Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging

Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Scientific Reports Springer Journals

Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging

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Publisher
Nature Publishing Group UK
Copyright
Copyright © 2017 by The Author(s)
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
eISSN
2045-2322
D.O.I.
10.1038/s41598-017-17018-5
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Scientific ReportsSpringer Journals

Published: Dec 1, 2017

References

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