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Article

Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions

by
Abdelbaset S. El-Sorogy
*,
Khaled Al-Kahtany
,
Talal Alharbi
,
Rakan Al Hawas
and
Naji Rikan
Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Land 2025, 14(2), 221; https://doi.org/10.3390/land14020221
Submission received: 30 December 2024 / Revised: 16 January 2025 / Accepted: 18 January 2025 / Published: 22 January 2025
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Abstract

:
Heavy metal contamination in soil is a global issue threatening human health and ecosystems. Accurate spatial maps of heavy metals (HMs) are vital to mitigating the adverse effects on the ecosystem. This study utilizes GIS and multivariate analysis to evaluate HMs in agricultural soils from Al Ghat Governorate, Saudi Arabia, analyzing Al, As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn using ICP-AES in 35 soil samples. Methods included contamination factor (CF), enrichment factor (EF), risk index (RI), geoaccumulation index (Igeo), pollution load index (PLI), soil quality guidelines (SQGs), and multivariate analysis. The soils, characterized by sandy texture, low organic matter, and alkalinity due to arid conditions and high calcium carbonate, had the following HM concentrations (mg/kg) in descending order: Fe (11,480) ˃ Al (7786) ˃ Mn (278) ˃ Zn (72.37) ˃ Ni (28.66) ˃ V (21.80) ˃ Cr (19.89) ˃ Co (19.00) ˃ Cu (12.46) ˃ Pb (5.46) ˃ As (2.69). EF, CF, and Igeo suggest natural sources for most HMs, predominantly from the sedimentary sequence, with localized Zn, Pb, Co, Mn, and Cu enrichment linked to mixed natural and agricultural influences. PLI and RI indicated acceptable contamination levels, posing no ecological risk. All samples fell below SQG thresholds for As, Cu, Pb, and Cr, confirming minimal ecological threat. Statistical analysis highlighted sedimentary cover as the primary HM source, with agricultural activities contributing to Co, Cu, Ni, and Pb levels.

1. Introduction

Agricultural soil is a vital component of ecosystems, supporting food production and sustaining biodiversity. However, contamination with heavy metals (HMs) is a growing concern. HMs, including cadmium, lead, mercury, arsenic, and chromium, often accumulate in agricultural soil due to both natural and anthropogenic activities [1,2]. Natural sources include the weathering of metal-rich rocks and volcanic activity, while anthropogenic sources are primarily linked to industrial discharges, agricultural practices, and urban runoff [3,4,5]. The use of fertilizers, pesticides, and sewage sludge in farming introduces metals like cadmium and lead into the soil. Additionally, atmospheric deposition from industrial emissions and vehicle exhaust contributes to the contamination of farmland, particularly in regions near urban centers [6,7].
The ecological risks of HMs in agricultural soils stem from their toxicity, persistence, and bioaccumulation. Once introduced into the soil, heavy metals disrupt microbial communities, impairing nutrient cycling and soil fertility. The bioavailability of metals like cadmium and lead can hinder plant growth by interfering with essential physiological processes, such as photosynthesis and nutrient uptake. Furthermore, these contaminants can enter food chains through plants and soil organisms, leading to biomagnification and ecological imbalances [8,9]. Contaminated soils also compromise the ecosystem services vital for sustainable agriculture [10,11]. The health risks associated with HMs in agricultural soils are significant, as they can be transferred to humans through the consumption of contaminated crops and water, causing severe health complications [12,13]. These health concerns are exacerbated in regions reliant on subsistence farming, where food safety measures are limited. Furthermore, occupational exposure to contaminated soil and dust during farming poses additional health hazards. Addressing these risks requires integrated strategies, including monitoring soil health, adopting remediation technologies, and regulating pollutant sources [14,15].
The evaluation of HMs in agricultural soil and other various environmental matrices, including industrial, urban, and natural soils, often involves several indices and factors such as contamination factor (CF), enrichment factor (EF), risk index (RI), geoaccumulation in-dex (Igeo), and pollution load index (PLI) to determine contamination levels, potential ecological risks, and pollution sources [16,17]. These indices collectively offer a comprehensive evaluation framework for HM contamination in agricultural soils. EF and Igeo focus on pollution sources and severity, while CF, PLI, and RI emphasize contamination levels and ecological risks [18,19]. Combining these indices helps in formulating effective management strategies for soil health. To identify potential sources and distribution patterns of HMs, hierarchical cluster analysis (HCA), Pearson’s correlation matrix, and principal component analysis (PCA) play crucial roles [20,21,22].
There is no study on the assessment of environmental and health risks of HMs in the agricultural soil in Al Ghat Governorate. However, for the central region, there are some scattered studies, such as on the Al Uyaynah–Al Jubailah region, Al-Majma’ah Governorate, and Al-Hariq area [4,23,24]. These studies confirmed that the rocks in the area are the primary natural source of heavy metals, with minor human impact likely resulting from agricultural activities, particularly for elements such as lead, zinc, and copper. The aims of this study are as follows: (i) to quantify the contamination levels of Al, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn in the agricultural soils of the Al Ghat area, Saudi Arabia; (ii) to assess the potential environmental risks associated with the presence of HMs in the soil; and (iii) to detect the potential sources of HMs in the examined soil.

