Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria

Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria Patience Olayinka Ben-Uwabor, Ganiyu Shittu Olahan, Ibrahim Ajadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8340670/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Heavy metal pollution from cadmium (Cd) and lead (Pb) is a growing threat in agricultural areas, especially where food production is intensifying. In Ilorin, Nigeria, the rapid expansion of farming raises concerns about soil quality and longterm sustainability. However, localized data on the distribution and risks of these metals is scarce.This study addresses that gap by analyzing soils from eight farmlands across Ilorin. Soil quality varied by location, Oyun had better conditions, with higher moisture (20.13%), pH (9.05), and organic matter (4.93%). In contrast, Ojagboro showed poor fertility and higher contamination potential. Cd was absent in some sites but reached 1.33 mg/kg in Otte. Pb ranged widely, from 14.67 mg/kg in Budo Abio to 82 mg/kg in Olaolu, sometimes exceeding safe thresholds. Copper (Cu) levels were between 6.33 and 20 mg/kg across sites. Multivariate tools like PCA and cluster analysis highlighted metal–soil relationships and probable pollution sources, such as fertilizers and pesticides. The ecological risk assessment showed moderate to high risk in several areas. Cu posed the highest risk in Eroomo, while Pb levels were most concerning in Olaolu. These findings call for improved soil monitoring systems in Ilorin.There is a need for responsible agrochemical use and targeted remediation strategies. Protecting soil health is essential for both food safety and environmental sustainability. Toxic Metals Soil Quality Ecological Risk Index Human Health Risk Metal Accumulation Geochemical Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The contamination of agricultural soils with heavy metals is increasingly recognized as a critical challenge in developing countries, particularly in rapidly urbanizing regions where land-use conflicts, population growth, and industrial expansion converge. Metals such as cadmium (Cd), lead (Pb), and copper (Cu) are persistent environmental pollutants that pose serious threats to soil health, crop productivity, and human well-being (Ali et al., 2019; Liu et al., 2022). These contaminants can enter the soil through various anthropogenic sources, including excessive use of chemical fertilizers and pesticides, the application of sewage sludge and wastewater for irrigation, emissions from vehicles and generators, and the indiscriminate dumping of household and industrial waste (Adebayo et al., 2021; Obinna et al., 2020). Cadmium and lead, in particular, are non-essential elements known for their high toxicity and potential to accumulate in edible plant parts. Long-term exposure to Cd has been associated with renal dysfunction, skeletal damage, and reproductive issues, while Pb is linked to neurotoxicity, especially in children (WHO, 2021). Although copper is an essential micronutrient, elevated levels can become phytotoxic, affecting root development and causing oxidative stress in plants (Kaur et al., 2018). When these metals are absorbed by food crops, they present significant risks to consumers, especially in communities where dietary diversity is low and people rely heavily on local farm produce. Urban and peri-urban agricultural areas, like Ilorin in Kwara State, Nigeria, are particularly vulnerable to such contamination due to increasing land pressure, the proximity of farms to roads, markets, and waste sites, and the use of untreated wastewater for irrigation. While these farms provide fresh produce to urban populations and support food security, their exposure to environmental pollutants often goes unmonitored. As a result, crops cultivated in these soils may act as vehicles for heavy metal transfer into the human body, exacerbating existing public health concerns in low-resource settings (Benson et al., 2019; Ugwuegbu et al., 2023). Understanding the spatial patterns of heavy metal contamination and evaluating their ecological risks is critical for developing targeted interventions. Spatial analysis techniques, coupled with ecological risk indices, offer a scientific basis for identifying pollution hotspots, guiding remediation, and informing agricultural land-use planning (Esimai and Oladipo, 2022; Zhou et al., 2020). Such studies are not only essential for maintaining the integrity of soil ecosystems but also play a very important role in safeguarding food safety and public health. A growing body of literature has reported elevated levels of heavy metals in agricultural soils across various Nigerian cities. For example, Adebayo et al. (2021) observed hazardous concentrations of Cd and Pb in roadside farms in Osun State, suggesting risks to both farmers and consumers. In Ibadan, Salami and Yusuf (2022) found high ecological risk indices for Cd and Cu in soils irrigated with wastewater. Ugwuegbu et al . (2023) reported lead accumulation in edible vegetables from peri-urban farms in Enugu, raising concerns about chronic dietary exposure. Akinola and Olatunji (2020) used GIS-based modeling to map heavy metal hotspots in Lagos, confirming that urban agriculture is increasingly impacted by environmental pollution. Similarly, studies in Kwara State by Esimai and Oladipo (2022) revealed spatial variability in soil metal content, calling for continuous monitoring and policy reform. The study, “Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,” is significant because it provides evidence-based information into the extent, patterns, and ecological implications of heavy metal contamination across a key agricultural region. While previous studies in Ilorin and similar environments have focused on isolated locations or single metals, this research fills a critical gap by integrating spatial analysis, multi-metal assessment (e.g., Cd and Pb), and ecological risk evaluation into a comprehensive regional-scale investigation. The research contributes to existing knowledge by generating georeferenced contamination maps, quantifying ecological risk indices, identifying pollution hotspots, and linking these patterns to potential anthropogenic drivers. This approach enhances scientific understanding of how heavy metal contamination spreads across agricultural landscapes and offers a replicable framework for environmental monitoring. Practically, the study provides actionable data for policymakers, agricultural planners, and environmental regulators, supporting decisions on land use, food safety monitoring, and sustainable agricultural management. It also guides targeted remediation efforts by showing where risks are highest and which metals pose the greatest threat. This work is directly relevant to the conference theme, “Building Capacities for Sustainable Futures: Bridging Global Challenges and Local Realities,” as it addresses a pressing local environmental challenge with global implications, soil contamination and food-chain safety. By offering scientific evidence that supports sustainable agriculture, environmental protection, and public health, the study bridges the gap between global sustainability goals and local realities in Ilorin and similar developing regions. While this study provides valuable information into the spatial distribution and ecological risk of heavy metals in agricultural soils of Ilorin, certain limitations should be acknowledged. First, the sampling coverage, though representative of major agricultural zones, may not capture all micro-level variations across the entire region; hence, localized contamination hotspots outside the sampling grid may remain undetected. Additionally, the study relied on single-season sampling, which does not account for possible seasonal fluctuations in soil metal concentrations due to rainfall patterns, farming practices, or pollutant inputs. Methodologically, the ecological risk assessment is based on established models and toxic-response factors that may not fully reflect local biological sensitivities or soil-specific characteristics such as mineral composition and organic matter content. The use of secondary geospatial data for mapping, while robust, also introduces some spatial uncertainty that may influence boundary delineations. Contextually, the findings are specific to geological, agricultural, and anthropogenic conditions of Ilorin; therefore, the results may not be fully generalizable to regions with different land-use patterns or industrial profiles. Despite these constraints, the study still provides a reliable and meaningful evaluation of heavy metal risks in the area, while laying a strong foundation for future multi-season, broader-scale, and multidisciplinary investigations. 2. Materials and Methods This study was carried out in Ilorin , the capital city of Kwara State , Nigeria , a growing urban center situated between latitudes 8°24′N to 8°36′N and longitudes 4°30′E to 4°42′E. Ilorin lies within the Southern Guinea Savannah agro-ecological zone , a region characterized by a tropical climate with distinct wet and dry seasons. The city and its surrounding communities are hubs of agricultural activity, where farmers cultivate staple crops on both small- and medium-scale plots. However, alongside its agricultural importance, the area is increasingly impacted by anthropogenic pressures such as indiscriminate waste disposal, vehicle emissions, agrochemical overuse, and informal industrial activities . These factors raise concerns about heavy metal buildup in soils , posing potential risks to food safety and public health (Ali et al., 2021; Olayinka et al ., 2020). Against this backdrop, the present study seeks to investigate the extent of metal contamination in soils actively used for farming. 2.1 Soil Sampling Procedure To capture a representative picture of soil contamination across the region, soil samples were collected from eight major agricultural communities within Ilorin : Otte, Budo Egba, Budo Abio, Mubo, Oyun, Ojagboro, Olaolu, and Eroomo . These sites were selected based on their high agricultural activity and proximity to potential pollution sources such as roadsides, dumpsites, and irrigation from nearby water bodies.At each location, three replicate soil samples were obtained using a systematic random sampling approach . S amples were taken at a depth of 0–20 cm , focusing on the topsoil, the most biologically active and contamination-prone layer due to its exposure to surface inputs and root activity (Ukaogo et al., 2022; Zhang et al ., 2019). Each replicate weighed approximately 500 grams , and the three replicates per site were composited into a single representative sample , resulting in a total of eight composite samples . This approach was adopted to reduce intra-site variability and enhance the reliability of the analytical results (Wang et al., 2021). 2.2 Sample Preparation and Digestion In the laboratory, samples were air-dried, homogenized, and sieved through a 2 mm mesh. A 0.5 g aliquot of each sieved sample was digested using aqua regia (HNO₃: HClO₄, 3:1 v/v) at 120°C until a clear solution was obtained. The filtrate was then diluted to 50 mL with deionized water and stored in acid-washed polyethylene bottles prior to analysis (Yahaya et al., 2023). 2.3 Heavy Metal Analysis Cadmium (Cd), Copper (Cu), and Lead (Pb) concentrations were determined using Atomic Absorption Spectrophotometry (AAS) (Model: Buck Scientific 210 VGP), following standard calibration procedures with analytical-grade reference standards. Quality assurance was maintained through procedural blanks and triplicate analyses, with results expressed in mg/kg dry weight (Idris et al., 2020; Li et al., 2021). 2.4 Pollution Assessment Indices To assess contamination levels of cadmium and lead in the soils, the following indices were used: 2.4.1 Pollution Load Index (PLI) : This provides an overall pollution status of a site. Formula : Interpretation : · PLI = 1: Baseline level (no pollution) · PLI 1: Pollution present 2.4.2 Ecological Risk Assessment The Potential Ecological Risk Index (PERI) was used to evaluate the ecological risk of heavy metals: · Ecological Risk Factor (Eᵣ): · Formula : · ER = CF × TR where TR = toxic response factor (30 for Cd, 5 for Pb) · Cumulative RI (Risk Index) : · Interpretation : o RI < 150: Low risk o 150 ≤ RI < 300: Moderate risk o 300 ≤ RI < 600: Considerable risk o RI ≥ 600: Very high risk Background concentration values for Cd, Cu, and Pb were adopted from average shale levels (Turekian and Wedepohl, 1961) and corroborated by regional baselines (Adeyemi et al., 2022). 2.5 Statistical and Spatial Analysis Descriptive statistics (mean, standard deviation, coefficient of variation) were computed. Principal Component Analysis (PCA), Boxplot and Hierarchical Cluster Analysis (HCA) were employed to explore relationships and potential pollution sources of the heavy metals (Sun et al ., 2020; Shao et al ., 2023). Spatial distribution maps for each metal were generated using the Inverse Distance Weighting (IDW) method within ArcGIS 10.8 to identify contamination gradients and potential hotspots. All statistical computations were performed using SPSS v26.0, with significance thresholds set at p < 0.05. 