title: Additional sampling based on regulation threshold and kriging variance to reduce the probability of false delineation in a contaminated site.
abstract: Kriging-based delineation when used to determine a cost-effective remediation plan should be based on the spatial distribution of the pollutant. This study proposed an adaptive cluster sampling (ACS) approach based on the regulation threshold and kriging variance for additional sampling to improve the reliability of delineating a heavy-metal contaminated site. A reliability index for reducing the probability of false delineation was used to determine the size and configuration of additional samples. A data set of Ni concentrations in soil was used for illustration. The results showed that the additional sampled observations during ACS were clustered where the Ni concentrations were close to the regulation threshold of 200 mg kg(-1), and were located where the first-phased sampling density was low. Compared with a simple random sampling (SRS), the relative frequency of misclassification over the whole study area (RFMW) using ACS in a 100 replicates simulation was lower when the same sample number of pooled data was used. In addition, the spatial distribution of the local misclassification rate (LMR) showed that the area with a high-valued LMR could be reduced and that the LMR gradients in the region could be lowered by using ACS instead of SRS. The above results suggest that the proposed ACS approach could improve the reliability of kriging-based delineation of heavy-metal contaminated soils.
authors: Department of Post-Modern Agriculture, MingDao University, 523 Pitou, Changhua, Taiwan.
authors: Kai-Wei
authors: KW
authors: Juang
authors: Wan-Jiun
authors: WJ
authors: Liao
authors: Ten-Lin
authors: TL
authors: Liu
authors: L
authors: L
authors: Tsui
authors: Dar-Yuan
authors: DY
authors: Lee
mesh_terms: D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000032:analysis*; D019216:Metals, Heavy / Q000032:analysis*; D015233:Models, Statistical; D011336:Probability
chemical_list: D004785:Environmental Pollutants; D019216:Metals, Heavy