Abstract
Introduction:
Soil contamination by heavy metals (HMs) represents an important concern for agricultural ecosystems and food security.
Methods:
Soil samples from 34 farms were analyzed for arsenic (As), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), and zinc (Zn) using ICP-AES. Geographic Information System (GIS) tools in combination with multivariate statistical analyses were applied to comprehensively assess heavy-metal contamination in the agricultural soils of Huraymala, Saudi Arabia. Additionally, contamination indices such as the Enrichment Factor (EF), Contamination Factor (CF), Pollution Load Index (PLI), and Risk Index (RI) were used to evaluate the levels of soil contamination in the study area. Non-carcinogenic and cancer risk indices were also computed. To measure vegetation density and distinguish between various types of land cover and land use, the Normalized Difference Vegetation Index (NDVI) and land use/land cover (LULC) were evaluated.
Results and discussion:
The mean concentrations (mg kg−1) revealed the order: Fe (11534) > Zn (47.97) > Ni (20.80) > Cu (11.26) > Pb (7.63) > As (2.54). The PLI was 0.29 (range: 0.11–0.56), confirming that the area is relatively uncontaminated (PLI < 1). The RI indicated low to moderate concern overall. Source identification via multivariate statistical analysis suggested mixed geogenic and anthropogenic origins. The human health risk assessment indicated no significant non-carcinogenic risk. The Lifetime Cancer Risk (LCR) was negligible for Pb (< 1 × 10−6) and within the acceptable range for As (1 × 10−6 to 1 × 10−4). NDVI ranged from −0.03 to 0.33.
Conclusion:
This work provides an important reference point for understanding soil quality and potential exposure pathways in arid agricultural settings. By offering the first comprehensive geospatial and health risk evaluation of heavy metal contamination in the agricultural soils of the Huraymala region.
1 Introduction
Soil is a finite resource essential to life, forming the foundation for approximately 95% of the world’s food supply. It also sustains ecosystems by producing biomass, protecting vital resources, and preserving biodiversity (Ferreira et al., 2022). However, these critical functions are being eroded by soil degradation, driven by the expansion of urban areas and intensive industrial and agricultural practices (Dubey et al., 2021). A primary form of this degradation is chemical pollution, which occurs when harmful substances concentrate in the ground, causing damaging and often toxic effects on the environment (Saljnikov et al., 2021). Metals and metalloids, including Pb, Cd, Cu, Hg, As, Sn, and Zn, are together referred to as HMs. These elements may be hazardous due to their comparatively high atomic mass (>4.5 g/cm3) (Alharbi et al., 2025). Geogenic and anthropogenic sources deposit metal contaminants in agricultural soils. The majority of geogenic sources are related to rock weathering. In contrast, the majority of environmental contamination is caused by human activities, including the mining and smelting operations, industrial and domestic effluents, and extensive use of inorganic and organic fertilizers, pesticides, and irrigation water (Goyer, 1993; Rehman et al., 2008). Pesticides and fertilizers used to boost yields continually introduce HMs into the soil ecosystem, where they eventually end up in food crops and fodder, producing considerable health risks for the public (Azizullah et al., 2011;Ullah et al., 2020).
Elevated levels of HMs induce abiotic stress in soil organisms by suppressing enzyme function, competing with vital nutrients, and promoting oxidative stress (Nowicka, 2022). This disruption negatively impacts the entire plant life cycle, from seed germination to full maturity, ultimately reducing both crop yield and quality (Yang et al., 2022). While some hyperaccumulator crops can tolerate this metal stress by absorbing and transferring HMs to their shoots, this trait introduces a significant risk (Abuzaid et al., 2019). The metals absorbed by plants can become concentrated in animals and humans through the food chain, posing a serious health concern (Song et al., 2022). Furthermore, HMs can leach from the soil into groundwater, contaminating vital drinking water sources (Abbas et al., 2020; Abuzaid and Jahin, 2021). The human body requires specific concentrations of essential elements like Cobalt (Co), Copper (Cu), Iron (Fe), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), and Zinc (Zn) for optimal health, with ideal ranges spanning from 0.1–10 mg kg−1 for Co to 2.5–4.5% for Fe (Iyengar, 1987). Concentrations of HMs that are too low result in deficiencies, while those that are too high cause toxicity (Berry and Wallace, 1981). Consequently, human nutritional status for these elements is classified as deficient, adequate, or toxic, a status directly influenced by the levels of these metals in the soil and the degree of human exposure (Steffan et al., 2018; Alarifi et al., 2023). Therefore, to create an appropriate remediation plan and lessen adverse effects, a precise assessment of soil pollution based on HMs is essential (Yang et al., 2021).
Geographic information systems (GIS) are extensively utilized in research domains to visualize the spatial distribution of heavy metals (Hu et al., 2013). An essential step in evaluating soil pollution is to determine the spatial distribution of HMs (Hammam et al., 2022). This process identifies contamination hotspots and is fundamental for managing associated risks (Wang et al., 2020). Consequently, Geographic Information Systems (GIS) are widely used globally to visualize the spatial variation of HMs in soil (Ahmad and Pandey, 2020; Gozukara et al., 2022; Gozukara, 2022). Studies in locations such as California and China have utilized geostatistical techniques like empirical Bayesian kriging, while research in the UK (Golden et al., 2020), Ireland, and Saudi Arabia has applied kriging, cokriging, and inverse distance weighting (Evans et al., 2019; Zhen et al., 2019; Jin et al., 2021; Alzahrani et al., 2024; El-Sorogy et al., 2025; Kahal et al., 2025). These geostatistical methods effectively model the spatial heterogeneity of soil properties and are routinely used to predict HMS concentrations at unmeasured locations based on sampled data points (Zhen et al., 2019; Golden et al., 2020;Jin et al., 2021).
Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are two multivariate statistical techniques that can be used to determine the origins of contamination, differentiating man-made from natural (Weissmannová et al., 2015; Mohamed et al., 2023). Furthermore, the soils’ contamination state can be categorized using contamination indices into four levels: low, moderate, strong, and high (Kahangwa and Protection, 2022). Combining Geographic Information Systems (GIS) with multivariate statistical techniques like PCA and HCA offers a powerful method for regionalization (Hammam et al., 2022). This approach can segment extensive study areas into zones, with each zone exhibiting a characteristic heavy metal signature defined by its specific concentration levels and spatial patterns. Unfortunately, studies on soil contamination in Huraymla, northwest Riyadh, Saudi Arabia, have rarely used such a comprehensive approach, which highlights the urgent need to test its effectiveness in soil ecosystems. Thus, the objectives of this investigation are threefold: first, to determine the concentration and spatial distribution of selected heavy metals (As, Cu, Fe, Ni, Pb, Zn) via a combined methodology of GIS and multivariate analyses (PCA, HCA); second, to contextualize the findings by comparing them with global soil values and established background levels; and third, to evaluate the potential environmental and public health risks arising from the presence of these HMs.