2. Materials and Methods

2.1. Study Area

Al Ghat Province is located in central Saudi Arabia and covers an approximate area of 1500 km2 (Figure 1). The province is situated to the northwest of Riyadh, with its central location making it an important site for traditional Saudi Arabian culture and agriculture. The area’s fertile soils and access to groundwater have historically made it a productive agricultural zone. Al Ghat Province is a historically and ecologically significant area characterized by its diverse landscape, including plateaus, valleys, and agricultural lands. According to agricultural surveys, Al Ghat is home to approximately 1500 farms. On average, the groundwater is generally accessible at depths ranging from 50 to 150 m. However, deeper aquifers may require drilling to depths exceeding 300 m. The vegetation in Al Ghat reflects the arid climate and its adaptation to desert conditions. Prominent types of vegetation include date palms, cereal crops (wheat and barley), fodder crops (alfalfa and other animal feed crops are cultivated to support livestock), and native flora (shrubs like ghaf and sidr trees are common, as well as desert grasses).
Wasia-Biyadh and Minjur Aquifers are the largest aquifers in the area, providing significant volumes of water for both irrigation and domestic purposes [25]. Additionally, alluvial Aquifers are found in valley regions, which are shallower and benefit from periodic recharge during rare rainfall events. The Al Ghat area is part of a complex sedimentary sequence primarily influenced by the geological history of the Arabian Peninsula. This region lies near the edge of the Arabian Shield, which serves as a stable foundation for sedimentary deposition.

2.2. Sampling and Analytical Methods

Thirty-five soil samples were taken from 0 to 30 cm depths utilizing a robust plastic hand shovel (Figure 1). A representative sample was created by amalgamating three subsamples into a composite sample, which was thereafter enclosed in plastic bags and preserved in an icebox. All samples were dried at 100 °C and ground to ensure a uniform particle size, with many being sieved to 63 µm. Aluminum, arsenic, cobalt, chromium, copper, iron, manganese, nickel, lead, vanadium, and zinc were analyzed using inductively coupled plasma–atomic emission spectrometry (ICP-AES) at the ALS Geochemistry Lab in Jeddah, Saudi Arabia. A 0.50 g portion of each sample was digested with aqua regia for 45 min at temperatures ranging from 60 to 120 °C to ensure complete digestion [26]. The digested sample was then filtered and diluted with deionized water to a final volume of 50 mL.
The ICP-AES was calibrated using certified multi-element standards with known concentrations prepared in an acid matrix similar to that of the soil samples. Certified Reference Materials (CRMs) with known compositions and matrix-matched to soil were analyzed alongside the samples to validate the method’s accuracy. Key suppliers for ALS-certified laboratories worldwide include reputable CRM producers such as the National Institute of Standards and Technology (NIST) and Sigma-Aldrich. Duplicate samples or splits were also analyzed to ensure precision and reproducibility.

2.3. Satellite Image and Statistical Analyses

This study utilized Landsat 8 imagery obtained from the USGS Earth Explorer for the years 2013 and 2024. The images, with a spatial resolution of 30 m, were well-suited for analyzing and classifying changes in land use and land cover over the study period. A supervised, pixel-based classification approach was applied using ArcGIS Pro, employing the Support Vector Machine (SVM) classifier. Classification accuracy was evaluated through cross-validation, yielding validation rates of 0.9585 for 2013 and 0.9010 for 2024. To identify potential sources and distribution patterns of HMs, hierarchical cluster analysis (HCA), Pearson’s correlation matrix, and principal component analysis (PCA) were applied using IBM SPSS Statistics 29 and OriginPro 2023b software.

