Soil Heavy Metal Pollution Research Articles

Lead and zinc chemical fraction alterations in multi-metal contaminated soil with pomelo peel biochar and biochar/apatite incubation

The issue of heavy metal soil contamination is widespread, and the negative effects of heavy metals on the ecosystem depend on their chemical speciation in contaminated soil. Pomelo peel-derived biochar produced at 300 °C (PPB300) and 500 °C (PPB500) and its combination with apatite ore (AP) was applied to immobilize lead (Pb) and zinc (Zn) in agricultural multi-metal polluted soil. Soil amendments including biochar (PPB300 and PPB500) at concentrations of 3%, 5%, and 10%, as well as a mixture of biochar and apatite (AP) at 3:3% and 5:5% weight ratios, were introduced into the soil matrix. The chemical forms of Pb and Zn in incubated soil samples and control soil (CS) were studied utilizing the Tessier’s sequential extraction procedure. The chemical partitioning of heavy metals was carried out using Tessier’s extraction protocol, yielding fractions representing exchangeable (F1), carbonate (F2), Fe/Mn oxide (F3), organic matter (F4), and residual (F5) forms. Quantification of Pb and Zn concentrations was accomplished via inductively coupled plasma mass spectrometry (ICP-MS). Observations revealed notable elevations in pH, organic carbon (OC), and electrical conductivity (EC) levels within the treated soil relative to the control soil (p < 0.05). After incubating PPB300, PPB500, and AP for 30 days, there was a reduction in the exchangeable fraction of Pb and Zn by approximately 64% and 58% respectively. This reduction was achieved by transforming heavy metals from mobile fractions to immobile fractions using five primary mechanisms: chemical and physical adsorption, electrostatic attraction, the formation of complexes via interactions with active groups, cation exchange processes, and precipitation events mediated by phosphate, carbonate ions, or hydroxyl ions within an alkaline environment. Therefore, pomelo peel-derived biochar and the blend of biochar/apatite show promise as materials for mitigating heavy metal pollution in soil.

Extraction of heavy metals from copper tailings by ryegrass (Lolium perenne L.) with the assistance of degradable chelating agents

Heavy metal contamination is an urgent ecological governance problem in mining areas. In order to seek for a green and environmentally friendly reagent with better plant restoration effect to solve the problem of low efficiency in plant restoration in heavy metal pollution soil. In this study, we evaluated the effects of three biodegradable chelating agents, namely citric acid (CA), fulvic acid (FA) and polyaspartic acid (PASP), on the physicochemical properties of copper tailings, growth of ryegrass (Lolium perenne L.) and heavy metal accumulation therein. The results showed that the chelating agent application improved the physicochemical properties of copper tailings, increased the biomass of ryegrass and enriched more Cu and Cd in copper tailings. In the control group, the main existing forms of Cu and Cd were oxidizable state, followed by residual, weak acid soluble and reducible states. After the CA, FA or PASP application, Cu and Cd were converted from the residual and oxidizable states to the reducible and weak acid soluble states, whose bioavailability in copper tailings were thus enhanced. Besides, the chelating agent incorporation improved the Cu and Cd extraction efficiencies of ryegrass from copper tailings, as manifested by increased root and stem contents of Cu and Cd by 30.29–103.42%, 11.43–74.29%, 2.98–110.98% and 11.11–111.11%, respectively, in comparison with the control group. In the presence of multiple heavy metals, CA, FA or PASP showed selectivity regarding the ryegrass extraction of heavy metals from copper tailings. PCA analysis revealed that the CA-4 and PASP-7 treatment had great remediation potentials against Cu and Cd in copper tailings, respectively, as manifested by increases in Cu and Cd contents in ryegrass by 90.98% and 74.29% compared to the CK group.

