Methods Used in the Detection of Heavy Metal Pollution in Soils

Mohammed Oday Al HAMDANI; Fatma Kaplan; Süreyya Betül RUFAİOĞLU

doi:10.18535/ijsrm/v13i03.ah01

Abstract

Heavy metal contamination in soils poses a major environmental risk to ecosystem health and human life. In this study, the methods used for the detection of heavy metals were analyzed. Although traditional laboratory techniques (AAS, ICP-MS, XRF) offer high sensitivity, they are costly and time-consuming. Geostatistical methods model the distribution of pollution by spatial analyses, and remote sensing techniques (satellite and UAV imaging) provide rapid detection over large areas. Machine learning approaches (RF, SVM, ANN) improve prediction accuracy by processing large data sets. Hybrid methods combine these techniques to provide more reliable and comprehensive analyses. In the future, heavy metal monitoring processes are projected to become more effective with real-time sensors, AI-based predictions, and cloud computing systems.

Keywords

Heavy metal pollutionlaboratory analysesgeostatisticsremote sensingmachine learning

References

Ahmed, A. Z. (2022). Cost analysis of soil contamination assessment methods: A comparative study. Journal of Environmental Monitoring, 245-261.Google Scholar ↗
Alloway, B. J. (2013). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability. Springer.Google Scholar ↗
Breiman, L. (2001). Machine Learning. Random forests., 5–32.Google Scholar ↗
Chabrillat, S. e. (2011). Remote sensing for soil mapping and monitoring. Advances in Agronomy, 243–289.Google Scholar ↗
Chen, T. &. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.Google Scholar ↗
Chen, T. e. (2022). Emerging trends in remote sensing and machine learning for soil heavy metal assessment. Emerging trends in remote sensing and machine learning for soil heavy metal assessment.Google Scholar ↗
Chiles, J. P. (2012). Geostatistics: Modeling Spatial Uncertainty. John Wiley & Sons.Google Scholar ↗
Cortes, C. &. (1995). Support-vector networks. Machine Learning, 273–297.Google Scholar ↗
Foody, G. M. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing,, 1335–1343.Google Scholar ↗
Garcia, M. L. (2018). Efficiency and limitations of traditional soil analysis techniques for heavy metal detection. Environmental Science & Technology, 675-689.Google Scholar ↗
Geremew, A. e. (2022). Application of machine learning algorithms for mapping heavy metals in agricultural soils. Journal of Environmental Management, 114295.Google Scholar ↗
Gholizadeh, A. B. (2018). Visible, near-infrared, and mid-infrared spectroscopy for the prediction of soil heavy metals. Soil Science Society of America Journal, 422–430.Google Scholar ↗
Gholizadeh, A. e. (2018). Proximal and remote sensing techniques for soil heavy metal studies: A review. Geoderma, 113–125.Google Scholar ↗
Goodfellow, I. B. (2016). Deep Learning. MIT Press.Google Scholar ↗
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.Google Scholar ↗
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.Google Scholar ↗
Grimm, R. B. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forest analysis. Geoderma, 102–113.Google Scholar ↗
Hastie, T. T. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.Google Scholar ↗
Hengl, T. e. (2017). Soil organic carbon maps of the world. Scientific Data, 170103.Google Scholar ↗
Isaaks, E. H. (1989). An Introduction to Applied Geostatistics. Oxford University Press.Google Scholar ↗
Isaaks, E. H. (1989). An Introduction to Applied Geostatistics. Oxford University Press.Google Scholar ↗
Jones, D. &. (2019). Standardized protocols for soil testing: Best practices for heavy metal analysis. Soil Science Journal, 123-140.Google Scholar ↗
Kabata-Pendias, A. (2011). Trace Elements in Soils and Plants. CRC Press.Google Scholar ↗
