Applying conditioned Latin hypercube sampling combined with stratifications in initial soil sampling of heavy metals
Date Issued
2012
Date
2012
Author(s)
Chen, Yen-Yu
Abstract
Soil pollution of heavy metals is one of the most important environment issues, and it is necessary to monitor and proceed remediation for contaminated area duo to the serious impact of heavy metal pollution to the environment and public health concerns. An efficient sampling strategy is needed to know the correct spatial distribution of soil pollutants to delineate the contaminated area, and reduce the follow-up sampling points to decrease the remediation costs. Conditioned Latin Hypercube Sampling (cLHS) is a sampling method using search algorithm based on heuristic rules combined with an annealing schedule, and it is demonstrated that cLHS could accurately reproduce the original distribution of the environmental covariates. In this study, the original 946 sampling data of Cr, Cu, Ni and Zn and correlated ancillary information in Chand-Hua County are used, and cLHS with two stratifications are applied to resample original data by selected ancillary variables. The errors of statistical and spatial of resampled data are calculated to discuss the influence of different sampling strategies and sample sizes on reproducibility. After proving that representative sampling could be selected by ancillary data, 1000 times sequential indicator simulation (SIS) are carried out in this study with original data, and the mean average error (MAE) is used to evaluate the efficiency of sampling strategies and sample sizes with ancillary information on the first time sampling. The results reveal that error of average value is not affected obviously by the change of sample size. However, the error of spatial variance decreases when the sample size increases. In conclusion, cLHS combined with irrigation stratification can efficiently preserve the statistic characteristics and spatial structure of the original data.
Subjects
soil heavy metal pollution
conditioned Latin Hypercube Sampling
sampling stategy
geostatistics
SDGs
Type
thesis
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