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应用生态学报 ›› 2018, Vol. 29 ›› Issue (6): 1893-1901.doi: 10.13287/j.1001-9332.201806.036

• 研究报告 • 上一篇    下一篇

基于GIS和地统计学的稻田土壤养分与重金属空间变异

杨之江1,陈效民1*,景峰1,郭碧林1,林高哲1   

  1. 南京农业大学资源与环境科学学院, 南京 210095
  • 收稿日期:2017-09-30 修回日期:2018-03-16 出版日期:2018-06-18 发布日期:2018-06-18
  • 通讯作者: E-mail: xmchen@njau.edu.cn
  • 作者简介:杨之江, 男, 1994年生, 硕士研究生. 主要从事水土资源利用管理及红壤改良的研究. E-mail: 2016103067@njau.edu.cn.
  • 基金资助:

    本文由国家重点基础研究发展计划项目(2016YFD0800306)资助

Spatial variability of nutrients and heavy metals in paddy field soils based on GIS and Geostatistics.

YANG Zhi-jiang1, CHEN Xiao-min1*, JING Feng1, GUO Bi-lin1, LIN Gao-zhe1   

  1. 1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2017-09-30 Revised:2018-03-16 Online:2018-06-18 Published:2018-06-18
  • Supported by:

    This work was supported by the National Key Research and Development Program of China (2016YFD0800306).

摘要: 以湖南省长沙县北山镇3.56 hm2的水稻田为研究区域,基于网格法(25 m×25 m)等距离取样,采用GIS和地统计学相结合的方法,对研究区土壤耕作层(0~20 cm)的pH值、有机质、全氮、速效磷、阳离子交换量(CEC)与3种典型重金属元素Cd、As、Pb的空间变异特征进行了定量分析.结果表明: 研究区内土壤pH值和Pb含量表现为弱变异,其他各项指标均表现出中等强度变异,变异顺序的大小为:速效磷>Cd>全氮>有机质>CEC>As>Pb>pH.半方差检验结果表明,有机质、速效磷、As的半方差函数的最佳拟合模型为指数模型;pH、全氮、CEC、Cd和Pb的最佳拟合模型为球状模型;除CEC呈中等空间相关外,其余指标均表现出强烈的空间相关.克里格插值分析表明: pH、全氮、CEC、Pb呈斑块状分布;有机质、速效磷、Cd、As呈块状和带状分布.植被、地形和人类活动是造成研究区土壤养分与重金属格局差异的主要因素.相关性分析表明,部分土壤养分与重金属含量的相关性达到显著水平,其中pH与有机质、Cd与Pb的相关性达到了极显著相关水平.

Abstract: Based on a grid (25 m × 25 m) equidistant sampling, the spatial variability of pH, organic matter, total nitrogen, available phosphorus, CEC and three typical heavy metal elements Cd, As and Pb in soil tillage layer (0-20 cm) were analyzed by using GIS and Geostatistics in the paddy field of 3.56 hm2 in Beishan Town, Changsha County, Hunan Province. The results showed that soil pH value and Pb content showed weak variation, and other indexes showed moderate variation. The order of variation was following available phosphorus > Cd > total nitrogen > organic matter > CEC > As > Pb > pH. Results of the semi-variance test showed that the best fitting model of the semi-variance function of organic matter, available phosphorus and As was exponential, and the best semi-variance function of pH, total nitrogen, CEC, Cd, Pb was spherical. All the indicators had a strong spatial correlation except for CEC, which showed moderate spatial correlation. Kriging interpolation analysis showed that pH, total nitrogen, CEC, Pb were plaque distribution, while organic matter, available phosphorus, Cd and As were block and banded distribution. Vegetation, topography and human activities were the main factors driving the variation of soil nutrients and heavy metals in the study area. The correlation between soil nutrients and heavy metals content was significant, among which pH and organic matter, Cd and Pb reached a very significant correlation level.