Assessment and Source Apportionment of Heavy Metals in Soils Around Idunmwowina, Ovia North East, Nigeria

516 Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 p ISSN: 2635-3342; e ISSN: 2635-3350 Original Research Article Assessment and Source Apportionment of Heavy Metals in Soils Around Idunmwowina, Ovia North East, Nigeria *1Ojeaga, K. and 2Ehinlaiye, A.O. 1Department of Science Laboratory Technology, Faculty of Life Sciences, University of Benin, Benin City, Nigeria. 2Department of Geology, Faculty of Physical Sciences, University of Benin, Benin City, Nigeria. *kenneth.ojeag[email protected]; [email protected] http://doi.org/10.5281/zenodo.18061772 ARTICLE INFORMATION ABSTRACT Article history: Received 20 Oct. 2025 Revised 03 Nov. 2025 Accepted 08 Nov. 2025 Available online 30 Dec. 2025 This study evaluates the concentration, contamination status, ecological risk, and potential sources of heavy metals in soils from Idunmowina, Ovia North East, Nigeria. Ten (10) soil samples were analyzed for Fe, Zn, Cu, Pb, Mn, Ni, Cd, Cr, and Co using Atomic Absorption Spectrophotometry (AAS). Mean concentrations were in the order: Mn (53.54 mg/kg) > Fe (25.7 mg/kg) > Cu (21.80 mg/kg) > Ni (8.43 mg/kg) > Cr (5.45 mg/kg) > Zn (2.51 mg/kg) > Pb (2.51 mg/kg) > Co (2.40 mg/kg) > Cd (0.72 mg/kg). Enrichment factors (EF < 2) indicated minimal enrichment and dominantly geogenic origin. Ecological risk assessment showed Cu (Eir = 10.9) as the most critical element, though the cumulative risk index (RI = 28.35) classified overall risk as low. The pollution load index (PLI = 0.2866–0.3073) and degree of contamination (Cd = 0.3586–2.6772) suggested slight to moderate contamination. Multivariate statistics provided further source differentiation. Correlation analysis revealed strong positive associations (Zn–Pb, Cr–Cd, Cu–Co) and a significant negative Cu–Mn relationship indicative of lateritic weathering. Cluster analysis grouped Fe–Cu separately from Cr, Ni, Cd, Co, Pb, and Zn, reflecting distinct input pathways. Principal component analysis (PCA) extracted four components accounting for 92.49% of the variance, with PC1 and PC2 dominated by anthropogenic signatures, while Mn was primarily geogenic. Overall, the soils exhibit heavy metal concentrations within regulatory thresholds. However, localized Cu enrichment highlights anthropogenic influence, necessitating continuous monitoring to mitigate potential ecological impacts. © 2025 RJEES. All rights reserved. Keywords: Heavy metals Source apportionment Lateritic weathering Environmental indices Mining 1. INTRODUCTION The process of industrialization and urbanization has escalated in recent years. During this process a large number of pollutants now enter the soil through different channels (Ha et al., 2014; Hu and Cheng, 517 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 2013), damaging its composition, structure and function (Sakizadeh and Zhang, 2021; Zhao et al., 2020). Heavy metals in soils are derived from natural and anthropogenic sources, such as mining, industrial waste, fertilizers, waste water, pesticides, ore smelting, rainfall and vegetation (Gil et al., 2018). Mining is an anthropogenic activity that introduce arrays of pollutants into the environment. Environmental pollution with heavy metals is particularly of serious concern because they accumulate and are persistent in soils (Guo et al., 2012; Luo et al., 2011). The accumulation of toxic metals in soil and by extension plants will compromise the food chain and it’s a major pathway by which contaminants enters the human body. Geochemical processes are responsible for their transport, mobility and fate in any environmental medium (Ojeaga and Ehinlaye 2025). It has been reported that concentration of heavy metals in soil alone cannot comprehensively deal with pollution status of an environmental component. The specific type of contamination found in contaminated soil is directly related to the operation that occurred at the