Prediction of Soil Heavy Metal Distribution Using Geographically Weighted Regression Kriging

Both soil heavy metals and the influencing factors are related to spatial location and are spatially heterogeneous. However, the global linear regression model assumes the regression coefficients to be spatially stationary throughout the study region and is unable to account for the spatially varyin...

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Bibliographic Details
Published in:Bulletin of environmental contamination and toxicology 2022-02, Vol.108 (2), p.344-350
Main Authors: Fu, Peihong, Yang, Yong, Zou, Yangsi
Format: Article
Language:English
Subjects:
Aquatic Pollution
China
Distribution patterns
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental Monitoring
Geographical distribution
Heavy metals
Lead
Metals
Metals, Heavy - analysis
Pollution
Regression coefficients
Regression models
Smoothness
Soil
Soil Pollutants - analysis
Soil Science & Conservation
Soils
Spatial Analysis
Spatial distribution
Spatial Regression
Waste Water Technology
Water Management
Water Pollution Control
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Summary:Both soil heavy metals and the influencing factors are related to spatial location and are spatially heterogeneous. However, the global linear regression model assumes the regression coefficients to be spatially stationary throughout the study region and is unable to account for the spatially varying relationships between soil heavy metals and influencing factors. Thus, the objectives of this study were to estimate the spatial distribution of soil heavy metals using a geographically weighted regression kriging (GWRK) approach, and compare the GWRK results with those obtained from ordinary kriging (OK) and regression kriging (RK). A dataset of soil lead (Pb) concentrations in Daye city, China, that was sampled in 2019 was used. According to the results of spatial smoothness, variability, and interpolation accuracy, GWRK was the best method and could provide the most reasonable spatial distribution pattern and the highest spatial interpolation accuracy in comparison with OK and RK.
ISSN:0007-4861
1432-0800
DOI:10.1007/s00128-021-03405-2