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Article

A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia

Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
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Author to whom correspondence should be addressed.
Land 2025, 14(8), 1697; https://doi.org/10.3390/land14081697
Submission received: 15 July 2025 / Revised: 13 August 2025 / Accepted: 16 August 2025 / Published: 21 August 2025

Abstract

This study employs Geographic Information Systems (GIS) combined with multivariate statistical techniques to evaluate soil contamination at two landfill sites in Al-Kharj, Saudi Arabia. A total of 32 soil samples were collected and analyzed for heavy metals and metalloids (HMs) using a range of contamination indices and established soil quality standards. GIS mapping revealed that the Al-Kharj landfill 1 (Kj1) experienced a steady area expansion from 2014 through 2025, while landfill Kj2 expanded from 2014 until 2022, after which its area contracted following the construction of additional facilities. The average values of HMs observed were as follows: Fe (9909 mg/kg), Al (6709 mg/kg), Mn (155.9 mg/kg), Zn (36.4 mg/kg), Cr (24.1 mg/kg), V (22.2 mg/kg), Ni (19.5 mg/kg), Cu (8.20 mg/kg), Pb (7.91 mg/kg), Co (4.32 mg/kg), and As (2.29 mg/kg). Notably, Kj2 exhibited overall higher HM concentrations than Kj1, with particularly elevated levels of Cr, Ni, and Pb. Although most HMs remained within internationally accepted safety limits, only three samples (9.4% of the total) exceeded the WHO threshold for Pb (>30 mg/kg). An analysis using contamination and enrichment factors pointed to increased concentrations of Pb, Zn, and Cr, suggesting localized anthropogenic contributions. Additionally, all samples recorded an ecological risk index (Eri) below 40, and the levels of As, Cr, and Pb consistently stayed under their respective effects range-low (ERL) thresholds, indicating minimal contamination risks. The variations in HM contamination between the sites are likely attributable to differences in the sources of metal inputs and removal processes. These findings highlight the need for continuous monitoring and localized remediation strategies to ensure environmental safety and sustainable landfill management.

1. Introduction

Modern waste management relies heavily on landfills, yet these facilities also introduce significant environmental challenges by potentially releasing harmful substances into adjacent ecosystems [1]. In particular, heavy metals and metalloids (HMs) are a major concern because of their long-term persistence, tendency to bioaccumulate, and toxic impacts on soil, water, and living organisms [2,3]. The migration of HMs from landfills into surrounding soils can lead to serious contamination issues that compromise agricultural productivity and degrade groundwater quality [4,5]. Therefore, evaluating HM pollution levels at landfill sites is crucial for gauging the extent of contamination and for guiding effective remediation strategies. Over the last two decades, increasing attention has been directed toward soil contamination in arid and semi-arid environments, where landfill leakage can significantly affect the limited water and soil resources. HMs such as Pb, Cr, and Cd persist in the environment and are difficult to remove through natural processes. Globally, researchers have used tools such as Geographic Information Systems (GIS), multivariate statistical methods, and contamination indices to assess HM pollution near waste disposal sites, particularly in developing regions where regulatory oversight is limited.
Numerous studies, both local and international, have explored HM contamination in landfill environments. For example, ref. [6] investigated the environmental risks of three landfills in southern Riyadh using GIS, soil quality guidelines, and contamination indices. Their findings revealed that while the soils showed only minimal enrichment in elements such as Al, Co, Mn, and V, there was clear evidence of contamination by HMs, including Pb, As, Zn, Cu, and Cr. In another study conducted in Egypt, researchers assessed soils around the El-Sadat City landfill and reported high levels of lead, cadmium, and nickel, which posed notable ecological risks [7]. That study, utilizing pollution indices such as Igeo and PLI, underscored the impact of both industrial and municipal waste disposal and highlighted the importance of ongoing monitoring and sustainable management practices. Similarly, research near the Ghazipur landfill in Delhi, India, reported concerning concentrations of chromium, copper, and zinc, with contamination factors strongly suggesting an anthropogenic origin; statistical methods such as PCA and HCA further identified industrial discharge and poor waste management as key contributors, with GIS mapping confirming that the areas closest to the landfill were most affected [8].
Additional studies from Turkey, Iran, and China confirm that unregulated waste disposal is a dominant factor in metal accumulation patterns in soils [9,10,11]. Despite these efforts, the majority of research on landfill-related soil contamination has focused on humid or temperate regions. There is a noticeable gap in assessing HM pollution patterns in arid environments such as Saudi Arabia, where both climate conditions and soil properties differ significantly. In Saudi Arabia, recent investigations have reported variable background values for local soils, depending on lithology and land use [12,13]. However, comprehensive spatial mapping of contamination and a comparative analysis between landfill sites are lacking.
The present study centers on the environmental risk assessment of HM contamination at two landfill sites in Al-Kharj, situated to the south of Riyadh, Saudi Arabia. The primary aim is to quantify the levels of selected HMs, including Al, As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn in soil samples collected from these locations. To achieve this, pollution indices such as the enrichment factor (EF), contamination factor (CF), and Potential Ecological Risk Index (RI) are employed, offering insights into the sources, accumulation trends, and potential ecological threats associated with these pollutants [14,15]. Furthermore, advanced statistical approaches, including Hierarchical Clustering Analysis (HCA), correlation matrix assessments, and Principal Component Analysis (PCA) are applied to elucidate the spatial distribution and origin of the HM contamination [16,17]. Geographic Information Systems (GIS) are also utilized to map the spatial variability of contamination across the study areas, enabling a detailed evaluation of contamination hotspots and supporting the development of targeted mitigation strategies [18,19]. The combination of GIS and multivariate statistical tools allows for a holistic understanding of contamination distribution and possible sources. One advantage of this approach is its ability to process spatial and statistical data concurrently, identifying patterns that are often missed by single-variable assessments. However, limitations include the dependence on accurate background values and sampling density, which may affect interpretation in heterogeneous terrains.
Ultimately, the insights derived from this research are expected to enhance our understanding of the environmental hazards posed by landfill-associated heavy metal pollution in arid regions. By rigorously assessing contamination levels and ecological risks, this study aims to provide a scientific basis for policymakers, urban planners, and waste management authorities to formulate sustainable strategies for reducing landfill-related pollution. Moreover, this work contributes to global efforts aimed at monitoring and managing heavy metal contamination in landfill sites, thereby advancing sustainable waste management and environmental conservation practices [20].

