A GIS and Multivariate Analysis Approach for Mapping Heavy Metals and Metalloids Contamination in Landfills: A Case Study from Al-Kharj, Saudi Arabia
Abstract
1. Introduction
2. Materials and Methods
Contamination Indices | Classification | |
---|---|---|
EF | EF < 2 | Minimal enrichment (natural origin) |
2 ≤ EF < 5 | Moderate enrichment | |
5 ≤ EF < 20 | Significant enrichment | |
20 ≤ EF < 40 | Very high enrichment | |
EF ≥ 40 | Extremely high enrichment | |
CF | CF < 1 | Low contamination |
1 ≤ CF <3 | Moderate contamination | |
3 ≤ CF < 6 | High contamination | |
CF ≥ 6 | Very high contamination | |
RI | Er < 40 | Low ecological risk |
40 < Er ≤ 80 | Moderate ecological risk | |
80 < Er ≤ 160 | Considerable ecological risk | |
160 < Er ≤ 320 | High ecological risk | |
Er > 320 | Serious ecological risk | |
RI < 150 | Low ecological risk | |
150 < RI < 300 | Moderate ecological risk | |
300 < RI < 600 | High potential ecological risk | |
RI ≥ 600 | Significantly high ecological risk |
3. Results and Discussion
3.1. Landfill Variation Through Time
3.1.1. Al Kharj Landfill 1 (Kj1)
3.1.2. Al Kharj Landfill 2 (Kj2)
3.2. Concentration and Spatial Distribution of HMs
3.3. Risk Assessment and Potential Sources of HMs
3.3.1. Contamination Factor (CF)
3.3.2. Enrichment Factor (EF)
3.3.3. Potential Risk Index (RI)
3.3.4. Soil Quality Guidelines
3.4. Potential Sources of HMs
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Alharbi, T.; El-Sorogy, A.S.; Rikan, N.; Salem, Y. Geographic Information System and contamination indices for environmental risk assessment of landfill disposal sites in Central Saudi Arabia. Sustainability 2024, 16, 9822. [Google Scholar] [CrossRef]
- Ali, H.; Khan, E.; Ilahi, I. Environmental chemistry and ecotoxicology of hazardous heavy metals: Environmental persistence, toxicity, and bioaccumulation. J. Environ. Sci. Health Part C 2019, 37, 81–117. [Google Scholar] [CrossRef]
- Alzahrani, H.; El-Sorogy, A.S.; Alghamdi, A.G.; Alasmary, Z.; Albugami, T.M.R. A multivariate and geographic-information-system approach to assess environmental and health hazards of Fe, Cr, Zn, Cu, and Pb in agricultural soils of Western Saudi Arabia. Sustainability 2025, 17, 1610. [Google Scholar] [CrossRef]
- Wu, Q.; Leung, J.Y.; Geng, X.; Chen, S.; Huang, X.; Li, H. Heavy metal contamination and ecological risk assessment in an estuary system: A case study from South China. Sci. Total Environ. 2020, 737, 139766. [Google Scholar]
- Gholizadeh, H.; Rahman, M.M.; Habib, M.A. The impact of solid waste disposal on heavy metal accumulation in soil and plants. Ecotoxicol. Environ. Saf. 2022, 234, 113427. [Google Scholar]
- Alharbi, T.; El-Sorogy, A.S.; Al-Katany, K.; Alhejji, S.S.S. Ecological health hazards and multivariate assessment of contamination sources of potentially toxic elements from Al-Lith coastal sediments, Saudi Arabia. Minerals 2024, 14, 1150. [Google Scholar] [CrossRef]
- El-Sergany, M.M.; El-Dine, W.F.; Saleh, H.M. Heavy metal contamination and environmental risk assessment around a landfill in Egypt. Environ. Monit. Assess 2021, 193, 98. [Google Scholar]
- Kumar, V.; Sharma, A.; Prasad, S. Spatial distribution and risk assessment of heavy metals in soils around Ghazipur landfill, India. Environ. Geochem. Health 2022, 44, 789–805. [Google Scholar]
- Han, Y.; Tang, Z.; Sun, J.; Xing, X.; Zhang, M.; Cheng, J. Heavy metals in soil contaminated through e-waste processing activities in a recycling area: Implications for risk management. Process Saf. Environ. Prot. 2019, 125, 189–196. [Google Scholar] [CrossRef]
- Ahmadi, M.; Akhbarizadeh, R.; Haghighifard, N.J.; Barzegar, G.; Jorfi, S. Geochemical determination and pollution assessment of heavy metals in agricultural soils of south western of Iran. J. Environ. Health Sci. Eng. 2019, 17, 657–669. [Google Scholar] [CrossRef] [PubMed]
- Wei, B.; Yang, L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchem. J. 2010, 94, 99–107. [Google Scholar] [CrossRef]
- Al-Khashman, O.A. The investigation of metal concentrations in street dust samples in Aqaba city, Jordan. Environ. Geochem. Health 2007, 29, 197–207. [Google Scholar] [CrossRef]
- Aloud, S.S.; Alotaibi, K.D.; Almutairi, K.F.; Albarakah, F.N. Assessment of Heavy Metals Accumulation in Soil and Native Plants in an Industrial Environment, Saudi Arabia. Sustainability 2022, 14, 5993. [Google Scholar] [CrossRef]
- Maanan, M.; Saddik, M.; Maanan, M.; Chaibi, M. Assessing metal contamination in coastal sediments using pollution indices and multivariate statistical approaches. Environ. Geochem. Health 2021, 43, 11–25. [Google Scholar]
- Kabata-Pendias, A.; Pendias, H. Trace Elements in Soils and Plants, 4th ed.; CRC Press: Boca Raton, FL, USA, 2011.
