Prediction of Soil Heavy Metal Distribution Using Geographically Weighted Regression Kriging

Author(s):  
Peihong Fu ◽  
Yong Yang ◽  
Yangsi Zou
2021 ◽  
Vol 11 (4) ◽  
pp. 1408
Author(s):  
Yuhong Dong ◽  
Shiliang Liu ◽  
Yongxiu Sun ◽  
Yixuan Liu ◽  
Fangfang Wang

Soil heavy metals along roadsides pose a great threat to ecosystems while their spatial variations and influencing factors still remain unclear in some regions, especially in tropical areas with complex landscape characteristics. Our study was carried out to determine how the land use, vegetation characteristics, topographical factors and distance to the road affect the soil heavy metal distribution. Taking Jinghong county in Yunnan Province, Southwest China as a case, soil samples were collected at different distances off roads and canonical correspondence analysis (CCA) methods were used to determine the relative importance of different factors. Our results showed that heavy metal sources were obtained mainly from the road, based on the principle component analysis (PCA) identification. There were no obvious trends of soil quality index (SQI) with distance to the road in natural soils, while SQI nutrients and SQI metals in farmlands had a decreasing and increasing trend, respectively, which could both be expressed by logarithm models. However, soil properties showed little differences for road levels while they showed significant differences under land use types. The CCA further showed that heavy metal variations in natural soils were jointly affected by distance, plant coverage, relative elevation and soil properties in decreasing order.


2011 ◽  
Vol 346 (1-2) ◽  
pp. 29-44 ◽  
Author(s):  
Dominique Proust ◽  
Jacinthe Caillaud ◽  
Claude Fontaine ◽  
Michel Fialin ◽  
Christian Courbe ◽  
...  

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