Next Article in Journal
Four Polygamous Families with Congenital Birth Defects from Fallujah, Iraq
Next Article in Special Issue
An Integrated Approach for Assessing Aquatic Ecological Carrying Capacity: A Case Study of Wujin District in the Tai Lake Basin, China
Previous Article in Journal
Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand
Previous Article in Special Issue
Spatiotemporal Trends in Oral Cancer Mortality and Potential Risks Associated with Heavy Metal Content in Taiwan Soil
Open AccessArticle

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

1
Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Section 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan
2
Department of Horticulture, National Chung Hsing University, 250, Kuo Kuang Road, Taichung 402, Taiwan
3
Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84, Gungjuan Road, Taishan, Taipei 24301, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2011, 8(1), 75-88; https://doi.org/10.3390/ijerph8010075
Received: 8 November 2010 / Revised: 24 December 2010 / Accepted: 28 December 2010 / Published: 30 December 2010
(This article belongs to the Special Issue Geostatistics in Environmental Pollution and Risk Assessment)
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. View Full-Text
Keywords: kernel density estimation (KDE); indicator Kriging (IK); sequential indicator simulation (SIS); heavy metal; soil contaminant kernel density estimation (KDE); indicator Kriging (IK); sequential indicator simulation (SIS); heavy metal; soil contaminant
MDPI and ACS Style

Lin, Y.-P.; Chu, H.-J.; Wu, C.-F.; Chang, T.-K.; Chen, C.-Y. Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques. Int. J. Environ. Res. Public Health 2011, 8, 75-88. https://doi.org/10.3390/ijerph8010075

AMA Style

Lin Y-P, Chu H-J, Wu C-F, Chang T-K, Chen C-Y. Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques. International Journal of Environmental Research and Public Health. 2011; 8(1):75-88. https://doi.org/10.3390/ijerph8010075

Chicago/Turabian Style

Lin, Yu-Pin; Chu, Hone-Jay; Wu, Chen-Fa; Chang, Tsun-Kuo; Chen, Chiu-Yang. 2011. "Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques" Int. J. Environ. Res. Public Health 8, no. 1: 75-88. https://doi.org/10.3390/ijerph8010075

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop