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Chinese Journal of Applied Ecology ›› 2018, Vol. 29 ›› Issue (5): 1695-1704.doi: 10.13287/j.1001-9332.201805.037

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Using sequential indicator simulation method to define risk areas of soil heavy metals in farmland.

YANG Hao1,2, SONG Ying-qiang1, HU Yue-ming1,2,3,4*, CHEN Fei-xiang1,2, ZHANG Rui1,2   

  1. 1College of Natural Resources and Environment, South China Agricultural University, Guang-zhou 510642, China;
    2Key Laboratory of Construction Land Improvement, Ministry of Land and Resources, Guangzhou 510642,China;
    3Guangdong Province Engineering Research Center for Land Information Technology, Guangzhou 510642, China;
    4Guangdong Province Key Laboratory for Land Use and Consolidation, Guangzhou 510642, China
  • Received:2017-08-18 Online:2018-05-18 Published:2018-05-18
  • Contact: *E-mail: ymhu163@163.com
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2016YFD0800301)

Abstract: The heavy metals in soil have serious impacts on safety, ecological environment and human health due to their toxicity and accumulation. It is necessary to efficiently identify the risk area of heavy metals in farmland soil, which is of important significance for environment protection, pollution warning and farmland risk control. We collected 204 samples and analyzed the contents of seven kinds of heavy metals (Cu, Zn, Pb, Cd, Cr, As, Hg) in Zengcheng District of Guangzhou, China. In order to overcame the problems of the data, including the limitation of abnormal values and skewness distribution and the smooth effect with the traditional kriging methods, we used sequential indicator simulation method (SISIM) to define the spatial distribution of heavy metals, and combined Hakanson index method to identify potential ecological risk area of heavy metals in farmland. The results showed that: (1) Based on the similar accuracy of spatial prediction of soil heavy metals, the SISIM had a better expression of detail rebuild than ordinary kriging in small scale area. Compared to indicator kriging, the SISIM had less error rate (4.9%-17.1%) in uncertainty evaluation of heavy-metal risk identification. The SISIM had less smooth effect and was more applicable to simulate the spatial uncertainty assessment of soil heavy metals and risk identification. (2) There was no pollution in Zengcheng’s farmland. Moderate potential ecological risk was found in the southern part of study area due to enterprise production, human activities, and river sediments. This study combined the sequential indicator simulation with Hakanson risk index method, and effectively overcame the outlier information loss and smooth effect of traditional kriging method. It provided a new way to identify the soil heavy metal risk area of farmland in uneven sampling.