• DocumentCode
    3445886
  • Title

    Using indicator kriging to delineate and map the spatial patterns of zinc soil pollution

  • Author

    Fengrui Chen ; Yu Liu ; Shujun Yang

  • Author_Institution
    Key Lab. of Geospatial Technol. for the Middle & Lower Yellow River Regions, Kaifeng, China
  • fYear
    2013
  • fDate
    20-22 June 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Mapping heavy metal pollution in soil is essential for environmental monitoring. Kriging is often used to characterize the spatial variability of heavy metal. However, due to its smooth effects, it is incapable of detecting the uncertainty of estimates. In this study, the indicator kriging was used to map the uncertainty of the estimate, and to provide the probability of pollution risk. Eight hundred and thirty nine samples were analyzed, according to the accumulating pollution index and the background value. Three thresholds which classified Zn pollution risk into safe, low, moderate and high levels were adopted. The result showed that there is high Zn content in the soil of the study area. Its mean reached 86.7 mg/kg, larger than the background value (80.3 mg/kg), which mainly resulted from the nearby Zn mine. The probability map indicated that the pollution risk were chiefly low and moderate levels, and the closer the distance to Zn mine, the greater the probability values.
  • Keywords
    environmental monitoring (geophysics); geophysical techniques; probability; soil pollution; statistical analysis; Zn; Zn mine; Zn pollution risk; environmental monitoring; heavy metal pollution; high Zn content; indicator kriging; pollution index; probability map; probability values; spatial patterns; spatial variability; zinc soil pollution; Distribution functions; Graphical models; Pollution; Soil; Uncertainty; Zinc; Geostatistics; Heavy metal; Indicator kriging; Soil pollution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoinformatics (GEOINFORMATICS), 2013 21st International Conference on
  • Conference_Location
    Kaifeng
  • ISSN
    2161-024X
  • Type

    conf

  • DOI
    10.1109/Geoinformatics.2013.6626102
  • Filename
    6626102