Abstract
It is extremely important to explore the heavy metal content and spatial distribution in different cultivated soils. In our study, cokriging (COK) method was used to investigate the relationship between heavy metals and land use types. A total of six heavy metals including Zn, Fe, Cu, Mn, B, S were selected to forecast the heavy metals in cultivated soils. Five types of cultivated land were considered in the study area, including vegetable land, irrigated land, irrigated paddy field, dry land, orchard. Test of normality was firstly carried out to assure that higher prediction accuracy can be obtained, and then correlation analysis and analysis of variance (ANOVA) were performed to find out the most sensitive heavy metals for spatial interpolation. The analysis results showed that Zn, Cu and Fe were the primary heavy metals in soil of the study area. It was obvious that the effect of Mn and Fe were much greater caused by land use types, while they were smaller for B and S. Three errors of mean standard (MS), root mean square error (RMSE), and root mean square standardized error (RMSSE) were derived to assess the prediction accuracy. In comparison with ordinary kriging (OK), the MS of COK approached to 1, the RMSE was much smaller and the RMSSE also approached to 1, which showed that COK can make better predictions than OK.
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References
DeFries, R.S., Foley, J.A., Asner, G.P.: Land-use choices: balancing human needs and ecosystem function. Front. Ecol. Environ. 2, 249–257 (2004)
Eklund, L., Persson, A., Pilesjö, P.: Cropland changes in times of conflict, reconstruction, and economic development in Iraqi Kurdistan. Ambio 45, 78–88 (2016)
Tournebize, J., Chaumont, C., Mander, Ü.: Implications for constructed wetlands to mitigate nitrate and pesticide pollution in agricultural drained watersheds. Ecol. Eng. 103, 415–425 (2017)
Ye, Y., Zhang, H., Liu, K., Wu, Q.: Research on the influence of site factors on the expansion of construction land in the Pearl River Delta, China: by using GIS and remote sensing. Int. J. Appl. Earth Obs. 21, 366–373 (2013)
Kumar, A., Devi, M., Deshmukh, B.: Integrated remote sensing and geographic information system based RUSLE modelling for estimation of soil loss in western Himalaya. India. Water Resour. Manag. 28, 3307–3317 (2014)
Pandey, B., Seto, K.C.: Urbanization and agricultural land loss in India: comparing satellite estimates with census data. J. Environ. Manage. 148, 53–66 (2015)
Gaubi, I., Chaabani, A., Mammou, A.B., Hamza, M.H.: A GIS-based soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) (Lebna watershed, Cap Bon, Tunisia). Nat. Hazards 86, 219–239 (2017)
Zhao, J.L., Xue, Y.A., Yang, H., Huang, L.S., Zhang, D.Y.: Evaluating and classifying field-scale soil nutrient status in Beijing using 3S technology. Int. J. Agric. Biol. 14, 689–696 (2012)
Ha, H., Olson, J.R., Bian, L., Rogerson, P.A.: Analysis of heavy metal sources in soil using kriging interpolation on principal components. Environ. Sci. Technol. 48, 4999–5007 (2014)
Shaw, D.J., Vrij, A., Leal, S., Mann, S., Hillman, J., Granhag, P.A., Fisher, R.P.: Expect the unexpected? Variations in question type elicit cues to deception in joint interviewer contexts. Appl. Cogn. Psych. 27, 336–343 (2013)
Yang, Q., Luo, W., Jiang, Z., Li, W., Yuan, D.: Improve the prediction of soil bulk density by cokriging with predicted soil water content as auxiliary variable. J. Soil Sediment. 16, 77–84 (2016)
Braga-Neto, U.M., Dougherty, E.R.: Is cross-validation valid for small-sample microarray classification? Bioinformatics 20, 374–380 (2004)
Boularbah, A., Schwartz, C., Bitton, G., Morel, J.L.: Heavy metal contamination from mining sites in South Morocco: 1. Use of a biotest to assess metal toxicity of tailings and soils. Chemosphere 63, 802–810 (2006)
Stein, A., Hoogerwerf, M., Bouma, J.: Use of soil-map delineations to improve (co-) kriging of point data on moisture deficits. Geoderma 43, 163–177 (1988)
Zhang, C., Li, W., Travis, D.J.: Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging. Int. J. Remote Sens. 30, 2173–2195 (2009)
Wong, T.T., Yang, N.Y.: Dependency analysis of accuracy estimates in k-fold cross validation. IEEE Trans. Knowl. Data Eng. 29, 2417–2427 (2017)
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Cross validation through two-dimensional solution surface for cost-sensitive SVM. IEEE Trans. Pattern Anal. 39, 1103–1121 (2017)
Acknowledgements
This work was supported by and Anhui Provincial Major Scientific and Technological Special Project (17030701062), National Key Research and Development Program of China (2016YFD0800904), Fund for “Integration of Cloud Computing and Big Data, Innovation of Science and Education” (2017A10014), and Application Research of Anhui Provincial Public Welfare Technology on Linkage Projects (1704f0704059).
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Zhao, J., Liu, C., Song, Q., Jiang, Y., Hong, Q., Huang, L. (2018). Mapping Heavy Metals in Cultivated Soils Based on Land Use Types and Cokriging. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_32
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DOI: https://doi.org/10.1007/978-981-13-0893-2_32
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