2.4. Indices Calculations

Multiple single and integrated contamination indices, such as the EF, Igeo, CF, RI, and PLI, were employed to assess the contamination levels and ecological risks of HMs. Equations (1)–(6) delineate the calculation methodologies of the contamination indices, together with the parameters employed in this study [16,27,28].
EF = (M/X) sample/(M/X) background
I-geo = Log2 (Cn/(1.5 × Bn))
CF = M/X
PLI = (CF1 × CF2 × CF3 × CF4… × CFn)1/n
Eri = Tri × Cfi
RI = Ʃ (Tri × Cfi)
X is the amount of a normalizer element (Fe), and M is the concentration of the metal under analysis. Due to its abundance in the Earth’s crust, iron serves as a stable and reliable baseline and is therefore employed as a reference element [29,30]. Bn is the geochemical background concentration of metal (n) in shale, Cn is the measured concentration of metal (n) in the soils, and a factor of 1.5 is added to account for possible variations in the background values. Tri denotes an element’s biological toxicity response factor, Eri denotes an element’s potential ecological risk factor, and Cfi denotes the contamination factor for each element. Zn = Co = Mn = 1, Cr = 2, Ni = 6, Cu = Pb = Ni = 5, and As = 10 is the toxicity response factor for metals [16].

3. Results

3.1. Soil Characteristics and HM Concentration

The geological map of the area presented in Figure S1 indicated that the sedimentary strata include a mix of Mesozoic and Cenozoic formations, reflecting varying depositional environments over time. The Mesozoic and Cenozoic sequence consists of Jurassic Dhruma, Tuwaiq Mountain limestone, Hanifa formations, and Quaternary deposits [31,32]. The Jurassic strata are characterized by extensive carbonate platforms, representing pure marine carbonate deposition. The later Mesozoic and Cenozoic periods brought further sedimentation, primarily of continental origin, with occasional marine influences in localized basins [33,34,35,36]. The agricultural soils of Al Ghat province exhibit specific intrinsic properties due to the arid climate and soil management practices in the region. Sandy soils are dominant in Al Ghat samples. Their texture often ranges from sandy to sandy loam, with a small proportion of clay content. The organic matter in soil samples is generally low (typically less than 1%). The pH levels of soils are slightly alkaline, often ranging between 7.5 and 8.5, with an average of 7.9. Al Ghat soil can be categorized into calciorthids (27 samples), torriorthents, and calciorthids (8 samples), as illustrated in Figure S2. Calciorthids are a type of aridisol that accumulates secondary carbonates, forming a calcic horizon and exhibiting textures that span from sandy to loamy [37]. Torriorthents, another type of Entisols, form mostly in residuum or colluvium on actively eroding slopes and in weathering-resistant materials. These soils are generally shallow, with a composition of loamy sand, fine sandy loam, sandy loam, loam, or clay loam, along with their gravely counterparts [37,38]. Figure 2 illustrates the land use within the study area, highlighting four categories—agricultural lands, developed lands, barren lands, and sand dunes—compared over an 11-year period from 2013 to 2024. Agricultural lands and developed lands, which constituted approximately 12.2% and 5.6% of the total area in 2013, respectively, showed a slight increase to 13.2% and 6.2% by 2024. This growth in the proportion of these categories was evidently at the expense of a reduction in barren lands and areas occupied by sand dunes.
The concentration of HMs in the study area is shown in Table S1. The average HM values (mg/kg) were in the order of 11,480 (Fe), 7786 (Al), 277.86 (Mn), 72.37 (Zn), 28.66 (Ni), 21.80 (V), 19.89 (Cr), 19.00 (Co), 12.46 (Cu), 5.46 (Pb), and 2.69 (As). Figure 3 presents the spatial distribution of HMs per sample location. The higher concentrations of Al, Fe, V, Ni, Mn, and Co were recorded in samples 1–5, 8, 9, 11, and 12 in the eastern and somewhat northern parts of the study area in slopes of the Jurassic sequences, whereas the lower values were reported towards the western part. This indicated the geogenic source of these HMs in the investigated farms. Most higher levels of Cu and Pb were reported in samples 28, 31, and 33 in the southern part of the study area, whereas the higher values of Zn were reported in the southern and eastern parts. Regarding the spatial distribution of As and Cr, it is noted that the higher values are scattered in the eastern, northern, and western parts of the study area.
In comparison with the effects range—low (ERL) and effects range—median (ERM) of the soil quality guidelines (SQGs) [39,40,41], it is noticed that all the investigated soil samples were below ERL for As, Cu, Pb, and Cr measurements (8.20, 34.0, 46.7, 81.0 mg/kg, respectively), implying that these samples do not pose a risk due to the presence of these four HMs. On the other hand, sample 28 showed Ni concentration between ERL (20.9 mg/kg) and ERM (51.6 mg/kg), and four samples (11, 30, 31, and 33) showed Zn levels between ERL (150 mg/kg) and ERM (410 mg/kg), implying occasional adverse effects due to presence of Ni and Zn in these soil samples. Additionally, in comparison to the EPA and WHO standard soil guideline values for HMs, it is noticed that As is of typical natural range (<10 mg/kg), Pb values mostly stay below 10 ppm, where background values are usually <20 mg/kg.
In this study, the Q-mode hierarchical cluster analysis (HCA) is used to classify sampling sites based on their similarities in HM concentrations. The dendrogram uses average linkage clustering to group the 35 sites into three clusters, indicating areas that share similar contamination levels or sources (Figure 4). The first cluster has 6 sites (1, 4, 5, 9, 11, and 12), the second has 13 sites (2, 3, 6, 10, 13, 16, 22–24, 31–33, and 35), whereas the third cluster accounts 16 sites (7, 8, 14, 15, 17–21, 25–30, and 34). Samples of cluster 1 were collected from farms located close or in the Jurassic mountain toes and obtained greater values of As, Co, Cr, Cu, Fe, Mn, Ni, Pb, and V (4.00, 27.00, 31.00, 17.33, 20,433, 693.67, 42.00, 6.00, and 36.17 mg/kg, respectively). These farms may share a specific contamination source or uniform natural conditions, mainly related to parent rock mineralogy and natural weathering processes. Samples of cluster 2 display lower values of Al, As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn (5154, 2.02, 29.62, 13.00, 10.54, 7092, 167.23, 28.77, 4.08, 13.62, and 93.77 mg/kg, respectively). The exception of these samples is sample 3, which obtained the highest concentrations of Co, Ni, and Zn (168.0, 109.0, and 289.0); proximity might be related to agricultural activities, such as the use of fertilizers and pesticides. Samples of cluster 3 indicate high similarity in HM concentrations and include intermediate HM values between those of clusters 1 and 2, except sample 28, which obtained the highest concentration of Pb (25.00 mg/kg), suggesting partial anthropogenic contamination.

