper in the study area. High concentrations are observed in the regions of the waste dumpsites and poultry farms for both Zn and Cu.

Close modal
Arsenic is a naturally occurring element in trace amounts found in rocks. Arsenic can be released from these geologic sources into groundwater depending on the chemical form of the arsenic, the geochemical conditions in the aquifer and the biogeochemical processes that occur. Arsenic also can be released into groundwater as a result of human activities, such as mining, and from its various uses in industry, in animal feed, as a wood preservative and as a pesticide. In drinking-water supplies, arsenic poses a problem because it is toxic at low levels and is a known carcinogen (USGS 2019). In the study area, high concentration of arsenic is observed in the regions of the waste dumpsite, dimension stone quarry and processing plant and the poultry farms (Figure 7(a)). This implies that in addition to the waste dumpsites and poultry farms, effluent from the dimension stone quarry and processing plant also impacts negatively on groundwater in the study area.
Figure 7

Spatial distribution of concentration of (a) arsenic and (b) nickel in the study area. High concentrations are observed in the regions of the dimension stone quarry and processing plant for and poultry farms for both As and Ni. High concentration of As is also observed aroung the waste dumpsite.

Figure 7

Spatial distribution of concentration of (a) arsenic and (b) nickel in the study area. High concentrations are observed in the regions of the dimension stone quarry and processing plant for and poultry farms for both As and Ni. High concentration of As is also observed aroung the waste dumpsite.

Close modal

Nickel in drinking water is primarily sourced by leaching from metals in contact with drinking water, such as pipes and fittings. However, nickel may also be present in some groundwater as a consequence of dissolution from nickel ore bearing rocks.. High concentration of nickel in the study area (Figure 7(b)) is observed in the regions of the dimension stone quarry and processing plant and the poultry farms, although significant concentrations are observed elsewhere.

Spatial distribution of the indices indicates that highest values are concentrated in the region of the poultry farms and the dimension stone quarry and processing plants (Figure 8). Poultry feed usually contains heavy metals as additives; thus excretion and wastes generally produced will have negative impacts on the environment since the wastes will contain these heavy metals (Okeke et al. 2015; Oyewale et al. 2019). Effluent from dimension stone quarrying and processing will also contain heavy metals; this will therefore impact on water that comes in contact with it. Figure 9 shows heavy metal concentration in effluents sampled at different points within the dimension stone processing plant in the study area; the effluent in all cases exceed the MAC used in this analysis for all heavy metals.
Figure 8

Spatial distribution of (a) degree of contamination; (b) HPI and (c) HEI. Highest values are observed in regions around the poultry farms and the dimension stone quarry and processing plant.

Figure 8

Spatial distribution of (a) degree of contamination; (b) HPI and (c) HEI. Highest values are observed in regions around the poultry farms and the dimension stone quarry and processing plant.

Close modal
Figure 9

Heavy metal concentration of effluent from the dimension stone processing plant. Concentration of the heavy metal exceeds the MAC as used in the present study.

Figure 9

Heavy metal concentration of effluent from the dimension stone processing plant. Concentration of the heavy metal exceeds the MAC as used in the present study.

Close modal

Heavy metal concentration in groundwater was evaluated using contamination indices with the aim of establishing its contamination status. Concentrations of Pb and As in all samples exceed the WHO limit of 10 μg/L. Concentration of Fe in all but two locations exceeds the WHO limit of 300 μg/L. Concentration of Ni in 75% of the samples are within the WHO limit of 70 μg/L. For Zn and Cu, water samples from 6 points had concentrations above the set limit of 3,000 and 2,000 μg/L respectively. Degree of contamination, heavy metal pollution and heavy metal evaluation indices showed that groundwater is significantly contaminated by heavy metals. Very strong correlation was observed between the degree of contamination and heavy metal evaluation index; between Pb, Fe and all indices; between TDS and Pb; Pb and Fe, Fe and Zn, Zn and Cu, Ni and Cu/Zn. The elements Pb, Zn, Cu, Fe and Ni contribute to the contamination observed relative to the others. TDS/EC is a reflection of contamination in the study area. Groundwater contamination is attributed to anthropogenic activities within the study area especially poultry farming and dimension stone quarrying and processing. The highest concentration of heavy metals was recorded from water points close to poultry farms, the dimension stone quarry and processing plant and a waste dumpsite, highlighting the need for proper solid waste/effluent disposal practices i.e. containment and treatment or evacuation to safe waste disposal facilities. Contamination indices were highest within these regions. Of the three sources, the poultry farms contribute most to the heavy metal load of groundwater. Groundwater in the study area is therefore not suitable for drinking purposes, poultry and fish farming. Results obtained here present a clear case for an environmental impact assessment and routine monitoring of water quality.

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

The authors declare there is no conflict.

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