2 Materials and methods
2.1 Area of study
Huraymla is located between 25°0.10′ - 25°0.5’ N and 46°0.5′ - 46°0.10′E (Figures 1a,b) with an area of 201 km2. Huraymla governorate is in the northwest of Riyadh, Saudi Arabia. Huraymla is characterized by agricultural land that produces dates, fruits, vegetables, and leafy green plants. The study area primarily consists of upper Jurassic marine carbonates and siliciclastics originating from the Twaiq Mountain Limestone, Hanifa, and Jubaila formations, along with Quaternary sediments (Powers et al., 1966; Tawfik et al., 2016) (Figure 2). The Twaiq Mountain Limestone, Hanifa, and Jubaila formations were developed within a wide epeiric, marine carbonate deposit (Aigner et al., 1989). The Hanifa formation differs from the Twaiq Mountain formation beneath it and is unconformable with the Jubaila formation above it (Youssef and El-Sorogy, 2015). The Jubaila is made up of clastic-carbonate facies with foraminiferal, stromatoporoids, and algal communities (Khalifa et al., 2021).
Figure 1
Figure 2
2.2 Calculation and mapping of normalize difference vegetation index (NDVI)
An important measure of the density and health of vegetation is the NDVI, which was obtained from remote sensing satellite images (Sentential 2 acquired in August 2025) (Tempa et al., 2024). The NDVI was computed by normalizing the difference between the red and near-infrared (NIR) spectral bands by their total using the ENVI software 5.7. By effectively capturing vegetation vitality and density, this indicator offers insights into shifts in plant growth and health. The spatial distribution of vegetation in Huraymla, Saudi Arabia, was visualized by mapping the NDVI value for each pixel defined in Equation 1. This was derived by applying the NDVI formula to bands 8 (NIR) and 4 (red).
2.3 Sampling and analytical methods
To assess soil contamination, 34 sampling points were randomly established across the farmlands in Huraymla, northwestern Riyadh, Saudi Arabia (Figure 1b). Samples were collected using a hard plastic hand shovel at a depth of about 10 centimeters. This depth is suitable to assess surface contamination, where pollutants tend to accumulate, making it relevant for biological activity, human and ecological exposure, nutrient cycling, and organic matter content (ITRC, 2022). A representative sample was obtained by combining three subsamples into a composite sample, which was placed in clean plastic bags and transported in ice boxes to the laboratory. The soil samples were air-dried at room temperature in the laboratory, where large pebbles and visible organic materials were manually removed. The samples were homogenized using an agate mortar and pestle, and sieved through a series of mesh sizes (>500, 250, 125, 65, and <65 μm). The <65 μm fraction was retained for chemical analysis. For heavy metal determination, a 0.50 g portion of the <65 μm fraction was digested on a hot plate at 60–120 °C for 45 min and subsequently diluted to 12.5 mL with deionized water. Analyses of total HMs were performed using inductively coupled plasma–atomic emission spectrometry (ICP-AES) at the ALS Geochemistry Laboratory in Jeddah, Saudi Arabia. The linearity, limits of detection (LODs), and limits of quantification (LOQs) of the ICP-AES method were evaluated, demonstrating a strong linear response (R2 = 0.997; Supplementary Table S1). Furthermore, one composite sample, created from four individual samples, was analyzed in duplicate to verify analytical precision. The accuracy and precision of the analytical methodology for multi-elemental soil assessment were rigorously validated. This was achieved through the analysis of certified reference materials (CRM11: EMOG17, and CRM2: GBM321-8) alongside reagent blanks to quantify procedural backgrounds. The recovery rates for the target elements ranged from 98.07 to 118.18%, confirming high reliability and accuracy of the applied techniques, consistent with established quality assurance protocols (Nazzal et al., 2016; Chandrasiri et al., 2019). The results are detailed in Supplementary Tables S2, S3.
2.4 Land surface analysis and GIS interpolation for mapping spatial distribution
The elevation of the research region was determined using the Digital Elevation Model (DEM), which was created by the NASA Shuttle Radar Topographic Mission (SRTM) and has a spatial resolution of 30 m (https://earthexplorer.usgs.gov, accessed on August 2025).
An interpolation technique was used to estimate unknown parameters for geospatial factors such as elevation, slope, concentration levels, rainfall, temperature, and noise levels using a limited range of collected data points when it is not practical, convenient, or economical to visit every site within the chosen study region (McCoy et al., 2001; Arumugam et al., 2019). Inverse distance weighting (IDW) is a commonly used interpolation technique for evaluating the spatial distribution of pollutants in soils due to its straightforward calculation and practical data analysis approach (Li et al., 2013; Dai et al., 2018; Wang et al., 2020). One kind of deterministic interpolation method that performs better with nearby data points than with distant ones is the IDW. With a weighting power of 2, the IDW technique was used to simplify the spatial variability of the metal concentration levels and source distribution factors (Wang et al., 2020; Saha et al., 2022). The weights are significantly impacted by the power values, which are the distances between the estimated and observed sampling locations. While weights are more uniformly distributed between neighboring points with lower power, the impact of the distant points diminishes as power increases (Keshavarzi and Sarmadian, 2012; Bhunia et al., 2018).
2.5 Calculation of contamination indices
The soil samples were evaluated for HM levels using the enrichment factor (EF), contamination factor (CF), and pollution load index (PLI), (Muller, 1969; Hakanson, 1980; Reimann and Caritat, 2000; Liu et al., 2005). Supplementary Table S4 categorizes these indices and calculates them using the algorithms mentioned below. Sutherland et al. (2000) describe the EF as a helpful tool for estimating the degree of metal pollution and establishing probable natural and/or anthropogenic sources. The EF was calculated using Equation 2.
is the metal being examined. The level of a normalizer element is denoted by, and a reference Fe concentration is used. Iron is selected as a reference element because it is one of the most abundant elements in the Earth’s crust, providing a stable and reliable baseline. Additionally, it is relatively immobile under most environmental conditions and is a conservative element, not significantly affected by human activities in most instances (Vineethkumar et al., 2020; Al-Kahtany et al., 2024; Youssef et al., 2024).
Contamination factor (CF) can be defined as (Equation 3)
In the context provided, “” represents the total metal content of the soil, while “” denotes the standard background value of the metal. The pollutant load index (PLI) is a comprehensive indicator employed to assess the degree of contamination level of specific metals at a given location (Shaheen et al., 2019). Equation 4 was used to determine the PLI.
The contamination factor of metal n is denoted by CFn.
The risk index (RI) is also calculated using Equations 5, 6 to estimate the degree of HMs pollution in samples (Hakanson, 1980).
In this context, the biological toxic reaction factor of a specific element is denoted as , the contamination factor of each element is represented by and stands for the potential ecological impact factor.
2.6 Statistical analysis
IBM SPSS Version 23 and Python software V3.10 were used to calculate all data input and basic descriptive statistical analysis, including the mean, standard deviation, and coefficient of variation for different heavy metal variables. The bivariate correlation in Python software V3.10 was used to ascertain the statistical significance of the Pearson correlation between the heavy metal content. To decrease data and improve the interpretability of the factors found, PCA carried out exploratory factor analysis of the elemental concentrations using varimax rotation (Makupa, 2013). To identify the elements that share a similar origin in soils, PCA was performed using Varimax rotation with Kaiser Normalization to identify potential contributing factors to the elemental concentrations. Python software V3.10 was used to determine the number of significant factors and the percentage of variation explained by each after the eigenvalues and eigenvectors were extracted from the correlation matrix. PCA helps to maximize the quantity and nature of data that are most useful for determining the soil’s heavy metal contamination (Gergen and Harmanescu, 2012). Hierarchical cluster analysis (HCA) was used to examine the classification of the HMs in the soil (Alzahrani et al., 2024; El-Sorogy et al., 2025; Kahal et al., 2025).