Kabata-Pendias, A. (2011). Trace Elements in Soils and Plants (4th ed.). CRC Press.Google Scholar ↗
Kerry, R. &. (2007). Comparison of geostatistical and deterministic methods for spatial interpolation of soil parameters. European Journal of Soil Science, 1127–1135.Google Scholar ↗
LeCun, Y. B. (2015). Deep learning. Nature, 436–444.Google Scholar ↗
Lee, S. &. (2023). Challenges in large-scale soil heavy metal assessments: A review of traditional and modern approaches. Geospatial Analysis Quarterly, 56-78.Google Scholar ↗
Li, J. e. (2022). Remote sensing and machine learning integration for heavy metal prediction in agricultural soils. Environmental Research.Google Scholar ↗
Liang, J. e. (2020). Application of machine learning models for heavy metal prediction in soils. Environmental Pollution, 114198.Google Scholar ↗
Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 36–43.Google Scholar ↗
Lu, G. Y. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 1044-1055.Google Scholar ↗
Lu, Y. e. (2012). Spatial distribution and source identification of heavy metals in agricultural soils: A case study in China. Journal of Environmental Management, 9-17.Google Scholar ↗
Marguí, E. &. (2013). X-Ray Fluorescence Spectrometry and Its Applications. Wiley.Google Scholar ↗
Marguí, E. &. (2013). X-Ray Fluorescence Spectrometry and Its Applications. Wiley.Google Scholar ↗
Mikkelsen, S. R. (2004). Bioanalytical Chemistry. Wiley-Interscience.Google Scholar ↗
Mikkelsen, S. R. (2004). Bioanalytical Chemistry. Wiley-Interscience.Google Scholar ↗
Mountrakis, G. I. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 247–259.Google Scholar ↗
Pal, M. &. (2010). Feature selection for classification of hyperspectral data by SVM. M.” IEEE Transactions on Geoscience and Remote Sensing, 2297–2307.Google Scholar ↗
Pebesma, E. &. (2005). Classes and methods for spatial data in R. R News,, 9-13.Google Scholar ↗
PerkinElmer. (2008). ICP. PerkinElmer Instruments.Google Scholar ↗
PerkinElmer. (2008). ICP Mass Spectrometry: A Primer. PerkinElmer Instruments.Google Scholar ↗
PerkinElmer. (2008). ICP Mass Spectrometry: A Primer. PerkinElmer Instruments.Google Scholar ↗
Rodriguez-Galiano, V. F.-O.-S. (2015). An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 93–104.Google Scholar ↗
Smith, P. J. (2020). Precision and accuracy in soil pollution studies: The role of traditional laboratory methods. Environmental Pollution Review, 89-105.Google Scholar ↗
Springer. (2012). Spatial distribution and source identification of heavy metals in agricultural soils: A case study in China. Journal of Environmental Management.Google Scholar ↗
Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications. Springer.Google Scholar ↗
Wang, L. e. (2020). Drone-based remote sensing for soil and crop monitoring: A review. Precision Agriculture, 1201–1226.Google Scholar ↗
Wang, X. L. (2021). Soil contamination and metal toxicity: Methods for soil quality assessment. Advances in Soil Science, 312-328.Google Scholar ↗
Webster, R. &. (2007). Geostatistics for Environmental Scientists. Wiley.Google Scholar ↗
Welz, B. &. (2008). Atomic Absorption Spectrometry. Wiley-VCH.Google Scholar ↗
Welz, B. &. (2008). Atomic Absorption Spectrometry. Wiley-VCH.Google Scholar ↗
Zhang, J. e. (2021). Remote sensing detection of heavy metals in soil. Science of the Total Environment, 755.Google Scholar ↗
Zhang, J. L. (2019). Machine learning in soil pollution: Methods and applications. Environmental Science & Technology, 2948–2962.Google Scholar ↗
Zhu, A. X. (2018). Using geostatistics and remote sensing for soil property prediction: A review. . Geoderma, 85-102.Google Scholar ↗
Zhu, X. e. (2021). Advances in machine learning techniques for environmental pollution monitoring. Environmental Pollution.Google Scholar ↗

