Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques

Yu-Pin Lin, Hone-Jay Chu, Chen-Fa Wu, Tsun-Kuo Chang and Chiu-Yang Chen
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Yu-Pin Lin: Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Section 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan
Hone-Jay Chu: Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Section 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan
Chen-Fa Wu: Department of Horticulture, National Chung Hsing University, 250, Kuo Kuang Road, Taichung 402, Taiwan
Tsun-Kuo Chang: Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Section 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan
Chiu-Yang Chen: Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84, Gungjuan Road, Taishan, Taipei 24301, Taiwan

IJERPH, 2010, vol. 8, issue 1, 1-14

Abstract: 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.

Keywords: kernel density estimation (KDE); indicator Kriging (IK); sequential indicator simulation (SIS); heavy metal; soil contaminant (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2010
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