Research Progress and Prospects in Soil Column Experiments to Study Soil Heavy Metal Pollution

Research Progress and Prospects in Soil Column Experiments to Study Soil Heavy Metal Pollution

Risk assessment and source analysis of heavy metals in soil around an asbestos mine in an arid plateau region, China

Asbestos is widely used in construction, manufacturing, and other common industrial fields. Human activities such as mining, processing, and transportation can release heavy metals from asbestos into the surrounding soil environment, posing a health hazard to the mining area's environment and its surrounding residents. The purpose of the present study was to determine the extent of ecological and human health damage caused by asbestos pollution, as well as the primary contributors to the contamination, by examining a large asbestos mine and the surrounding soil in China. The level of heavy metal pollution in soil and sources were analyzed using methods such as the geo-accumulation index (Igeo), potential ecological risk index (RI), and positive matrix factorization (PMF) model. A Monte Carlo simulation-based health risk model was employed to assess the health risks of heavy metals in the study area’s soil to human beings. The results showed that the concentrations of As, Pb, Cr, Cu, and Ni in the soil were 1.74, 0.13, 13.31, 0.33, and 33.37 times higher than the local soil background values, respectively. The Igeo assessment indicated significant accumulation effects for Ni, Cr, and As. The RI evaluation revealed extremely high comprehensive ecological risks (RI ≥ 444) in the vicinity of the waste residue heap and beneficiation area, with Ni exhibiting strong individual potential ecological risk (Eir ≥ 320). The soil health risk assessment demonstrated that As and Cr posed carcinogenic risks to adults, with mean carcinogenic indices (CR) of 1.56E − 05 and 4.14E − 06, respectively. As, Cr, and Cd posed carcinogenic risks to children, with mean CRs of 1.08E − 04, 1.61E − 05, and 2.68E − 06, respectively. Cr also posed certain non-carcinogenic risks to both adults and children. The PMF model identified asbestos contamination as the primary source of heavy metals in the soil surrounding the asbestos mining area, contributing to 79.0%. According to this study, it is recommended that management exercise oversight and regulation over the concentrations of Ni, Cr, Cd, and As in the soil adjacent to asbestos mines, establish a designated control zone to restrict population activities, and locate residential zones at a safe distance from the asbestos mine production zone.

Evaluation and Source Apportionment on Potentially Toxic Elements Pollution of Agricultural Soil in Mango Planting Areas in Panzhihua City, China

ABSTRACT Heavy metal pollution in agricultural soil has a wide range of sources and an enormous scope of influence, bringing about severe environmental risk. In this study, we used Pearson correlation analysis, combined with principal component and cluster analysis (PCA/CA) to comprehensively analyze the sources of soil heavy metal contamination in a mango plantation area in Panzhihua City. We performed a spatial distribution of heavy metal contamination levels via ArcGIS for single factor and Nemerow Contamination Index. The results showed that PTEs exceeded background values of soil pollution in Sichuan Province. The enrichment of Cr, Ni and Zn were caused by the development of local industry. Cu is mainly from atmospheric dust. Various pollution sources controlled the accumulation of Cd, whereas that of As and Pb were related to agricultural activities and transportation. The spatial distribution of the pollution indices indicated that the soil in the study area was mild to severe pollution with compound PTEs. Chemical speciation analysis showed that the ecological risk of PTEs enrichment in the study area was slight. This study highlights the geographic distribution of heavy metal contamination levels and source resolution, providing targeting for local regulatory authorities to improve soil environmental quality.

Role of Plant-Growth-Promoting Rhizobacteria in Plant Machinery for Soil Heavy Metal Detoxification.

Heavy metals migrate easily and are difficult to degrade in the soil environment, which causes serious harm to the ecological environment and human health. Thus, soil heavy metal pollution has become one of the main environmental issues of global concern. Plant-growth-promoting rhizobacteria (PGPR) is a kind of microorganism that grows around the rhizosphere and can promote plant growth and increase crop yield. PGPR can change the bioavailability of heavy metals in the rhizosphere microenvironment, increase heavy metal uptake by phytoremediation plants, and enhance the phytoremediation efficiency of heavy-metal-contaminated soils. In recent years, the number of studies on the phytoremediation efficiency of heavy-metal-contaminated soil enhanced by PGPR has increased rapidly. This paper systematically reviews the mechanisms of PGPR that promote plant growth (including nitrogen fixation, phosphorus solubilization, potassium solubilization, iron solubilization, and plant hormone secretion) and the mechanisms of PGPR that enhance plant-heavy metal interactions (including chelation, the induction of systemic resistance, and the improvement of bioavailability). Future research on PGPR should address the challenges in heavy metal removal by PGPR-assisted phytoremediation.

Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints

Monitoring heavy metal concentrations in soils is central to assessing agricultural production safety. Satellite observations permit inferring concentrations from spectrum, thereby contributing to the prevention and control of soil heavy metal pollution. However, heavy metals exhibit weak spectral responses, particularly at low and medium concentrations, and are predominantly influenced by other soil components. Machine learning (ML)-driven modelling can produce predictions but lacks interpretability. Here, we present an interpretable ML framework for concentration quantification modelling and investigated the contributions of spectral and environmental factors—pH and organic carbon—to the estimation of metals with multiple concentration gradients, as analysed through SHAP (SHapley Additive exPlanations) data derived from four learning-based scenarios. The results indicated that scenarios SHC (spectral, pH, and organic carbon) and SH (spectral and pH) were the most optimal for chromium (Cr) [RPD = 1.42, Adj R2 = 0.62], and cadmium (Cd) [RPD = 1.80, Adj R2 = 0.80]. Under environmental constraints, the spectral predictability for Cr and Cd was improved by 67 % and 87 %, respectively. We concluded that interpretable modelling, utilising both spectral and soil environmental factors, holds significant potential for estimating heavy metals across concentration gradients. It is recommended that samples with higher organic carbon content and lower pH be selected to enhance Cr and Cd predictions. An advanced grasp of interpretable predictions facilitates earlier warning of heavy metal contamination and guides the formulation of robust sampling strategies.

Combined effects of biochar and biodegradable mulch film on chromium bioavailability and the agronomic characteristics of tobacco

Biochar (BC) and biodegradable mulch film (BMF) are both commonly used means of production in agriculture. In recent years, most studies have focused on the effects of BC or BMF on soil heavy metal pollution, while they have neglected the combined effects. In this study, a pot experiment was conducted to examine the impacts of BMF, BC, and combined BMF and BC (CMB) on the mobility of chromium (Cr) and the agronomic characteristics of flue-cured tobacco. Compared with the control, BMF, BC, and CMB significantly reduced the concentrations of diethylenetriamine pentaacetic acid (DTPA) extractable Cr in soils by 29.07–29.75%, 45.35–48.54%, and 34.21–37.92%, respectively. In comparison to the application of BMF and BC alone, co-application reduced the availability of Cr in soil via increasing the adsorption of soil Cr and soil enzyme activity, which resulted in the decrease of Cr content and bioconcentration factor and in plants. Moreover, the combined application increased the plant height, stem diameter, leaf area, total root area, root tip number, and root activity of tobacco, which leaded to increase in leaf and root biomass by 11.40–67.01% and 23.91–50.74%, respectively. Therefore, the application of CMB can reduce the heavy metal residues in tobacco leaves and improve tobacco yield and quality.

Research on the Adsorption of Soil Heavy Metals by Microalgal Biofilms: Mechanisms, Applications, and Prospects

Soil heavy metal pollution stands as one of the significant challenges facing the global environment today, posing serious threats to human health and ecosystems. In addressing this issue, microalgal biofilms have emerged as a promising bioremediation tool due to their efficient adsorption capabilities. This article provides a comprehensive review of the current status, application cases, and future developments of microalgal biofilms in soil heavy metal adsorption research, aiming to offer insights and guidance for research and practices in the field of environmental remediation.

Characteristics of Soil Heavy Metal Pollution and Health Risks in Chenzhou City

The objective of this inquiry is to illuminate the attributes of heavy metal contamination and evaluate the potential ecological hazards inherent in the surface soil of Chenzhou City. A comprehensive analysis was conducted on 600 systematically collected soil samples within the study area, utilizing enrichment factors, geo-accumulation indices, comprehensive pollution indices, potential ecological hazard indices, and health risk assessment models to evaluate the degree of heavy metal contamination in the soil, potential ecological risks, and associated health hazards. The findings reveal that the average enrichment factor (EF) for each heavy metal is below 2, with the hierarchy from highest to lowest being Hg &gt; Cd &gt; Cu &gt; Pb &gt; Ni &gt; Zn &gt; Cr &gt; As. Approximately 78.67% of soil samples exhibit no pollution to weak pollution levels based on heavy metal enrichment factors. Moreover, the comprehensive pollution index (IPIN) indicates that 95.17% of soil samples are within safe and pollution-free levels, indicating an overall environmentally secure setting. However, 2.67% of samples display heightened potential ecological risk levels, primarily concentrated in the southwestern region of the study area, influenced by nearby industrial activities. Additionally, it is noteworthy that both the non-carcinogenic and carcinogenic health hazards emanating from soil heavy metals to adult individuals lie within tolerable thresholds. Among these, arsenic (As), chromium (Cr), and lead (Pb) have been discerned as the principal non-carcinogenic agents. It is of particular significance that only a solitary soil specimen, located in the southwestern quadrant of the investigative region, manifests detectable health perils for children.

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques.

The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.

Potential Ecological Risk Assessment of Soil Heavy Metals in Fengdong New District Based on Information Diffution Model

The large-scale construction of new districts has led to severe soil heavy metal pollution. Therefore, taking Fengdong New District as the target research area, the descriptive statistics of heavy metal content characteristics and Kriging interpolation analysis have been conducted, and the potential ecological risk index and information diffusion theory were further combined to create an information diffusion model based on risk assessment. Finally, the pollution degree, ecological risk, and risk occurrence probability of Pb, Cu, Cd, and Hg were discussed. The findings revealed that the average concentrations of the four heavy metals far exceeded the background value of soil heavy metals by a factor of 1.943 (Pb), 1.419 (Cu), 3.074 (Cd), and 3.567 (Hg), respectively. Moreover, the distribution of soil heavy metals showed strong variability(CV>65%)owing to human interference. The distribution of Pb and Cu pollution were predominantly influenced by industrial production and land development for construction purposes, whereas industrial activities, agricultural practices, and transportation served as the primary sources of Cd contamination. On the other hand, industrial construction emerged as the major factor contributing to Hg pollution. The average values of individual potential ecological risk index for heavy metals of 9.716 (Pb), 7.095 (Cu), 92.292 (Cd), and 142.469 (Hg), coupled with the regional comprehensive potential ecological risk index (RI) average of 251.573, signified that the region was overall characterized by a relatively high potential ecological risk status. The overall potential ecological risk for Pb and Cu in the region were mild, whereas Cd and Hg posed moderate to high risks, indicating that Cd and Hg were the dominant driving factors behind regional heavy metal pollution. The evaluation results of the information diffusion model based on the potential ecological risk indicated that the probability ranking of different levels of comprehensive potential ecological risk was as follows:slightly high (38.98%) > moderate (38.55%) > high (5.89%) > slight (5.15%) > extremely high (3.56%). The exceeding probabilities of potential ecological risk levels for Cd and Hg were significantly higher than those for Pb and Cu. The exceeding probability of different pollution levels of Hg were slight (94.89%), moderate (66.85%), slightly high (23.62%), high (3.9%), and extremely high (2%), of which only the surpassing probability of the slight level was lower than that of Cd. The prediction error of pollution probability of each potential ecological risk level was less than 5%, demonstrating the reliability of the information diffusion model based on the risk assessment. This research will provide technical reference and support for the monitoring and management of potential ecological risks from soil heavy metals in limited sample data regions.

Traceability Analysis and Environmental Quality Assessment of Soil Heavy Metal Pollution in West Hunan Province

In order to explore the status of soil heavy metal pollution and environmental quality in west Hunan, relevant areas of Phoenix County were selected as the study area. Using data from 440 soil samples collected in the study area from June to August 2022, the pH value of the soil and contents of eight heavy metal elements, namely, As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were analyzed. The PMF model was used for traceability analysis and geochemical evaluation of soil environmental quality. The results showed that the average values of soil heavy metals ω(Zn), ω(Cr), ω(Pb), ω(Ni), ω(Cu), ω(As), ω(Cd), and ω(Hg) were 81.02, 64.67, 31.63, 29.27, 25.52, 9.93, 0.28, and 0.13 mg·kg-1, respectively. The soil in the study area was mainly weakly acidic, and the contents of the Cd and Hg elements were relatively high compared to the national soil background values and were highly variable. The contents of the Hg and Cd elements in forest land were higher than that in other land uses. The PMF model results showed that the contribution rates of heavy metal pollution sources in the study area were mining sources (37.4%), atmospheric sedimentation sources (7.7%), natural sources (41.1%), and agricultural activity sources (13.8%) and provided suggestions on pollution control measures according to the spatial distribution of the four types of pollution sources. Through the comprehensive assessment of soil environmental geochemistry, the study area was divided into three types of plots, namely, non-risk areas (94.27 km2), accounting for 76.38%; risk-controllable areas (27.45 km2), accounting for 22.24%; and high-risk areas (1.7 km2), accounting for 1.38%. This study provided data support for the prevention and control measures of land pollution in the research area, as well as the delineation of the prevention and control scope.

Contaminated soil remediation with nano-FeS loaded lignin hydrogel: A novel strategy to produce safe rice grains while reducing cadmium in paddy field

Cadmium (Cd) contamination in agricultural soil has been an elevated concern due to the high health risks associated with the transfer through the soil-food chain, particularly in the case of rice. Recently, there has numerous researches on the use of nanoparticle-loaded materials for heavy metal-polluted soil remediation, resulting in favorable outcomes. However, there has been limited research focus on the field-scale application and recovery. This study was aimed to validate the Cd reduction effect of the nano-FeS loaded lignin hydrogel composites (FHC) in mildly polluted paddies, and to propose a field-scale application method. Hence, a multi-site field experiment was conducted in southern China. After the application for 94–103 days, the FHC exhibited a high integrity and elasticity, with a recovery rate of 91.90%. The single-round remediation led to decreases of 0.42–31.72% in soil Cd content and 1.52–49.11% in grain Cd content. Additionally, this remediation technique did not adversely impact rice production. Consequently, applying FHC in the field was demonstrated to be an innovative, efficient, and promising remediation technology. Simultaneously, a strategy was proposed for reducing Cd levels while cultivating rice in mildly polluted fields using the FHC.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

Soil heavy metal pollution and the associated toxicity risk assessment in Ajdabiya and Zueitina, Libya

Soil heavy metal pollution and the associated toxicity risk assessment in Ajdabiya and Zueitina, Libya

Experimental Study on the Effects of Heavy Metal Pollution on Soil Physical Properties and Microstructure Evolution

Soil heavy metal pollution poses a formidable challenge for environmental protection professionals. This study delves into the impact of zinc and lead pollution on the particle size distribution, liquid-plastic limit, permeability, shear strength, and other pertinent physical and engineering properties of clay. The alterations in the microstructure of soil contaminated by heavy metals were scrutinized using a scanning electron microscope. The findings reveal that as the concentration of heavy metals in contaminated soil rises, there is a concurrent decrease in the liquid limit, plasticity index, and silt content. This, in turn, leads to the deterioration of the original fluidity and plasticity of the soil, accompanied by a reduction in fine particles. Resistivity tests indicate that an escalation in water content results in a decrease in resistivity, an increase in porosity leads to an increase in resistivity, and an elevation in the concentration of heavy metals precipitates a sharp decline in resistivity due to the heightened conductivity of heavy metal ions. Heavy metal pollution induces structural changes in the soil, particularly in pore size, thereby influencing the permeability coefficient.

