Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. (June 2021)

• Record Type: Journal Article
• Authors: Xu, Haofan; Croot, Peter; Zhang, Chaosheng
• Is Part Of: Environment international. Volume 151(2021)
• Publication Date: 2021-06
• Subjects: Potentially toxic elements -- Hot spot analysis -- K-means clustering analysis -- Hidden spatial patterns -- Geochemical association; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106456
• Languages: English
• ISSNs: 0160-4120

Abstract

Graphical abstract: Highlights: Hot spot analysis identified the spatial clustering patterns for topsoil PTEs. K-means clustering analysis is effective in revealing hidden patterns of soil samples. Consistent spatial patterns were revealed between PTEs and soil samples. Peat was associated with high concentrations of Bi, Pb, Sb and Sn. Basalt was associated with high concentrations of Cr, Co, Cu, Mn, Ni, V and Zn. Abstract: The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord Gi * statistic and K-means clustering analysis were performed on 15 PTEs in 6, 862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6, 862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.