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• W2622610865 startingPage "986" @default.
• W2622610865 abstract "Measuring the spatial distribution of heavy metal contaminants is the basis of pollution evaluation and risk control. Considering the cost of soil sampling and analysis, spatial interpolation methods have been widely applied to estimate the heavy metal concentrations at unsampled locations. However, traditional spatial interpolation methods assume the sample sites can be located stochastically on a plane and the spatial association between sample locations is analyzed using Euclidean distances, which may lead to biased conclusions in some circumstances. This study aims to analyze the spatial distribution characteristics of copper and lead contamination in river sediments of Daye using network spatial analysis methods. The results demonstrate that network inverse distance weighted interpolation methods are more accurate than planar interpolation methods. Furthermore, the method named local indicators of network-constrained clusters based on local Moran’ I statistic (ILINCS) is applied to explore the local spatial patterns of copper and lead pollution in river sediments, which is helpful for identifying the contaminated areas and assessing heavy metal pollution of Daye." @default.
• W2622610865 created "2017-06-15" @default.
• W2622610865 creator A5000667041 @default.
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• W2622610865 date "2017-06-07" @default.
• W2622610865 modified "2023-09-29" @default.
• W2622610865 title "Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China" @default.
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• W2622610865 doi "https://doi.org/10.3390/su9060986" @default.
• W2622610865 hasPublicationYear "2017" @default.
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