[refR-1] Ahmed, A. Z. (2022). Cost analysis of soil contamination assessment methods: A comparative study. Journal of Environmental Monitoring, 245-261.Google Scholar ↗

[refR-2] Alloway, B. J. (2013). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability. Springer.Google Scholar ↗

[refR-3] Breiman, L. (2001). Machine Learning. Random forests., 5–32.Google Scholar ↗

[refR-4] Chabrillat, S. e. (2011). Remote sensing for soil mapping and monitoring. Advances in Agronomy, 243–289.Google Scholar ↗

[refR-5] Chen, T. &. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.Google Scholar ↗

[refR-6] Chen, T. e. (2022). Emerging trends in remote sensing and machine learning for soil heavy metal assessment. Emerging trends in remote sensing and machine learning for soil heavy metal assessment.Google Scholar ↗

[refR-7] Chiles, J. P. (2012). Geostatistics: Modeling Spatial Uncertainty. John Wiley & Sons.Google Scholar ↗

[refR-8] Cortes, C. &. (1995). Support-vector networks. Machine Learning, 273–297.Google Scholar ↗

[refR-9] Foody, G. M. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing,, 1335–1343.Google Scholar ↗

[refR-10] Garcia, M. L. (2018). Efficiency and limitations of traditional soil analysis techniques for heavy metal detection. Environmental Science & Technology, 675-689.Google Scholar ↗

[refR-11] Geremew, A. e. (2022). Application of machine learning algorithms for mapping heavy metals in agricultural soils. Journal of Environmental Management, 114295.Google Scholar ↗

[refR-12] Gholizadeh, A. B. (2018). Visible, near-infrared, and mid-infrared spectroscopy for the prediction of soil heavy metals. Soil Science Society of America Journal, 422–430.Google Scholar ↗

[refR-13] Gholizadeh, A. e. (2018). Proximal and remote sensing techniques for soil heavy metal studies: A review. Geoderma, 113–125.Google Scholar ↗

[refR-14] Goodfellow, I. B. (2016). Deep Learning. MIT Press.Google Scholar ↗

[refR-15] Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.Google Scholar ↗

[refR-16] Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.Google Scholar ↗

[refR-17] Grimm, R. B. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forest analysis. Geoderma, 102–113.Google Scholar ↗

[refR-18] Hastie, T. T. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.Google Scholar ↗

[refR-19] Hengl, T. e. (2017). Soil organic carbon maps of the world. Scientific Data, 170103.Google Scholar ↗

[refR-20] Isaaks, E. H. (1989). An Introduction to Applied Geostatistics. Oxford University Press.Google Scholar ↗

[refR-21] Isaaks, E. H. (1989). An Introduction to Applied Geostatistics. Oxford University Press.Google Scholar ↗

[refR-22] Jones, D. &. (2019). Standardized protocols for soil testing: Best practices for heavy metal analysis. Soil Science Journal, 123-140.Google Scholar ↗

[refR-23] Kabata-Pendias, A. (2011). Trace Elements in Soils and Plants. CRC Press.Google Scholar ↗

[refR-24] Kabata-Pendias, A. (2011). Trace Elements in Soils and Plants (4th ed.). CRC Press.Google Scholar ↗

[refR-25] Kerry, R. &. (2007). Comparison of geostatistical and deterministic methods for spatial interpolation of soil parameters. European Journal of Soil Science, 1127–1135.Google Scholar ↗

[refR-26] LeCun, Y. B. (2015). Deep learning. Nature, 436–444.Google Scholar ↗

[refR-27] Lee, S. &. (2023). Challenges in large-scale soil heavy metal assessments: A review of traditional and modern approaches. Geospatial Analysis Quarterly, 56-78.Google Scholar ↗