2. Materials and Methods

Two landfill sites in Al-Kharj City were chosen for this investigation using Esri basemaps and Google Earth satellite imagery (Figure 1). The first site, referred to as the Al-Kharj landfill 1 (Kj1), is situated in the northeastern part of the city (latitude 24.158933–24.191809° N; longitude 47.405572–47.415119° E), while the second site, the Al-Kharj landfill 2 (Kj2), is located in the southeastern region (latitude 24.099717–24.127597° N; longitude 47.362169–47.389898° E). The study area is located in the arid central region of Saudi Arabia, characterized by extremely low rainfall (<100 mm/year), high evaporation, and hot desert climate. The surface geology of Al-Kharj consists predominantly of Quaternary deposits overlying Cretaceous carbonate formations (mainly limestone and dolomite). The soil type is primarily sandy loam to loamy sand, with low organic content and moderate permeability. Groundwater in the region flows in a southwesterly direction, and shallow aquifers exist beneath the study sites, posing a potential risk for leachate infiltration. Landfill Kj1 is an older, partially engineered municipal solid waste (MSW) site with limited leachate collection and unlined cells, while Kj2 is newer, semi-engineered, and includes waste segregation zones and partial containment infrastructure. The types of waste include household waste, construction debris, discarded furniture, plastics, and metals, with evidence of mixed domestic and light industrial inputs.
Field surveys were conducted in December 2024 and January 2025 to document the waste composition and to systematically collect samples. During these visits, 16 surface soil samples were acquired from each site using a GPS device to accurately record the sampling locations. Samples were collected from the undisturbed surface layers at a depth of 0–15 cm between waste piles, to capture the contamination influenced by both the deposited waste and environmental factors such as wind and rainfall. Although the number of samples (n = 16 per site) may limit the statistical generalizability to the entire region, this density was chosen due to logistical and safety constraints and is considered sufficient for preliminary spatial and contamination pattern assessments. Immediately after collection, the soil samples were sealed in plastic bags and stored at 4 °C until further analysis. In the laboratory, the samples were first rinsed with distilled water, then dried at 100 °C, and subsequently ground to a uniform fine powder using a non-metallic mortar and pestle. The concentrations of Fe, Al, Pb, Zn, Mn, Cr, Cu, Ni, V, As, and Co were measured by inductively coupled plasma-atomic emission spectrometry (ICP-AES) at the ALS Geochemistry Laboratory in Jeddah, Saudi Arabia.
For each analysis, 0.50 g of soil was digested with aqua regia (a blend of one-part nitric acid to three parts hydrochloric acid) on a sand-heated hot plate at temperatures between 60 and 120 °C for 45 min to achieve complete decomposition of the sample matrix. The resultant solution was then filtered and diluted to 50 mL with deionized water. During the ICP-AES process, the solution was introduced into an argon plasma torch operating at temperatures above 6000 °C, causing the elements to be atomized and ionized, thereby emitting light at wavelengths characteristic of each element. To ensure the accuracy and precision of the ICP-AES analysis, rigorous quality assurance and quality control (QA/QC) protocols were implemented. Detection limits for the analyzed elements ranged from 0.01 to 0.1 mg/kg, depending on the metal. Spike recovery tests yielded results between 89% and 106%, indicating satisfactory analytical recovery. Certified Reference Materials (CRMs) with known concentrations were analyzed alongside the samples to validate instrument performance [21,22]. The CRM recovery results ranged from 92% to 105% across all analyzed metals, indicating high analytical accuracy. Precision was evaluated through duplicate sample analyses, with relative standard deviations (RSDs) consistently below 5% for all metals, confirming excellent reproducibility. These measures collectively ensured the reliability and robustness of the analytical results.
To monitor temporal changes in landfill areas, historical satellite imagery was retrieved from Esri’s World Imagery database, which offers global satellite data dating back to 20 December 2014. For consistency, images from December in the years 2014, 2018, and 2022 were analyzed, with January images used specifically for both landfill sites. Archived images were accessed via the Wayback service on the ArcGIS Online platform, which organizes historical layers in a timeline and list format, making it easier to pinpoint local changes. Geographic Information System (GIS) techniques were then applied to digitize the landfill boundaries for each time period, thereby enabling precise quantification of area expansion or contraction over time.
To evaluate the contamination levels and assess ecological risks posed by HMs, several contamination indices were utilized. These include the enrichment factor (EF), contamination factor (CF), and Potential Ecological Risk Index (RI). The following equations and classification criteria (Table 1) were used to compute these indices [23,24,25]:
EF = (M/X) sample/(M/X) background
CF = M/Mb
Eri = Tri × Cfi
RI = Ʃ (Tri × Cfi)
In these equations, M denotes the concentration of the metal in the sample, Mb indicates the typical background value of the metal, and X is the level of the normalizing element iron (Fe), selected for its abundance and stability in the Earth’s crust [26]. The ecological risk factor (Eri) is calculated using the toxic response factor (Tri) and the contamination factor (Cfi) for each metal. The assigned toxic response factors are as follows: Zn = Co = Mn = 1, Cr = 2, Ni = 6, Cu = Pb = Ni = 5, and As = 10 [6,23]. This multi-index approach is justified, as it allows for a comprehensive and comparative evaluation of soil contamination across spatial and temporal scales. EF identifies anthropogenic enrichment, CF quantifies contamination intensity, and RI estimates ecological risk, together providing a robust framework for interpreting pollution patterns in complex landfill environments.
Prior to conducting statistical analyses, the dataset was evaluated for normality using the Shapiro–Wilk test. Variables that did not meet the assumption of normal distribution were subjected to appropriate transformations, such as logarithmic transformation, to stabilize variance and improve normality. These steps were essential to ensure the validity and reliability of the multivariate techniques applied, including Pearson’s correlation analysis, HCA, and PCA, which assume approximately normally distributed input data.
Table 1. Classification of the contamination indices [27,28,29].
Table 1. Classification of the contamination indices [27,28,29].
Contamination IndicesClassification
EFEF < 2Minimal enrichment (natural origin)
2 ≤ EF < 5Moderate enrichment
5 ≤ EF < 20Significant enrichment
20 ≤ EF < 40Very high enrichment
EF ≥ 40Extremely high enrichment
CFCF < 1Low contamination
1 ≤ CF <3Moderate contamination
3 ≤ CF < 6High contamination
CF ≥ 6 Very high contamination
RIEr < 40Low ecological risk
40 < Er ≤ 80Moderate ecological risk
80 < Er ≤ 160Considerable ecological risk
160 < Er ≤ 320High ecological risk
Er > 320Serious ecological risk
RI < 150Low ecological risk
150 < RI < 300Moderate ecological risk
300 < RI < 600High potential ecological risk
RI ≥ 600Significantly high ecological risk

