Juang, K.-W.K.-W.JuangDAR-YUAN LEETeng, Y.-L.Y.-L.Teng2018-09-102018-09-102005http://www.scopus.com/inward/record.url?eid=2-s2.0-22844433980&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/317175Correctly 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. © 2005 Elsevier Ltd. All rights reserved.Adaptive sampling; Cumulative distribution function of order statistics (CDFOS); Kriging; Soil contamination[SDGs]SDG12[SDGs]SDG15Contamination; Environmental impact; Heavy metals; Impurities; Statistical methods; Clean-up actions; Cumulative distribution function of order statistics (CDFOS); Kriging; Pollutant mapping; Soils; heavy metal; sampling; soil pollution; article; error; evolutionary adaptation; kriging; quantitative assay; randomization; sampling; soil pollution; statistics; Taiwan; Environmental Monitoring; Environmental Pollution; Metals, Heavy; Models, Statistical; Sampling Studies; Soil Pollutants; Statistical Distributions; Taiwanjournal article10.1016/j.envpol.2005.04.0032-s2.0-22844433980WOS:000231201800009