[refR-28] Li, J. e. (2022). Remote sensing and machine learning integration for heavy metal prediction in agricultural soils. Environmental Research.Google Scholar ↗

[refR-29] Liang, J. e. (2020). Application of machine learning models for heavy metal prediction in soils. Environmental Pollution, 114198.Google Scholar ↗

[refR-30] Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 36–43.Google Scholar ↗

[refR-31] Lu, G. Y. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 1044-1055.Google Scholar ↗

[refR-32] Lu, Y. e. (2012). Spatial distribution and source identification of heavy metals in agricultural soils: A case study in China. Journal of Environmental Management, 9-17.Google Scholar ↗

[refR-33] Marguí, E. &. (2013). X-Ray Fluorescence Spectrometry and Its Applications. Wiley.Google Scholar ↗

[refR-34] Marguí, E. &. (2013). X-Ray Fluorescence Spectrometry and Its Applications. Wiley.Google Scholar ↗

[refR-35] Mikkelsen, S. R. (2004). Bioanalytical Chemistry. Wiley-Interscience.Google Scholar ↗

[refR-36] Mikkelsen, S. R. (2004). Bioanalytical Chemistry. Wiley-Interscience.Google Scholar ↗

[refR-37] Mountrakis, G. I. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 247–259.Google Scholar ↗

[refR-38] Pal, M. &. (2010). Feature selection for classification of hyperspectral data by SVM. M.” IEEE Transactions on Geoscience and Remote Sensing, 2297–2307.Google Scholar ↗

[refR-39] Pebesma, E. &. (2005). Classes and methods for spatial data in R. R News,, 9-13.Google Scholar ↗

[refR-40] PerkinElmer. (2008). ICP. PerkinElmer Instruments.Google Scholar ↗

[refR-41] PerkinElmer. (2008). ICP Mass Spectrometry: A Primer. PerkinElmer Instruments.Google Scholar ↗

[refR-42] PerkinElmer. (2008). ICP Mass Spectrometry: A Primer. PerkinElmer Instruments.Google Scholar ↗

[refR-43] Rodriguez-Galiano, V. F.-O.-S. (2015). An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 93–104.Google Scholar ↗

[refR-44] Smith, P. J. (2020). Precision and accuracy in soil pollution studies: The role of traditional laboratory methods. Environmental Pollution Review, 89-105.Google Scholar ↗

[refR-45] Springer. (2012). Spatial distribution and source identification of heavy metals in agricultural soils: A case study in China. Journal of Environmental Management.Google Scholar ↗

[refR-46] Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications. Springer.Google Scholar ↗

[refR-47] Wang, L. e. (2020). Drone-based remote sensing for soil and crop monitoring: A review. Precision Agriculture, 1201–1226.Google Scholar ↗

[refR-48] Wang, X. L. (2021). Soil contamination and metal toxicity: Methods for soil quality assessment. Advances in Soil Science, 312-328.Google Scholar ↗

[refR-49] Webster, R. &. (2007). Geostatistics for Environmental Scientists. Wiley.Google Scholar ↗

[refR-50] Welz, B. &. (2008). Atomic Absorption Spectrometry. Wiley-VCH.Google Scholar ↗

[refR-51] Welz, B. &. (2008). Atomic Absorption Spectrometry. Wiley-VCH.Google Scholar ↗

[refR-52] Zhang, J. e. (2021). Remote sensing detection of heavy metals in soil. Science of the Total Environment, 755.Google Scholar ↗

[refR-53] Zhang, J. L. (2019). Machine learning in soil pollution: Methods and applications. Environmental Science & Technology, 2948–2962.Google Scholar ↗

[refR-54] Zhu, A. X. (2018). Using geostatistics and remote sensing for soil property prediction: A review. . Geoderma, 85-102.Google Scholar ↗

[refR-55] Zhu, X. e. (2021). Advances in machine learning techniques for environmental pollution monitoring. Environmental Pollution.Google Scholar ↗