A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes
Soil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soi...
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Elsevier
2025-06-01
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| author | Magboul M. Sulieman Fuat Kaya Abdullah S. Al-Farraj Eric C. Brevik |
| author_facet | Magboul M. Sulieman Fuat Kaya Abdullah S. Al-Farraj Eric C. Brevik |
| author_sort | Magboul M. Sulieman |
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| description | Soil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soils and preindustrial areas from human activities that lead to the concentration of heavy metals (HMs) is a priority. Here, a novel methodology was proposed to establish background concentrations of eight soil HMs, cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), and digitally map their spatial distributions in an area (i.e., harrats region) that has not yet been impacted by industrial activity. The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. The methodology is important for any future environmental pollution/monitoring studies in the area and can be applied in other similar environments. Machine learning algorithms show great ability to use available environmental variables and investigate the relationships between the factors influencing HMs accumulation under a given soil environment. The proposed methodology was effective for describing HMs spatial variability in the environments investigated. • The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas. • Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies. • Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods. |
| format | Article |
| id | doaj-art-12502ded049c412d8e51efbb6f950b0d |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | MethodsX |
| spelling | doaj-art-12502ded049c412d8e51efbb6f950b0d2025-01-28T04:14:39ZengElsevierMethodsX2215-01612025-06-0114103180A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributesMagboul M. Sulieman0Fuat Kaya1Abdullah S. Al-Farraj2Eric C. Brevik3Department of Soil Science, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia; Department of Soil and Environment Sciences, Faculty of Agriculture, University of Khartoum, P.O. Box 32, 13314, Khartoum North, Shambat, Sudan; Department of Physical Geography, University of Göttingen, 37077, Göttingen, Germany; Corresponding author.Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, 32260, Isparta, TürkiyeDepartment of Soil Science, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi ArabiaSchool of Agricultural Sciences and School of Earth Systems and Sustainability, Southern Illinois University, Carbondale, IL, USASoil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soils and preindustrial areas from human activities that lead to the concentration of heavy metals (HMs) is a priority. Here, a novel methodology was proposed to establish background concentrations of eight soil HMs, cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), and digitally map their spatial distributions in an area (i.e., harrats region) that has not yet been impacted by industrial activity. The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. The methodology is important for any future environmental pollution/monitoring studies in the area and can be applied in other similar environments. Machine learning algorithms show great ability to use available environmental variables and investigate the relationships between the factors influencing HMs accumulation under a given soil environment. The proposed methodology was effective for describing HMs spatial variability in the environments investigated. • The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas. • Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies. • Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods.http://www.sciencedirect.com/science/article/pii/S2215016125000287Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives |
| spellingShingle | Magboul M. Sulieman Fuat Kaya Abdullah S. Al-Farraj Eric C. Brevik A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes MethodsX Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives |
| title | A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes |
| title_full | A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes |
| title_fullStr | A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes |
| title_full_unstemmed | A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes |
| title_short | A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes |
| title_sort | novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing terrain attributes |
| topic | Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000287 |
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