site. The range of contaminant concentration and the physical and chemical forms of contaminants will depend on activities and disposal patterns for contaminated wastes on the sites. Other factors that may influence the form, concentration and distribution of toxic metals include soil and groundwater chemistry and local transport mechanism (Mahato et al., 2023). The study area Idunmowina, located in Ovia North East of Edo State is known for hosting an existing compressed natural gas station; sand mine and several abandoned mine pits which residents have turned into an open dump site. These activities are potential sources of heavy metal enrichment in the environment. Currently there are no published work on assessment, sources and human health risk associated with toxic metals in the area. Therefore, this study combined environmental tools (geo-accumulation index, enrichment factor, pollution load index, potential ecological risk index) and statistical tools (correlation, cluster analysis and principal component analysis) to assess metal load in the soils. 2. MATERIALS AND METHODS 2.1. The Study Area The study area, Idunmwowina is located in Ovia North East local government area in Edo State. The area lies between Latitude N60 and Longitude E0050. The area is accessible from the Oluku and Uselu axis of the Benin-Lagos Road. The study area is known for hosting an active compressed natural gas station; active sand mining sites. Many of these sand mines have been turned into open dumpsites by residents due to nonadherence to international best practices by mine developers. These activities are potential sources of heavy metal enrichment in the environment. 2.2. The Geology of the Study Area The study area, Idunmwowina (latitude 6°25′59″N and longitude 5°35′59″E), lies within the Benin Formation, a major stratigraphic unit of the Southern Sedimentary Basin as depicted in Figure 1. The Benin Formation is composed predominantly of reddish to reddish-brown lateritic sands, sandy clays, silts, gravels, and ferruginized sandstones (Short and Stauble, 1967; Akujieze and Irabor, 2014). The surface layer is typically lateritized, underlain by loose sands and sandy clays, often with reticulate mud cracks. Parkinson (1907) first described this lithologic unit as “Benin Sand,” representing deposits of a paleocoastal environment dating from the Paleocene to Pleistocene. Subsequent studies (Tattam, 1943) referred to the sequence as “Coastal Plain Sands,” extending across parts of Benin, Calabar, Owerri, and Onitsha. These sediments constitute the upper continental facies of the Niger Delta, reflecting ancient fluvial and coastal plain depositional systems. 2.3. Soil Sampling A total of ten (10) surface soil samples were randomly collected from different locations in Idunmwowina community, Edo State, an area influenced by mining activities. Sampling was carried out using a hand auger, and each sample was stored in clean, labeled plastic containers. Samples were transported to the Ecotoxicology Laboratory, University of Benin, for further preparation and analysis. 518 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 2.4. Sample Preparation In the laboratory, soil samples were air-dried at room temperature, homogenized, and sieved through a 2 mm mesh to remove coarse debris. One gram (1 g) of each sample was digested using a mixed-acid procedure (HF–HNO₃–HClO₄–H₂SO₄ in aqua regia), diluted to 50 ml with distilled water, and transferred into polyethylene bottles for analysis (AOAC, 1990). Figure 1: Geology of the study area 2.5. Heavy Metal Analysis Concentrations of Fe, Zn, Cu, Pb, Mn, Ni, Cd, Cr, and Co were quantified using Atomic Absorption Spectrophotometry (AAS, VGP210 Bulk Scientific). Quality control was ensured by analyzing blanks, duplicates, and certified reference materials. Additionally, Na, K, and Ca were determined using a flame photometer. 