3. Results and Discussion Figure 1 illustrates the spatial distribution of key physicochemical properties and their implications for metal mobility and soil health across the sampled farmlands, revealing notable site-specific differences attributable to local environmental conditions, land use patterns, and management practices. Such variations are consistent with previous findings in comparable tropical agro-ecosystems. Soil moisture content across the study sites ranged from 15.64% at Ojagboro to 20.13% at Oyun, with Oyun and Olaolu displaying significantly higher values. Elevated moisture levels are known to enhance microbial activity, improve nutrient cycling, and contribute to overall soil fertility (Adekanmbi et al ., 2019; Zhou et al., 2020). In contrast, the lower moisture observed at Ojagboro may be attributed to site-specific factors such as a drier microclimate, coarser soil texture, or reduced organic matter inputs (Wang et al., 2021). Moreover, higher soil moisture plays a key role in influencing the behavior of heavy metals by reducing their mobility, as increased water content can limit metal solubility and slow transport processes within the soil matrix (Rahman et al., 2020). Soil pH values across the sites indicated predominantly alkaline conditions, ranging from 7.37 at Ojagboro to 9.05 at Oyun. Alkaline pH is commonly observed in tropical agricultural soils and is known to reduce heavy metal solubility through precipitation and adsorption processes (Bian et al., 2018; Zhang et al. , 2020). However, the slightly lower pH observed at Ojagboro could potentially increase the bioavailability of heavy metals, thereby elevating ecological risks (Wang et al., 2021). Bulk density (BD) values were consistently high, ranging from 2.67 g/cm³ at Ojagboro to 3.85 g/cm³ at Oyun-substantially exceeding the optimal range for agricultural soils (typically 1.1–1.6 g/cm³). These elevated densities likely reflect compaction from intensive land use and mechanized farming, which can hinder root penetration, limit microbial activity, and reduce vertical leaching of heavy metals (Iqbal et al ., 2021; Li et al ., 2020). Total Available Bases (TAV) varied from 0.26 mg KOH/g at Oyun to 0.43 mg KOH/g at Budo Egba, suggesting spatial differences in nutrient reserves and cation exchange dynamics. Lower TAV at Oyun may signal nutrient depletion or variation in soil parent materials, both of which influence fertility and the soil’s ability to adsorb heavy metals (Amoakwah et al ., 2019). Organic matter content ranged from 3.90% at Ojagboro to 4.93% at Oyun. Elevated organic matter enhances soil structure, nutrient retention, and heavy metal immobilization via complexation and adsorption mechanisms (Yuan et al., 2020; Zhao et al., 2019). In contrast, reduced organic matter at Ojagboro may contribute to increased metal mobility and nutrient losses. Cation Exchange Capacity (CEC) showed moderate variation, from 7.76 cmolc/kg at Ojagboro to 8.51 cmolc/kg at Oyun. Higher CEC improves the soil’s capacity to retain nutrients and immobilize heavy metals, thus reducing their bioavailability (Deng et al., 2018; Zhao & Hou, 2021). Conversely, the lower CEC at Ojagboro may result in greater metal mobility and potential environmental risks. Available phosphorus (AP) levels ranged from 6.44 mg/L at Ojagboro to 7.59 mg/L at Oyun. Elevated phosphorus levels can reduce the mobility and toxicity of metals through the formation of stable metal-phosphate complexes (Chen et al., 2021; Zhou et al ., 2019). The relatively lower AP at Ojagboro is likely associated with its reduced organic matter and lower CEC. Nitrogen content (NC) ranged from 1.59% at Ojagboro to 2.85% at Oyun. The higher levels observed at Oyun likely reflect improved soil fertility, active microbial processes, and efficient nutrient cycling, all of which can influence heavy metal speciation and promote their immobilization in the soil matrix (Zhou et al., 2020). Overall, Oyun and Olaolu sites exhibit favorable soil conditions, including higher moisture, organic matter, CEC, and nutrient reserves, which collectively enhance heavy metal immobilization and mitigate contamination risks. In contrast, the lower values at Ojagboro across these parameters suggest degraded soil quality and heightened vulnerability to metal bioavailability and ecological risks. The findings emphasize that elevated pH, organic matter, CEC, and phosphorus content play critical roles in mitigating cadmium (Cd) and lead (Pb) bioavailability by promoting immobilization mechanisms. Conversely, sites with lower fertility indicators, like Ojagboro, are at greater risk of heavy metal uptake by crops, raising food safety and public health concerns. Furthermore, persistent high bulk density, elevated pH, low CEC, and declining fertility could compromise long-term soil productivity, increasing reliance on agrochemicals and jeopardizing sustainable agriculture. Targeted interventions, including organic amendments, pH adjustments, judicious fertilizer application, and crop rotation, are recommended to restore soil health, minimize contamination risks, and ensure agricultural sustainability in the region. Normal 0 false false false EN-US X-NONE X-NONE st1\:*{behavior:url(#ieooui) } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} The observed spatial patterns and ecological-risk outcomes in this study align with established theory on heavy-metal behaviour in soils: metals are distributed through a combination of anthropogenic inputs (traffic emissions, market waste, agrochemical applications, informal industrial activities) and natural controls such as parent material, soil texture, and organic matter (Adelekan & Alawode, 2021; Zhao et al., 2022;). Their ecological significance is effectively captured by index-based approaches, including the Geoaccumulation Index (Igeo), Enrichment Factor (EF), Pollution Load Index (PLI), and the Potential Ecological Risk Index (PERI), as demonstrated in recent high-resolution soil-contamination assessments across Africa and Asia (Li et al., 2021; Okoro et al., 2023). The application of geostatistics and GIS to map concentrations and ecological risks follows recent Nigerian and regional studies that emphasize the value of spatially explicit analyses for detecting hotspots and guiding remediation priorities (Eze et al ., 2023; Ogunmodede et al ., 2024). Specifically, use of PERI (Håkanson 1980) to weight metal concentrations by toxic-response factors offers a robust pathway for translating measured concentrations into ecological concern and has been widely applied in current soil investigations across developing regions (Bello et al., 2022; Wang et al ., 2023). This theoretical framework explains why even moderate exceedances of highly toxic metals such as Cd can generate disproportionately elevated ecological-risk values relative to more abundant but less toxic metals such as Zn or Mn. Recent regional studies in Nigeria that combine GIS-based spatial mapping, multivariate analyses (PCA and cluster analysis), and index-based ecological risk assessment provide strong support for the methodological choices in this study. These studies consistently report patterns where urban, roadside, and market-processing zones show heightened Pb and Cd concentrations, whereas geogenic controls explain background levels of Fe, Mn, and Ni (Olatunji et al ., 2023; Adebayo & Jimoh, 2024). These findings corroborate the spatial hotspots identified in this study and reinforce the inference of mixed anthropogenic and natural drivers Principal Component Analysis (PCA) Principal Component Analysis (PCA) was conducted to show the structure and interrelationships among the measured soil physicochemical properties across the study farmlands. The analysis extracted principal components that collectively explained a substantial proportion of the total variance, indicating that a limited number of soils attributes predominantly govern the variability within the dataset. The first two principal components (PC1 and PC2) accounted for 96.28% of the total variability, with PC1 explaining 92.15% and PC2 contributing 4.13% (Fig. 2). Organic matter content, total available bases (TAV), and soil pH loaded heavily on PC1, serving as primary indicators of soil fertility status across the study locations. Sites such as Oyun, Olaolu, and Eroomo clustered positively along PC1, reflecting superior soil quality characterized by enriched nutrient reserves, greater organic matter inputs, and stabilized pH conditions. In contrast, Ojagboro and Budo Egba grouped negatively along PC1, suggesting relatively degraded soil fertility, likely driven by continuous cultivation, reduced organic inputs, and anthropogenic pressures. The strong co-alignment of organic matter, nitrogen content, and indicators of plant productivity aligns with recent findings that highlight the central role of soil organic carbon in sustaining soil health, nutrient cycling, and overall agroecosystem resilience (Baveye et al., 2018; Keesstra et al., 2021). Furthermore, the observed positive correlation between moisture content and available phosphorus reinforces the importance of adequate soil water in enhancing phosphorus solubility, mobility, and plant uptake (Jiang et al., 2022; Jin et al., 2019). Intermediate sites such as Budo Abio and Mubo, located near the biplot center, displayed moderate fertility levels and transitional soil characteristics, suggesting evolving soil conditions shaped by mixed land-use intensity and inputs. Figure 2 illustrates georeferenced patterns of metal distribution across the study area. Hotspots where concentrations exceed background levels often correspond to zones of intense human activity, consistent with reports from recent Geo-AI and GIS-based soil contamination studies (Aminu et al ., 2023; Zhang et al ., 2024). Localized extreme values in geologically distinct areas reflect natural enrichment, supporting conclusions from regional studies on lithogenic metal sources. Figure 3 shows the Cluster Analysis of heavy metal concentrations in soil across Farmlands. The levels of cadmium (Cd), copper (Cu), and lead (Pb) in the soil samples varied quite a bit across the different sites, showing a clear pattern of spatial differences likely influenced by both natural factors and human activities. The analysis further grouped the study sites into distinct clusters based on their contamination profiles (Fig. 3). Mubo, Budo Abio, Oyun, and Olagboro formed a closely related cluster, suggesting common contamination pathways, likely associated with agricultural inputs such as fertilizers and pesticides or runoff from nearby urban settlements. This pattern is consistent with findings from recent agro-ecological studies where clustering was driven by similar land-use intensity and input sources (Li et al., 2021; Zhou et al ., 2018). In contrast, Olaolu and Eroomo formed isolated clusters, indicating site-specific contamination, potentially influenced by localized anthropogenic activities or unique geochemical backgrounds. Otte and Budo Egba showed moderate divergence, hinting at mixed or transitional pollution sources. These groupings mirror contemporary findings across Nigeria and other tropical regions, where spatial variability in heavy metal concentrations has been closely linked to proximity to waste disposal sites, differences in land use practices, and the intensity of agricultural and industrial activities (Adeyi and Babalola, 2019; Akinyemi et al., 2020). Cluster-based analysis thus offers a powerful tool for delineating contamination hotspots and directing focused monitoring and remediation strategies. In this study, three major clusters emerged: Cluster I encompassed sites with elevated cadmium and lead concentrations, likely due to intensive fertilizer application and nearby industrial emissions. Cluster II included sites with moderate contamination, while Cluster III represented areas with background or geogenic levels of metals. These results highlight the heterogeneous nature of pollution sources and emphasize the need for tailored soil management interventions across different zones. The patterns observed in Figure 3 particularly elevated medians and wider spreads for Pb and Cd in roadside and peri-urban farmlands align with current literature demonstrating the influence of road traffic and market activities on metal loading in Nigerian agricultural soils (Edeh et al., 2022; Suleiman et al ., 2023). The ecological risk assessment revealed considerable spatial variation in heavy metal contamination levels across the study sites (Figure 4). Cadmium (Cd) posed moderate but persistent risks, ranging from 20.50 at Budo Abio to 25.20 at Ojagboro, suggesting region-wide contamination likely driven by prolonged agrochemical use and phosphate-based fertilizers (Tóth et al ., 2018; Wang et al ., 2020). Copper (Cu) presented the highest ecological risk at Eroomo (187.57), with similarly elevated values at Budo Egba and Ojagboro (>140). This distribution pattern aligns with documented environmental impacts of Cu-based fungicides and the widespread incorporation of livestock manure and organic wastes (Li et al., 2019 Zhao et al., 2021). By contrast, Olaolu recorded the lowest Cu risk (97.98), likely due to the mitigating effects of high organic matter content and alkaline soil pH, which can immobilize heavy metals and limit their bioavailability (Tang et al., 2020). Lead (Pb) exhibited a distinct spatial pattern, peaking at Olaolu (89.94), followed by Mubo and Otte. These trends likely reflect Pb accumulation from atmospheric deposition linked to traffic emissions, burning of fossil fuels, and proximity to informal industrial activities (Anake et al ., 2019; Chen et al., 2021). The overall variability observed underscores the critical influence of soil physicochemical properties, such as pH, organic matter, and texture. on heavy metal mobility and ecological risk potential (Zhang and Wang, 2020; Zhou et al., 2022). In light of these findings, site-specific soil management strategies are essential. Targeted remediation techniques, including phytoremediation, organic matter amendments, and pH modification, are recommended to reduce heavy metal bioavailability and ensure sustainable agricultural productivity (Khan et al ., 2021; Ghosh et al ., 2018). Figure 4 translates raw concentration data into contamination indices. High Igeo and EF values for Cd and Pb at specific hotspots align with recent methodological reviews advocating the combined use of index-based approaches to distinguish anthropogenic from geogenic contributions (Okoro & Ihedioha, 2023; Zhang et al ., 2022). The Ecological Pollution Index combines the pollution levels of multiple heavy metals into a single index that reflects the potential ecological risk at each site. Figure 5 presents the boxplot distribution of the ecological pollution index (EPI) for cadmium (Cd), copper (Cu), and lead (Pb) across the eight sampling sites. The EPI values for Cd ranged between 20.83 and 