3.2. Risk Assessment of HMs

The contamination of soil with HMs may adversely affect ecosystems and human health through direct contact with polluted soil and the consumption of food derived from contaminated soil [1,5]. The EF effectively distinguishes between anthropogenic and natural sources of metals in environmental samples [42,43,44]. Al and V show EF values < 1, suggesting natural sources. Most soil samples obtained EF values less than 2 or 3 for Pb and Co, indicating natural or minor anthropogenic influence, except for sample 28 (EF = 5.51 for Pb), showing significant enrichment (Table S2). Zn shows high variability, with some samples (e.g., samples 3, 6, 13, 28, 30) having EF > 5, indicating significant to very high enrichment, mainly due to agricultural sources (Table 1). Cr and As showed EF values remain below 2 in all samples, suggesting natural sources dominate. Most samples obtained EF < 3 for Ni, but moderate enrichment is observed in a few (e.g., sample 28). Moderate to significant enrichment for Cu is observed in some samples (e.g., samples 28, 30, 31), suggesting anthropogenic sources like agricultural activities. EF values for Mn are generally low, except for sample 11 (EF = 4.97), indicating moderate enrichment in this sample. The spatial distribution of EF values for Pb, Cu, Zn, Ni, Mn, and Co per sample locations is directly proportional to the concentrations of these HMs (Figure 5).
The average values of CF for all HMs were less than 1, suggesting a low contamination factor. Fe, Al, As, and V mostly exhibit CF < 1 across all samples. However, elevated values of CF were recorded as individual samples. For instance, samples 3 and 28 show CF of 1.60 and 1.25 for Ni and Pb, respectively, suggesting moderate contamination. Zn shows higher contamination levels in sample 3 (CF = 3.04), indicating considerable contamination, and samples 31 and 33 (CF = 1.88 and 1.60, respectively) imply moderate contamination (Table 1). Samples showed low contamination for Mn, while sample 11 has a CF of 2.41, showing moderate contamination. Most samples have low contamination levels for Co, while samples 2 and 3 show very high CF values (Table S3). The Igeo is a widely utilized metric for assessing HM contamination in diverse environmental media, including soils [13,45,46], by comparing present concentration levels to those from preindustrial periods(Table S4). Most HMs such as Cr, Cu, As, Al, Fe, Mn, Co, and V have negative Igeo values, indicating they are in the uncontaminated category. Pb is practically uncontaminated overall. However, sample 28 (Igeo = 0.51) falls into the uncontaminated to moderately contaminated range. Sample 3 is moderately contaminated for Zn (Igeo = 1.4I), indicating potential localized zinc pollution (Table 1).
Pollution load index (PLI) is a quantitative tool used to assess pollution levels in a given environment, particularly for soil contamination by HMs [45]. It is calculated by combining the contamination factors (CF) of individual HMs for each sample. All investigated samples have PLI < 1, suggesting the soil is relatively uncontaminated or within baseline pollution levels. Sample 11 has the highest PLI value of 0.58, while sample 35 has the lowest PLI value of 0.09. Zn and Ni tend to show higher variability in their concentrations, with sample 3 showing a significant spike in Zn (3.04) and sample 11 in Ni (1.01). Samples 11, 28, 31, and 33 show slightly elevated PLI values compared to others, potentially due to a combination of increased levels of multiple metals such as Zn, Ni, and Cu. On the other hand, samples such as 10, 13, and 35 consistently show the lowest levels of both individual metal concentrations and overall PLI values (Table S5). The risk index (RI) values range from 3.96 (minimum) to 23.42 (maximum). All RI values fall into the low-risk category, suggesting that the combined ecological risk of HMs in these soil samples is minimal (Table 1). Sample 11 (RI = 23.42) has elevated Eri for Pb (1.50), Ni (6.09), Cu (3.44), and As (3.08), making it the highest risk sample, while in sample 28 (RI = 17.47), the high Eri for Pb, Ni, and As drive the elevated RI. In contrast, sample 35 (RI = 3.96) exhibits uniformly low Eri values for all HMs, making it the least concerning from an ecological risk perspective (Table S6).