2.6. Environmental Indices To assess contamination and ecological risk, several geochemical indices were applied, including the geoaccumulation index (Igeo) (Müller, 1969), enrichment factor (EF) (Liaghati et al., 2004 and Zahra et al., (2015), contamination factor (CF) and degree of contamination (Cd) (Sadhu et al., 2012; Likuku et al., (2013), modified degree of contamination (mCd) (Abraham and Parker, 2008), pollution load index (PLI), (Tomlinson et al., 1980), and the potential ecological risk index (PERI) as modified by, (Hakanson, 1980) is presented in Table 1. Table 1: Grade standards for potential ecological risks (Hakanson, 1980) Risk level Low risk Moderate risk High risk Very high risk Disastrous risk Grade I II III IV V Eir <30 30-60 60-120 120-240 >240 PERI <100 100-200 200-400 >400 519 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 3. RESULTS AND DISCUSSION 3.1. Heavy Metal Assessment The result of heavy metals concentration, their minimum, maximum, mean and standard deviation is presented in Table 2. The summary of univariate statistical analysis with comparison with (WHO, 2012) permissible standard is presented in Table 3. The mean concentrations of heavy elements Mn, Fe, Cu, Zn, Ni, Pb, Cr, Co, and Cd in surface soil samples were 53.54, 25.7, 21.8, 2.51, 8.43, 2.51, 5.45, 2.40 and 0.727 mg/kg, respectively. According to Table 2, the concentration of all analyzed metals were below the (WHO, 2012) values indicating that that underlying lithology and conditions of soil formation contributes to the metal concentration in the soil. It was observed that the Cd concentration 0.727mg/kg was almost at par with the (0.8mg/kg) permissible limit for WHO. The high Cd content could be attributed to materials (used batteries, plant materials, pipes) dump in the area. This value is higher than the Cd content recorded by (Soltani –Gerdefaramarzi et al., 2021). Table 2: Heavy metal concentrations in soil samples with statistical parameters Table 3: Summary of univariate statistical heavy metals parameter in soil sample Heavy metal parameter (mg/kg) Mean WHO Permissible Limit (WHO, 2012) Fe 25.70 0.3 Zn 2.51 50 Cu 21.8 36 Pb 2.51 50 Cd 0.73 0.8 Mn 53.54 200 Ni 8.43 35 Cr 5.45 30 Co 2.4 50 3.2. Environmental Assessment 3.2.1. Geo-accumulation index (Igeo) The Geo-Accumulation Index (Igeo) was employed to evaluate the degree of heavy metal contamination in soils of the study area. The results as shown in Table 4 indicate that all computed Igeo values for the analyzed metals were negative, thereby classifying the soils as practically uncontaminated according to Müller’s (1969) classification scheme. Among the metals, copper (Cu) exhibited the least negative Igeo value (- 0.4756), suggesting a slight enrichment but still within the uncontaminated threshold. In contrast, zinc (Zn), lead (Pb), and cadmium (Cd) displayed the most negative values (-5.3961, -4.1737, and -4.4401, respectively), reflecting negligible accumulation and no contamination risk. Similarly, Fe (-1.2364), Mn (- 2.4920), Ni (-2.7589), Cr (-3.0471), and Co (-2.6463) all fell within the uncontaminated category. (Likuku et al., 2013; Zahra et al., 2015) reported similar negative Igeo values confirming background contributions of metals. This demonstrates that the soils are largely influenced natural geochemical background levels with limited evidence of anthropogenic inputs. The slight enrichment of Cu may reflect minor contributions from lithogenic sources. Therefore, the soils in the study area can be considered unpolluted with heavy metals and rather are controlled by parent rock material composition. 