- Kahal, A.Y.; El-Sorogy, A.S.; Meroño de Larriva, J.E.; Shokr, M.S. Mapping soil contamination in arid regions: A GIS and multivariate analysis approach. Minerals 2025, 15, 124. [Google Scholar] [CrossRef]
- Li, J.; Zhang, H.; Shi, W.; Sun, H. Multivariate statistical approaches for source apportionment of heavy metal pollution: A review. Environ. Pollut. 2022, 301, 119013. [Google Scholar]
- 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. [Google Scholar] [CrossRef]
- Shokr, M.S.; Mustafa, A.-r.A.; Alharbi, T.; Meroño de Larriva, J.E.; El-Sorogy, A.S.; Al-Kahtany, K.; Abdelsamie, E.A. Integration of VIS–NIR spectroscopy and multivariate technique for soils discrimination under different land management. Land 2024, 13, 2056. [Google Scholar] [CrossRef]
- Chen, H.; Teng, Y.; Lu, S.; Wang, Y.; Wang, J. Contamination features and health risk of soil heavy metals in China. Sci. Total Environ. 2015, 512–513, 143–153. [Google Scholar] [CrossRef]
- Thompson, M.; Ellison, S.L.R. The International Harmonized Protocol for the Proficiency Testing of Analytical Chemistry Laboratories. Pure Appl. Chem. 2011, 78, 145–196. [Google Scholar] [CrossRef]
- Gao, X.; Chen, C. Heavy metal pollution status in surface sediments of the coastal Bohai Bay. Water Res. 2012, 46, 1901–1911. [Google Scholar] [CrossRef]
- Hakanson, L. An ecological risk index for aquatic pollution control: A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
- Reimann, C.; de Caritat, P. Distinguishing between natural and anthropogenic sources for elements in the environment: Regional geochemical surveys versus enrichment factors. Sci. Total Environ. 2005, 337, 91–107. [Google Scholar] [CrossRef]
- Weissmannová, H.D.; Pavlovský, J. Indices of soil contamination by heavy metals: Methodology of calculation for pollution assessment (minireview). Environ. Monit. Assess. 2017, 189, 616. [Google Scholar] [CrossRef]
- Vineethkumar, V.; Narayana, A.C.; Prakash, T.N. Assessment of heavy metal contamination in coastal sediments using geochemical indices and spatial distribution patterns: A case study from the southwest coast of India. Mar. Pollut. Bull. 2020, 153, 111006. [Google Scholar]
- Sutherland, R.A. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environ. Geol. 2000, 39, 611–627. [Google Scholar] [CrossRef]
- Zhang, J.; Liu, C.L. Riverine composition and estuarine geochemistry of particulate metals in China. Estuar. Coast. Shelf Sci. 2002, 54, 1051–1070. [Google Scholar] [CrossRef]
- Youssef, M.; Al Otaibi, S.; El-Sorogy, A.S. Distribution, source, and contamination of heavy metals in coastal sediments of Jeddah, Red Sea, Saudi Arabia. Bull. Environ. Contam. Toxicol. 2024, 113, 12. [Google Scholar] [CrossRef] [PubMed]
- Al-Kahtany, K.; Al-Hashim, M.H.; El-Sorogy, A.S. Heavy metal(loid)s contamination and ecological-health risk assessment of coastal sediment from Salwa Bay, Saudi Arabia. Arab. J. Chem. 2024, 17, 105868. [Google Scholar] [CrossRef]
- El-Sorogy, A.S.; Nour, H.E.; Al-Kahtany, K.; Youssef, M.; Alharbi, T.; Giacobbe, S. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba. Open Geosci. 2025, 17, 20250807. [Google Scholar] [CrossRef]
- United States Environmental Protection Agency (US EPA). Soil Screening Guidance: Technical Background Document; United States Environmental Protection Agency (US EPA): Washington, DC, USA, 1996.