3. Results and Discussion

3.1. Landfill Variation Through Time

3.1.1. Al Kharj Landfill 1 (Kj1)

Al Kharj landfill 1 is characterized by a diverse assortment of municipal solid waste, including materials such as concrete, bricks, wood, plastics, textiles, discarded furniture, scrap HMs, and various chemicals. Figure 2 illustrates the significant expansion of Kj1 over time. Initially, in 2014, the landfill occupied roughly 36,892 m2, mainly concentrated in the western portion, which represented about 1.2% of the total designated area. By 2018, the site had extended into the northeastern sector, reaching an area of approximately 362,775 m2 (11.7% of the total). This growth continued into 2022, as the landfill expanded further into the eastern and southern parts, covering around 653,348 m2 (21.0%). As of January 2025, the footprint of Kj1 had increased to about 877,214 m2, amounting to 28.2% of the overall area.

3.1.2. Al Kharj Landfill 2 (Kj2)

Al Kharj landfill 2 exhibits a diverse array of municipal waste, including packaging, wood, plastics, glass, textiles, outdated furniture, scrap HMs, ceramics, concrete, bricks, and paints. In December 2014, the actively used area measured approximately 2,610,569 m2, accounting for about 61.2% of the total site. By December 2018, this area had slightly expanded to around 2,699,473 m2 (63.3% of the site). However, by December 2022, the utilized area declined to approximately 2,368,549 m2 (55.5%), a reduction primarily due to the mid-2022 development of a residential project and park (Figure 3). This downward trend continued, and by January 2024, the area in active use had further contracted to about 877,214 m2, representing only 28.2% of the total site.

3.2. Concentration and Spatial Distribution of HMs

Persistent heavy metal (HM) contamination can severely impact ecosystems, affecting vegetation, wildlife, water sources, soil, and air quality. HMs, such as Pb, Cd, Hg, and As, are particularly hazardous because they tend to accumulate in organisms and magnify through food chains, causing extensive ecological disruption [3,30,31]. Table 2 presents the concentrations of HMs (mg/kg, dry weight) recorded at the two landfill sites, while Figure 4 and Figure 5 illustrate their spatial distribution. The observed HM levels (mg/kg), arranged from highest to lowest average concentrations, are as follows: Fe (5600–17,800; mean = 9909), Al (3100–14,500; mean = 6709), Mn (72–294; mean = 155.91), Zn (12.0–137.0; mean = 36.40), Cr (9.0–71.0; mean = 24.1), V (7.0–43.0; mean = 22.20), Ni (8.0–49.0; mean = 19.50), Cu (1.0–26.0; mean = 8.20), Pb (2.00–39.0; mean = 7.91), Co (2.00–10.00; mean = 4.32), and As (1.00–4.00; mean = 2.29).
Overall, landfill Kj2 exhibited higher HM concentrations compared to Kj1. Notably, Kj2 registered peak values for Cr (71 mg/kg in sample 22), Ni (49 mg/kg in sample 22), and Pb (39 mg/kg in sample 21). In contrast, Kj1 recorded significant concentrations of Ni (39 mg/kg in sample 11) and Zn (137 mg/kg in sample 7). When compared to the international guidelines set by the US EPA [32] and WHO [33], most HMs, such as As, Co, Cr, Cu, Mn, V, and Zn fall within acceptable ranges. Although Fe does not have a defined regulatory limit, its measured concentration was the highest (17,800 mg/kg). Ni reached a borderline level at 49 mg/kg, which necessitates continuous monitoring. Of particular concern is Pb, where three samples (21, 22 in Kj2 and 15 in Kj1) exceeded the WHO safety threshold (>30 mg/kg), representing 9.4% of the dataset, signaling potential environmental risks [15]. These results indicate that while most HMs are within safe limits, specific areas with elevated Pb concentrations might benefit from targeted remediation efforts.
Spatial mapping (Figure 4 and Figure 5) reveals distinct distribution patterns for the HMs across the landfill sites. In Kj1, high levels of Al, Co, Cr, Cu, Fe, Mn, Ni, and V were primarily found in the northern and southern sectors (notably at sites 2, 8, 9, and 11), as depicted by the red and orange zones. In contrast, the northern part also showed increased concentrations of As and Zn (sites 7, 8, 9, and 11), while Pb was predominantly concentrated in the southeastern area (sites 15 and 16). The central areas of Kj1 (sites 3, 4, 5, 6, and 14) exhibited comparatively lower HM levels, which could be attributed to better soil conditions, reduced exposure to contamination sources, or more effective natural drainage. For Kj2, the highest HM concentrations (illustrated by red and orange regions) were mainly located in the eastern and southwestern areas (e.g., sites 21, 22, 24, 30, and 32), whereas lower concentrations were observed in the northwestern and select southern sites (sites 26, 27, 29, and 17).
The temporal analysis of landfill boundaries indicates that changes in landfill area may influence HM contamination patterns. For instance, the steady expansion of Kj1 from 2014 to 2025 likely increased the volume of waste and leachate production, enhancing the potential for HM migration into surrounding soils. Conversely, Kj2’s reduction in surface area after 2022 coincided with the construction of engineered containment facilities, which may have curtailed the further spread of contamination despite historically higher HM levels. This pattern is evident in Kj2, where elevated Pb, Cr, and Ni concentrations were observed near areas expanded between 2014 and 2022, as confirmed by spatial distribution maps (Figure 4 and Figure 5). This suggests that both landfill management practices and spatial extent play important roles in determining contamination intensity and distribution.
Figure 6 displays the outcome of a hierarchical cluster analysis (HCA) performed using average linkage to group soil samples based on their HM concentrations. The dendrogram clearly distinguishes two main clusters. The larger cluster comprises 29 samples (specifically samples 1, 3–10, 12–21, and 23–32), while a smaller, separate cluster consists of samples 2, 22, and 11. These three samples are separated by a high rescaled distance, which indicates that they possess distinct contamination profiles particularly marked by elevated levels of Pb and Ni, with sample 22 showing the highest contamination. This suggests that these samples may be influenced by localized industrial waste deposits or other concentrated sources of pollution that require focused remediation. Notably, the elevated Pb levels at sites 21 and 22 may also be partially attributed to traffic emissions from nearby roads, as vehicular activity in landfill-access corridors can contribute to atmospheric deposition of lead-containing particles, a pattern also reported in other Saudi cities [15].
Moreover, the observed concentrations are comparable to those reported in other regions of Saudi Arabia. For example, Alzahrani et al. (2025) recorded Pb levels between 3 and 37 mg/kg in agricultural soils in Western Saudi Arabia [3], while Cr and Zn reported maximum values of 904 and 2230 mg/kg, respectively, in central Saudi landfill sites [1]. These similarities suggest common anthropogenic pressures across Saudi urban regions, including mixed MSW, traffic emissions, and inadequate containment. Overall, the analysis confirms that HM contamination is not uniformly distributed, with clear spatial heterogeneity across both sites. This spatial variability is driven by localized pollution inputs, such as unsegregated waste disposal, surface runoff, and possibly wind-driven redistribution.