520 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 Table 4: Igeo (Geo-Accumulation Index) for heavy metals and sampling points 3.2.2. Enrichment factor (EF) The results of enrichment factor (EF) of analyzed metals in this study is presented in Table 5. In this study, EF value of Cu, Mn , Co, Ni, Cr, Pb, Zn, Cd, were 1.7257, 0.4245, 0.3814, 0.3513, 0.2889,0.1335, 0.0572, and 0.0688 respectively. Cu had the highest EF value of 1.7257 while Zn had the least value of 0.0572. The EF values of Mn , Co, Ni, Cr, Pb, Zn, Cd ( EF< 1), suggests that they are depleted in the environment derived entirely from natural sources. However, the slight increase in the EF value of Cu 1.7257 (EF > 1), though indicates minor enrichment, it could possibly be influenced by anthropogenic activities in the area linked to open dumpsites in the area (Poggere et al., 2025). Table 5: Enrichment factor of heavy metals and the sampling points of the study area 3.2.3. Contamination factor The Contamination factor (CF) and Pollution Load Index (PLI) help assess the extent of metal contamination in the soil. Table 6 shows contamination factor of heavy metals in the soil samples analyzed from the study area. The results revealed that Fe (CF = 0.6366), Mn (CF = 0.2666), Zn (CF = 0.0356), Ni (CF = 0.2216), Pb (CF = 0.0831), Cr (CF = 0.1815), Co (CF = 0.2396), and Cd (CF = 0.0691) exhibited low contamination in the sampling locations (CF < 1) indicating a predominantly geogenic origin. However, Cu (CF = 1.0787) (1<CF <3) shows moderate contamination, indicating a possible anthropogenic (industrial or agricultural) influence that may likely be linked to degradation of mechanical hardwares (Owamah et al., 2023). 3.2.4. Degree of contamination and pollution load index The pollution load index (PLI) values for the investigated soils are presented in Table 6. The PLI ranged from 0.0356 (Zn) to 0.2666 (Ni). All values were ≤ 1, which indicates that the soils are within baseline levels of pollution and are not critically impacted by anthropogenic inputs (Tomlinson et al., 1980). This finding is consistent with studies by (Adesanya et al., 2020) and (Ololade et al., 2010), who similarly reported PLI values < 1 in agricultural and peri-urban soils of southwestern Nigeria, suggesting limited anthropogenic enrichment. The degree of contamination (Cd) values ranged between 0.3586 (Zn) and 2.6772 (Mn). According to Hakanson’s (1980) classification, Cd < 7 reflects a low degree of contamination. Hence, all soils in the present study fall within the “low contamination” category. Mn recorded the highest Cd value, 521 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 which may be attributed to natural geogenic inputs such as lateritic weathering, while Zn recorded the lowest (Cd) value. Comparable results have been reported in similar geologic terrains of West Africa, where Mn is often elevated due to pedogenic processes, whereas Zn remains relatively low due to limited anthropogenic input (Iwegbue et al., 2013; Ayrault et al., 2012). Overall, both PLI and Cd values suggest that the soils are not significantly polluted and are within acceptable ecological thresholds. However, the slightly elevated (Cd) values for Mn highlight the contribution of geogenic processes, whereas other metals show limited anthropogenic influence. Continuous monitoring is therefore recommended to prevent possible build-up of contaminants from mining and related activities in the area. Table 6: Contamination factor, degree of contamination factor and pollution load index 3.2.5. Potential ecological risk and ecological risk index (RI) The potential ecological risk factor (Eir) values for individual metals and the overall risk index (RI) are presented in Table 7. The order of decreasing (Eir) was Cu > Fe > Mn > Co > Ni > Cr > Pb > Cd > Zn. Among the analyzed metals, copper (Cu) recorded the highest Eir value of 10.9, which falls within the category of considerable risk (Hakanson, 1980). In contrast, the (Eir ) values of Fe (6.42), Mn (2.67), Co (2.40), Ni (2.21), Cr (1.81), Pb (0.83), Cd (0.72), and Zn (0.35) all indicated low ecological risk. The cumulative RI value of 28.35 confirmed that the heavy metals in soils within the study area pose an overall low risk to the environment (RI < 150; Hakanson, 1980). Although the cumulative ecological risk remains low, the elevated contribution of Cu is notable. Table 7: Ecological risk Indices of heavy