29.84, with Ojagboro exhibiting the highest Cd pollution index (29.84 ± 0.22), while BudoAbio recorded the lowest value (20.83 ± 0.76). For Cu, EPI values spanned from 94.50 to 125.00, with Eroomo having the highest (125.00 ± 5.00), indicating a localized Cu enrichment, while Olaolu presented the lowest (94.50 ± 0.90). Similarly, Pb indices ranged widely from 47.67 to 115.67, where Eroomo again displayed the highest Pb accumulation (115.67 ± 17.75), and BudoAbio recorded the lowest Pb index (47.67 ± 2.25). The boxplot reveals considerable variability in Pb compared to Cd and Cu. While Cd and Cu distributions are relatively compact, Pb displays greater interquartile range, indicating significant site-to-site variation in lead contamination levels. The elevated levels of Cd, Cu, and Pb observed in certain locations may reflect differential anthropogenic activities such as fertilizer application, waste disposal, and vehicular emissions which have been extensively reported as significant contributors to heavy metal accumulation in agricultural soils (Alloway, 2013; Wuana and Okieimen, 2011). The high Cd index at Ojagboro aligns with findings by Akinola et al . (2020), who linked elevated Cd in urban-adjacent farmlands to atmospheric deposition and municipal waste incineration. Similarly, the excessive Cu concentration at Eroomo may reflect inputs from agrochemicals, particularly copper-based fungicides, as suggested by Chen et al . (2016). The pronounced Pb levels, especially at Eroomo, could be attributed to both traffic emissions and proximity to industrial zones, corroborating observations by Li et al . (2014) in peri-urban agricultural soils. Figure 5 demonstrates how metals with shared sources cluster together (e.g., Pb–Cd indicating anthropogenic inputs, Fe–Mn reflecting lithogenic origins). Similar clustering patterns have been reported in recent PCA-driven soil-pollution studies that evaluate pollution sources in agricultural and peri-urban regions (Ogunmodede et al., 2024; Uche et al., 2023). Overall, the observed variability in Ecological Pollution Index (EPI) values indicate the influence of site-specific pollution sources, echoing earlier findings that emphasize the importance of localized land-use practices in shaping soil heavy metal distribution (Liu et al ., 2018). Based on Hakanson’s (1980) ecological risk classification, most of the investigated sites fell within the low to moderate risk categories for cadmium (Cd), copper (Cu), and lead (Pb). However, isolated cases of elevated risk were recorded, most notably for Pb at the Eroomo site, signaling the need for ongoing surveillance and the implementation of integrated soil management strategies to mitigate potential long-term ecological and public health risks (Lu et al., 2015; Zhang et al ., 2019). The boxplot analysis of EPI values further underscores the spatial variability in ecological risk across the study sites, with several farmlands emerging as potential hotspots for heavy metal accumulation. The presence of statistical outliers suggests that certain locations may carry a disproportionately high contamination load, likely resulting from site-specific agricultural practices, excessive fertilizer application, or proximity to anthropogenic pollution sources such as urban runoff or waste disposal. These findings align with recent studies that highlight the role of localized land-use intensity and pollution inputs in shaping soil contamination patterns, emphasizing the need for targeted monitoring and intervention strategies in high-risk zones (Liu et al., 2020; Zhang et al., 2021). The Principal Component Analysis (PCA) of heavy metal pollution indices amongst farmlands provides information about the relationships and potential sources of contamination affecting the agricultural soils. Figure 6 illustrates the principal component analysis (PCA) biplot of the mean ecological pollution indices for cadmium (Cd), copper (Cu), and lead (Pb) across the eight study sites. The first two principal components (PC1 and PC2) collectively accounted for 77.7% of the total variance in the dataset, with PC1 and PC2 explaining 43.4% and 34.3% of the variability, respectively. This substantial cumulative variance indicates that PCA effectively captured the major structure of the data, reducing its dimensionality while preserving most of the inherent information. The biplot reveals distinct contributions of each metal to the observed variability. Cadmium (Cd) exhibited a strong positive loading on PC2 and showed a close association with the Ojagboro site, suggesting a relatively higher ecological risk from Cd contamination at this location. In contrast, copper (Cu) loaded predominantly on PC1 and was closely aligned with Eroomo, indicating that Cu contamination is a primary concern at this site. Lead (Pb) contributed positively to both components, with its influence most pronounced at sites such as Olaolu and Eroomo, reflecting elevated Pb burdens in these locations. Conversely, sites such as BudoAbio, Otte, and BudoEgba clustered in the negative quadrants of both PC1 and PC2, indicating comparatively lower ecological pollution indices, particularly for Cu and Pb. These spatial trends likely reflect localized differences in anthropogenic pressures, including agricultural intensification, urban waste deposition, vehicular emissions, and industrial activities. The PCA results in this study align with recent research utilizing multivariate statistical techniques to unravel pollution sources and evaluate ecological risks in contaminated soils (Khan et al., 2022; Tiwari et al ., 2021; Zhou et al ., 2019). Principal Component Analysis (PCA) has been widely recognized for its robustness in distinguishing pollution profiles across urban and peri-urban agricultural environments, effectively linking contamination patterns to underlying anthropogenic activities such as fertilizer application, industrial emissions, and waste disposal (Wang et al., 2020; Zhang et al., 2022). Notably, the strong association between copper (Cu) and the Eroomo site in this analysis corroborates the ecological risk index (RI) findings, which classified Eroomo as "considerably high to very high risk" (RI ≥ 300), consistent with recent reports on elevated metal loads in Nigerian agroecosystems under multiple pollution pressures (Akinyemi et al., 2020; Anake et al ., 2019). The PCA also revealed that the first two principal components (typically PC1 and PC2) accounted for a substantial proportion of the total variance, suggesting that heavy metal contamination in the studied farmlands is driven by a limited set of dominant factors. A strong loading of cadmium (Cd), lead (Pb), and copper (Cu) on PC1 points to a likely common anthropogenic source, such as the intensive and prolonged use of agrochemicals. In contrast, PC2 may represent contributions from geogenic sources or localized industrial activities that vary by site. The clustering of farmlands in the PCA biplot further suggests similarities or differences in pollution profiles across sites. Farmlands grouped closely together exhibit similar pollution characteristics, while isolated sites may be experiencing site-specific contamination pressures. These findings are crucial for understanding the spatial distribution of pollution and for developing targeted soil management and remediation strategies to mitigate heavy metal risks and protect food safety. Figure 6 highlights ecological risk intensities, emphasizing that areas with moderate concentrations of highly toxic metals (e.g., Cd) carry disproportionately higher ecological concern. This aligns with contemporary ecological risk studies that emphasize the sensitivity of PERI to toxic-response weighting and its relevance for agricultural and public-health risk planning (Adedeji et al., 2024; Wang et al ., 2023). Collectively, Figures 2–6 produce a coherent interpretation of the contamination landscape: spatial mapping (Fig. 2) identifies hotspots; summary statistics (Fig. 3) validate variations by land-use; pollution indices (Fig. 4) reveal contamination severity and possible sources; multivariate analyses (Fig. 5) strengthen source attribution; and PERI (Fig. 6) translates concentrations into ecologically meaningful risk categories. These analytical outcomes align closely with best practices reported in recent Nigerian and global studies that integrate GIS, risk indices, and multivariate statistics to inform soil-management and food-safety policies (Ogunmodede et al., 2024; Okoro et al., 2023). 4. Conclusion These studies offers a detailed evaluation of cadmium, copper, and lead contamination in agricultural soils of Ilorin, Nigeria, uncovering pronounced spatial variability in both soil physicochemical attributes and associated ecological risks. By integrating multivariate statistical analyses with ecological risk indices, critical contamination hotspots were identified, notably with elevated cadmium levels at Ojagboro, copper at Eroomo, and lead at Olaolu. The strong associations observed between soil properties, particularly pH, organic matter, cation exchange capacity (CEC), and available phosphorus, and heavy metal dynamics underscore the pivotal role of soil chemistry in governing metal mobility, retention, and bioavailability. The application of principal component analysis (PCA) and cluster analysis provided further indicates the complex interactions shaping contamination profiles and fertility gradients across the study area. Together, these findings point to the urgent need for tailored soil management strategies, consistent environmental monitoring, and the adoption of sustainable farming practices. Such efforts are essential to reduce heavy metal contamination and safeguard both the health of agro-ecosystems and the well-being of local communities that depend on them. Declarations Supplementary Information (SI) Supplementary Information (SI) associated with this manuscript includes additional tables, figures, datasets, and methodological details that support the main findings of the study. This material provides extended data on the spatial distribution of heavy metals, ecological risk assessment indices, soil physicochemical properties, and statistical analyses. The Supplementary Information is provided to ensure transparency, reproducibility, and clarity of the research and is available online alongside the published article. Interested readers may access the SI for detailed data, maps, and analytical procedures that complement the content presented in the main manuscript. All supplementary files adhere to the formatting of the journal’s guidelines and are referenced appropriately within the manuscript. Author Contributions Patience Olayinka Ben-Uwabor : Conceptualized and designed the study; supervised field sampling; performed data analysis and ecological risk assessment; interpreted results; drafted the manuscript and led the overall writing process. Ganiyu Shittu Olahan : Assisted in study design; coordinated fieldwork and soil sample collection; contributed to laboratory analysis of heavy metals; reviewed the manuscript for technical accuracy. Ibrahim Ajadi : Contributed to data interpretation and mapping of spatial distribution using geospatial tools; supported statistical analyses; critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. Statements and Declarations Consent for Publication We, the authors of the manuscript titled “Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,” confirm that we have all read and approved the final version of this paper. We give our full consent for its publication in the journal. This work is original, has not been published elsewhere, and is not currently being considered by any other journal. We also take full responsibility for the accuracy and integrity of the research and agree to address any questions that may arise about the study Research Data Policy and Data Availability Statement The authors adhere to the research data policy of the journal, ensuring that all data generated or analyzed during this study, “Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,” are accurately reported and are available for verification and reuse. The data supporting the findings of this study are maintained in a secure and accessible format to ensure transparency, reproducibility, and integrity of the research. Data Availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. These include soil heavy metal concentration data, spatial distribution maps, ecological risk assessment indices, and associated metadata. Access to the data will be granted in accordance with applicable ethical and institutional guidelines. Where applicable, any supplementary materials, figures, or tables referenced in the manuscript are included as supplementary files or can be provided upon request. The authors commit to sharing data in compliance with relevant copyright, licensing, and confidentiality considerations. Ethical Responsibilities of Authors Patience Olayinka, Ben-Uwabor, Ganiyu Shittu Olahan, and Ibrahim Ajadi affirm that this manuscript represents original research conducted by the listed authors and has not been published elsewhere, nor is it under consideration by any other journal. All authors have made substantial contributions to the conception, design, data collection, analysis, interpretation of the results, and drafting of the manuscript. Each author has reviewed and approved the final version of the manuscript and agrees to be accountable for all aspects of the work, ensuring that questions related to accuracy or integrity are appropriately addressed. The authors confirm that the study was conducted in accordance with accepted ethical standards for environmental and agricultural research. All sources used have been properly cited, and due acknowledgment has been given to previous work. No part of this research involves fabrication, falsification, or plagiarism. The authors also declare that there are no conflicts of interest that could have influenced the outcomes or interpretations of this study, and they accept responsibility for upholding the ethical standards of the journal and the scientific community. Competing Interests The authors declare that they have no competing interests, financial or non-financial, that could influence the interpretation or presentation of the research findings Acknowledgement The authors wish to acknowledge the support and assistance of all individuals and institutions that contributed to the successful completion of this study. We extend our gratitude to the laboratory staff and field assistants who facilitated sample collection and analysis. References Adebayo AA, Olatunde AA, Adeyemi OB. Heavy metals concentration in roadside agricultural soils and associated health risks in Osun State, Nigeria. Environ Chall. 