3.3. Risk Assessment and Potential Sources of HMs

3.3.1. Contamination Factor (CF)

The contamination factor (CF) serves as a vital metric for evaluating soil pollution by comparing the concentration of a metal in a sample to its natural background level [15]. Our CF analysis (Table S1) indicates that HMs such as Pb, Zn, and Cr have elevated values, suggesting significant anthropogenic input that could pose environmental and health risks [34]. For instance, in Kj2 sample 21, Pb exhibited a CF of 1.95, likely reflecting contributions from industrial processes, residues from lead-based paints, or vehicle emissions [35], whereas Pb levels in Kj1 were consistently lower (ranging between 0.15 and 0.25). Elevated CF values for Zn were also noted, with Kj1 sample 7 recording a value of 1.44 and Kj2 sample 21 showing a value of 0.73; such increases may be linked to processes such as galvanization, battery production, or the use of agricultural chemicals [20]. Additionally, the Cu concentration in Kj1 sample 7 reached a CF of 0.43, potentially indicative of industrial discharge or contamination from electrical equipment, while other samples maintained moderate Cu levels (CF < 0.3). Similarly, Ni showed slight elevations in Kj1 sample 8 (CF = 0.43) and Kj2 sample 21 (CF = 0.40), implying influences from metal refining or waste disposal activities [15].
The chromium levels in Kj2 sample 20 were moderately elevated (CF = 0.74), further hinting at industrial contributions, and As also exhibited noticeable contamination in Kj2, particularly in sample 20 (CF = 0.31) and in samples 21 and 22, possibly due to industrial emissions or pesticide application. In contrast, Mn, Co, and V generally maintained low CF values across most samples, suggesting these HMs predominantly originate from natural, lithogenic sources with minimal anthropogenic input [36]. Spatial distribution maps (Figure 7) reveal localized high CF values for Pb and Zn at specific sites, especially site 15 and site 7 in Kj1, and in the southeastern and central sectors of Kj2. These spatial patterns align closely with the locations of mixed municipal waste zones and suggest improper waste segregation as a contributing factor.

3.3.2. Enrichment Factor (EF)

EF values are used to evaluate contamination by comparing the concentration of a metal in the sample against a reference element, commonly aluminum or iron, to differentiate between naturally occurring levels and those resulting from human activity [35]. An analysis of the EF values for HMs in the Al Kharj landfill sites (Table S2) reveals localized pollution, with Pb, Zn, and Cr showing marked enrichment, pointing to anthropogenic influences [36]. For example, in Kj2 sample 21, Pb is highly enriched with an EF of 7.08, while several other samples exhibit moderate enrichment (EF > 2), suggesting contributions from industrial waste or vehicular emissions. Similarly, Zn displays elevated EF values (greater than 2.5) in samples 7, 18, and 21, possibly linked to sources such as battery manufacturing, galvanized materials, or other industrial discharges. Cr also shows notable enrichment in sample 20 (EF = 3.7), which could be associated with activities such as leather tanning, metal plating, or waste incineration. Ni presents moderate enrichment in samples 21 and 22 (EF > 1.5) but appears largely to derive from natural sources.
In the case of Cu, most samples record low EF values (EF < 2), except for samples 10, 18, 22, and 30, where elevated levels may be attributed to industrial residues or the application of fungicides. Arsenic is enriched in samples 9, 10, 18, and 30, likely due to pesticide usage or industrial emissions, whereas Mn, Co, and V exhibit minimal enrichment (EF < 2), indicating their predominantly lithogenic origin [15,37]. The spatial distribution of EF values (Figure 8) provides clear evidence of pollution hotspots. In Kj1, Zn shows maximum EF (8.62) in the northern region (site 7), while Pb peaks at sites 3 and 15. Cr’s enrichment is concentrated around sampling point 3. In Kj2, the highest Pb enrichment (EF = 7.08) is seen in the southeastern zone (site 21), Zn peaks in the north-central area, and Cr is most enriched near sites 18 and 19. These spatial patterns strongly suggest targeted pollution sources, potentially linked to vehicular access routes, surface runoff, and improper landfill engineering.

3.3.3. Potential Risk Index (RI)

The potential risk index is proposed to measure the detrimental impacts of HM concentrations [23,37]. The RI applied in this study is based on the method introduced by [23] and is widely recognized as a comparative screening tool in environmental geochemistry. While RI provides a standardized way to compare contamination severity across metals and sites, it is important to note that it is species-independent and does not account for site-specific bioavailability or local ecological sensitivities. As such, the RI values reported here should be interpreted as indicative of relative risk rather than definitive measures of ecological harm. This limitation highlights the need for complementary site-specific bioassays in future assessments. In this study, all landfill samples recorded an Eri below 40 for HMs, including As, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn (Table S3), indicating low ecological risk. However, individual site-level risk evaluation reveals more nuanced threats. In Kj1, most samples exhibited RI values under 8.0, reflecting low to moderate contamination. However, samples 11 (RI = 10.91) and 15 (RI = 7.86) showed slightly elevated risk levels, largely attributable to increased contributions from Ni (3.44 in sample 11) and Pb (2.75 in sample 15) [38,39].
In contrast, Kj2 displayed higher risk, with samples 21 (RI = 18.00), 22 (RI = 18.11), and 28 (RI = 11.02) exceeding the threshold of 10, signaling substantial ecological concern. These values suggest that bioaccumulation and toxicity risks may occur, especially for organisms in direct contact with or consuming matter from these areas. Sample 22 is especially critical, with contributions from Ni (4.32), Cu (2.89), Pb (4.5), and As (3.08), all nearing or exceeding moderate hazard thresholds [40,41,42]. Although the absolute RI values are not extreme, their spatial clustering and alignment with enrichment data reinforce the need for site-specific ecological monitoring and control strategies.

