Juang Kai-Wei, Lee Dar-Yuan, Teng Yun-Lung
Department of Post-Modern Agriculture, MingDao University, Pitou, Changhua, Taiwan.
Environ Pollut. 2005 Nov;138(2):268-77. doi: 10.1016/j.envpol.2005.04.003.
Correctly classifying "contaminated" areas in soils, based on the threshold for a contaminated site, is important for determining effective clean-up actions. Pollutant mapping by means of kriging is increasingly being used for the delineation of contaminated soils. However, those areas where the kriged pollutant concentrations are close to the threshold have a high possibility for being misclassified. In order to reduce the misclassification due to the over- or under-estimation from kriging, an adaptive sampling using the cumulative distribution function of order statistics (CDFOS) was developed to draw additional samples for delineating contaminated soils, while kriging. A heavy-metal contaminated site in Hsinchu, Taiwan was used to illustrate this approach. The results showed that compared with random sampling, adaptive sampling using CDFOS reduced the kriging estimation errors and misclassification rates, and thus would appear to be a better choice than random sampling, as additional sampling is required for delineating the "contaminated" areas.
根据污染场地的阈值对土壤中的“污染”区域进行正确分类，对于确定有效的清理行动至关重要。通过克里金法进行污染物绘图越来越多地用于划定污染土壤的范围。然而，克里金法得出的污染物浓度接近阈值的那些区域很有可能被错误分类。为了减少由于克里金法的高估或低估导致的错误分类，开发了一种使用顺序统计量累积分布函数（CDFOS）的自适应采样方法，以便在进行克里金法的同时抽取额外样本以划定污染土壤的范围。台湾新竹的一个重金属污染场地被用来阐述这种方法。结果表明，与随机采样相比，使用CDFOS的自适应采样减少了克里金法估计误差和错误分类率，因此，由于划定“污染”区域需要额外采样，它似乎是比随机采样更好的选择。