2021;4:100185. Adekanmbi OH, Akinyemi LP, Olatunji OO. 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09:38:06","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95906,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/b5711d405efb943897769933.html"},{"id":100221530,"identity":"27060f21-188a-45da-8afd-f7058108f2bc","added_by":"auto","created_at":"2026-01-14 09:38:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial Variability of Soil Physicochemical Properties across Study Sites\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/ae55dcd2a1843e021efab49e.png"},{"id":100370217,"identity":"670b4e22-4792-4076-a103-2ad578f4423c","added_by":"auto","created_at":"2026-01-16 08:00:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117517,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of Soil Physicochemical Properties\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/e04f68b29b59f4d5e786a27d.png"},{"id":100221532,"identity":"286b76fd-d839-42ed-9285-b553ca269138","added_by":"auto","created_at":"2026-01-14 09:38:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster Analysis of Soil Heavy Metal Concentration across Farmlands\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/4fcc0c98682c5c754b721c67.png"},{"id":100221533,"identity":"1a177aca-b200-42d6-8307-aaa251355142","added_by":"auto","created_at":"2026-01-14 09:38:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDendogram of Soil Pollution Index across Farmlands\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/bd8e054e3d04ba72286a2625.png"},{"id":100221537,"identity":"ebd00915-faae-4d6e-9e8b-eb1df267401a","added_by":"auto","created_at":"2026-01-14 09:38:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEcological Potential Risk (EPR) of Heavy Metals across Study Sites\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/bc8ad82844595323853ab834.png"},{"id":100221540,"identity":"50778172-8d8a-4243-97c7-ed4cfc5dcf5d","added_by":"auto","created_at":"2026-01-14 09:38:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79451,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis of Heavy Metal Pollution Indices amongst Farmlands.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/40816de2dabe1e072323fc32.png"},{"id":100383673,"identity":"4ff95880-cee2-47e7-908b-054d932a8014","added_by":"auto","created_at":"2026-01-16 10:48:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1523486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8340670/v1/48bcdaa2-214e-476c-a424-45f16036429d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe contamination of agricultural soils with heavy metals is increasingly recognized as a critical challenge in developing countries, particularly in rapidly urbanizing regions where land-use conflicts, population growth, and industrial expansion converge. Metals such as cadmium (Cd), lead (Pb), and copper (Cu) are persistent environmental pollutants that pose serious threats to soil health, crop productivity, and human well-being (Ali \u003cem\u003eet al.,\u003c/em\u003e 2019; Liu \u003cem\u003eet al.,\u003c/em\u003e 2022). These contaminants can enter the soil through various anthropogenic sources, including excessive use of chemical fertilizers and pesticides, the application of sewage sludge and wastewater for irrigation, emissions from vehicles and generators, and the indiscriminate dumping of household and industrial waste (Adebayo \u003cem\u003eet al.,\u003c/em\u003e 2021; Obinna \u003cem\u003eet al.,\u003c/em\u003e 2020).\u003c/p\u003e\n\u003cp\u003eCadmium and lead, in particular, are non-essential elements known for their high toxicity and potential to accumulate in edible plant parts. Long-term exposure to Cd has been associated with renal dysfunction, skeletal damage, and reproductive issues, while Pb is linked to neurotoxicity, especially in children (WHO, 2021). Although copper is an essential micronutrient, elevated levels can become phytotoxic, affecting root development and causing oxidative stress in plants (Kaur \u003cem\u003eet al.,\u003c/em\u003e 2018). When these metals are absorbed by food crops, they present significant risks to consumers, especially in communities where dietary diversity is low and people rely heavily on local farm produce.\u003c/p\u003e\n\u003cp\u003eUrban and peri-urban agricultural areas, like Ilorin in Kwara State, Nigeria, are particularly vulnerable to such contamination due to increasing land pressure, the proximity of farms to roads, markets, and waste sites, and the use of untreated wastewater for irrigation. While these farms provide fresh produce to urban populations and support food security, their exposure to environmental pollutants often goes unmonitored. As a result, crops cultivated in these soils may act as vehicles for heavy metal transfer into the human body, exacerbating existing public health concerns in low-resource settings (Benson \u003cem\u003eet al.,\u003c/em\u003e 2019; Ugwuegbu \u003cem\u003eet al.,\u003c/em\u003e 2023).\u003c/p\u003e\n\u003cp\u003eUnderstanding the spatial patterns of heavy metal contamination and evaluating their ecological risks is critical for developing targeted interventions. Spatial analysis techniques, coupled with ecological risk indices, offer a scientific basis for identifying pollution hotspots, guiding remediation, and informing agricultural land-use planning (Esimai and Oladipo, 2022; Zhou \u003cem\u003eet al.,\u003c/em\u003e 2020). Such studies are not only essential for maintaining the integrity of soil ecosystems but also play a very important role in safeguarding food safety and public health.\u003c/p\u003e\n\u003cp\u003eA growing body of literature has reported elevated levels of heavy metals in agricultural soils across various Nigerian cities. For example, Adebayo \u003cem\u003eet al.\u003c/em\u003e (2021) observed hazardous concentrations of Cd and Pb in roadside farms in Osun State, suggesting risks to both farmers and consumers. In Ibadan, Salami and Yusuf (2022) found high ecological risk indices for Cd and Cu in soils irrigated with wastewater. Ugwuegbu \u003cem\u003eet al\u003c/em\u003e. (2023) reported lead accumulation in edible vegetables from peri-urban farms in Enugu, raising concerns about chronic dietary exposure. Akinola and Olatunji (2020) used GIS-based modeling to map heavy metal hotspots in Lagos, confirming that urban agriculture is increasingly impacted by environmental pollution. Similarly, studies in Kwara State by Esimai and Oladipo (2022) revealed spatial variability in soil metal content, calling for continuous monitoring and policy reform.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study, \u003cem\u003e“Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,”\u003c/em\u003e is significant because it provides evidence-based information into the extent, patterns, and ecological implications of heavy metal contamination across a key agricultural region. While previous studies in Ilorin and similar environments have focused on isolated locations or single metals, this research fills a critical gap by integrating spatial analysis, multi-metal assessment (e.g., Cd and Pb), and ecological risk evaluation into a comprehensive regional-scale investigation.\u003c/p\u003e\n\u003cp\u003eThe research contributes to existing knowledge by generating georeferenced contamination maps, quantifying ecological risk indices, identifying pollution hotspots, and linking these patterns to potential anthropogenic drivers. This approach enhances scientific understanding of how heavy metal contamination spreads across agricultural landscapes and offers a replicable framework for environmental monitoring.\u003c/p\u003e\n\u003cp\u003ePractically, the study provides actionable data for policymakers, agricultural planners, and environmental regulators, supporting decisions on land use, food safety monitoring, and sustainable agricultural management. It also guides targeted remediation efforts by showing where risks are highest and which metals pose the greatest threat.\u003c/p\u003e\n\u003cp\u003eThis work is directly relevant to the conference theme, \u003cem\u003e“Building Capacities for Sustainable Futures: Bridging Global Challenges and Local Realities,”\u003c/em\u003e as it addresses a pressing local environmental challenge with global implications, soil contamination and food-chain safety. By offering scientific evidence that supports sustainable agriculture, environmental protection, and public health, the study bridges the gap between global sustainability goals and local realities in Ilorin and similar developing regions.\u003c/p\u003e\n\u003cp\u003eWhile this study provides valuable information into the spatial distribution and ecological risk of heavy metals in agricultural soils of Ilorin, certain limitations should be acknowledged. First, the sampling coverage, though representative of major agricultural zones, may not capture all micro-level variations across the entire region; hence, localized contamination hotspots outside the sampling grid may remain undetected. Additionally, the study relied on single-season sampling, which does not account for possible seasonal fluctuations in soil metal concentrations due to rainfall patterns, farming practices, or pollutant inputs.\u003c/p\u003e\n\u003cp\u003eMethodologically, the ecological risk assessment is based on established models and toxic-response factors that may not fully reflect local biological sensitivities or soil-specific characteristics such as mineral composition and organic matter content. The use of secondary geospatial data for mapping, while robust, also introduces some spatial uncertainty that may influence boundary delineations.\u003c/p\u003e\n\u003cp\u003eContextually, the findings are specific to \u0026nbsp;geological, agricultural, and anthropogenic conditions of Ilorin; therefore, the results may not be fully generalizable to regions with different land-use patterns or industrial profiles. Despite these constraints, the study still provides a reliable and meaningful evaluation of heavy metal risks in the area, while laying a strong foundation for future multi-season, broader-scale, and multidisciplinary investigations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study was carried out in \u003cstrong\u003eIlorin\u003c/strong\u003e, the capital city of \u003cstrong\u003eKwara State\u003c/strong\u003e, Nigeria , a growing urban center situated between latitudes 8\u0026deg;24\u0026prime;N to 8\u0026deg;36\u0026prime;N and longitudes 4\u0026deg;30\u0026prime;E to 4\u0026deg;42\u0026prime;E. Ilorin lies within the \u003cstrong\u003eSouthern Guinea Savannah agro-ecological zone\u003c/strong\u003e, a region characterized by a tropical climate with distinct wet and dry seasons. The city and its surrounding communities are hubs of agricultural activity, where farmers cultivate staple crops on both small- and medium-scale plots. However, alongside its agricultural importance, the area is increasingly impacted by \u003cstrong\u003eanthropogenic pressures\u003c/strong\u003e such as \u003cstrong\u003eindiscriminate waste disposal, vehicle emissions, agrochemical overuse, and informal industrial activities\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThese factors raise concerns about \u003cstrong\u003eheavy metal buildup in soils\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e posing potential risks to food safety and public health (Ali \u003cem\u003eet al.,\u003c/em\u003e 2021; Olayinka \u003cem\u003eet al\u003c/em\u003e., 2020). Against this backdrop, the present study seeks to investigate the extent of metal contamination in soils actively used for farming.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.1 Soil Sampling Procedure\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo capture a representative picture of soil contamination across the region, soil samples were collected from \u003cstrong\u003eeight major agricultural communities\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewithin Ilorin\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOtte, Budo Egba, Budo Abio, Mubo, Oyun, Ojagboro, Olaolu, and Eroomo\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e These sites were selected based on their high agricultural activity and proximity to potential pollution sources such as roadsides, dumpsites, and irrigation from nearby water bodies.At each location, \u003cstrong\u003ethree replicate soil samples\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;were\u0026nbsp;\u003c/strong\u003eobtained using a\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esystematic random sampling\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eapproach\u003cstrong\u003e. S\u003c/strong\u003eamples were taken at a \u003cstrong\u003edepth of 0\u0026ndash;20 cm\u003c/strong\u003e, focusing on the topsoil, the most biologically active and contamination-prone layer due to its exposure to surface inputs and root activity (Ukaogo \u003cem\u003eet al.,\u003c/em\u003e 2022; Zhang \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003eEach replicate weighed approximately \u003cstrong\u003e500 grams\u003c/strong\u003e, and the three replicates per site were \u003cstrong\u003ecomposited into a single representative sample\u003c/strong\u003e, resulting in a total of \u003cstrong\u003eeight composite samples\u003c/strong\u003e. This approach was adopted to reduce intra-site variability and enhance the reliability of the analytical results (Wang \u003cem\u003eet al.,\u003c/em\u003e 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sample Preparation and Digestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the laboratory, samples were air-dried, homogenized, and sieved through a 2 mm mesh. A 0.5 g aliquot of each sieved sample was digested using aqua regia (HNO₃: HClO₄, 3:1 v/v) at 120\u0026deg;C until a clear solution was obtained. The filtrate was then diluted to 50 mL with deionized water and stored in acid-washed polyethylene bottles prior to analysis (Yahaya \u003cem\u003eet al.,\u003c/em\u003e 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Heavy Metal Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCadmium (Cd), Copper (Cu), and Lead (Pb) concentrations were determined using Atomic Absorption Spectrophotometry (AAS) (Model: Buck Scientific 210 VGP), following standard calibration procedures with analytical-grade reference standards. Quality assurance was maintained through procedural blanks and triplicate analyses, with results expressed in mg/kg dry weight (Idris \u003cem\u003eet al.,\u003c/em\u003e 2020; Li \u003cem\u003eet al.,\u003c/em\u003e 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 \u0026nbsp; Pollution Assessment Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess contamination levels of cadmium and lead in the soils, the following indices were used:\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.4.1 Pollution Load Index (PLI)\u003c/strong\u003e:\u0026nbsp;This provides an overall pollution status of a site.\u003c/h3\u003e\n\u003cp\u003e\u003cimg width=\"48\" height=\"37\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAlBAMAAAD2N3ecAAAAAXNSR0IArs4c6QAAACFQTFRFAAAAAAAAAAA6AABmOgAAZrb/kNv/tv//25Bm/7Zm/9uQcDbJLQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAJ0lEQVQ4T2NgGO6AwykAuxeXCJpjl5goqEaiDpx2DPfAHfUfLUMAALV5A9W789rKAAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u003cstrong\u003eFormula\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"216\" height=\"38\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026middot;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;PLI = 1: Baseline level (no pollution)\u003c/p\u003e\n\u003cp\u003e\u0026middot;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;PLI \u0026lt; 1: No pollution\u003c/p\u003e\n\u003cp\u003e\u0026middot;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;PLI \u0026gt; 1: Pollution present\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 Ecological Risk Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003ePotential Ecological Risk Index (PERI)\u003c/strong\u003e was used to evaluate the ecological risk of heavy metals:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eEcological Risk Factor (Eᵣ):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eFormula\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eER\u003c/strong\u003e\u003cstrong\u003e=\u003c/strong\u003e\u003cstrong\u003eCF\u003c/strong\u003e\u0026times;\u003cstrong\u003eTR\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;where TR = toxic response factor (30 for Cd, 5 for Pb)\u003c/p\u003e\n\u003cp\u003e\u0026middot;\u0026nbsp;\u003cstrong\u003eCumulative RI (Risk Index)\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cimg width=\"72\" height=\"22\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eInterpretation\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eo RI \u0026lt; 150: Low risk\u003c/p\u003e\n\u003cp\u003eo 150 \u0026le; RI \u0026lt; 300: Moderate risk\u003c/p\u003e\n\u003cp\u003eo 300 \u0026le; RI \u0026lt; 600: Considerable risk\u003c/p\u003e\n\u003cp\u003eo RI \u0026ge; 600: Very high risk\u003c/p\u003e\n\u003cp\u003eBackground concentration values for Cd, Cu, and Pb were adopted from average shale levels (Turekian and Wedepohl, 1961) and corroborated by regional baselines (Adeyemi \u003cem\u003eet al.,\u003c/em\u003e 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical and Spatial Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics (mean, standard deviation, coefficient of variation) were computed. Principal Component Analysis (PCA), Boxplot and Hierarchical Cluster Analysis\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(HCA) were employed to explore relationships and potential pollution sources of the heavy metals (Sun \u003cem\u003eet al\u003c/em\u003e., 2020; Shao \u003cem\u003eet al\u003c/em\u003e., 2023). Spatial distribution maps for each metal were generated using the Inverse Distance Weighting (IDW) method within ArcGIS 10.8 to identify contamination gradients and potential hotspots. All statistical computations were performed using SPSS v26.0, with significance thresholds set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eFigure 1 illustrates the spatial distribution of key physicochemical properties and their implications for metal mobility and soil health across the sampled farmlands, revealing notable site-specific differences attributable to local environmental conditions, land use patterns, and management practices. Such variations are consistent with previous findings in comparable tropical agro-ecosystems.\u003c/p\u003e\n\u003cp\u003eSoil moisture content across the study sites ranged from 15.64% at Ojagboro to 20.13% at Oyun, with Oyun and Olaolu displaying significantly higher values. Elevated moisture levels are known to enhance microbial activity, improve nutrient cycling, and contribute to overall soil fertility (Adekanmbi \u003cem\u003eet al\u003c/em\u003e., 2019; Zhou \u003cem\u003eet al.,\u003c/em\u003e 2020). In contrast, the lower moisture observed at Ojagboro may be attributed to site-specific factors such as a drier microclimate, coarser soil texture, or reduced organic matter inputs (Wang \u003cem\u003eet al.,\u003c/em\u003e 2021). Moreover, higher soil moisture plays a key role in influencing the behavior of heavy metals by reducing their mobility, as increased water content can limit metal solubility and slow transport processes within the soil matrix (Rahman \u003cem\u003eet al.,\u003c/em\u003e 2020).\u003c/p\u003e\n\u003cp\u003eSoil pH values across the sites indicated predominantly alkaline conditions, ranging from 7.37 at Ojagboro to \u0026nbsp;9.05 at Oyun. Alkaline pH is commonly observed in tropical agricultural soils and is known to reduce heavy metal solubility through precipitation and adsorption processes (Bian \u003cem\u003eet al.,\u003c/em\u003e 2018; Zhang \u003cem\u003eet al.\u003c/em\u003e, 2020). However, the slightly lower pH observed at Ojagboro could potentially increase the bioavailability of heavy metals, thereby elevating ecological risks (Wang \u003cem\u003eet al.,\u003c/em\u003e 2021).\u003c/p\u003e\n\u003cp\u003eBulk density (BD) values were consistently high, ranging from 2.67 g/cm\u0026sup3; at Ojagboro to 3.85 g/cm\u0026sup3; at Oyun-substantially exceeding the optimal range for agricultural soils (typically 1.1\u0026ndash;1.6 g/cm\u0026sup3;). These elevated densities likely reflect compaction from intensive land use and mechanized farming, which can hinder root penetration, limit microbial activity, and reduce vertical leaching of heavy metals (Iqbal \u003cem\u003eet al\u003c/em\u003e., 2021; Li \u003cem\u003eet al\u003c/em\u003e., 2020).\u003c/p\u003e\n\u003cp\u003eTotal Available Bases (TAV) varied from 0.26 mg KOH/g at Oyun to 0.43 mg KOH/g at Budo Egba, suggesting spatial differences in nutrient reserves and cation exchange dynamics. Lower TAV at Oyun may signal nutrient depletion or variation in soil parent materials, both of which influence fertility and the soil\u0026rsquo;s ability to adsorb heavy metals (Amoakwah \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003eOrganic matter content ranged from 3.90% at Ojagboro to 4.93% at Oyun. Elevated organic matter enhances soil structure, nutrient retention, and heavy metal immobilization via complexation and adsorption mechanisms (Yuan \u003cem\u003eet al.,\u003c/em\u003e 2020; Zhao \u003cem\u003eet al.,\u003c/em\u003e 2019). In contrast, reduced organic matter at Ojagboro may contribute to increased metal mobility and nutrient losses.\u003c/p\u003e\n\u003cp\u003eCation Exchange Capacity (CEC) showed moderate variation, from 7.76 cmolc/kg at Ojagboro to 8.51 cmolc/kg at Oyun. Higher CEC improves the soil\u0026rsquo;s capacity to retain nutrients and immobilize heavy metals, thus reducing their bioavailability (Deng et al., 2018; Zhao \u0026amp; Hou, 2021). Conversely, the lower CEC at Ojagboro may result in greater metal mobility and potential environmental risks.\u003c/p\u003e\n\u003cp\u003eAvailable phosphorus (AP) levels ranged from 6.44 mg/L at Ojagboro to 7.59 mg/L at Oyun. Elevated phosphorus levels can reduce the mobility and toxicity of metals through the formation of stable metal-phosphate complexes (Chen \u003cem\u003eet al.,\u003c/em\u003e 2021; Zhou \u003cem\u003eet al\u003c/em\u003e., 2019). The relatively lower AP at Ojagboro is likely associated with its reduced organic matter and lower CEC.\u003c/p\u003e\n\u003cp\u003eNitrogen content (NC) ranged from 1.59% at Ojagboro to 2.85% at Oyun. The higher levels observed at Oyun likely reflect improved soil fertility, active microbial processes, and efficient nutrient cycling, all of which can influence heavy metal speciation and promote their immobilization in the soil matrix (Zhou \u003cem\u003eet al.,\u003c/em\u003e 2020).\u003c/p\u003e\n\u003cp\u003eOverall, Oyun and Olaolu sites exhibit favorable soil conditions, including higher moisture, organic matter, CEC, and nutrient reserves, which collectively enhance heavy metal immobilization and mitigate contamination risks. In contrast, the lower values at Ojagboro across these parameters suggest degraded soil quality and heightened vulnerability to metal bioavailability and ecological risks.\u003c/p\u003e\n\u003cp\u003eThe findings emphasize that elevated pH, organic matter, CEC, and phosphorus content play critical roles in mitigating cadmium (Cd) and lead (Pb) bioavailability by promoting immobilization mechanisms. Conversely, sites with lower fertility indicators, like Ojagboro, are at greater risk of heavy metal uptake by crops, raising food safety and public health concerns. Furthermore, persistent high bulk density, elevated pH, low CEC, and declining fertility could compromise long-term soil productivity, increasing reliance on agrochemicals and jeopardizing sustainable agriculture. Targeted interventions, including organic amendments, pH adjustments, judicious fertilizer application, and crop rotation, are recommended to restore soil health, minimize contamination risks, and ensure agricultural sustainability in the region.\u003c/p\u003e\u003c!--[if gte mso 9]\u003e\u003cxml\u003e \u003co:OfficeDocumentSettings\u003e \u003co:AllowPNG/\u003e \u003c/o:OfficeDocumentSettings\u003e\u003c/xml\u003e\u003c![endif]--\u003e\u003c!--[if gte mso 9]\u003e\u003cxml\u003e \u003cw:WordDocument\u003e \u003cw:View\u003eNormal\u003c/w:View\u003e \u003cw:Zoom\u003e0\u003c/w:Zoom\u003e \u003cw:TrackMoves/\u003e \u003cw:TrackFormatting/\u003e \u003cw:PunctuationKerning/\u003e \u003cw:ValidateAgainstSchemas/\u003e \u003cw:SaveIfXMLInvalid\u003efalse\u003c/w:SaveIfXMLInvalid\u003e \u003cw:IgnoreMixedContent\u003efalse\u003c/w:IgnoreMixedContent\u003e \u003cw:AlwaysShowPlaceholderText\u003efalse\u003c/w:AlwaysShowPlaceholderText\u003e \u003cw:DoNotPromoteQF/\u003e 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Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Bullet 5\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Number 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Number 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Number 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Number 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"10\" QFormat=\"true\" Name=\"Title\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Closing\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Signature\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"1\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Default Paragraph Font\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text Indent\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Continue\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Continue 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Continue 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Continue 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"List Continue 5\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Message Header\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"11\" QFormat=\"true\" Name=\"Subtitle\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Salutation\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Date\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text First Indent\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text First Indent 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Note Heading\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text Indent 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Body Text Indent 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Block Text\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Hyperlink\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"FollowedHyperlink\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"22\" QFormat=\"true\" Name=\"Strong\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"20\" QFormat=\"true\" Name=\"Emphasis\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Document Map\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Plain Text\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"E-mail Signature\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Top of Form\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Bottom of Form\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Normal (Web)\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Acronym\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Address\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Cite\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Code\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Definition\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Keyboard\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Preformatted\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Sample\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Typewriter\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"HTML