3.3.4. Soil Quality Guidelines

The HM data from Kj1 and Kj2 reveal differing contamination profiles, with particularly notable levels of Cu, Ni, and Zn, some of which surpass sediment quality guideline (SQG) thresholds [43,44]. All Cu concentrations remain below the effects range-low (ERL) limit of 34 mg/kg (Table 3). However, the Ni concentrations in 18.75% of the samples fall between the ERL of 20.9 mg/kg and the effects range-median (ERM) of 51.6 mg/kg, suggesting moderate risk of bioaccumulation. Zn mostly remains below the ERL limit (150 mg/kg), though sample 7 in Kj1 approaches this value. Pb and Cr remain below the ERL thresholds, while As levels are consistently low (Table 3).
In Kj2, sample 22 stands out, with Cu (26 mg/kg), Ni (49 mg/kg), Zn (82 mg/kg), and Pb (18 mg/kg) approaching or exceeding the ERL limits, indicating potential sediment toxicity. Sample 21, with Pb at 39 mg/kg, closely approaches the ERL limit for Pb and poses a possible chronic exposure risk. When compared to studies from Jeddah and Riyadh, where Zn values above 140 mg/kg and Pb values up to 60 mg/kg have been reported [1,3,41], Al-Kharj landfills display similar contamination trends. However, the values remain lower than those reported in humid tropical sites in Southeast Asia, where landfill leachate significantly elevates HM mobility due to persistent rainfall and leaching [45].
In arid environments such as Al-Kharj, limited rainfall reduces leachate generation, potentially slowing vertical HM migration. However, strong evaporation and dust transport can redistribute contaminants horizontally via wind and surface crusting, as reported in arid-zone landfills in Jordan and Egypt [46,47]. These conditions may partially explain the localized surface-level enrichment observed in our study.

3.4. Potential Sources of HMs

A Pearson’s correlation analysis revealed several strong positive relationships (r > 0.7, p < 0.01) among the studied elements (Table 4). For example, aluminum showed robust correlations with cobalt (r = 0.945), iron (r = 0.935), nickel (r = 0.947), and manganese (r = 0.889), suggesting that these elements may share a common source or exhibit similar behavior within the landfill environment [43,44]. Cobalt also demonstrated strong associations with iron (r = 0.918), nickel (r = 0.923), and vanadium (r = 0.863), which may point to joint contributions from industrial waste or metal-rich landfill materials [45,46]. In addition, chromium was significantly correlated with arsenic (r = 0.736), cobalt (r = 0.749), copper (r = 0.727), and nickel (r = 0.783), indicating that these HMs could be derived from similar pollution sources, such as electronic waste or industrial discharges [47,48].
Moderate correlations (r = 0.5–0.7) were observed between pairs such as copper–nickel, lead–copper, lead–nickel, zinc–cobalt, and zinc–copper. These moderate relationships likely stem from industrial processes, including metal plating, electronics manufacturing, the use of lead-based alloys, or battery production [49,50]. On the other hand, lead exhibited only weak correlations with most of the other HMs, with the exception of a moderate association with copper (r = 0.574) and a weaker link with chromium (r = 0.442), suggesting that lead may originate from different sources, compared to the other metals [35]. Furthermore, zinc’s correlations were generally weak, except for slightly higher values with cobalt (r = 0.535) and copper (r = 0.476), implying that its behavior may be more independent due to its distinct mobility or origin [51]. The Al-Kharj region contains light industrial zones, vehicle maintenance workshops, and solid waste transfer stations, which may contribute Pb from leaded fuels (legacy contamination), lubricants, and battery waste. Paints, electronic equipment, and discarded plastics can also be secondary sources of Pb, Zn, and Cu. In addition, high traffic volumes near landfill perimeters and wind dispersion in this arid zone (prevailing NW to SE direction) likely influence spatial HM deposition.
The Kaiser–Meyer–Olkin (KMO) Measure and Bartlett’s Test of Sphericity are employed to determine whether a dataset is appropriate for factor analysis [52,53]. The KMO statistic quantifies the proportion of variance among variables that might be attributed to underlying common factors [54], and a value exceeding 0.80 indicates excellent suitability. In this study, a KMO value of 0.860 (Table S4) suggests that the dataset is highly amenable to meaningful factor extraction. Meanwhile, Bartlett’s test evaluates if the correlation matrix differs significantly from an identity matrix, where variables would otherwise be uncorrelated, and a p-value of 0.000 (<0.05) confirms that there are adequate inter-variable correlations for factor analysis [55,56].
Principal Component Analysis (PCA) is then applied as a multivariate technique to uncover relationships among HMs and identify potential pollution sources in the landfill environments [48,57]. The first principal component (PC1) accounts for 70.941% of the total variance (Table 5), suggesting that a single latent factor dominates the variability in heavy metal concentrations, which may indicate co-migration from shared contamination sources [51,56]. Specifically, high factor loadings (greater than 0.80) for Al (0.930), Co (0.958), Cr (0.828), Cu (0.871), Fe (0.932), Mn (0.916), Ni (0.951), and V (0.904) point to a combined influence of both natural processes, such as mountain weathering, and anthropogenic activities such as industrial waste deposition, leachate percolation, or electronic waste disposal [35,49]. In contrast, moderate loadings for As (0.772) and Pb (0.541) imply a partial connection to the main contamination source but also suggest the influence of independent or legacy sources, such as old lead-based paint residues, pesticide use in surrounding agricultural areas, or atmospheric deposition from urban vehicle emissions. Zinc, with the lowest loading (0.508), behaves independently, potentially due to its high mobility and sensitivity to pH and redox conditions in arid soils.
The scree plot is a visual tool in PCA that plots eigenvalues against component numbers to help determine the optimal number of principal components to retain. It highlights the key factors that account for most of the data variance [57]. In our study, the first principal component (PC1) exhibits a very high eigenvalue (greater than 8), indicating that it captures the majority of the variance (Figure S1). A marked drop in eigenvalues, often referred to as an “elbow” appears at PC2, and beyond this point, the eigenvalues level off, suggesting that subsequent components add little meaningful information and likely represent noise [56,58]. Site-specific PCA could be applied in future studies with a larger sample size, which would further enhance the characterization of differences between landfill sites.