- World Health Organization (WHO). Guidelines for Drinking Water Quality, 4th ed.; WHO: Geneva, Switzerland, 2017.
- Nriagu, J.; Pacyna, J. Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature 1988, 333, 134–139. [Google Scholar] [CrossRef] [PubMed]
- Alloway, B.J. Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability, 3rd ed.; Springer: Dordrecht, The Netherlands, 2013. [Google Scholar]
- Wuana, R.A.; Okieimen, F.E. Heavy metals in contaminated soils: A review of sources, chemistry, risks, and best available strategies for remediation. ISRN Ecol. 2011, 2011, 402647. [Google Scholar] [CrossRef]
- El-Sorogy, A.S.; Al Khathlan, M.H. Assessment of potentially toxic elements and health risks of agricultural soil in Southwest Riyadh, Saudi Arabia. Open Chem. 2024, 22, 20240017. [Google Scholar] [CrossRef]
- Li, Y.; Wang, X.; Zhang, H. Heavy metal mobility in landfill sites and risk assessment of zinc and lead leaching. Environ. Sci. Pollut. Res. 2019, 26, 15412–15423. [Google Scholar]
- MacDonald, D.D.; Ingersoll, C.G.; Berger, T.A. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 2000, 39, 20–31. [Google Scholar] [CrossRef] [PubMed]
- Chapman, P.M.; Smith, E.P.; Munns, W.R. Sediment quality assessment: A triad approach to weight-of-evidence evaluation. Environ. Toxicol. Chem. 2013, 32, 513–522. [Google Scholar]
- Tessier, A.; Garnier, J.M.; Campbell, P.G.C. The role of manganese oxides in metal bioavailability and sediment toxicity. Aquat. Geochem. 2023, 29, 145–162. [Google Scholar]
- Burton, G.A.; Nguyen, L.T.H.; Janssen, C.; Baalousha, M. Advances in sediment quality assessment: From guidelines to bioavailability-based approaches. Environ. Sci. Technol. 2022, 56, 2345–2357. [Google Scholar]
- Long, E.; MacDonald, D.; Smith, S.; Calder, F. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environ. Manag. 1995, 19, 81–97. [Google Scholar] [CrossRef]
- Long, E.R.; Ingersoll, C.G.; MacDonald, D.D. Calculation and uses of mean sediment quality guideline quotients: A critical review. Environ. Sci. Technol. 2006, 40, 1726–1736. [Google Scholar] [CrossRef]
- Sethi, S.; Gupta, P. Soil contamination: A menace to life. In Soil Contamination–Threats and Sustainable Solutions; IntechOpen: London, UK, 2020; p. 13. [Google Scholar] [CrossRef]
- Abdullah, M.; Ahmed, A.; Khan, S. Heavy metal contamination in landfill leachates and its impact on surrounding soil and water systems. Environ. Pollut. 2020, 264, 114721. [Google Scholar]
- Shoaib, M.; Junaid, M.; Hussain, M. The mobility and bioavailability of heavy metals in landfill sites and their impact on environmental health. Environ. Geochem. Health 2021, 43, 569–583. [Google Scholar]
- Al-Khashman, O. The role of industrial waste in heavy metal contamination in municipal landfills. J. Hazard. Mater. 2019, 379, 120799. [Google Scholar]
- Zhang, L.; Yang, Y.; Wei, J. The environmental impact of vanadium and arsenic in landfill sites: A case study. Chemosphere 2021, 283, 131196. [Google Scholar]
