HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (23) Citing Articles via Google Scholar Google Scholar Articles by Cattle, J. A. Articles by Minasny, B. Search for Related Content PubMed PubMed Citation Articles by Cattle, J. A. Articles by Minasny, B. GeoRef GeoRef Citation Agricola Articles by Cattle, J. A. Articles by Minasny, B. Related Collections Ecological Risk Assessment Heavy Metals Soil Pollution Spatial Distribution Statistics

TECHNICAL REPORTS
Heavy Metals in the Environment

Kriging Method Evaluation for Assessing the Spatial Distribution of Urban Soil Lead Contamination

Department of Agricultural Chemistry and Soil Science, The Univ. of Sydney, NSW 2006 Australia

* Corresponding author (cattlej{at}epa.nsw.gov.au)

Received for publication July 25, 2001. Describing contaminant spatial distribution is an integral component of risk assessment. Application of geostatistical techniques for this purpose has been demonstrated previously. These techniques may provide both an estimate of the concentration at a given unsampled location, as well as the probability that the concentration at that location will exceed a critical threshold concentration. This research is a comparative study between multiple indicator kriging and kriging with the cumulative distribution function of order statistics, with both local and global variograms. The aim was to determine which of the four methods is best able to delineate between "contaminated" and "clean" soil. The four methods were validated with a subset of data values that were not used in the prediction. Method performance was assessed by calculating the root mean square error (RMSE), analysis of variance, the proportion of sites misclassified by each method as either "clean" when they were actually "contaminated" or vice versa, and the expected loss for each misclassification type. The data used for the comparison were 807 topsoil Pb concentrations from the inner-Sydney suburbs of Glebe and Camperdown, Australia. While there was very little difference between the four methods, multiple indicator kriging was found to produce the most accurate predictions for delineating "clean" from "contaminated" soil.

Abbreviations: CCDF, conditional cumulative distribution function • CDF, cumulative distribution function • EIL, environmental investigation limit • RMSE, root mean square error

This article has been cited by other articles:

				J.A. Chipres, J.C. Salinas, J. Castro-Larragoitia, and M.G. Monroy Geochemical mapping of major and trace elements in soils from the Altiplano Potosino, Mexico: a multi-scale comparison Geochemistry: Exploration, Environment, Analysis, November 1, 2008; 8(3-4): 279 - 290. [Abstract] [Full Text] [PDF]