Variable\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Normal Table\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"annotation subject\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"No List\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Outline List 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Outline List 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Outline List 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Simple 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Simple 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Simple 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Classic 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Classic 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Classic 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Classic 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Colorful 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Colorful 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Colorful 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Columns 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Columns 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Columns 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Columns 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Columns 5\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 5\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 6\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 7\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Grid 8\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 4\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 5\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 6\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 7\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table List 8\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table 3D effects 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table 3D effects 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table 3D effects 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Contemporary\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Elegant\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Professional\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Subtle 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Subtle 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Web 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Web 2\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Web 3\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Balloon Text\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"39\" Name=\"Table Grid\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Table Theme\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" Name=\"Placeholder Text\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"1\" QFormat=\"true\" Name=\"No Spacing\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" SemiHidden=\"true\" Name=\"Revision\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"34\" QFormat=\"true\" Name=\"List Paragraph\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"29\" QFormat=\"true\" Name=\"Quote\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"30\" QFormat=\"true\" Name=\"Intense Quote\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"60\" Name=\"Light Shading Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"61\" Name=\"Light List Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"62\" Name=\"Light Grid Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"63\" Name=\"Medium Shading 1 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"64\" Name=\"Medium Shading 2 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"65\" Name=\"Medium List 1 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"66\" Name=\"Medium List 2 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"67\" Name=\"Medium Grid 1 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"68\" Name=\"Medium Grid 2 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"69\" Name=\"Medium Grid 3 Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"70\" Name=\"Dark List Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"71\" Name=\"Colorful Shading Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"72\" Name=\"Colorful List Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"73\" Name=\"Colorful Grid Accent 6\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"19\" QFormat=\"true\" Name=\"Subtle Emphasis\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"21\" QFormat=\"true\" Name=\"Intense Emphasis\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"31\" QFormat=\"true\" Name=\"Subtle Reference\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"32\" QFormat=\"true\" Name=\"Intense Reference\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"33\" QFormat=\"true\" Name=\"Book Title\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"37\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" Name=\"Bibliography\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"39\" SemiHidden=\"true\" UnhideWhenUsed=\"true\" QFormat=\"true\" Name=\"TOC Heading\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"41\" Name=\"Plain Table 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"42\" Name=\"Plain Table 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"43\" Name=\"Plain Table 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"44\" Name=\"Plain Table 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"45\" Name=\"Plain Table 5\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"40\" Name=\"Grid Table Light\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"46\" Name=\"Grid Table 1 Light\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"47\" Name=\"Grid Table 2\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"48\" Name=\"Grid Table 3\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"49\" Name=\"Grid Table 4\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"50\" Name=\"Grid Table 5 Dark\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"51\" Name=\"Grid Table 6 Colorful\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"52\" Name=\"Grid Table 7 Colorful\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"46\" Name=\"Grid Table 1 Light Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"47\" Name=\"Grid Table 2 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"48\" Name=\"Grid Table 3 Accent 1\"/\u003e \u003cw:LsdException Locked=\"false\" Priority=\"49\" Name=\"Grid Table 4 Accent 1\"/\u003e 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!mso]\u003e\u003cobject classid=\"clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D\" id=ieooui\u003e\u003c/object\u003e\u003cstyle\u003est1\\:*{behavior:url(#ieooui) }\u003c/style\u003e\u003c![endif]--\u003e\n\u003cstyle\u003e\n \u003c!-- /* Font Definitions */ @font-face \t{font-family:\"Cambria Math\"; \tpanose-1:2 4 5 3 5 4 6 3 2 4; \tmso-font-charset:0; \tmso-generic-font-family:roman; \tmso-font-pitch:variable; \tmso-font-signature:-536869121 1107305727 33554432 0 415 0;} @font-face \t{font-family:Calibri; \tpanose-1:2 15 5 2 2 2 4 3 2 4; \tmso-font-charset:0; \tmso-generic-font-family:swiss; \tmso-font-pitch:variable; \tmso-font-signature:-469750017 -1040178053 9 0 511 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal \t{mso-style-unhide:no; \tmso-style-qformat:yes; \tmso-style-parent:\"\"; \tmargin-top:0in; \tmargin-right:0in; \tmargin-bottom:10.0pt; \tmargin-left:0in; \tline-height:115%; \tmso-pagination:widow-orphan; \tfont-size:11.0pt; \tfont-family:\"Calibri\",sans-serif; \tmso-ascii-font-family:Calibri; \tmso-ascii-theme-font:minor-latin; \tmso-fareast-font-family:Calibri; \tmso-fareast-theme-font:minor-latin; \tmso-hansi-font-family:Calibri; \tmso-hansi-theme-font:minor-latin; \tmso-bidi-font-family:\"Times New Roman\"; \tmso-bidi-theme-font:minor-bidi;} .MsoChpDefault \t{mso-style-type:export-only; \tmso-default-props:yes; \tfont-size:11.0pt; \tmso-ansi-font-size:11.0pt; \tmso-bidi-font-size:11.0pt; \tmso-ascii-font-family:Calibri; \tmso-ascii-theme-font:minor-latin; \tmso-fareast-font-family:Calibri; \tmso-fareast-theme-font:minor-latin; \tmso-hansi-font-family:Calibri; \tmso-hansi-theme-font:minor-latin; \tmso-bidi-font-family:\"Times New Roman\"; \tmso-bidi-theme-font:minor-bidi; \tmso-font-kerning:0pt; \tmso-ligatures:none;} .MsoPapDefault \t{mso-style-type:export-only; \tmargin-bottom:10.0pt; \tline-height:115%;} @page WordSection1 \t{size:8.5in 11.0in; \tmargin:1.0in 1.0in 1.0in 1.0in; \tmso-header-margin:.5in; \tmso-footer-margin:.5in; \tmso-paper-source:0;} div.WordSection1 \t{page:WordSection1;} \n --\u003e\n\u003c/style\u003e\u003c!--[if gte mso 10]\u003e\u003cstyle\u003e /* Style Definitions */ table.MsoNormalTable\t{mso-style-name:\"Table Normal\";\tmso-tstyle-rowband-size:0;\tmso-tstyle-colband-size:0;\tmso-style-noshow:yes;\tmso-style-priority:99;\tmso-style-parent:\"\";\tmso-padding-alt:0in 5.4pt 0in 5.4pt;\tmso-para-margin-top:0in;\tmso-para-margin-right:0in;\tmso-para-margin-bottom:10.0pt;\tmso-para-margin-left:0in;\tline-height:115%;\tmso-pagination:widow-orphan;\tfont-size:11.0pt;\tfont-family:\"Calibri\",sans-serif;\tmso-ascii-font-family:Calibri;\tmso-ascii-theme-font:minor-latin;\tmso-hansi-font-family:Calibri;\tmso-hansi-theme-font:minor-latin;\tmso-bidi-font-family:\"Times New Roman\";\tmso-bidi-theme-font:minor-bidi;}\u003c/style\u003e\u003c![endif]--\u003e\u003c!--StartFragment--\u003e\n\u003cp class=\"MsoNormal\"\u003eThe observed spatial patterns and ecological-risk outcomes in this study align with established theory on heavy-metal behaviour in soils: metals are distributed through a combination of anthropogenic inputs (traffic emissions, market waste, agrochemical applications, informal industrial activities) and natural controls such as parent material, soil texture, and organic matter (Adelekan \u0026amp; Alawode, 2021; Zhao \u003cem\u003eet al.,\u003c/em\u003e 2022;). Their ecological significance is effectively captured by index-based approaches, including the Geoaccumulation Index (Igeo), Enrichment Factor (EF), Pollution Load Index (PLI), and the Potential Ecological Risk Index (PERI), as demonstrated in recent high-resolution soil-contamination assessments across Africa and Asia (Li \u003cem\u003eet al.,\u003c/em\u003e 2021; Okoro \u003cem\u003eet al.,\u003c/em\u003e 2023). The application of geostatistics and GIS to map concentrations and ecological risks follows recent Nigerian and regional studies that emphasize the value of spatially explicit analyses for detecting hotspots and guiding remediation priorities (Eze \u003cem\u003eet\u0026nbsp;al\u003c/em\u003e., 2023; Ogunmodede \u003cem\u003eet\u0026nbsp;al\u003c/em\u003e.,\u0026nbsp;2024).\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eSpecifically, use of PERI (H\u0026aring;kanson 1980) to weight metal concentrations by toxic-response factors offers a robust pathway for translating measured concentrations into ecological concern and has been \u003cem\u003ewidely applied in current soil investigations across developing regions (Bello et al., 2022; Wang et al\u003c/em\u003e., 2023). This theoretical framework explains why even moderate exceedances of highly toxic metals such as Cd can generate disproportionately elevated ecological-risk values relative to more abundant but less toxic metals such as Zn or Mn.\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eRecent regional studies in Nigeria that combine GIS-based spatial mapping, multivariate analyses (PCA and cluster analysis), and index-based ecological risk assessment provide strong support for the methodological choices in this study. These studies consistently report patterns where urban, roadside, and market-processing zones show heightened Pb and Cd concentrations, whereas geogenic controls explain background levels of Fe, Mn, and Ni (Olatunji \u003cem\u003eet al\u003c/em\u003e., 2023; Adebayo \u0026amp; Jimoh, 2024). These findings corroborate the spatial hotspots identified in this study and reinforce the inference of mixed anthropogenic and natural drivers\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003ePrincipal Component Analysis (PCA) was conducted to show the structure and interrelationships among the measured soil physicochemical properties across the study farmlands. The analysis extracted principal components that collectively explained a substantial proportion of the total variance, indicating that a limited number of soils attributes predominantly govern the variability within the dataset.\u0026nbsp;The first two principal components (PC1 and PC2) accounted for 96.28% of the total variability, with PC1 explaining 92.15% and PC2 contributing 4.13% (Fig. 2). Organic matter content, total available bases (TAV), and soil pH loaded heavily on PC1, serving as primary indicators of soil fertility status across the study locations.\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eSites such as Oyun, Olaolu, and Eroomo clustered positively along PC1, reflecting superior soil quality characterized by enriched nutrient reserves, greater organic matter inputs, and stabilized pH conditions. In contrast, Ojagboro and Budo Egba grouped negatively along PC1, suggesting relatively degraded soil fertility, likely driven by continuous cultivation, reduced organic inputs, and anthropogenic pressures. The strong co-alignment of organic matter, nitrogen content, and indicators of plant productivity aligns with recent findings that highlight the central role of soil organic carbon in sustaining soil health, nutrient cycling, and overall agroecosystem resilience (Baveye \u003cem\u003eet al.,\u003c/em\u003e 2018; Keesstra \u003cem\u003eet al.,\u003c/em\u003e 2021). Furthermore, the observed positive correlation between moisture content and available phosphorus reinforces the importance of adequate soil water in enhancing phosphorus solubility, mobility, and plant uptake (Jiang \u003cem\u003eet al.,\u003c/em\u003e 2022; Jin \u003cem\u003eet al.,\u0026nbsp;\u003c/em\u003e2019). Intermediate sites such as Budo Abio and Mubo, located near the biplot center, displayed moderate fertility levels and transitional soil characteristics, suggesting evolving soil conditions shaped by mixed land-use intensity and inputs.