2.7 Health risk assessment
The health risks associated with ingesting, inhaling, and dermal contact pathways for both adults and children were calculated (Waste and Response, 2002). The chronic daily intake (CDI) for the three pathways (milligrams/kilogram. day) can be calculated using the below formulas (Luo et al., 2012; Mondal et al., 2021) (Equations 7–9):
The exposure factors utilized in the calculation of CDI are detailed in Supplementary Table S2. Lead (Pb), and Arsenic (As) were specifically chosen to calculate the potential carcinogenic risks (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2012). Additionally, Fe, Zn, As, Ni, Pb, and Cu were assessed for the non-carcinogenic risks. The total non-carcinogenic risk for a single element is obtained by adding together all of the Hazard Quotients (HQs), which is known as the Hazard Index (HI) (Chonokhuu et al., 2019) (Equations 10, 11):
Supplementary Tables S5, S6 show the exposure factors used in the estimation of CDI for non-carcinogenic risk, along with the reference dose (RfD) and the cancer slope factors (CSF) for HMs (Waste and Response, 2002; Miletić et al., 2023; El-Sorogy and Al Khathlan, 2024). Supplementary Table S7 does not include a CDI value for iron because there are no reference dose values for inhalation and dermal absorption of Fe. This absence could be due to inconsistencies in published data (Miletić et al., 2023). Additionally, the impact of Pb on humans through dermal contact remains uncertain, which is why CSF values for dermal exposure to Pb are rarely mentioned in the literature. An HI number exceeding one indicates a chance of possible non-carcinogenic risk, with the likelihood increasing as HI increases (Waste and Response, 2002; ITRC, 2022). The total lifetime cancer risk (LCR) was calculated using the below Equations 12, 13:
“CSF” refers to the carcinogenic slope factor values for Pb and As, which are specified as 0.0085 and 1.5 (mg/kg. day), respectively (ITRC, 2022). The LCR values less than 1 × 10−6 mean there are no substantial health risks, LCR values between 1 × 10−6 and 1 × 10−4 suggest an acceptable carcinogenic hazard, and LCR values higher than 1 × 10−4 indicate an unacceptable level of risk (Mondal et al., 2021).
3 Results and discussion
3.1 Land surface and vegetation status of the study area
The ground surface elevation ranges from roughly 724 meters above sea level (asl) to 915 meters (asl), according to the Digital Elevation Model (DEM) analysis (Figure 3a). The NDVI values in the research area are modest, ranging from −0.03 to 0.33 (Figure 3b). Liang (2003) defines cultivated land as having an NDVI value between 0.2 and 0.7, and bare soil as having a value between 0 and 0.2. Water is connected with negative values. High NDVI values are typically found in the middle part of the research area.
Figure 3
3.2 Concentration and distribution of HMs
The recorded soil concentrations of HMs (dry weight, mg kg−1) were organized in Table 1. The mean concentrations of the HMs had the order of Fe (11,534+/−3,374), Zn (47.97+/−6.22), Ni (20.80+/−9.26), Cu (11.26+/−6.22), Pb (7.63+/−17.80), and As (2.54+/−1) (Table 1). Based on the geographical analysis, the highest concentrations of As (6 mg kg−1) were found at site 22 in the central region, while the highest concentrations of Cu and Pb (35 and 108 mg kg−1, respectively) were found at site 1 in the middle of the study area (Figure 4). However, site 16 in the research area’s north had the greatest zinc concentration (242 mg kg−1). According to the statistics, all heavy metals except As the research area’s content fluctuates greatly (STD > 2) (Ali et al., 2013). The abundance of individual elements in soils and other surficial materials is influenced by biological and climatic factors, as well as by agricultural and industrial processes that have acted on the materials over time, in addition to the element content of the bedrock or other deposits from which the materials originated (Al-Bagawi et al., 2021).
Table 1
| S. no. | As | Cu | Fe | Ni | Pb | Zn |
|---|---|---|---|---|---|---|
| mg kg−1 | ||||||
| 1 | 4.00 | 35.00 | 15,000 | 33.0 | 108 | 73.0 |
| 2 | 3.00 | 10.00 | 10,900 | 23.0 | 8.00 | 65.0 |
| 3 | 4.00 | 19.00 | 17,600 | 33.0 | 9.00 | 75.0 |
| 4 | 3.00 | 11.00 | 12,300 | 22.0 | 7.00 | 70.0 |
| 5 | 4.00 | 8.00 | 14,200 | 22.0 | 6.00 | 27.0 |
| 6 | 2.00 | 15.00 | 10,100 | 14.0 | 6.00 | 70.0 |
| 7 | 3.00 | 10.00 | 11,200 | 18.0 | 7.00 | 46.0 |
| 8 | 1.00 | 4.00 | 8,300 | 10.0 | 3.00 | 17.0 |
| 9 | 2.00 | 8.00 | 11,000 | 20.0 | 4.00 | 56.0 |
| 10 | 2.00 | 7.00 | 9,100 | 21.0 | 4.00 | 35.0 |
| 11 | 2.00 | 9.00 | 11,400 | 16.0 | 4.00 | 37.0 |
| 12 | 2.00 | 8.00 | 11,100 | 18.0 | 4.00 | 30.0 |
| 13 | 2.00 | 7.00 | 10,300 | 15.0 | 3.00 | 22.0 |
| 14 | 1.00 | 3.00 | 6,800 | 8.0 | 1.00 | 9.0 |
| 15 | 2.00 | 8.00 | 10,300 | 18.0 | 4.00 | 30.0 |
| 16 | 3.00 | 20.00 | 10,600 | 19.0 | 6.00 | 242.0 |
| 17 | 4.00 | 12.00 | 16,400 | 37.0 | 5.00 | 47.0 |
| 18 | 3.00 | 19.00 | 14,900 | 31.0 | 6.00 | 70.0 |
| 19 | 3.00 | 12.00 | 12,500 | 24.0 | 4.00 | 54.0 |
| 20 | 3.00 | 15.00 | 13,800 | 26.0 | 6.00 | 48.0 |
| 21 | 3.00 | 12.00 | 14,100 | 26.0 | 5.00 | 43.0 |
| 22 | 6.00 | 22.00 | 23,200 | 56.0 | 10.00 | 65.0 |
| 23 | 3.00 | 12.00 | 13,000 | 21.0 | 4.00 | 39.0 |
| 24 | 2.00 | 6.00 | 7,200 | 12.0 | 4.00 | 16.0 |
| 25 | 2.00 | 5.00 | 7,000 | 11.0 | 3.00 | 56.0 |
| 26 | 2.00 | 11.00 | 8,100 | 15.0 | 3.00 | 33.0 |
| 27 | 3.00 | 9.00 | 12,600 | 23.0 | 5.00 | 30.0 |
| 28 | 2.00 | 10.00 | 11,700 | 23.0 | 4.00 | 33.0 |
| 29 | 1.00 | 18.00 | 8,900 | 9.0 | 3.00 | 58.0 |
| 30 | 3.00 | 12.00 | 13,900 | 25.0 | 4.00 | 34.0 |
| 31 | 2.00 | 5.00 | 7,400 | 19.0 | 3.00 | 18.0 |
| 32 | 2.00 | 10.00 | 10,000 | 16.0 | 3.00 | 58.0 |
| 33 | 2.00 | 9.00 | 9,700 | 14.0 | 6.00 | 29.0 |
| 34 | 2.00 | 10.00 | 12,300 | 22.00 | 4.00 | 35.00 |
| Min. | 1.00 | 3.00 | 6,800 | 8.00 | 1.00 | 9.00 |
| Max. | 6.00 | 35.00 | 23,200 | 56.00 | 108.00 | 242.00 |
| Aver. | 2.64 | 11.92 | 11,858 | 21.78 | 10.42 | 53.36 |
| STD | 1.02 | 6.22 | 3,374 | 9.26 | 17.80 | 38.70 |
The heavy metal concentrations in the Huraymla soil.