Soil heavy metal pollution and ecological risk assessment in disturbed and undisturbed soil of Morigaon, Assam

Soil heavy metal pollution and ecological risk assessment in disturbed and undisturbed soil of Morigaon, Assam

Phytoremediation of copper-contaminated soils by rapeseed (Brassica napus L.) and underlying molecular mechanisms for copper absorption and sequestration

High levels of copper released in the soil, mainly from anthropogenic activity, can be hazardous to plants, animals, and humans. The present research aimed to estimate the suitability and effectiveness of rapeseed (Brassica napus L.) as a possible soil remediation option and to uncover underlying adaptive mechanisms A pot experiment was conducted to explore the effect of copper stress on agronomic and yield traits for 32 rapeseed genotypes. The copper-tolerant genotype H2009 and copper-sensitive genotype ZYZ16 were selected for further physiological, metabolomic, and transcriptomic analyses. The results exhibited a significant genotypic variation in copper stress tolerance in rapeseed. Specifically, the ratio of seed yield under copper stress to control ranged from 0.29 to 0.74. Furthermore, the proline content and antioxidant enzymatic activities in the roots were greater than those in the shoots. The accumulated copper in the roots accounted for about 50% of the total amount absorbed by plants; thus, the genotypes possessing high root volumes can be used for rhizofiltration to uptake and sequester copper. Additionally, the pectin and hemicellulose contents were significantly increased by 15.6% and 162%, respectively, under copper stress for the copper-tolerant genotype, allowing for greater sequestration of copper ions in the cell wall and lower oxidative stress. Comparative analysis of transcriptomes and metabolomes revealed that excessive copper enhanced the up-regulation of functional genes or metabolites related to cell wall binding, copper transportation, and chelation in the copper-tolerant genotype. Our results suggest that copper-tolerant rapeseed can thrive in heavily copper-polluted soils with a 5.85% remediation efficiency as well as produce seed and vegetable oil without exceeding food quality standards for the industry. This multi-omics comparison study provides insights into breeding copper-tolerant genotypes that can be used for the phytoremediation of heavy metal-polluted soils.

Heavy Metal Pollution in Soils near Ilesha Gold Mining Area, Nigeria

Background The discovery of extensive gold deposits has raised concerns about potential heavy metal contamination in waterways and adjacent soils, particularly in developing nations like Nigeria where environmental regulations may not be stringent enough. Objective A research study was conducted to assess the levels of heavy metals in surface soils (0 to 25 cm deep) across 30 sites within three selected areas (Epe, Igun, and Ijana) located in the gold mining region of Ilesha, Osun State Nigeria. Methods The study employed an atomic absorption spectrophotometer to measure the total concentrations of heavy metals, specifically arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn). Furthermore, the research encompassed an examination of various physicochemical properties of the soil samples, including organic matter content, pH levels, organic carbon content, calcium content, phosphorus levels, cation exchange capacity, and particle size distribution. Results Results showed a range mean concentration with standard deviation of As, Cd, Co, Cr, Cu, Ni, Pb and Zn of 1.0 – 7.1 ± 1.77 mg kg-1, 0.9 – 4.2 ± 0.84 mg kg-1, 1.0 – 6.0 ± 1.29 mg kg-1, 1.1 – 6.4 ± 1.57 mg kg-1, 2.90 – 20.9± 4.00 mg kg-1, 1.1 – 6.4 ±1.56 mg kg-1, 0.7 – 6.7 ±1.26 mg kg-1 and 11.7 – 70.7± 17.75 mg kg-1, respectively. The investigation of soils in three specific locations revealed significant contamination by heavy metals. However, it is worth noting that the concentrations of these metals fell below the established intervention levels outlined by environmental protection agencies such as the World Health Organization and the Food and Agriculture Organization. The distribution of heavy metals and their availability exhibited a direct correlation with the distance from the mining sites, spanning from 0 to 900 meters. Two critical factors influencing the concentration of heavy metals in these areas were identified as soil particle size, specifically the clay percentage, and pH levels. Conclusion Despite the presence of heavy metal pollution in the soils of Epe, Igun, and Ijana, they managed to maintain metal levels below the intervention thresholds set by environmental protection agencies, with the exception of cadmium (Cd).