A two-step sampling strategy to improve the prediction accuracy of contamination hotspots and identify hotspot boundariesopen access
- Authors
- Kim, Joonmyoung; Lee, Seonwoo; Ryu, Taekseon; Na, Jonghyun; Yun, Taehyun; Lee, Jeongho; Kim, Hansuk; Kwon, Man Jae; Jo, Ho Young; Joo, Yongsung
- Issue Date
- Oct-2025
- Publisher
- Elsevier B.V.
- Keywords
- Geostatistics; Nonlinear optimization; Sampling design; Soil contamination; Spatial modeling; Spatial sampling
- Citation
- Spatial Statistics, v.69, pp 1 - 13
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- Spatial Statistics
- Volume
- 69
- Start Page
- 1
- End Page
- 13
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/58904
- DOI
- 10.1016/j.spasta.2025.100918
- ISSN
- 2211-6753
2211-6753
- Abstract
- Efficient soil remediation, both economically and environmentally, depends on accurate mapping of contaminant concentrations and boundaries of hotspots (areas with concentrations exceeding a critical threshold) through an effective allocation of limited soil sampling sites. This paper introduces a novel two-step sampling location selection method, referred to as the weighted stepwise spatial sampling (WSSS) method. The WSSS method is specifically designed to provide accurate estimates of contaminant concentrations within hotspots and their boundaries. In the first step, dispersed sampling locations are selected for broad exploration, while in the second step, guided by the digital soil mapping results based on the first-step sampling data, sampling locations are selected to focus on identifying potential hotspots. A simulation study using total petroleum hydrocarbon soil data from South Korea demonstrates the superior accuracy and stability of the WSSS in identifying hotspot boundaries and predicting contaminant concentrations within hotspots, compared to other sampling location selection methods. This performance is achieved through an objective function specifically designed to ensure that the selection of sampling locations in the second step is robust to potential inaccuracies or uncertainties in the initial predictions. © 2025
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Collections - College of Natural Science > Department of Statistics > 1. Journal Articles

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