STD, Stander division; S.no., sample number.
Figure 4
3.3 Spatial distribution of heavy metals
The spatial distribution of heavy metals, interpolated using the Inverse Distance Weighting (IDW) method, reveals distinct and heteroaligeneous patterns across the study area, suggesting possible differences in sources and transport pathways (Figure 4). The highest concentrations of the majority of the analyzed elements, namely Cu, Fe, Ni, and Pb, were predominantly clustered in the southern sector. In contrast, As exhibited a more complex and dispersed distribution, with elevated concentrations forming notable hotspots in both the north-eastern and southern parts of the area. Zn was uniquely concentrated in the south, aligning with the other elements but potentially originating from a distinct, specific source. The IDW interpolation effectively highlights these zones of enrichment, providing a critical visual tool for identifying priority areas for further investigation and targeted remediation efforts to protect soil health and food safety (El-Sorogy et al., 2025).
3.4 Comparative analysis of heavy metal concentrations
Comparing the HMs concentrations in current study those in other locations is essential since it helps determine whether the levels of HMs in our research area are within a normal or acceptable range when compared to recommended and regional values (Table 2). Upon comparing these values with HM concentrations in soils from other regions in Saudi Arabia, background values, and global averages (Table 2), it is evident that the current average concentrations were lower than those reported for background levels and the global average (Turekian and Wedepohl, 1961; Kabata-Pendias, 2000). The average concentrations of Pb, and Cu in Huraymla area were noticed to be higher than those observed in the soils of Al-Ahsa and Al-Ammariah in Saudi Arabia (Alarifi et al., 2022; Alharbi and El-Sorogy, 2022). Conversely, the current values for Fe, As, Zn, and Pb were lower than the average concentrations reported for soils in Wadi Jazan and Al Uyaynah in Saudi Arabia (Al-Boghdady et al., 2019; Alharbi and El-Sorogy, 2021). Notably, all measured metals in Huraymala fall significantly below the permissible limits for agricultural soil set by the (FAO/WHO, 2021) and the European Union (EU, 2002). Even if our investigation reveals that the heavy metal concentrations in the study area are now low, indicating a favorable environmental status, the region’s agricultural activity must be taken into account. Despite its economic importance, agriculture may cause long-term issues with soil quality, particularly when it comes to the accumulation of heavy metals from sources like fertilizers, pesticides, and irrigation water. As a result, it is essential to routinely check and assess the concentrations of heavy metals in the research area’s soils. Maintaining these low levels is necessary to achieve sustainable growth in the region. One of the UN’s Sustainable Development Goals (SDGs), specifically SDG 2: Zero Hunger, is ensuring healthy soil for the production of safe and ample food (Lile et al., 2023; Einar et al., 2025).
Table 2
| Location and references | Fe | Ni | Zn | Cu | As | Pb |
|---|---|---|---|---|---|---|
| Measuring unit | mg kg−1 | |||||
| Huraymla, northwestern Riyadh, Saudi Arabia (present study) | 11,534 | 20.80 | 47.97 | 11.26 | 2.54 | 7.63 |
| Al-Ahsa, Saudi Arabia (Alharbi and El-Sorogy, 2022) | 11,790 | 14.53 | 54.43 | 10.83 | 2.27 | 5.23 |
| Al Uyaynah, Saudi Arabia (Alharbi and El-Sorogy, 2021) | 65,200 | 19.25 | 64.33 | 10.56 | 13.8 | 28.48 |
| Jazan, Saudi Arabia (Al-Boghdady et al., 2019) | 23,811 | 48.66 | 75.80 | 72.85 | 14.13 | 19.41 |
| Al-Ammariah, Saudi Arabia (Alarifi et al., 2022) | 11,581 | 26.94 | 52.16 | 11.36 | 3.78 | 5.08 |
| World average (Kabata-Pendias, 2000) | 35,000 | 29 | 70 | 38.9 | 6.83 | 27 |
| (FAO/WHO, 2021) Permissible Limit for Agricultural Soil | 50,000 | 50 | 300 | 100 | – | 60 |
| (EU, 2002) | - | 75 | 300 | 140 | – | 300 |
| Background value (Turekian and Wedepohl, 1961) | 47,200 | 68 | 95 | 45 | 13 | 20 |
Comparison of different local and global backdrops with the mean concentration of HMs in the research region.
3.5 Multivariate analysis
3.5.1 Pearson correlation
Details on the carrier substances and chemical relationships of metals in the study area are revealed by matrix analysis, along with evidence of the relationships between specific factors (El-Sorogy et al., 2025). Many elemental pairs, such as Fe-As (r = 0.90), Fe-Cu (r = 0.61), and Fe-Ni (r = 0.90), showed substantial positive correlations, indicating comparable origins for these HMs (Figure 5) (Nazzal et al., 2016). The elevated concentrations of Fe suggested that these HMs naturally originated primarily from the weathering of Jurassic and Cretaceous to Quaternary rocks in the research area (Alarifi et al., 2022). Ferric hydroxide, widely present in argillaceous sediments and alluvial soils, plays a role in regulating soil and soil solution concentrations, as highlighted by Kabata-Pendias (2000). The soil’s average EF for As was 6.05, indicating a human source, particularly industrial wastes, fertilizers, and insecticides such as Pb-arsenates and Cu-acetate-arsenates (Kabata-Pendias, 2000). Moreover, despite the strong correlation of Pb and Zn with Cu (r = 0.714 and 0.536, respectively), their association with other HMs was weak, suggesting a common source for Pb, Zn, and Cu. Zinc concentrations are likely to be higher in argillaceous sediments, calcareous soils, and organic soils in sedimentary rocks, influenced by hydrous oxides, clay minerals, and pH during soil weathering processes. The average EF value for Zn was 2.36, which falls within the range commonly associated with typical agricultural activities like fertilizer application (Ziko et al., 2001; Alharbi and El-Sorogy, 2021).
