Optimizing Soil Sampling for Accurately Prediction of the Potential Remediation-Effective Area in a Contaminated Agricultural Land

Abstract

To achieve food security in a contaminated agricultural land, the remediation areas usually need more samples to obtain accurate contamination information and implement appropriate measures. In this study, we propose an optimal encryption sampling design to instead of the detailed survey, which is determined by the variation of heavy metals and the technology capability of remediation, to guide soil sampling for accurately remediation in the potential remediation-effective areas (PRA). The coefficient of screening variation threshold (CSVT), considering spatial variation, technology capacity and acceptable error of sampling, together with the spatial cyclic statistics method of neighbourhood analysis, is introduced to identify and delineate the PRA. Both of the hypothetical analysis and application case studies are conducted to illustrate the advantages and disadvantages of the optimization. The results show that, compared with the detailed survey, the optimal design shows a lower overall accuracy due to its sparsely sampling at the clean area, but it exhibits a similar effect of accurately prediction in boundary delineation and further classification in the PRA in both simulation and application studies. This work provides an effective method for subsequent accurate remediation at the investigation stage and valuable insights into application combination of technology capacity and contaminated agricultural land investigation.