3.3. Multivariate Analysis

The R-mode HCA is a statistical technique used to group HMs based on their similarity in behavior or distribution [46]. It groups the HMs based on their similarity into two clusters (Figure 6). The shorter the linkage distance, the more similar the HMs are [47]. The first cluster accounts for Fe and Al, which cluster closely, indicating they have a strong geogenic origin, likely from the same parent rock material or weathering processes. The second cluster includes Mn, Zn, Ni, Co, Pb, Cu, As, V, and Cr, suggesting some shared geochemical behavior from natural co-occurrence in soil mineralogy or anthropogenic influence, such as the use of fertilizers or pesticides, especially for Pb, Cu, and Zn [11].
High positive correlations were observed between Al and Cr (0.929), Al and V (0.952), and Fe and Al (0.905), underscoring their central role in influencing the mobility and availability of these metals (Table 2), suggesting that these HMs may originate from a common geogenic (natural) source, such as the parent rock material [27]. The high positive correlations between Ni and Co (0.913) likely point to their co-occurrence in mineralogical sources or may share anthropogenic inputs, mainly agricultural fertilizers.
The moderate correlations between Mn and Cu (0.521) and Mn and Fe (0.627) may indicate interactions between these metals in the soil, such as co-precipitation or adsorption onto the same soil particles [48]. They may also share partial input from agricultural activities like the use of metal-containing pesticides. Fe and Mn are often considered regulators in soil chemistry due to their role in redox reactions and their ability to bind with other metals [11]. Moreover, the moderate correlations between Cu and Pb (0.558) suggest a potential anthropogenic influence. The moderate correlations involving Cu, Pb, and Zn may suggest contributions from human activities, such as the application of fertilizers and pesticides. On the other hand, the weak correlation or negative correlations between Zn and Pb (0.009), Co and Cr (−0.122), and Al and Zn (−0.105) indicate no significant relationship, suggesting that these metals are influenced by independent sources or mechanisms in the soil.
The principal component analysis (PCA) reduces data dimensionality, enabling the identification of key components that explain variance in the dataset [49]. Three PCs were extracted, explaining 83.739% of the variance and accounting for 46.772%, 24.894%, and 12.073% of the total variance, respectively (Table 3). PC1 captures nearly half of the total variance, suggesting it represents the dominant source or process influencing the HMs. It shows strong positive loadings of Al (0.924), Cr (0.927), Fe (0.948), V (0.934), As (0.804), Cu (0.536), Mn (0.645), and V (0.934), suggesting they share a common geogenic source, likely derived from the parent rock material in the Al Ghat region. PC2 represents secondary factors contributing to the dataset. Co (0.884), Ni (0.820), and Zn (0.890) are strongly associated with PC2, while PC3 captures finer-scale variations, representing additional but less influential factors. Pb (0.784) and Cu (0.670) are strongly associated with PC3. However, while natural sedimentary processes are significant, human activities such as the use of fertilizers, pesticides, and irrigation water also may add to HM concentrations. Fertilizers often contain HMs such as Cu, Zn, and Cd, while pesticides can introduce Pb and As into the soil system, potentially compounding natural sources (13).
The scatter plot presented in Figure 7 visualizes the relationship between the samples and the principal components while also using the PCA loadings to interpret the contributions of each variable to these components. Most samples form a dense cluster near the center, indicating similar characteristics, while samples such as 3, 11, and 13 are outliers, separated from the main cluster, indicating unique compositions of HMs, which may be due to localized agricultural practices. Al, Cr, Fe, and V are strongly correlated with PC1, which appears to represent that these HMs are commonly found in geological formations, while Co, Ni, and Zn are strongly correlated with PC2 and may represent anthropogenic influences. Cu and Pb have moderate contributions to both PCs, with Pb being distinct in the upper PC2 axis.

