Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

Abstract

The application of multivariate geostatistical and statistical methods remain valuable tools for environmental pollution assessment. In particular, stochastic simulation techniques like sequential Gaussian simulation (SGS) and the self-organizing map artificial neural networks (SeOM-ANNs) have facilitated the understanding of the spatial distribution of potentially toxic elements (PTEs) in polluted soils. However, there is a dearth of literature on the application of SGS and SeOM-ANN in mapping potentially toxic elements (PTE) in heavily polluted mining and smelter affected floodplain soils. This study shows the applicability SGS and SeOM–ANN which is a powerful visualization tool for the categorization of PTEs [Cadmium (Cd), Arsenic (As), Antimony (Sb), Lead (Pb) and Zinc (Zn)] levels together with selected soil properties [oxidizable carbon (Cox) and soil reaction (pH_H2O)] in one of the most polluted mining floodplain soils in Europe. A k-means algorithm was used to classify distinct clusters which were visually unclear based on the SeOM–ANN Neighbor distance plot (U-Matrix). The k-means resulted in 5 distinct clusters. Cluster 1 to 5 based on SeOM–ANN for all PTEs revealed an increase in concentration levels in the same order (1–5) while for soil properties the trend was not clear. The soils were successfully assessed based on different intensity level combinations and k-means clustering results efficiently mapped into a spatial distribution map. High concentration levels of the PTEs were noticed in the northern parts of the study area based on the conditional Gaussian simulations (CGSs) generated through SGS, while low levels were prominent in the southwestern parts. The hotspot areas were comparable with the k-means spatial distribution maps. It is recommended that special attention be paid to the identified hotspots for possible remediation. This study further demonstrates the usefulness of geostatistics and advanced statistical methods in site-specific planning and implementation of remediation measures for polluted mining floodplain soils.