Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing
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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.
KeywordsSpatial interpolation Ordinary kriging (OK) Inverse distance weighted (IDW) Prediction accuracy Sample concentration prediction High pollution area identification
The authors are grateful to Dongnan for language help and writing assistance.
- Chaplot V, Darboux F, Bourennane H, Leguédois S, Silvera N, Phachomphon K (2006) Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology 77(1–2):126–141. https://doi.org/10.1016/j.geomorph.2005.12.010 CrossRefGoogle Scholar
- Gotway CA, Ferguson RB, Hergert GW, Peterson TA (1996) Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci Soc Am J 60(4):1237–1247. https://doi.org/10.2136/sssaj1996.03615995006000040040x CrossRefGoogle Scholar
- Li XW, Xie YF, Wang JF, Christakos G, Si JL, Zhao HN, Ding YQ, Li J (2013) Influence of planting patterns on fluoroquinolone residues in the soil of an intensive vegetable cultivation area in northern China. Sci Total Environ 458:63–69. https://doi.org/10.1016/j.scitotenv.2013.04.002 CrossRefGoogle Scholar
- Nael M, Khademi H, Jalalian A, Schulin R, Kalbasi M, Sotohian F (2009) Effect of geo-pedological conditions on the distribution and chemical speciation of selected trace elements in forest soils of western Alborz, Iran. Geoderma 152(1–2):157–170. https://doi.org/10.1016/j.geoderma.2009.06.001 CrossRefGoogle Scholar
- U.S.EPA (1996) Method 3050B: acid digestion of sediments, sludges and soils, revision 2. Washington, DCGoogle Scholar
- Webster R, Oliver MA (1992) Sample adequately to estimate variograms of soil properties. Eur J Soil Sci 43(1):177–192. https://doi.org/10.1111/j.1365-2389.1992.tb00128.x CrossRefGoogle Scholar
- Yasrebi J, Saffari M, Fathi H, Karimian N, Mosazallahi M, Gazni R (2009) Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters. Res J Biol Sci 4(1):93–102Google Scholar