\u0026nbsp;\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eFigure 2 illustrates georeferenced patterns of metal distribution across the study area. Hotspots where concentrations exceed background levels often correspond to zones of intense human activity, consistent with reports from recent Geo-AI and GIS-based soil contamination studies (Aminu \u003cem\u003eet al\u003c/em\u003e., 2023; Zhang \u003cem\u003eet al\u003c/em\u003e., 2024). Localized extreme values in geologically distinct areas reflect natural enrichment, supporting conclusions from regional studies on lithogenic metal sources.\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the\u003cstrong\u003e\u0026nbsp;Cluster Analysis of heavy metal concentrations in soil across Farmlands.\u003c/strong\u003e The levels of cadmium (Cd), copper (Cu), and lead (Pb) in the soil samples varied quite a bit across the different sites, showing a clear pattern of spatial differences likely influenced by both natural factors and human activities.\u003c/p\u003e\n\u003cp\u003eThe analysis further grouped the study sites into distinct clusters based on their contamination profiles (Fig. 3). Mubo, Budo Abio, Oyun, and Olagboro formed a closely related cluster, suggesting common contamination pathways, likely associated with agricultural inputs such as fertilizers and pesticides or runoff from nearby urban settlements. This pattern is consistent with findings from recent agro-ecological studies where clustering was driven by similar land-use intensity and input sources (Li \u003cem\u003eet al.,\u003c/em\u003e 2021; Zhou \u003cem\u003eet al\u003c/em\u003e., 2018). In contrast, Olaolu and Eroomo formed isolated clusters, indicating site-specific contamination, potentially influenced by localized anthropogenic activities or unique geochemical backgrounds. Otte and Budo Egba showed moderate divergence, hinting at mixed or transitional pollution sources.\u003c/p\u003e\n\u003cp\u003eThese groupings mirror contemporary findings across Nigeria and other tropical regions, where spatial variability in heavy metal concentrations has been closely linked to proximity to waste disposal sites, differences in land use practices, and the intensity of agricultural and industrial activities (Adeyi and Babalola, 2019; Akinyemi \u003cem\u003eet al.,\u003c/em\u003e 2020). Cluster-based analysis thus offers a powerful tool for delineating contamination hotspots and directing focused monitoring and remediation strategies. In this study, three major clusters emerged: Cluster I encompassed sites with elevated cadmium and lead concentrations, likely due to intensive fertilizer application and nearby industrial emissions. Cluster II included sites with moderate contamination, while Cluster III represented areas with background or geogenic levels of metals. These results highlight the heterogeneous nature of pollution sources and emphasize the need for tailored soil management interventions across different zones.\u003c/p\u003e\n\u003cp\u003eThe patterns observed in Figure 3 particularly elevated medians and wider spreads for Pb and Cd in roadside and peri-urban farmlands align with current literature demonstrating the influence of road traffic and market activities on metal loading in Nigerian agricultural soils (Edeh et al., 2022; Suleiman \u003cem\u003eet\u0026nbsp;al\u003c/em\u003e., 2023).\u003c/p\u003e\u003c!--EndFragment--\u003e\n\u003cp\u003eThe ecological risk assessment revealed considerable spatial variation in heavy metal contamination levels across the study sites (Figure 4). Cadmium (Cd) posed moderate but persistent risks, ranging from 20.50 at Budo Abio to 25.20 at Ojagboro, suggesting region-wide contamination likely driven by prolonged agrochemical use and phosphate-based fertilizers (T\u0026oacute;th \u003cem\u003eet al\u003c/em\u003e., 2018; Wang \u003cem\u003eet al\u003c/em\u003e., 2020). Copper (Cu) presented the highest ecological risk at Eroomo (187.57), with similarly elevated values at Budo Egba and Ojagboro (\u0026gt;140). This distribution pattern aligns with documented environmental impacts of Cu-based fungicides and the widespread incorporation of livestock manure and organic wastes (Li \u003cem\u003eet al.,\u003c/em\u003e 2019 Zhao \u003cem\u003eet al.,\u003c/em\u003e 2021). By contrast, Olaolu recorded the lowest Cu risk (97.98), likely due to the mitigating effects of high organic matter content and alkaline soil pH, which can immobilize heavy metals and limit their bioavailability (Tang \u003cem\u003eet al.,\u003c/em\u003e 2020).\u003c/p\u003e\n\u003cp\u003eLead (Pb) exhibited a distinct spatial pattern, peaking at Olaolu (89.94), followed by Mubo and Otte. These trends likely reflect Pb accumulation from atmospheric deposition linked to traffic emissions, burning of fossil fuels, and proximity to informal industrial activities (Anake \u003cem\u003eet al\u003c/em\u003e., 2019; Chen \u003cem\u003eet al.,\u003c/em\u003e 2021). The overall variability observed underscores the critical influence of soil physicochemical properties, such as pH, organic matter, and texture. on heavy metal mobility and ecological risk potential (Zhang and Wang, 2020; Zhou \u003cem\u003eet al.,\u003c/em\u003e 2022).\u003c/p\u003e\n\u003cp\u003eIn light of these findings, site-specific soil management strategies are essential. Targeted remediation techniques, including phytoremediation, organic matter amendments, and pH modification, are recommended to reduce heavy metal bioavailability and ensure sustainable agricultural productivity (Khan \u003cem\u003eet al\u003c/em\u003e., 2021; Ghosh \u003cem\u003eet al\u003c/em\u003e., 2018).\u003c/p\u003e\n\u003cp\u003eFigure 4 translates raw concentration data into contamination indices. High Igeo and EF values for Cd and Pb at specific hotspots align with recent methodological reviews advocating the combined use of index-based approaches to distinguish anthropogenic from geogenic contributions (Okoro \u0026amp; Ihedioha, 2023; Zhang\u0026nbsp;\u003cem\u003eet\u0026nbsp;al\u003c/em\u003e.,\u0026nbsp;2022).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Ecological Pollution Index combines the pollution levels of multiple heavy metals into a single index that reflects the potential ecological risk at each site. Figure 5 presents the boxplot distribution of the ecological pollution index (EPI) for cadmium (Cd), copper (Cu), and lead (Pb) across the eight sampling sites. The EPI values for Cd ranged between 20.83 and 29.84, with Ojagboro exhibiting the highest Cd pollution index (29.84 \u0026plusmn; 0.22), while BudoAbio recorded the lowest value (20.83 \u0026plusmn; 0.76). For Cu, EPI values spanned from 94.50 to 125.00, with Eroomo having the highest (125.00 \u0026plusmn; 5.00), indicating a localized Cu enrichment, while Olaolu presented the lowest (94.50 \u0026plusmn; 0.90). Similarly, Pb indices ranged widely from 47.67 to 115.67, where Eroomo again displayed the highest Pb accumulation (115.67 \u0026plusmn; 17.75), and BudoAbio recorded the lowest Pb index (47.67 \u0026plusmn; 2.25).\u003c/p\u003e\n\u003cp\u003eThe boxplot reveals considerable variability in Pb compared to Cd and Cu. While Cd and Cu distributions are relatively compact, Pb displays greater interquartile range, indicating significant site-to-site variation in lead contamination levels.\u003c/p\u003e\n\u003cp\u003eThe elevated levels of Cd, Cu, and Pb observed in certain locations may reflect differential anthropogenic activities such as fertilizer application, waste disposal, and vehicular emissions which have been extensively reported as significant contributors to heavy metal accumulation in agricultural soils (Alloway, 2013; Wuana and Okieimen, 2011). The high Cd index at Ojagboro aligns with findings by Akinola \u003cem\u003eet al\u003c/em\u003e. (2020), who linked elevated Cd in urban-adjacent farmlands to atmospheric deposition and municipal waste incineration. Similarly, the excessive Cu concentration at Eroomo may reflect inputs from agrochemicals, particularly copper-based fungicides, as suggested by Chen \u003cem\u003eet al\u003c/em\u003e. (2016). The pronounced Pb levels, especially at Eroomo, could be attributed to both traffic emissions and proximity to industrial zones, corroborating observations by Li \u003cem\u003eet al\u003c/em\u003e. (2014) in peri-urban agricultural soils.\u003c/p\u003e\n\u003cp\u003eFigure 5 demonstrates how metals with shared sources cluster together (e.g., Pb\u0026ndash;Cd indicating anthropogenic inputs, Fe\u0026ndash;Mn reflecting lithogenic origins). Similar clustering patterns have been reported in recent PCA-driven soil-pollution studies that evaluate pollution sources in agricultural and peri-urban regions (Ogunmodede \u003cem\u003eet al.,\u003c/em\u003e 2024; Uche\u0026nbsp;\u003cem\u003eet al.,\u003c/em\u003e 2023).\u003c/p\u003e\n\u003cp\u003eOverall, the observed variability in Ecological Pollution Index (EPI) values indicate the influence of site-specific pollution sources, echoing earlier findings that emphasize the importance of localized land-use practices in shaping soil heavy metal distribution (Liu \u003cem\u003eet al\u003c/em\u003e., 2018). Based on Hakanson\u0026rsquo;s (1980) ecological risk classification, most of the investigated sites fell within the low to moderate risk categories for cadmium (Cd), copper (Cu), and lead (Pb). However, isolated cases of elevated risk were recorded, most notably for Pb at the Eroomo site, signaling the need for ongoing surveillance and the implementation of integrated soil management strategies to mitigate potential long-term ecological and public health risks (Lu \u003cem\u003eet al.,\u003c/em\u003e 2015; Zhang \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003eThe boxplot analysis of EPI values further underscores the spatial variability in ecological risk across the study sites, with several farmlands emerging as potential hotspots for heavy metal accumulation. The presence of statistical outliers suggests that certain locations may carry a disproportionately high contamination load, likely resulting from site-specific agricultural practices, excessive fertilizer application, or proximity to anthropogenic pollution sources such as urban runoff or waste disposal. These findings align with recent studies that highlight the role of localized land-use intensity and pollution inputs in shaping soil contamination patterns, emphasizing the need for targeted monitoring and intervention strategies in high-risk zones (Liu \u003cem\u003eet\u0026nbsp;al.,\u003c/em\u003e 2020; Zhang \u003cem\u003eet\u0026nbsp;al.,\u003c/em\u003e 2021).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Principal Component Analysis (PCA) of heavy metal pollution indices amongst farmlands provides information about the relationships and potential sources of contamination affecting the agricultural soils. Figure 6 illustrates the principal component analysis (PCA) biplot of the mean ecological pollution indices for cadmium (Cd), copper (Cu), and lead (Pb) across the eight study sites. The first two principal components (PC1 and PC2) collectively accounted for 77.7% of the total variance in the dataset, with PC1 and PC2 explaining 43.4% and 34.3% of the variability, respectively. This substantial cumulative variance indicates that PCA effectively captured the major structure of the data, reducing its dimensionality while preserving most of the inherent information.\u003c/p\u003e\n\u003cp\u003eThe biplot reveals distinct contributions of each metal to the observed variability. Cadmium (Cd) exhibited a strong positive loading on PC2 and showed a close association with the Ojagboro site, suggesting a relatively higher ecological risk from Cd contamination at this location. In contrast, copper (Cu) loaded predominantly on PC1 and was closely aligned with Eroomo, indicating that Cu contamination is a primary concern at this site. Lead (Pb) contributed positively to both components, with its influence most pronounced at sites such as Olaolu and Eroomo, reflecting elevated Pb burdens in these locations.\u003c/p\u003e\n\u003cp\u003eConversely, sites such as BudoAbio, Otte, and BudoEgba clustered in the negative quadrants of both PC1 and PC2, indicating comparatively lower ecological pollution indices, particularly for Cu and Pb. These spatial trends likely reflect localized differences in anthropogenic pressures, including agricultural intensification, urban waste deposition, vehicular emissions, and industrial activities.\u003c/p\u003e\n\u003cp\u003eThe PCA results in this study align with recent research utilizing multivariate statistical techniques to unravel pollution sources and evaluate ecological risks in contaminated soils (Khan \u003cem\u003eet al.,\u003c/em\u003e 2022; Tiwari \u003cem\u003eet al\u003c/em\u003e., 2021; Zhou \u003cem\u003eet al\u003c/em\u003e., 2019). Principal Component Analysis (PCA) has been widely recognized for its robustness in distinguishing pollution profiles across urban and peri-urban agricultural environments, effectively linking contamination patterns to underlying anthropogenic activities such as fertilizer application, industrial emissions, and waste disposal (Wang \u003cem\u003eet al.,\u003c/em\u003e 2020; Zhang \u003cem\u003eet al.,\u003c/em\u003e 2022). Notably, the strong association between copper (Cu) and the Eroomo site in this analysis corroborates the ecological risk index (RI) findings, which classified Eroomo as \u0026quot;considerably high to very high risk\u0026quot; (RI \u0026ge; 300), consistent with recent reports on elevated metal loads in Nigerian agroecosystems under multiple pollution pressures (Akinyemi \u003cem\u003eet al.,\u003c/em\u003e 2020; Anake \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003eThe PCA also revealed that the first two principal components (typically PC1 and PC2) accounted for a substantial proportion of the total variance, suggesting that heavy metal contamination in the studied farmlands is driven by a limited set of dominant factors. A strong loading of cadmium (Cd), lead (Pb), and copper (Cu) on PC1 points to a likely common anthropogenic source, such as the intensive and prolonged use of agrochemicals. In contrast, PC2 may represent contributions from geogenic sources or localized industrial activities that vary by site.