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|標題:||Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques||作者:||Lin, Y.P.
|關鍵字:||kernel density estimation (KDE);indicator Kriging (IK);sequential;indicator simulation (SIS);heavy metal;soil contaminant;sequential indicator simulation;heavy-metal pollution;particulate;matter;contaminated soil;cadmium;uncertainty;patterns;system;probability;bangladesh||Project:||International Journal of Environmental Research and Public Health||期刊/報告no：:||International Journal of Environmental Research and Public Health, Volume 8, Issue 1, Page(s) 75-88.||摘要:||
Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere.
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