Open AccessSpatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging
Author(s) -
Bei Zhang,
Yong Yang
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-017-17018-5
Subject(s) - variogram , kriging , soil science , environmental science , geostatistics , interpolation (computer graphics) , heavy metals , sampling (signal processing) , spatial variability , soil water , environmental chemistry , statistics , mathematics , computer science , chemistry , computer graphics (images) , filter (signal processing) , computer vision , animation
Soil heavy metals exhibit significant spatiotemporal variability and are strongly correlated with other soil heavy metals. Thus, other heavy metals can be used to improve the accuracy of predictions when performing spatiotemporal predictions of soil heavy metals within a given area. In this study, we propose the spatiotemporal cokriging (STCK) method to enable the use of historical sampling points and co-variables in the spatial prediction of soil heavy metals. Moreover, experimental spatiotemporal (ST) semivariogram and ST cross-semivariogram computational methods, a fitting strategy to the ST semivariogram and ST cross-semivariogram models based on the Bilonick model, and the STCK interpolation algorithm are introduced; these methods are based on spatiotemporal kriging (STK) and cokriging (CK). The data used in this study consist of measurements of soil heavy metals from 2010 to 2014 in Wuhan City, China. The results show that the behavior of predictions of the concentrations of heavy metals in soils is physically more realistic, and the prediction uncertainties are slightly smaller, when STCK is used with greater numbers of co-variables and neighboring points.