Applications of machine learning in potentially toxic elemental contamination in soils: A review

Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational methods are often inadequate for large-scale assessments because they are time-consuming, costly, and have a limited accuracy. Machine learning (ML) techniques have emerged as promising tools in environmental studies because of their superiority in processing high-dimensional and unstructured data. However, critical evaluations of contemporary ML applications and methods in PTEs content, distribution, and identification remain scarce. To address this research gap, this study reviews applications of ML to soil PTEs contamination including content prediction, spatial distribution, source identification, and other related tasks. Hyperspectral data combined with ML methods can predict the content of PTEs in large-scale areas at a low cost. In addition, ML algorithms that integrate environmental covariates offer superior performance in spatial predictions compared with traditional geostatistical methods. Moreover, ML techniques incorporated with receptor models provide important advances in the quantitative identification and apportioning of PTE sources, thereby supporting effective environmental management and risk assessment. Based on the frequency of the variables used, we propose that soil pH, soil organic matter (SOM), industrial activities, soil texture, and other relevant factors are key environmental variables that enhance the accuracy of predictions regarding the spatial distribution and source identification of PTEs. From these findings, ML techniques, through their powerful data processing capabilities, provide new perspectives and tools for the efficient assessment and management of soil PTEs contamination.