4. Conclusions

This study successfully applied GIS and multivariate statistical methods to map soil contamination at two landfill sites in Al-Kharj City. GIS was employed to monitor changes in landfill boundaries from 2014 to 2025 and to analyze the distribution patterns of HMs. In landfill Kj1, the highest concentrations of Al, Co, Cr, Cu, Fe, Mn, Ni, and V were found in its northern and southern sectors, while in Kj2, elevated HM levels predominantly occurred in the eastern and southwestern areas. Among all measured elements, Pb, Zn, Cr, and Cu demonstrated the highest contamination and enrichment levels, especially in samples 7, 21, and 22, with Pb concentrations. The CF, EF, and RI results showed that while overall ecological risks remained low to moderate, localized hotspots, particularly in Kj2, pose potential long-term environmental and public health concerns.
Contamination indices revealed that Pb, Zn, Cu, and Cr were significantly impacted by anthropogenic activities, whereas Al, Mn, Ni, Cu, As, Co, and V exhibited minimal enrichment. HCA divided the HMs into two principal clusters, and Pearson’s correlation analysis showed strong positive correlations among pairs such as Al–Co, Al–Fe, Al–Ni, Al–Mn, Co–Fe, Co–Ni, Co–V, Cr–As, Cr–Co, Cr–Cu, and Cr–Ni. These associations imply that the HMs are likely derived from both natural processes and industrial waste or metal-rich materials present in the landfill. Additionally, PCA further corroborated these findings, with PC1 accounting for 70.941% of the total variance, indicating a combined influence of natural weathering such as from adjacent mountains and industrial waste deposition. The PCA and the correlation analysis also suggested multiple pollution sources, including landfill waste, road traffic emissions, and possibly pesticide or e-waste residues.
The integration of GIS with multivariate statistical tools proved to be a major strength of this study. GIS enabled detailed spatial visualization of contamination hotspots, while statistical methods helped uncover relationships among HMs and identify potential sources. This combination provided a comprehensive and robust framework for understanding the extent and origin of soil contamination in complex landfill environments. To curb HM leaching and enhance landfill management, it is recommended that measures such as bioremediation or phytoremediation for HMs such as Pb and Zn be adopted, alongside improved waste segregation and containment strategies. Further research into metal bioavailability is also needed to assess long-term environmental risks and develop sustainable remediation solutions. We suggest that future work should include dedicated reference site sampling to establish site-specific baseline values for more robust comparisons.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land14081697/s1, Table S1: The concentration of HMs (mg/kg) from the two investigated landfills; Table S2: The enrichment factor for HMs from the two landfills; Table S3: The risk index for HMs from the two landfills; Table S4: KMO and Bartlett’s Test of the analyzed HMs; Figure S1: Scree plot analysis for principal component selection.

Author Contributions

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

Funding

Ongoing Research Funding Program, (ORF-2025-791), King Saud University, Riyadh, Saudi Arabia.