Figure 5
3.5.2 Principal components analysis
The correlation analysis results received strong validation through PCA. Two principal components (PCs) (eigenvalue>1) were identified, collectively explaining 61 and 19.87% of the overall variation, as illustrated in Table 3 and Figure 6. The initial PC exhibited notable loadings for As, Cu, Fe, and Ni. High loadings in these HMs on the first PC suggested a potential geogenic origin. In the sedimentary sequences of central Saudi Arabia, various minerals are associated with elements like As, Ni, Fe, Pb, Cu, and Zn, like arsenopyrite (FeASS), realgar (AS₄S₄), pentlandite [(Fe, Ni)₉S₈], millerite (NiS), hematite (Fe₂O₃), magnetite (Fe₃O₄), goethite (FeO(OH)), galena (PbS), chalcopyrite (CuFeS₂), bornite (Cu₅FeS₄), malachite (Cu₂CO₃ (OH)₂), and sphalerite (ZnS) (Obasi and Akudinobi, 2020; Hassan Ahmed, 2022). PC2, on the other hand, demonstrated significant loadings for Pb and Zn, accompanied by average enrichment factor (EF) values exceeding 1 and 3. This implies a minor enrichment, possibly originating from both geogenic and human sources, wherein the use of phosphate fertilizer and fungicides in agricultural fields for increased yield could play a contributory role.
Table 3
| Component matrixa | Component | |
|---|---|---|
| 1 | 2 | |
| As | 0.912 | −0.287 |
| Cu | 0.844 | 0.454 |
| Fe | 0.901 | −0.379 |
| Ni | 0.906 | −0.364 |
| Pb | 0.547 | 0.575 |
| Zn | 0.429 | 0.546 |
| % of Variance | 61 | 19.87 |
| Cumulative % | 61 | 80.88 |
Component matrix of studied elements.
Highly loaded variables are indicated by bold face numbers.
Figure 6
3.5.3 Hierarchical agglomerative cluster of studied elements
Hierarchical agglomerative cluster analysis’s (HCA) primary objective is to naturally organize data into clusters based on similarity by looking for objects in the n-dimensional space that are nearest to one another. It also aims to distinguish stable clusters from other clusters (Astel et al., 2011). The Q-mode HCA categorized the 34 examined samples into four distinct groups (Table 4 and Figure 7). The largest group (cluster 2), consisting of S6, S8, S9-S16, S24-S26, S28, S29, S32, S33, S34, exhibited the lowest amounts of As, Cu, Fe, Ni, Pb, and Zn (1.83+/−0.38, 8.50+/−3.70, 9,483+/−1718, 15.61+/−4.47, 3.76+/−1.14,and 35.67+/−17.28 mg kg−1, respectively). The intermediate group (cluster 3), comprising S2-S5, S7, S10, S17, S18-S21, S27, S30 displayed varied metal concentrations (Table 4 and Figure 7). Notably, S1 had the greatest values for Cu (35.00 mg kg−1) and Pb (108.00 mg kg−1) in cluster 1. The smallest group, represented by S16, exhibited the highest amounts of As, Fe, and Ni (3.00, 10,600, and 19.00 mg kg−1, respectively), this implies that even while anthropogenic farming methods are a discernible contributing factor, the environmental impact is not yet an important concern. These results align with research from other Saudi Arabian agricultural districts, including Al Qassim and Jazan, which similarly found that soils in these areas have comparatively low levels of heavy metal contamination (El-Sorogy et al., 2025; Kahal et al., 2025).
Table 4
| n | As | Cu | Fe | Ni | Pb | Zn | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | STD | Mean | STD | Mean | STD | Mean | STD | Mean | STD | Mean | STD | ||
| Cluster | |||||||||||||
| 1 | 2 | 5 | 1.41 | 28.50 | 9.19 | 19,100 | 5,798 | 44.50 | 16.26 | 59 | 69.29 | 69 | 5.65 |
| 2 | 18 | 1.83 | 0.38 | 8.50 | 3.70 | 9,483 | 1718 | 15.61 | 4.47 | 3.67 | 1.14 | 35.67 | 17.28 |
| 3 | 13 | 3.23 | 0.44 | 12.38 | 3.40 | 13,646 | 1905 | 25.46 | 5.29 | 5.85 | 1.57 | 49.85 | 15.94 |
| 4 | 1 | 3 | 20 | 10,600 | 19 | 6 | 242 | ||||||
Mean concentration of HMs in different clusters.
n = number of samples for each cluster; STD = stander deviation.
Figure 7
3.6 Contamination and risk assessment
Contamination by HMs in agricultural land arises from the extensive application of both inorganic and organic fertilizers, pesticides, irrigation water, and animal dung (Azizullah et al., 2011; Ullah et al., 2020). While As and Pb are significant environmental concerns and pollutants, Fe, Ni, Cu, and Zn are necessary heavy metals crucial to various biological activities, albeit in low amounts (González et al., 2021; Alharbi and El-Sorogy, 2022). To evaluate potential HM contamination in the examined soil, CF and EF were employed (Table 5 and Figure 8). The mean EF values for the HMs were as follows: As (0.80+/−0.17), Cu (1.02+/−0.44), Ni (1.23+/−0.24), Pb (1.45+/−0.24), and Zn (2.14+/−2.76). Implying that the Huraymla soil is minor enriched for HMs. Select HMs were significantly enriched in select individual samples, such as Pb in S1 (EF = 16.99) and Zn in S16 (EF = 11.34). Chronic health concerns caused by lead buildup in human bodies include blood, brain, and nerve abnormalities, structural damage, digestive and cardiovascular problems, and hypertension (Abadin et al., 2007; Yuan et al., 2014). Figure 8 illustrates the distribution of enrichment factor values for HMs within the study area. The highest EF values of As in the soil, located particularly in the northeastern and southwestern farms, which are located on Cretaceous and Quaternary deposits. The EF values for Pb and Zn are generally low throughout the study area, except for some hotspots like farm 1 in the south-central region for Pb, and farm 16 for Zn, which are situated on Quaternary alluvial deposits.