4. Discussion

This depth of sampling (0–30 cm) is appropriate for evaluating surface pollution, where pollutants often concentrate, and is significant for biological relevance, human and ecological exposure, nutrient cycling, and organic matter [50]. The dominance of sandy soils in the Al Ghat agricultural soils is attributed to the sedimentary origin and arid conditions, impacting water retention and nutrient-holding capacity. The arid conditions limit organic matter inputs and reduce decomposition rates, highlighting a need for amendments to enhance fertility, as well as the high calcium carbonate content and low organic matter in soil increase the alkalinity of the agricultural soils [51,52,53]. The fine-grained soil in Al Ghat exhibits a higher tendency for HM adsorption compared to coarse-grained soil. This is due to the presence of soil colloids like clay minerals and iron/manganese oxy-hydroxides, which are highly chemically active. Their large surface area and chemical structure enable strong HM adsorption and eco-friendly properties [54,55].
Results of EF indicated that HMs like Al, As, Cr, Mn, and V are closer to natural levels, showing limited anthropogenic impact. Zn, Pb, and Cu exhibit notable enrichment in certain samples, suggesting mixed natural and localized anthropogenic impact, possibly due to agricultural activities [56]. Results of CF revealed that Fe, Al, As, and V mostly exhibit CF < 1, indicating low contamination levels across all samples. Zn, Mn, Co, Pb, and Ni showed moderate to high contamination and may need further investigation. Most HMs such as Cr, Cu, Fe, As, Mn, Co, Al, and V have negative Igeo values, indicating they are in the uncontaminated category. Igeo findings implied that sample 28 shows a slightly elevated Igeo for Pb, possibly indicating slight anthropogenic input. Sample 3 shows moderate contamination of Zn. Samples 10, 13, 23, and 35 exhibit the lowest values across many metals, indicating these areas may represent natural or least-contaminated conditions. The PLI dataset indicates that the majority of soil samples fall within acceptable contamination levels (PLI < 1). However, localized areas (e.g., samples 11 and 28) may require monitoring and further investigation to assess potential sources of metal deposition. The variability in Zn and Ni concentrations suggests that these metals could be influenced by anthropogenic activities, such as agricultural inputs [57,58]. The cumulative RI values across all samples are below the threshold for moderate risk, indicating that the soils in this study pose no ecological threat from HMs. Some samples (e.g., 11 and 28) show relatively higher risks, highlighting localized contamination.
The statistical tools like R-mode HCA, CM, and PCA indicated that the HMs like Al, As, Cu, Fe, Mn, and V are mainly derived from geogenic sources from the nearby parent rock material after weathering, transportation, and deposition in the soil. On the other hand, in addition to natural sources, Co, Cu, Ni, and Pb may also share partial input from agricultural activities [11]. The primary source of most HMs in the agricultural soils in the Ghat area might be released from the Paleozoic to Cenozoic sedimentary cover in the study area. Some minerals provide the main sources of HMs in agricultural soils, e.g., kaolinite (Al2Si2O5(OH)4), albite NaAlSi3O8, pyrolusite (MnO2), arsenopyrite (FeAsS), cobaltite (CoAsS), chromite (FeCr2O4), chalcopyrite (CuFeS2), hematite (Fe2O3), rhodochrosite (MnCO3), pentlandite ((Fe,Ni)9S8), galena (PbS), vanadinite (Pb5(VO4)3Cl), and sphalerite (ZnS). These minerals are common in clay-rich soils derived from weathered feldspar, sandstones, black shales, and sulfide (carbonate)-rich and oxidized zones of sedimentary rocks in arid environments [59,60].