- Singh, R.; Kumar, P.; Chauhan, S. Metal pollution from e-waste disposal and its environmental consequences. J. Environ. Manag. 2023, 325, 116764. [Google Scholar]
- Abdi, H.; Williams, L.J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
- Kabir, M.; Hossain, K.; Alam, S. Chromium and nickel interactions in landfill leachates: Implications for soil and groundwater pollution. Sci. Total Environ. 2021, 792, 148621. [Google Scholar]
- Costello, A.B.; Osborne, J.W. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pract. Assess. Res. Eval. 2021, 26, 7. [Google Scholar] [CrossRef]
- Rezaei, M.; Taghavi, L.; Mohammadi, S. Iron and manganese transport in landfill leachate: A case study. Water Res. 2020, 182, 115917. [Google Scholar]
- Kaiser, H.F. An index of factorial simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
- Anwar, S.; Naz, A.; Ashraf, M.Y.; Malik, A. Evaluation of inorganic contaminants emitted from automobiles and dynamics in soil, dust, and vegetation from major highways in Pakistan. Environ. Sci. Pollut. Res. Int. 2020, 27, 32494–32508. [Google Scholar] [CrossRef] [PubMed]
- Field, A. Discovering Statistics Using IBM SPSS Statistics, 5th ed.; Sage Publications: London, UK, 2018. [Google Scholar]
- Gálvez-Cloutier, R.; Beaulieu, P.; Bell, C. Lead and zinc contamination in urban landfills: Sources and leachate interactions. Waste Manag. 2022, 138, 34–46. [Google Scholar]
S.N. | Al | As | Co | Cr | Cu | Fe | Mn | Ni | Pb | V | Zn | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Kj1 | 1 | 5900 | 2.00 | 3.00 | 17.0 | 6.0 | 8600 | 125 | 15.0 | 3.00 | 17.0 | 20.0 |
2 | 14,500 | 2.00 | 9.00 | 33.0 | 11.0 | 17,500 | 261 | 31.0 | 5.00 | 33.0 | 38.0 | |
3 | 3200 | 1.00 | 2.00 | 14.0 | 7.0 | 11,200 | 141 | 8.0 | 4.00 | 13.0 | 26.0 | |
4 | 4600 | 1.00 | 3.00 | 14.0 | 3.0 | 7700 | 109 | 13.0 | 4.00 | 15.0 | 16.0 | |
5 | 3600 | 1.00 | 2.00 | 11.0 | 1.0 | 6600 | 89 | 10.0 | 2.00 | 12.0 | 14.0 | |
6 | 4500 | 2.00 | 2.00 | 14.0 | 3.0 | 7600 | 108 | 12.0 | 3.00 | 14.0 | 29.0 | |
7 | 4800 | 2.00 | 5.00 | 14.0 | 5.0 | 7900 | 113 | 14.0 | 3.00 | 16.0 | 137.0 | |
8 | 9600 | 2.00 | 6.00 | 24.0 | 8.0 | 12,200 | 203 | 29.0 | 5.00 | 25.0 | 53.0 | |
9 | 6800 | 3.00 | 5.00 | 25.0 | 8.0 | 9300 | 137 | 19.0 | 5.00 | 23.0 | 37.0 | |
10 | 5900 | 2.00 | 4.00 | 19.0 | 14.0 | 9500 | 144 | 18.0 | 7.00 | 19.0 | 48.0 | |
11 | 12,400 | 4.00 | 8.00 | 32.0 | 12.0 | 15,400 | 260 | 39.0 | 5.00 | 32.0 | 41.0 | |
12 | 5300 | 2.00 | 3.00 | 17.0 | 5.0 | 8300 | 123 | 15.0 | 4.00 | 18.0 | 26.0 | |
13 | 4600 | 2.00 | 3.00 | 14.0 | 4.0 | 7300 | 107 | 14.0 | 3.00 | 14.0 | 22.0 | |
14 | 4300 | 2.00 | 2.00 | 12.0 | 2.0 | 6500 | 87 | 12.0 | 2.00 | 13.0 | 18.0 | |
15 | 5700 | 2.00 | 3.00 | 17.0 | 10.0 | 8000 | 119 | 17.0 | 11.00 | 16.0 | 26.0 | |
16 | 5700 | 1.00 | 3.00 | 16.0 | 6.0 | 8300 | 120 | 15.0 | 5.00 | 16.0 | 17.0 | |
Kj2 | 17 | 4100 | 1.00 | 2.00 | 12.0 | 4.0 | 6000 | 82 | 11.0 | 3.00 | 11.0 | 13.0 |