Table 5
| S. no. | EF | CF | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| As | Cu | Ni | Pb | Zn | As | Cu | Fe | Ni | Pb | Zn | |
| 1 | 0.97 | 2.45 | 1.53 | 16.99 | 2.42 | 0.31 | 0.78 | 0.32 | 0.49 | 5.40 | 0.77 |
| 2 | 1.00 | 0.96 | 1.46 | 1.73 | 2.96 | 0.23 | 0.22 | 0.23 | 0.34 | 0.40 | 0.68 |
| 3 | 0.83 | 1.13 | 1.30 | 1.21 | 2.12 | 0.31 | 0.42 | 0.37 | 0.49 | 0.45 | 0.79 |
| 4 | 0.89 | 0.94 | 1.24 | 1.34 | 2.83 | 0.23 | 0.24 | 0.26 | 0.32 | 0.35 | 0.74 |
| 5 | 1.02 | 0.59 | 1.08 | 1.00 | 0.94 | 0.31 | 0.18 | 0.30 | 0.32 | 0.30 | 0.28 |
| 6 | 0.72 | 1.56 | 0.96 | 1.40 | 3.44 | 0.15 | 0.33 | 0.21 | 0.21 | 0.30 | 0.74 |
| 7 | 0.97 | 0.94 | 1.12 | 1.48 | 2.04 | 0.23 | 0.22 | 0.24 | 0.26 | 0.35 | 0.48 |
| 8 | 0.44 | 0.51 | 0.84 | 0.85 | 1.02 | 0.08 | 0.09 | 0.18 | 0.15 | 0.15 | 0.18 |
| 9 | 0.66 | 0.76 | 1.26 | 0.86 | 2.53 | 0.15 | 0.18 | 0.23 | 0.29 | 0.20 | 0.59 |
| 10 | 0.80 | 0.81 | 1.60 | 1.04 | 1.91 | 0.15 | 0.16 | 0.19 | 0.31 | 0.20 | 0.37 |
| 11 | 0.64 | 0.83 | 0.97 | 0.83 | 1.61 | 0.15 | 0.20 | 0.24 | 0.24 | 0.20 | 0.39 |
| 12 | 0.65 | 0.76 | 1.13 | 0.85 | 1.34 | 0.15 | 0.18 | 0.24 | 0.26 | 0.20 | 0.32 |
| 13 | 0.71 | 0.71 | 1.01 | 0.69 | 1.06 | 0.15 | 0.16 | 0.22 | 0.22 | 0.15 | 0.23 |
| 14 | 0.53 | 0.46 | 0.82 | 0.35 | 0.66 | 0.08 | 0.07 | 0.14 | 0.12 | 0.05 | 0.09 |
| 15 | 0.71 | 0.81 | 1.21 | 0.92 | 1.45 | 0.15 | 0.18 | 0.22 | 0.26 | 0.20 | 0.32 |
| 16 | 1.03 | 1.98 | 1.24 | 1.34 | 11.34 | 0.23 | 0.44 | 0.22 | 0.28 | 0.30 | 2.55 |
| 17 | 0.89 | 0.77 | 1.57 | 0.72 | 1.42 | 0.31 | 0.27 | 0.35 | 0.54 | 0.25 | 0.49 |
| 18 | 0.73 | 1.34 | 1.44 | 0.95 | 2.33 | 0.23 | 0.42 | 0.32 | 0.46 | 0.30 | 0.74 |
| 19 | 0.87 | 1.01 | 1.33 | 0.76 | 2.15 | 0.23 | 0.27 | 0.26 | 0.35 | 0.20 | 0.57 |
| 20 | 0.79 | 1.14 | 1.31 | 1.03 | 1.73 | 0.23 | 0.33 | 0.29 | 0.38 | 0.30 | 0.51 |
| 21 | 0.77 | 0.89 | 1.28 | 0.84 | 1.52 | 0.23 | 0.27 | 0.30 | 0.38 | 0.25 | 0.45 |
| 22 | 0.94 | 0.99 | 1.68 | 1.02 | 1.39 | 0.46 | 0.49 | 0.49 | 0.82 | 0.50 | 0.68 |
| 23 | 0.84 | 0.97 | 1.12 | 0.73 | 1.49 | 0.23 | 0.27 | 0.28 | 0.31 | 0.20 | 0.41 |
| 24 | 1.01 | 0.87 | 1.16 | 1.31 | 1.10 | 0.15 | 0.13 | 0.15 | 0.18 | 0.20 | 0.17 |
| 25 | 1.04 | 0.75 | 1.09 | 1.01 | 3.97 | 0.15 | 0.11 | 0.15 | 0.16 | 0.15 | 0.59 |
| 26 | 0.90 | 1.42 | 1.29 | 0.87 | 2.02 | 0.15 | 0.24 | 0.17 | 0.22 | 0.15 | 0.35 |
| 27 | 0.86 | 0.75 | 1.27 | 0.94 | 1.18 | 0.23 | 0.20 | 0.27 | 0.34 | 0.25 | 0.32 |
| 28 | 0.62 | 0.90 | 1.36 | 0.81 | 1.40 | 0.15 | 0.22 | 0.25 | 0.34 | 0.20 | 0.35 |
| 29 | 0.41 | 2.12 | 0.70 | 0.80 | 3.24 | 0.08 | 0.40 | 0.19 | 0.13 | 0.15 | 0.61 |
| 30 | 0.78 | 0.91 | 1.25 | 0.68 | 1.22 | 0.23 | 0.27 | 0.29 | 0.37 | 0.20 | 0.36 |
| 31 | 0.98 | 0.71 | 1.78 | 0.96 | 1.21 | 0.15 | 0.11 | 0.16 | 0.28 | 0.15 | 0.19 |
| 32 | 0.73 | 1.05 | 1.11 | 0.71 | 2.88 | 0.15 | 0.22 | 0.21 | 0.24 | 0.15 | 0.61 |
| 33 | 0.75 | 0.97 | 1.00 | 1.46 | 1.49 | 0.15 | 0.20 | 0.21 | 0.21 | 0.30 | 0.31 |
| 34 | 0.59 | 0.85 | 1.24 | 0.77 | 1.41 | 0.15 | 0.22 | 0.26 | 0.32 | 0.20 | 0.37 |
| Min | 0.41 | 0.46 | 0.70 | 0.35 | 0.66 | 0.08 | 0.07 | 0.14 | 0.12 | 0.05 | 0.09 |
| max | 1.04 | 2.45 | 1.78 | 16.99 | 11.34 | 0.46 | 0.78 | 0.49 | 0.82 | 5.40 | 2.55 |
| Mean | 0.80 | 1.02 | 1.23 | 1.45 | 2.14 | 0.20 | 0.26 | 0.25 | 0.31 | 0.39 | 0.52 |
| STD | 0.17 | 0.44 | 0.24 | 2.76 | 1.81 | 0.08 | 0.14 | 0.07 | 0.14 | 0.89 | 0.41 |
Minimum, maximum, and average levels of raw data on trace metal concentrations.
Figure 8
The HMs in the Huraymla had the following mean CF values: As (0.20+/−0.08), Cu (0.26+/−0.14), Fe (0.25+/−0.07), and Ni (0.31 +/− 0.14), and Pb (0.39 +/− 0.89), and Zn (0.52+/−0.41). These values suggest that the soil was low in contamination with the other HMs. Moreover, certain individual samples exhibited significant values, such as S1 for Pb (CF = 5.40), and moderate values, such as S16 for Zn (CF = 2.55) (Table 5 and Figure 8). The transformation of industrial wastes, irrigation waters, and pesticides into nonphytotoxic forms in soils may require decades. Furthermore, Zn tends to accumulate in argillaceous sediments and is very mobile during weathering processes. Agricultural practices and the nonferrous metal industry are examples of anthropogenic sources of zinc (Kabata-Pendias, 2000).
The total concentrations of all measured HMs were utilized in integrated indices, and Figure 9 illustrates the distribution of average PLI and RI values in this study. PLI serves to indicate the degradation of soil conditions resulting from the accumulation of HMs (Varol, 2011). It ranged from 0.11 in S14 to 0.56 in S1, with an average of 0.29, suggesting relatively clean soil conditions (PLI < 1) (Table 6 and Figure 9). RI, which quantifies the degree of ecological danger posed by HM concentrations in water, air, and soil (Hakanson, 1980), varied from 8.35 in S14 to 61.53 in S1, with an average of 23.79. This indicates that, on average, the HMs in the soil did not pose a significant threat. However, Farms S1 and S22 exhibited higher RI values (61.53 and 52.01, respectively), suggesting a moderate value associated with HMs. The elevated RI in these two outlier soil samples could be attributed to higher CF values compared to the other samples (Table 6).
