Cattle Julie A, McBratney Alex B, Minasny Budiman
Department of Agricultural Chemistry and Soil Science, Univ. of Sydney, NSW 2006, Australia.
J Environ Qual. 2002 Sep-Oct;31(5):1576-88. doi: 10.2134/jeq2002.1576.
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.
描述污染物的空间分布是风险评估的一个重要组成部分。此前已经证明了为此目的应用地质统计技术。这些技术既可以提供给定未采样位置处浓度的估计值，也可以提供该位置处浓度超过临界阈值浓度的概率。本研究是对多重指示克里金法与基于顺序统计量累积分布函数的克里金法进行的比较研究，同时使用了局部和全局变异函数。目的是确定这四种方法中哪一种最能区分“受污染”土壤和“清洁”土壤。这四种方法用预测中未使用的数据值子集进行了验证。通过计算均方根误差（RMSE）、方差分析、每种方法将实际“受污染”误分类为“清洁”或反之的站点比例以及每种误分类类型的预期损失来评估方法性能。用于比较的数据是来自澳大利亚悉尼内西区格利布和坎珀当的807个表层土壤铅浓度。虽然这四种方法之间差异很小，但发现多重指示克里金法在区分“清洁”土壤和“受污染”土壤方面能产生最准确的预测。