metals in soil obtained from the area of study Potential ecological risk indices for single heavy metals Soil Sample Parameters SS1 SS2 SS3 SS4 SS5 SS6 SS7 SS8 SS9 SS10 Eirz Mn 0.2901 0.2530 0.2550 0.2382 0.2953 0.3156 0.2407 0.2608 0.2513 0.2769 2.6772 Fe 0.675 0.525 0.825 0.7 0.725 0.55 0.6 0.65 0.55 0.625 6.425 Cu 0.9 0.8 1.1 1 1.2 1.3 1.1 1.05 1.15 1.3 10.9 Zn 0.0414 0.0371 0.0286 0.0314 0.0343 0.0386 0.0414 0.0386 0.0343 0.0328 0.3586 Ni 0.2310 0.2395 0.2253 0.2221 0.225 0.2110 0.2168 0.2131 0.2142 0.2195 2.2176 Pb 0.0967 0.0867 0.0667 0.0733 0.08 0.09 0.0967 0.09 0.08 0.0767 0.8367 Cr 0.1797 0.1947 0.172 0.1843 0.1767 0.173 0.1667 0.182 0.1967 0.1917 1.8173 Co 0.209 0.236 0.23 0.238 0.244 0.263 0.254 0.247 0.239 0.24 2.4 Cd 0.105 0.081 0.062 0.064 0.072 0.051 0.038 0.052 0.109 0.093 0.727 Mean Conc. 0.3031 0.2725 0.3294 0.3057 0.3391 0.3325 0.3060 0.3093 0.3138 0.3395 Total ∑RI =28.3594 Poggere et al. (2023) highlighted that Cu toxicity in soils may disrupt trophic interactions and bioaccumulation patterns in food chains, depending on organismal sensitivity to edaphic conditions. Similarly, Simoes et al. (2020) reported neurotoxic effects of excessive Cu exposure on earthworms, a keystone soil biota that enhances porosity, permeability, nutrient cycling, and plant productivity. These findings align with the present study, suggesting that localized Cu enrichment may pose a latent ecological 522 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 threat despite the low overall risk index. Comparable research in Nigerian mining districts and sub-urban environments has also identified Cu as a dominant contributor to ecological risk, often associated with anthropogenic activities such as mining, smelting, and vehicular emissions (Iwegbue et al., 2013; Adesanya et al., 2020). Figure 2: Potential ecological risk of heavy metals in the study area 3.3. Source Apportionment of Heavy Metals in Soil Samples 3.3.1. Correlation The result of the correlation analysis of heavy metals is presented in Table 8. It was observed that Zn had strong positive relationship with Pb. Cr had a strong positive relationship with Cd. Cu had a strong positive relationship with Co. These relationship indicates that they share similar sources that could be linked to industrial contamination, as these elements are commonly associated with metal processing, mining, paint, and battery industries. The strong negative relationship between Mn and Fe also confirms an acidic environment. Fe and Mn in the soil could be due to their adsorbent properties, precipitating as hydroxides, forming solid particles as they leave acidic water conditions around weathered ore deposits (Rengel 2015). (Mandal et al., 2022) reported a link between metals and acidic environments. These metals with strong associations suggest that they formed from the same source and travel together (ref). The Fe-Zn negative relationship suggests different geochemical sources—Fe might be linked to iron oxide formation, while Zn might be more mobile in the soil. The Mn-Cu relationship could indicate lateritic weathering processes, where Mn oxides tend to concentrate Cu with the loose superficial material (Rengel, 2015). The negative Ni-Cu correlation suggests that these elements compete for adsorption sites in soil minerals. Table 8: Correlation matrix of heavy metal parameters of the study area Mn Fe Cu Zn Ni Pb Cr Co Cd Mn 1 Fe -0.0585 1 Cu 0.4448 0.0315 1 Zn 0.2468 -0.5459 -0.2390 1 Ni -0.1155 0.1387 -0.7377 -0.0550 1 Pb 0.2468 -0.5459 -0.2390 1 -0.0550 1 Cr -0.2117 -0.4414 -0.2217 -0.2246 0.2368 -0.2246 1 Co 0.1057 -0.3921 0.5829 0.1053 -0.6556 0.1053 -0.2591 1 Cd 0.1099 -0.1414 -0.1610 -0.1283 0.3512 -0.1283 0.7236 -0.6704 1 523 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 3.3.2. Clusters analysis The results of cluster analysis, used to group variables of the same source, are presented in the dendrogram of Figure 3. In the cluster analysis diagram, the distance between the clusters indicates the degree of relationship between the variables. In this study, it was observed that iron (Fe) and Copper (Cu) were placed in a strong cluster, suggesting similar sources, which could be attributed to anthropogenic sources linked to metal scraps and vehicular hardware. The second cluster includes Chromium (Cr), Nickel (Ni), Cadmium (Cd), Cobalt (Co), Lead (Pb), and Zinc (Zn), whose sources could be linked to industrial activity especially mining currently carried out in the area (Mahato et al., 2023). The third cluster indicates manganese standing alone from other metals. It has been reported that the concentration of Mn in the soil could be influenced by weathering and dissolution of ore containing Mn (Khozyem et al., 2019) Figure 3: Dendrogram using average linkage (between groups) of the Heavy metal parameters 3.3.3. Principal component analysis Principal component analysis provides insight pertinent to source identification and contributing factors of metals in environmental components (Giri and Singh 2013). The results of Principal component analysis (PCA) of analyzed metals in the soil, as shown in Tables 9 and 10 indicates four (4) principal components which account for 92.49% of the total variance. PC1 accounted for 28.36%, PC2 accounted for 28.13%, and PC3 and PC4 accounted for 21.37% and 14.63%, respectively. From the results of PCA, it was observed that PC1 exhibited high loadings of (>0.5) of Cu and Co. The implication is that they represent common sources linked to industrial activities, such as their uses in car radiators, wear and tear of vehicular parts (Guo et al., 2012). PC2 indicated high loadings of Zn and Pb. It has been reported that high levels of Pb in urban soils are related to the use of lead gasoline (De Miguel et al., 1997). In addition, the compounds of Zn have been used as antioxidants and as dispersants of lubricating oils (De Miguel et al., 1999). PC3 shows corresponding loadings of Cr and Cd linked to the manufacture of car batteries. PC4 indicate distinct and separate loading of Mn. The isolated standing of Mn in the soil could be attributed to geogenic origin. Previous studies (Zhu et al., 2006; Zeng, 2004 ) linked the presence of Mn in soils to weathering of ore minerals (maganiferous laterites), the ability to form precipitates from solution, and redox environment. 524 K. Ojeaga and A.O. Ehinlaiye / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 516-526 Table 9: Principal component analysis Table 10: Unrotated component matrixa and rotated component matrixa 4. CONCLUSION This study has demonstrated that heavy metals in the investigated soils are influenced by both geogenic and anthropogenic activities, with industrial operations, mining, and traffic emissions serving as contributors, while the lithology of underlying rocks and weathering processes account for geogenic input. The overall concentrations of the analyzed metals were within acceptable limits, and the negative Igeo values confirmed the absence of significant contamination. Although copper posed a considerable ecological risk, the overall ecological risk index (RI) suggested that the cumulative effect of all metals remains low. Multivariate statistical analyses (correlation, cluster, and PCA) further reinforced the anthropogenic dominance in the sources of Chromium (Cr), Nickel (Ni), Cadmium (Cd), Cobalt (Co), Lead (Pb), and Zinc (Zn), whereas Manganese (Mn) largely reflected natural/geogenic contributions. Thus, while the soils in Idunmowina currently reflect low ecological risk, periodic monitoring is recommended to prevent potential accumulation and ecological impairment from Cu and other anthropogenic metal inputs. In essence, while the soils in the study area are not presently threatened by heavy metal pollution, continuous monitoring and mitigation measures are recommended to prevent potential build-up from ongoing human activities. 5. CONFLICT OF INTEREST There is no conflict of interest associated with this work.