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 the Ongoing Research Funding Program, (ORF-2025-791), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location map of the three studied landfills and sample locations.
Figure 1. Location map of the three studied landfills and sample locations.
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Figure 2. Temporal change in surface area of Kj1 from 2014 to 2025.
Figure 2. Temporal change in surface area of Kj1 from 2014 to 2025.
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Figure 3. Temporal change in surface area of Kj2 from 2014 to 2025.
Figure 3. Temporal change in surface area of Kj2 from 2014 to 2025.
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Figure 4. Spatial distribution of Al, As, Co, Cr, Cu, and Fe in the two studied landfills.
Figure 4. Spatial distribution of Al, As, Co, Cr, Cu, and Fe in the two studied landfills.
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Figure 5. Spatial distribution of Mn, Ni, Pb, V, and Zn in the two studied landfills.
Figure 5. Spatial distribution of Mn, Ni, Pb, V, and Zn in the two studied landfills.
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Figure 6. Q-mode HCA of soil samples from the two studied landfills.
Figure 6. Q-mode HCA of soil samples from the two studied landfills.
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Figure 7. Spatial distribution of CF per sample locations for Pb and Zn in the two studied landfills.
Figure 7. Spatial distribution of CF per sample locations for Pb and Zn in the two studied landfills.
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Figure 8. Spatial distributions of EF per sample locations for Pb, As, and Cr in the two studied landfills.
Figure 8. Spatial distributions of EF per sample locations for Pb, As, and Cr in the two studied landfills.
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Table 2. The concentrations of HMs (mg/kg) from the two investigated landfills.
Table 2. The concentrations of HMs (mg/kg) from the two investigated landfills.
S.N.AlAsCoCrCuFeMnNiPbVZn
Kj1159002.003.0017.06.0860012515.03.0017.020.0
214,5002.009.0033.011.017,50026131.05.0033.038.0
332001.002.0014.07.011,2001418.04.0013.026.0
446001.003.0014.03.0770010913.04.0015.016.0
536001.002.0011.01.066008910.02.0012.014.0
645002.002.0014.03.0760010812.03.0014.029.0
748002.005.0014.05.0790011314.03.0016.0137.0
896002.006.0024.08.012,20020329.05.0025.053.0
968003.005.0025.08.0930013719.05.0023.037.0
1059002.004.0019.014.0950014418.07.0019.048.0
1112,4004.008.0032.012.015,40026039.05.0032.041.0
1253002.003.0017.05.0830012315.04.0018.026.0
1346002.003.0014.04.0730010714.03.0014.022.0
1443002.002.0012.02.065008712.02.0013.018.0
1557002.003.0017.010.0800011917.011.0016.026.0
1657001.003.0016.06.0830012015.05.0016.017.0
Kj21741001.002.0012.04.060008211.03.0011.013.0
1869003.004.0024.015.010,40016021.013.0026.048.0
1970003.005.0019.08.011,30026517.04.0026.027.0
1065004.005.0067.08.0950015122.04.0028.021.0
2190003.006.0040.012.013,00020627.039.0031.069.0
2214,2004.0010.0071.026.017,80029449.018.0043.082.0
2345002.003.0017.06.0810012914.07.0019.022.0
2486002.005.0026.010.011,30019424.016.0026.035.0
2576002.004.0023.08.0990015723.06.0023.031.0
2636002.002.0013.05.064009711.06.0012.020.0
2731001.002.009.04.056007211.04.007.012.0
2877003.005.0024.011.011,40019123.016.0025.041.0
2956002.003.0019.06.0810013313.03.0027.016.0
3068004.006.0027.08.010,20018219.05.0039.025.0
3153002.004.0021.06.010,00017914.04.0027.017.0
3286004.006.0033.011.012,60019727.09.0040.041.0
Table 3. Distribution of samples in the ranges established by the Sediment Quality Guideline (SQG) according to the HM levels (mg/kg).
Table 3. Distribution of samples in the ranges established by the Sediment Quality Guideline (SQG) according to the HM levels (mg/kg).
HMsMean
Concentration
SQG [40]% of Samples Within Ranges of the SQG
ERLERM<ERL>ERL and <ERM>ERM
Cu8.234270100 (32)00
Ni19.520.951.681.25 (26)18.75 (6)0
Zn36.415041096.86 (31)3.14 (1)0
As2.298.270100 (32)00
Cr24.181370100 (32)00
Pb7.9146.7218100 (32)00
Table 4. The correlation matrix of the analyzed HMs.
Table 4. The correlation matrix of the analyzed HMs.
AlAsCoCrCuFeMnNiPbVZn
Al1
As0.592 **1
Co0.945 **0.698 **1
Cr0.693 **0.736 **0.749 **1
Cu0.756 **0.619 **0.761 **0.727 **1
Fe0.935 **0.584 **0.918 **0.687 **0.785 **1
Mn0.889 **0.655 **0.898 **0.654 **0.746 **0.943 **1
Ni0.947 **0.681 **0.923 **0.783 **0.835 **0.884 **0.844 **1
Pb0.405 *0.3420.388 *0.442 *0.574 **0.437 *0.408 *0.471 **1
V0.803 **0.824 **0.863 **0.756 **0.720 **0.817 **0.848 **0.798 **0.416 *1
Zn0.395 *0.3230.535 **0.3240.476 **0.410 *0.357 *0.454 **0.383 *0.3251
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
Table 5. Principal component loadings and the single extracted PC with varimax normalized rotation.
Table 5. Principal component loadings and the single extracted PC with varimax normalized rotation.
HMsComponent
PC1
Al0.930
As0.772
Co0.958
Cr0.828
Cu0.871
Fe0.932
Mn0.916
Ni0.951
Pb0.541
V0.904
Zn0.508
% of Variance70.941
Cumulative %70.941
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Alharbi, T.; El-Sorogy, A.S.; Rikan, N. A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia. Land 2025, 14, 1697. https://doi.org/10.3390/land14081697

AMA Style

Alharbi T, El-Sorogy AS, Rikan N. A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia. Land. 2025; 14(8):1697. https://doi.org/10.3390/land14081697

Chicago/Turabian Style

Alharbi, Talal, Abdelbaset S. El-Sorogy, and Naji Rikan. 2025. "A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia" Land 14, no. 8: 1697. https://doi.org/10.3390/land14081697

APA Style

Alharbi, T., El-Sorogy, A. S., & Rikan, N. (2025). A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia. Land, 14(8), 1697. https://doi.org/10.3390/land14081697

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