\u003c/p\u003e\n\u003cp\u003eThe clustering of farmlands in the PCA biplot further suggests similarities or differences in pollution profiles across sites. Farmlands grouped closely together exhibit similar pollution characteristics, while isolated sites may be experiencing site-specific contamination pressures. These findings are crucial for understanding the spatial distribution of pollution and for developing targeted soil management and remediation strategies to mitigate heavy metal risks and protect food safety.\u003c/p\u003e\n\u003cp\u003eFigure 6 highlights ecological risk intensities, emphasizing that areas with moderate concentrations of highly toxic metals (e.g., Cd) carry disproportionately higher ecological concern. This aligns with contemporary ecological risk studies that emphasize the sensitivity of PERI to toxic-response weighting and its relevance for agricultural and public-health risk planning (Adedeji \u003cem\u003eet\u0026nbsp;al.,\u003c/em\u003e 2024; Wang \u003cem\u003eet\u0026nbsp;al\u003c/em\u003e.,\u0026nbsp;2023).\u003c/p\u003e\n\u003cp\u003eCollectively, Figures 2\u0026ndash;6 produce a coherent interpretation of the contamination landscape: spatial mapping (Fig. 2) identifies hotspots; summary statistics (Fig. 3) validate variations by land-use; pollution indices (Fig. 4) reveal contamination severity and possible sources; multivariate analyses (Fig. 5) strengthen source attribution; and PERI (Fig. 6) translates concentrations into ecologically meaningful risk categories. These analytical outcomes align closely with best practices reported in recent Nigerian and global studies that integrate GIS, risk indices, and multivariate statistics to inform soil-management and food-safety policies (Ogunmodede \u003cem\u003eet al.,\u003c/em\u003e 2024; Okoro \u003cem\u003eet al.,\u003c/em\u003e 2023).\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThese studies offers a detailed evaluation of cadmium, copper, and lead contamination in agricultural soils of Ilorin, Nigeria, uncovering pronounced spatial variability in both soil physicochemical attributes and associated ecological risks. By integrating multivariate statistical analyses with ecological risk indices, critical contamination hotspots were identified, notably with elevated cadmium levels at Ojagboro, copper at Eroomo, and lead at Olaolu. The strong associations observed between soil properties, particularly pH, organic matter, cation exchange capacity (CEC), and available phosphorus, and heavy metal dynamics underscore the pivotal role of soil chemistry in governing metal mobility, retention, and bioavailability. The application of principal component analysis (PCA) and cluster analysis provided further indicates the complex interactions shaping contamination profiles and fertility gradients across the study area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTogether, these findings point to the urgent need for tailored soil management strategies, consistent environmental monitoring, and the adoption of sustainable farming practices. Such efforts are essential to reduce heavy metal contamination and safeguard both the health of agro-ecosystems and the well-being of local communities that depend on them.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information (SI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Information (SI) associated with this manuscript includes additional tables, figures, datasets, and methodological details that support the main findings of the study. This material provides extended data on the spatial distribution of heavy metals, ecological risk assessment indices, soil physicochemical properties, and statistical analyses.\u003c/p\u003e\n\u003cp\u003eThe Supplementary Information is provided to ensure transparency, reproducibility, and clarity of the research and is available online alongside the published article. Interested readers may access the SI for detailed data, maps, and analytical procedures that complement the content presented in the main manuscript.\u003c/p\u003e\n\u003cp\u003eAll supplementary files adhere to the \u0026nbsp;formatting of the journal’s guidelines and are referenced appropriately within the manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003ePatience Olayinka Ben-Uwabor\u003c/strong\u003e: Conceptualized and designed the study; supervised field sampling; performed data analysis and ecological risk assessment; interpreted results; drafted the manuscript and led the overall writing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGaniyu Shittu Olahan\u003c/strong\u003e: Assisted in study design; coordinated fieldwork and soil sample collection; contributed to laboratory analysis of heavy metals; reviewed the manuscript for technical accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIbrahim Ajadi\u003c/strong\u003e: Contributed to data interpretation and mapping of spatial distribution using geospatial tools; supported statistical analyses; critically revised the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors\u003c/strong\u003e read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe, the authors of the manuscript titled \u003cem\u003e“Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,”\u003c/em\u003e confirm that we have all read and approved the final version of this paper.\u003c/p\u003e\n\u003cp\u003eWe give our full consent for its publication in the journal. This work is original, has not been published elsewhere, and is not currently being considered by any other journal. We also take full responsibility for the accuracy and integrity of the research and agree to address any questions that may arise about the study\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eResearch Data Policy and Data Availability Statement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors adhere to the research data policy of the journal, ensuring that all data generated or analyzed during this study, \u003cem\u003e“Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria,”\u003c/em\u003e are accurately reported and are available for verification and reuse. The data supporting the findings of this study are maintained in a secure and accessible format to ensure transparency, reproducibility, and integrity of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;Availability:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. These include soil heavy metal concentration data, spatial distribution maps, ecological risk assessment indices, and associated metadata. Access to the data will be granted in accordance with applicable ethical and institutional guidelines.\u003c/p\u003e\n\u003cp\u003eWhere applicable, any supplementary materials, figures, or tables referenced in the manuscript are included as supplementary files or can be provided upon request. The authors commit to sharing data in compliance with relevant copyright, licensing, and confidentiality considerations.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEthical Responsibilities of Authors\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePatience Olayinka, Ben-Uwabor, Ganiyu Shittu Olahan, and Ibrahim Ajadi affirm that this manuscript represents original research conducted by the listed authors and has not been published elsewhere, nor is it under consideration by any other journal.\u003c/p\u003e\n\u003cp\u003eAll authors have made substantial contributions to the conception, design, data collection, analysis, interpretation of the results, and drafting of the manuscript. Each author has reviewed and approved the final version of the manuscript and agrees to be accountable for all aspects of the work, ensuring that questions related to accuracy or integrity are appropriately addressed.\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the study was conducted in accordance with accepted ethical standards for environmental and agricultural research. All sources used have been properly cited, and due acknowledgment has been given to previous work. No part of this research involves fabrication, falsification, or plagiarism.\u003c/p\u003e\n\u003cp\u003eThe authors also declare that there are no conflicts of interest that could have influenced the outcomes or interpretations of this study, and they accept responsibility for upholding the ethical standards of the journal and the scientific community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests, financial or non-financial, that could influence the interpretation or presentation of the research findings\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors wish to acknowledge the support and assistance of all individuals and institutions that contributed to the successful completion of this study. We extend our gratitude to the laboratory staff and field assistants who facilitated sample collection and analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdebayo AA, Olatunde AA, Adeyemi OB. Heavy metals concentration in roadside agricultural soils and associated health risks in Osun State, Nigeria. Environ Chall. 2021;4:100185.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Adekanmbi OH, Akinyemi LP, Olatunji OO. Influence of soil moisture and organic matter on crop productivity in tropical regions. Int J Environ Stud. 2019;76(4):623\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Adeyemi AA, Ogunfowokan AO, Akinremi CA. Human and ecological risk assessment of heavy metals in agricultural soils of southwestern Nigeria. Environ Monit Assess. 2022;194(3):219.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Adeyi AA, Babalola BA. Heavy metal pollution in urban soil: A case study of an open dumpsite in Southwestern Nigeria. 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Sci Total Environ. 2020;706:135710.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-planet","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Clean Planet](https://link.springer.com/journal/44480)","snPcode":"44480","submissionUrl":"https://submission.springernature.com/new-submission/44480/3?","title":"Clean Planet","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Toxic Metals, Soil Quality, Ecological Risk Index, Human Health Risk, Metal Accumulation, Geochemical Assessment","lastPublishedDoi":"10.21203/rs.3.rs-8340670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8340670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeavy metal pollution from cadmium (Cd) and lead (Pb) is a growing threat in agricultural areas, especially where food production is intensifying. In Ilorin, Nigeria, the rapid expansion of farming raises concerns about soil quality and longterm sustainability. However, localized data on the distribution and risks of these metals is scarce.This study addresses that gap by analyzing soils from eight farmlands across Ilorin.\u003cbr\u003e\nSoil quality varied by location, Oyun had better conditions, with higher moisture (20.13%), pH (9.05), and organic matter (4.93%). In contrast, Ojagboro showed poor fertility and higher contamination potential. Cd was absent in some sites but reached 1.33 mg/kg in Otte.\u003cbr\u003e\nPb ranged widely, from 14.67 mg/kg in Budo Abio to 82 mg/kg in Olaolu, sometimes exceeding safe thresholds. Copper (Cu) levels were between 6.33 and 20 mg/kg across sites.\u003cbr\u003e\nMultivariate tools like PCA and cluster analysis highlighted metal–soil relationships and probable pollution sources, such as fertilizers and pesticides. The ecological risk assessment showed moderate to high risk in several areas. Cu posed the highest risk in Eroomo, while Pb levels were most concerning in Olaolu. These findings call for improved soil monitoring systems in Ilorin.There is a need for responsible agrochemical use and targeted remediation strategies.\u003cbr\u003e\nProtecting soil health is essential for both food safety and environmental sustainability.\u003c/p\u003e","manuscriptTitle":"Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Regional Agricultural Soils, Ilorin, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 09:38:01","doi":"10.21203/rs.3.rs-8340670/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T15:50:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T09:39:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60667827538154021648414315023294364450","date":"2026-02-11T15:11:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90621436773917208797202774710782274452","date":"2026-01-18T12:06:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T05:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59547094936971060221370514572682663704","date":"2026-01-14T05:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T12:49:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T23:06:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T23:05:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Planet","date":"2025-12-12T01:20:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-planet","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Clean Planet](https://link.springer.com/journal/44480)","snPcode":"44480","submissionUrl":"https://submission.springernature.com/new-submission/44480/3?","title":"Clean Planet","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07c406a0-2196-4736-85f2-fe9cede4269f","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T15:54:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 09:38:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8340670","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8340670","identity":"rs-8340670","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}