Figure 9
Table 6
| S. no. | Pollution load index (PLI) | Risk index (RI) |
|---|---|---|
| 1 | 0.56 | 61.53 |
| 2 | 0.32 | 25.36 |
| 3 | 0.46 | 35.48 |
| 4 | 0.33 | 26.01 |
| 5 | 0.3 | 30.52 |
| 6 | 0.25 | 18.66 |
| 7 | 0.29 | 24.8 |
| 8 | 0.16 | 9.3 |
| 9 | 0.27 | 18.28 |
| 10 | 0.22 | 17.57 |
| 11 | 0.23 | 17.73 |
| 12 | 0.25 | 17.78 |
| 13 | 0.2 | 16.59 |
| 14 | 0.11 | 8.35 |
| 15 | 0.24 | 17.78 |
| 16 | 0.36 | 27.9 |
| 17 | 0.38 | 33.51 |
| 18 | 0.4 | 28.11 |
| 19 | 0.32 | 25.1 |
| 20 | 0.35 | 26.46 |
| 21 | 0.34 | 25.87 |
| 22 | 0.53 | 52.01 |
| 23 | 0.29 | 24.61 |
| 24 | 0.18 | 15.88 |
| 25 | 0.19 | 15.83 |
| 26 | 0.22 | 17.1 |
| 27 | 0.3 | 24.64 |
| 28 | 0.27 | 18.65 |
| 29 | 0.2 | 10.8 |
| 30 | 0.31 | 25.4 |
| 31 | 0.17 | 16.28 |
| 32 | 0.23 | 17 |
| 33 | 0.2 | 17.1 |
| 34 | 0.27 | 18.54 |
| Min | 0.11 | 8.35 |
| Max | 0.56 | 61.53 |
| Mean | 0.29 | 23.79 |
| STD | 1.02 | 6.22 |
PLI, and RI of the studied HMs.
Different phylogenetic relationships and grouping patterns among the 34 samples are revealed by the Hierarchical Clustering Analysis (HCA) results, which are displayed as a distance heatmap. Samples 2, 4, 7, 19, 23, 27, and 30 form a closely connected group, as indicated by the extraordinarily low pairwise distances (e.g., 0.49 between Sample 2 and Sample 4 and 0.53 between S 19 and S 23) in the dendrogram, which clearly segregates into several primary clusters (Figure 7). This implies that there is a great deal of commonality among this group. Samples 8, 14, 24, 25, 26, 31, and 33 are part of a distinct, well-characterized cluster that shows another closely related cohort. Sample1 is positioned as an outgroup because it appears to be the most divergent, with the biggest average distance to all other samples. Likewise, S16 and S22 show notable genetic separations from the primary clusters, emphasizing their distinct features (Figure 10). Instead of a tight bifurcation, the intermediate distances between the major clusters indicate a graduated scale of diversity. Overall, the HCA offers compelling evidence for a structured population, clearly defining very similar subgroups and pointing out possible outliers.
Figure 10
The outcomes in Figure 11 demonstrate how well the combined PCA–HCA method works to identify various patterns in the dataset. The spatial or compositional heterogeneity among the examined samples is reflected in the separation between the clusters, which offers important information about the environmental factors affecting the samples’ distribution throughout the study area. Four major clusters (Clusters 1–4) were found, and each color denoted a different sample group.
Figure 11
The samples that are highlighted in red are clearly closely clustered, suggesting that their assessed attributes are highly comparable. However, other samples (such as the ones in pink or cyan) are situated further away from the major clusters, indicating significant variations that might be related to certain environmental circumstances (Figure 11). Reduced agricultural output from contaminated soils can cost producers money and raise the cost of food for consumers. Furthermore, soil remediation can be expensive and frequently necessitates large human and technological inputs (Liu et al., 2012). For sustained agricultural development, it is therefore both economically and environmentally necessary to remediate soil contamination and promote soil health (Boularbah et al., 2025).
3.7 Health risk assessment
Numerous HMs play vital roles in nutrition, being essential in minimal quantities. For instance, Cd is a key component of cobalamin, Mn regulates numerous enzymes, Fe is integral to hemoglobin and myoglobin, Ni is essential for active urease synthesis in plant cells, and Zn acts as a cofactor for specific enzymes (González et al., 2021). Nevertheless, excessive exposure to these HMs can be toxic and cause serious illnesses (Khoshakhlagh et al., 2024; Hendawy et al., 2025). For instance, an excess of Fe can lead to life-threatening conditions, such as cancer, diabetes, heart disease, and neurological disorders (Abbaspour et al., 2014). Ni exposure in occupational settings has been associated with kidney disorders, cardiovascular disorders, lung fibrosis, and respiratory tract cancer (Genchi et al., 2020). Elevated levels of Mn can result in nervous system disorders (Neal and Guilarte, 2013).
3.7.1 Chronic daily intake (CDI) and hazard index (HI)
Table 7 displays the outcomes of CDI, HI, and HQ concerning the non-carcinogenic risks associated with heavy metals through different pathways for both adults and children. The risk of non-carcinogenic CDI values for children and adults follows the order of the ingestion pathway > dermal pathway > inhalation pathway. Regarding the non-carcinogenic risk, adult CDI values (mg/kg/day) varied from 3.615E-06 (As) to 0.0162 (Fe) through ingestion, from 1.44E-08 (As) to 2.91659E-07 (Zn) through dermal contact, and from 5.31605E-11 (As) to 1.07496E-09 (Zn) through inhalation. For children, CDI ranged from 3.37E-05 (As) to 0.152 (Fe) through ingestion, from 6.731E-08 (As) to 5.55482E-07 (Zn) through dermal contact, and from 2.481E-10 (As) to 5.01649E-09 (Zn) through inhalation.
Table 7
| HMs | Adults | ||||||
|---|---|---|---|---|---|---|---|
| CDIIng | CDIDerm | CDIInh | HQIng | HQDem | HQInh | HI | |
| As | 3.61492E-06 | 1.44235E-08 | 5.31605E-11 | 0.0120 | 4.80784E-05 | 1.77202E-07 | 0.012 |
| Pb | 1.42694E-05 | 5.69349E-08 | 2.09844E-10 | 0.00408 | 1.62671E-05 | 5.99555E-08 | 0.004 |
| Cu | 1.63242E-05 | 6.51336E-08 | 2.40062E-10 | 0.00044 | 1.75562E-06 | 6.47067E-09 | 0.0004 |
| Ni | 2.98326E-05 | 1.19032E-07 | 4.38714E-10 | 0.0014 | 5.9516E-06 | 2.12089E-08 | 0.0014 |
| Zn | 7.30974E-05 | 2.91659E-07 | 1.07496E-09 | 0.00024 | 9.72196E-07 | 3.58321E-09 | 0.0002 |
| Fe | 0.0162 | – | – | 0.0233 | – | – | 0.0233 |
| HMs | Children | ||||||
|---|---|---|---|---|---|---|---|
| CDIIng | CDIDerm | CDIInh | HQIng | HQDem | HQInh | Hi | |
| As | 3.37392E-05 | 6.73097E-08 | 2.48082E-10 | 0.1125 | 0.00022 | 8.26942E-07 | 0.113 |
| Pb | 0.00013 | 2.65696E-07 | 9.79273E-10 | 0.0381 | 7.59132E-05 | 2.79792E-07 | 0.0383 |
| Cu | 0.00015 | 3.03957E-07 | 1.12029E-09 | 0.0041 | 8.1929E-06 | 3.01965E-08 | 0.0043 |
| Ni | 0.00028 | 5.55482E-07 | 2.04733E-09 | 0.0139 | 2.77741E-05 | 1.02367E-07 | 0.014 |
| Zn | 0.00068 | 1.36107E-06 | 5.01649E-09 | 0.0023 | 4.53691E-06 | 1.67216E-08 | 0.0023 |
| Fe | 0.152 | – | – | 0.217 | – | – | 0.217 |
The HQ, HI, and CDI (in mg/kg/day) for non-carcinogenic hazards in adults and children.