5. Conclusions

Most HMs, such as Al, Fe, Mn, As, Cu, Pb, and Cr, were primarily of geogenic origin. These metals were linked to the chemical weathering of Paleozoic–Quaternary sedimentary cover in the Al Ghat region.
Elevated levels of Co, Cu, Ni, Zn, and Pb in specific samples were attributed to both natural sources and agricultural practices. Fertilizers, pesticides, and irrigation water might be the potential contributors to HM concentrations.
Multivariate analyses identified minerals in nearby sedimentary rocks as primary sources of HMs. These minerals included kaolinite, albite, pyrolusite, arsenopyrite, cobaltite, chromite, chalcopyrite, hematite, rhodochrosite, pentlandite, galena, vanadinite, and sphalerite.
The results of enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), potential ecological risk index (RI), and pollution load index (PLI) provided evidence supporting geogenic origins for most HMs.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land14020221/s1. Figure S1: Geologic map of the study area with sampling locations; Figure S2: Soil types at Al Ghat province, central Saudi Arabia; Table S1: Concentration of HMs (mg/kg) in Al Ghat agricultural soils; Table S2: Enrichment factor (EF) in Al Ghat agricultural soils; Table S3: Contamination factor (CF) in Al Ghat agricultural soils; Table S4: Geo-accumulation index (I-geo) in Al Ghat agricultural soils; Table S5: Pollution load index (PLI) in Al Ghat agricultural soils; Table S6: Ecological risk factor and potential ecological risk index of Al Ghat agricultural soils.

Author Contributions

Conceptualization, A.S.E.-S., K.A.-K. and R.A.H.; methodology, T.A. and N.R.; software; writing—original draft preparation, A.S.E.-S., K.A.-K. and T.A.; writing—review and editing, A.S.E.-S., K.A.-K. and T.A.; funding acquisition, K.A.-K. All authors have read and agreed to the published version of the manuscript.

Funding

King Saud University, Researchers Supporting Project number (RSP 2025R139).

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its Supplementary Materials.