18 | 6900 | 3.00 | 4.00 | 24.0 | 15.0 | 10,400 | 160 | 21.0 | 13.00 | 26.0 | 48.0 | |
19 | 7000 | 3.00 | 5.00 | 19.0 | 8.0 | 11,300 | 265 | 17.0 | 4.00 | 26.0 | 27.0 | |
10 | 6500 | 4.00 | 5.00 | 67.0 | 8.0 | 9500 | 151 | 22.0 | 4.00 | 28.0 | 21.0 | |
21 | 9000 | 3.00 | 6.00 | 40.0 | 12.0 | 13,000 | 206 | 27.0 | 39.00 | 31.0 | 69.0 | |
22 | 14,200 | 4.00 | 10.00 | 71.0 | 26.0 | 17,800 | 294 | 49.0 | 18.00 | 43.0 | 82.0 | |
23 | 4500 | 2.00 | 3.00 | 17.0 | 6.0 | 8100 | 129 | 14.0 | 7.00 | 19.0 | 22.0 | |
24 | 8600 | 2.00 | 5.00 | 26.0 | 10.0 | 11,300 | 194 | 24.0 | 16.00 | 26.0 | 35.0 | |
25 | 7600 | 2.00 | 4.00 | 23.0 | 8.0 | 9900 | 157 | 23.0 | 6.00 | 23.0 | 31.0 | |
26 | 3600 | 2.00 | 2.00 | 13.0 | 5.0 | 6400 | 97 | 11.0 | 6.00 | 12.0 | 20.0 | |
27 | 3100 | 1.00 | 2.00 | 9.0 | 4.0 | 5600 | 72 | 11.0 | 4.00 | 7.0 | 12.0 | |
28 | 7700 | 3.00 | 5.00 | 24.0 | 11.0 | 11,400 | 191 | 23.0 | 16.00 | 25.0 | 41.0 | |
29 | 5600 | 2.00 | 3.00 | 19.0 | 6.0 | 8100 | 133 | 13.0 | 3.00 | 27.0 | 16.0 | |
30 | 6800 | 4.00 | 6.00 | 27.0 | 8.0 | 10,200 | 182 | 19.0 | 5.00 | 39.0 | 25.0 | |
31 | 5300 | 2.00 | 4.00 | 21.0 | 6.0 | 10,000 | 179 | 14.0 | 4.00 | 27.0 | 17.0 | |
32 | 8600 | 4.00 | 6.00 | 33.0 | 11.0 | 12,600 | 197 | 27.0 | 9.00 | 40.0 | 41.0 |
HMs | Mean Concentration | SQG [40] | % of Samples Within Ranges of the SQG | |||
---|---|---|---|---|---|---|
ERL | ERM | <ERL | >ERL and <ERM | >ERM | ||
Cu | 8.2 | 34 | 270 | 100 (32) | 0 | 0 |
Ni | 19.5 | 20.9 | 51.6 | 81.25 (26) | 18.75 (6) | 0 |
Zn | 36.4 | 150 | 410 | 96.86 (31) | 3.14 (1) | 0 |
As | 2.29 | 8.2 | 70 | 100 (32) | 0 | 0 |
Cr | 24.1 | 81 | 370 | 100 (32) | 0 | 0 |
Pb | 7.91 | 46.7 | 218 | 100 (32) | 0 | 0 |
Al | As | Co | Cr | Cu | Fe | Mn | Ni | Pb | V | Zn | |
---|---|---|---|---|---|---|---|---|---|---|---|
Al | 1 | ||||||||||
As | 0.592 ** | 1 | |||||||||
Co | 0.945 ** | 0.698 ** | 1 | ||||||||
Cr | 0.693 ** | 0.736 ** | 0.749 ** | 1 | |||||||
Cu | 0.756 ** | 0.619 ** | 0.761 ** | 0.727 ** | 1 | ||||||
Fe | 0.935 ** | 0.584 ** | 0.918 ** | 0.687 ** | 0.785 ** | 1 | |||||
Mn | 0.889 ** | 0.655 ** | 0.898 ** | 0.654 ** | 0.746 ** | 0.943 ** | 1 | ||||
Ni | 0.947 ** | 0.681 ** | 0.923 ** | 0.783 ** | 0.835 ** | 0.884 ** | 0.844 ** | 1 | |||
Pb | 0.405 * | 0.342 | 0.388 * | 0.442 * | 0.574 ** | 0.437 * | 0.408 * | 0.471 ** | 1 | ||
V | 0.803 ** | 0.824 ** | 0.863 ** | 0.756 ** | 0.720 ** | 0.817 ** | 0.848 ** | 0.798 ** | 0.416 * | 1 | |
Zn | 0.395 * | 0.323 | 0.535 ** | 0.324 | 0.476 ** | 0.410 * | 0.357 * | 0.454 ** | 0.383 * | 0.325 | 1 |
HMs | Component |
---|---|
PC1 | |
Al | 0.930 |
As | 0.772 |
Co | 0.958 |
Cr | 0.828 |
Cu | 0.871 |
Fe | 0.932 |
Mn | 0.916 |
Ni | 0.951 |
Pb | 0.541 |
V | 0.904 |
Zn | 0.508 |
% of Variance | 70.941 |
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
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 StyleAlharbi, 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 StyleAlharbi, 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