Nonetheless, the average CDI from the ingestion pathway in children indicates an approximate 9.33 times increase compared to adults and a substantial 4.49E+03 times increase through dermal and inhalation pathways. This suggests that compared to adults, children are more likely to be exposed to non-carcinogenic material. The elevated CDI in children, particularly through sediment ingestion during outdoor play activities, is perhaps attributed to their increased sensitivity to exposure, leading to the absorption of toxic HMs. This phenomenon is more pronounced than in adults (Tepanosyan et al., 2024).
The results confirmed that ingestion was the primary exposure pathway through which individuals were exposed to heavy metals (Chonokhuu et al., 2019). The contribution of ingested exposure (HQing) to the overall HI for both adults and children constituted 99.90 and 99.56% of the total risk, respectively. Among adults, the descending order of HI values was Fe > As > Ni > Pb > Cu > Zn, while in children, the order was Fe > As > Pb > Ni > Cu > Zn. The HI values ranged from 0.0002 (Zn) to 0.0233 (Fe) for adults and from 0.0023 (Zn) to 0.217 (Fe) for children (Table 7). Importantly, all of the HMs in the research region had HI values that were not as high as 1.0, suggesting that there is no considerable non-carcinogenic risk for the population residing in the study region (Bello et al., 2019; Tian et al., 2020). However, it is noteworthy that children, due to oral and finger practices, are more vulnerable to health impacts and appear highly susceptible to the impacts of HMs (Agyeman et al., 2021a; Agyeman et al., 2021b).
3.7.2 Carcinogenic risks (CRs) and total lifetime cancer risk (LCR)
The mean CR values for As and Pb in both children and adults, across the three pathways, follow the descending order of As > Pb. The CR values varied for adults and children in the ingestion pathway from 5.31829E-06 to 4.96374E-05 for As and from 9.10959E-08 to 8.50228E-07 for Pb (Table 8). The LCR levels for As and Pb across all examined sites were higher in children than in adults. They ranged from 5.34E-06 to 4.97E-05 for As and from 5.11E-08 to 8.50E-07 for Pb in adults and children, respectively (Table 8; Figure 12; Supplementary Table S8). The carcinogenic risk (CR) from the ingestion pathway was the primary contributor to the LCR, accounting for 99.60% in adults and 99.80% in children. Regarding the LCR values, both adults and children were found to have an acceptable carcinogenic risk for arsenic (As), with all values falling within the range of 1 × 10−6 to 1 × 10−4. For lead (Pb), the LCR was below 1 × 10−6, indicating a negligible carcinogenic risk.
Table 8
| HMs | Adults | Children | ||||||
|---|---|---|---|---|---|---|---|---|
| CRIng | CRDerm | CRInh | LCR | CRIng | CRDerm | CRInh | LCR | |
| As | 5.31829E-06 | 2.122E-08 | 7.82102E-11 | 5.33959E-06 | 4.96374E-05 | 9.90266E-08 | 3.64981E-10 | 4.97368E-05 |
| Pb | 9.10959E-08 | – | 1.33965E-12 | 5.11E-08 | 8.50228E-07 | – | 6.25168E-12 | 8.50E-07 |
Carcinogenic hazards for Cr, Pb, and As, as well as the overall cancer risk (LCR) for both adults and children through different pathways.
Figure 12
4 Conclusion
This study achieved its objective of conducting a comprehensive, geospatially-explicit assessment of heavy metal (HM) contamination in the agricultural soils of Huraymala, Saudi Arabia. The analysis of 34 soil samples established a clear order of mean concentrations (mg kg−1): Fe (11674) > Zn (47.12) > Ni (21.10) > Cu (11.50) > Pb (7.82) > As (2.59). An overall Pollution Load Index (PLI) of 0.29 confirmed the area is relatively unpolluted on average.
Source apportionment via Principal Component Analysis (PCA) indicated that the HMs contamination originates from mixed geogenic and anthropogenic sources. The human health risk assessment provided a nuanced profile, determining no significant non-carcinogenic hazard (Hazard Index < 1). For carcinogenic risk, the Lifetime Cancer Risk (LCR) was negligible for lead (Pb < 1 × 10−6) but fell within the acceptable range for arsenic (As = 1 × 10−6 to 1 × 10−4), with soil ingestion identified as the dominant exposure pathway.
The primary contribution of this work is the establishment of a high-resolution, quantitative baseline for Huraymala. By moving beyond average concentrations to map discrete hotspots and quantify specific risks, this study provides a scientific framework for precision soil conservation. The findings provide general insights that may support considerations related to monitoring and agricultural management and may also serve as a useful reference for application in other agricultural settings. This proactive, data-driven approach is fundamental to maintaining soil health, safeguarding groundwater quality, and ensuring the long-term productivity and safety of the local food supply, thereby directly supporting the region’s food security goals.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
SA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. AE-S: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. KA-K: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. TA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. SA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. JL: Writing – original draft, Writing – review & editing. MS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was supported by Ongoing Research Funding program, (ORF-Ctr-2025-5), King Saud University, Riyadh, Saudi Arabia.
Acknowledgments
The authors extend their appreciation Ongoing Research Funding program, (ORF-Ctr-2025-5), King Saud University, Riyadh, Saudi Arabia
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2025.1726872/full#supplementary-material
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Summary
Keywords
agriculture soil, food security, GIS, HMs, multivariate analysis, Saudi Arabia
Citation
Alarifi SS, El-Sorogy AS, Al-Kahtany K, Alharbi T, Alhejji SSS, de Larriva JEM and Shokr MS (2026) Exploring soil heavy metal(loid) levels in Huraymla, Saudi Arabia: implications for sustainable agriculture and food supply. Front. Sustain. Food Syst. 9:1726872. doi: 10.3389/fsufs.2025.1726872
Received
17 October 2025
Revised
23 December 2025
Accepted
24 December 2025
Published
14 January 2026
Volume
9 - 2025
Edited by
Sylvester Chibueze Izah, Bayelsa Medical University, Nigeria
Reviewed by
Milan Hait, Dr. C.V. Raman University, India
Chinwe Azuka Onwudiegwu, Obafemi Awolowo University, Nigeria
Updates
Copyright
© 2026 Alarifi, El-Sorogy, Al-Kahtany, Alharbi, Alhejji, de Larriva and Shokr.
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*Correspondence: Mohamed S. Shokr, mohamed_shokr@agr.tanta.edu.eg
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