Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/se
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REVIEW PAPER
Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/ sediments: a bibliometric review Prince Chapman Agyeman . Samuel Kudjo Ahado . Lubosˇ Boru˚vka . James Kobina Mensah Biney . Vincent Yaw Oppong Sarkodie . Ndiye M. Kebonye . John Kingsley
Received: 6 May 2020 / Accepted: 6 October 2020 Springer Nature B.V. 2020
Abstract The rising and continuous pollution of the soil from anthropogenic activities is of great concern. Owing to this concern, the advent of digital soil mapping (DSM) has been a tool that soil scientists use in this era to predict the potentially toxic element (PTE) content in the soil. The purpose of this paper was to conduct a review of articles, summarize and analyse the spatial prediction of potentially toxic elements, determine and compare the models’ usage as well as their performance over time. Through Scopus, the Web of Science and Google Scholar, we collected papers between the year 2001 and the first quarter of 2019, which were tailored towards the spatial PTE prediction using DSM approaches. The results indicated that soil pollution emanates from diverse sources. However, it provided reasons why the authors investigate a piece of land or area, highlighting the uncertainties in mapping, number of publications per journal and continental efforts to research as well as published on trending issues regarding DSM. This paper reveals the complementary role machine learning algorithms and the geostatistical models play in DSM. Nevertheless, geostatistical approaches remain P. C. Agyeman (&) S. K. Ahado L. Boru˚vka J. K. M. Biney V. Y. O. Sarkodie N. M. Kebonye J. Kingsley Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamy´cka´ 129, 165 00 Praha 6, Suchdol, Czech Republic e-mail: [email protected]
the most preferred model compared to machine learning algorithms. Keywords Digital soil mapping Spatial prediction Geostatistics Machine learning Algorithms Potentially toxic elements Soil pollution
Introduction Potentially toxic elements (PTEs) are abundant natural components of the earth’s crust soils (Kabata-Pendias and Mukherjee 2007; In˜igo et al. 2011). PTE is a generic nomenclature given to poisonous metal(loid)s that are detrimental to either human well-being or sustainable environment or both. The term soil contamination alludes to the presence of a chemical(s) or strange substance higher than the average concentration that has adverse effects on any nontargeted organism (FAO and ITPS 2015). Although part of PTEs has anthropogenic sources, some contaminants can happen naturally in soils as components of minerals and can be toxic at high concentrations. Soil contamination cannot be regularly evaluated or outwardly seen, making it a concealed threat. The diverse variety of contaminants is continuously advancing due to agrochemical and industrial deve
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