Acknowledgments

The authors extend their appreciation to Researchers Supporting Project number (RSP 2025R139), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 14 00221 g001
Figure 1. Location map of the sampling sites from Al Ghat farms, central Saudi Arabia.
Figure 1. Location map of the sampling sites from Al Ghat farms, central Saudi Arabia.
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Figure 2. Comparison between LULC for the study area in 2013 and 2024.
Figure 2. Comparison between LULC for the study area in 2013 and 2024.
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Figure 3. Spatial distribution of Al, As, Co, and Cr in Al Ghat agricultural soils.
Figure 3. Spatial distribution of Al, As, Co, and Cr in Al Ghat agricultural soils.
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Figure 4. Q-mode HCA of soil samples.
Figure 4. Q-mode HCA of soil samples.
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Figure 5. Spatial distribution of EF per sample locations for Pb, Zn, Ni, Cu, Mn, and Co in Al Ghat agricultural soils.
Figure 5. Spatial distribution of EF per sample locations for Pb, Zn, Ni, Cu, Mn, and Co in Al Ghat agricultural soils.
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Figure 6. R-mode HCA of the investigated HMs.
Figure 6. R-mode HCA of the investigated HMs.
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Figure 7. The PCA scatter plot between the soil samples from the Al Ghat region and the HM variables.
Figure 7. The PCA scatter plot between the soil samples from the Al Ghat region and the HM variables.
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Table 1. Class distribution (sample %) of contamination indices for HMs examined in the studied soil.
Table 1. Class distribution (sample %) of contamination indices for HMs examined in the studied soil.
IndicesClassesAlAsCoCrCuMnNiPbVZnFe
EFEF < 2Deficiency to minimal enrichment35352435313327333519-
EF = 2–5Moderate enrichment0040426109-
EF = 5–20Significant enrichment0050002107-
EF = 20–40Very high enrichment0010000000-
EF > 40Extremely high enrichment0010000000-
CFCf < 1Low contamination factor3635313535343334352635
1 ≤ Cf < 3Moderate contamination factor00300121080
3 ≤ Cf < 6Considerable contamination factor00100000010
Cf ≥ 6Very high contamination factor00000000000
IgeoIgeo < 0Uncontaminated3435353535343234352535
0 < Igeo < 1Unpolluted to moderately contaminated10100131090
1 < Igeo < 2Moderately contaminated00100000010
2 < Igeo < 3Moderately to strongly contaminated00000000000
3 < Igeo > 4Strongly contaminated00000000000
4 < Igeo < 5Strongly to extremely contaminated00000000000
Igeo > 5Extremely high contamination00000000000
EriEr < 40Low ecological risk-353535353535353535-
40 < Er ≤ 80Moderate ecological risk-000000000-
80 < Er ≤ 160Considerable ecological risk-000000000-
160 < Er ≤ 320High ecological risk-000000000-
Er > 320Serious ecological risk-000000000-
Table 2. The correlation matrix of the analyzed HMs.
Table 2. The correlation matrix of the analyzed HMs.
AlAsCoCrCuFeMnNiPbVZn
Al1
As0.748 **1
Co−0.062−0.1031
Cr0.929 **0.735 **−0.1221
Cu0.3290.2310.0870.391 *1
Fe0.905 **0.704 **0.0280.904 **0.385 *1
Mn0.422 *0.391 *0.2630.461 **0.521 **0.627 **1
Ni0.2430.2160.913 **0.2080.3050.3080.448 **1
Pb0.2220.355 *−0.1310.2590.558 **0.2060.0710.1051
V0.952 **0.769 **−0.0710.924 **0.3200.938 **0.450 **0.2250.2421
Zn−0.105−0.2500.646 **−0.1090.524 **−0.0040.3140.636 **0.009−0.1361
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
Table 3. Principal component loadings and the three extracted PCs with varimax normalized rotation.
Table 3. Principal component loadings and the three extracted PCs with varimax normalized rotation.
Component
123
Al0.924−0.204−0.152
As0.804−0.261−0.053
Co0.0690.884−0.337
Cr0.927−0.219−0.071
Cu0.5360.3780.670
Fe0.948−0.076−0.153
Mn0.6450.367−0.025
Ni0.4070.820−0.215
Pb0.369−0.0280.784
V0.934−0.223−0.148
Zn0.0630.8900.165
% of Variance46.77224.89412.073
Cumulative %46.77271.66683.739
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El-Sorogy, A.S.; Al-Kahtany, K.; Alharbi, T.; Al Hawas, R.; Rikan, N. Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions. Land 2025, 14, 221. https://doi.org/10.3390/land14020221

AMA Style

El-Sorogy AS, Al-Kahtany K, Alharbi T, Al Hawas R, Rikan N. Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions. Land. 2025; 14(2):221. https://doi.org/10.3390/land14020221

Chicago/Turabian Style

El-Sorogy, Abdelbaset S., Khaled Al-Kahtany, Talal Alharbi, Rakan Al Hawas, and Naji Rikan. 2025. "Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions" Land 14, no. 2: 221. https://doi.org/10.3390/land14020221

APA Style

El-Sorogy, A. S., Al-Kahtany, K., Alharbi, T., Al Hawas, R., & Rikan, N. (2025). Geographic Information System and Multivariate Analysis Approach for Mapping Soil Contamination and Environmental Risk Assessment in Arid Regions. Land, 14(2), 221. https://doi.org/10.3390/land14020221

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