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Sustainability 2017, 9(6), 986; doi:10.3390/su9060986

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

1,2,3,4,* and 4
1
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
2
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
3
College of Information Engineering, Shenzhen University, Shenzhen 518060, China
4
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
*
Author to whom correspondence should be addressed.
Academic Editor: Fei Li
Received: 14 May 2017 / Revised: 2 June 2017 / Accepted: 3 June 2017 / Published: 7 June 2017
View Full-Text   |   Download PDF [3182 KB, uploaded 8 June 2017]   |  

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. View Full-Text
Keywords: heavy metal contamination; river sediments; network inverse distance weighted interpolation; local indicators of network-constrained clusters heavy metal contamination; river sediments; network inverse distance weighted interpolation; local indicators of network-constrained clusters
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Wang, Z.; Nie, K. Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China. Sustainability 2017, 9, 986.

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