Abstract
Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.
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Acknowledgements
The authors are grateful to Dongnan for language help and writing assistance.
Funding
This project was supported by the National High Technology Research and Development Program of China (“863” Program, 2014AA06A513) and the Project of Heavy Metal Risk Warning and Phytoremediation in Mining Concentrated Area (GJHZ201308).
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Qiao, P., Lei, M., Yang, S. et al. Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environ Sci Pollut Res 25, 15597–15608 (2018). https://doi.org/10.1007/s11356-018-1552-y
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DOI: https://doi.org/10.1007/s11356-018-1552-y