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Studies in spatial sampling strategies and contaminated area delineations in regional scale

Soil sampling strategies usually include judgmental sampling, simple random sampling, stratified sampling, systematic and grid sampling, adaptive cluster sampling, composite sampling. Each one contains various objectives and the appropriateness. However, the sampling strategy should consider multivariate and then should be reliable for delineating the pollution hazard. The research aims to resample the multiple soil heavy metals at Chang-Hua County. The sampling model that can consider multivariate, statistic distribution and spatial information is developed. Moreover, the method is reliable for the analysis in delineating the pollution hazard. The method is a stratified conditional Latin hypercube sampling (scLHS) that includes the stratified sampling and grid sampling. Meanwhile, the consideration in spatial aspect for sampling sites in conditioned Latin hypercube sampling is also unignorable. First, sampling is applied based on sampling data or the other correlated data. So the incorporation of spatial data, which is regarded as the spatial cLHS, might be able to drive the data closer to their original spatial allocation. Then, the spatial distribution and uncertainty of each technique, including original data without sampling, were evaluated by the sequential indicator simulation (SIS). Furthermore, the spatial cLHS could better imitate the distribution and spatial allocation of the original data. The study considers two scenarios: with and without soil pollution data. Wherever the soil pollution occurs, the model evaluates the variogram and the spatial distribution of soil pollution based on the information offered. And then the model compares both the variogram and the spatial distribution with original data. After all the local and spatial uncertainty of the model are calculated. If there is no soil pollution at all, the model evaluates variogram and spatial distribution of soil pollution based on other information and then compares them with the original data, and calculates the local and spatial uncertainty. By the end of this project, the model and the graph presentation for related results would be showed by a built-in interface of geographic information system.
Soil pollution, Heavy metal, Stratified conditioned Latin hypercube sampling, Conditional simulation, Spatial uncertainty