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基于PPC模型与RI指数法的茶产地土壤重金属污染评价
引用本文:王 历,周忠发,李丹丹,黄登红.基于PPC模型与RI指数法的茶产地土壤重金属污染评价[J].土壤,2017,49(6):1203-1209.
作者姓名:王 历  周忠发  李丹丹  黄登红
作者单位:贵州师范大学喀斯特研究院,贵州师范大学地理与环境科学学院/喀斯特研究院,贵州师范大学喀斯特研究院,贵州师范大学喀斯特研究院
基金项目:国家自然科学基金项目(41661088)、贵州省重大科技专项计划(黔科合重大专项字[2013]6024号)、贵州省科技计划项目(黔科合GY字[2015]3001)、贵州省高层次创新型人才培养计划—“百”层次人才项目(黔科合平台人才[2016]5674)、贵州省国内一流学科建设项目 (黔教科研发[2017]85号)和国家遥感中心贵州分部平台建设项目(黔科合计Z字[2012]4003)资助。
摘    要:以某喀斯特山区茶产地表层土壤为研究对象,依托GIS空间分析技术,采用投影寻踪聚类(PPC)模型和潜在生态风险指数(RI)法两种较新的评价方法对土壤重金属污染进行综合评价。投影寻踪聚类模型将236个土壤样本中的Hg、As、Cd、Pb、Cr含量指标作为多维投影参数寻求其最优投影方向,由投影指标函数得出土壤重金属污染投影值来反映重金属含量的特征,建立投影寻踪聚类模型,从而得出研究区土壤重金属污染实际现况,结合潜在生态风险指数法,进一步对研究区土壤重金属污染风险程度和演变趋势进行预测。结果表明:(1)在指标投影方向上Hg和As对研究区土壤污染影响最大,Cd对土壤污染影响最小;(2)根据PPC评价标准,研究区土壤环境质量状况良好,无污染的样点区域达到96.61%,清洁占总样点数的30.08%,尚清洁占总样点数的66.53%;(3)在潜在生态风险上存在低度、中度,分别占采样点的89.41%和10.59%,风险程度较低,但有向中度演变的趋势;(4)在污染情况上,清洁和尚清洁的空间分布广阔,轻度污染进行内插后空间分布不明显。本文研究结果有利于综合评价该地的土壤环境质量状况,在实践中将为喀斯特山区的土壤资源的管理和茶叶种植区的合理布局起到参考作用。

关 键 词:茶产地  GIS  投影寻踪聚类模型  潜在生态风险指数法  综合评价
收稿时间:2016/11/7 0:00:00
修稿时间:2016/12/21 0:00:00

Assessment of Heavy Metal Pollution in Tea-planting Soils Based on PPC Model and RI Index Method
WANG Li,ZHOU Zhongf,LI Dandan and HUANG Denghong.Assessment of Heavy Metal Pollution in Tea-planting Soils Based on PPC Model and RI Index Method[J].Soils,2017,49(6):1203-1209.
Authors:WANG Li  ZHOU Zhongf  LI Dandan and HUANG Denghong
Institution:School of Karst Science, Guizhou Normal University,School of Geography & Environmental Science/ School of Karst Science, Guizhou Normal University,School of Karst Science, Guizhou Normal University and School of Karst Science, Guizhou Normal University
Abstract:Taking the topsoils of the tea-planting area in Karst mountainous regions of Guizhou Province as the study object, based on GIS spatial analysis technology, projection pursuit cluster (PPC) model and potential ecological risk index (RI), as two new evaluation methods, were used to comprehensively evaluate the heavy mental pollution in soil. PPC model takes the contents of Hg, As, Cd, Pb and Cr in 236 soil samples as the multi-dimensional projection parameter to seek its optimal projection direction, obtains the projection value of soil heavy mental pollution by the function of projection index to reflect the characteristic of heavy metal content, establishes the poly class model of projection pursuit, knows the actual situation of soil heavy mental pollution, and further to predict the pollution degree of soil heavy metals and its evolution trend in the study area combined with the method of potential ecological risk index. The results showed that: 1) Hg and As had the greatest influences on soil pollution in the projection direction of index, while Cd had minimal impact on soil pollution. 2) according to the criteria of PPC evaluation, soil environmental quality in the study area was in good condition, the areas of no-pollution level accounted for 96.61% of the study area, the numbers of clean level and still clean level accounted for 30.08% and 66.53% of the total samples, respectively. 3) There were low level and moderate level in the potential ecological risk, accounted for 89.41% and 10.59% of the total samples, respectively, their risk degree were low, but trended to worsen into moderate level. 4) On pollution level, the clean level and still clean level distributed widely in the study area, the spatial distribution of light pollution level was not obviously after interpolation. The above results are favorable for the comprehensive evaluation of soil environmental quality and can play a reference role in the management of soil resources and the rational distribution of tea-planting in Karst areas.
Keywords:Tea-planting region  GIS  Projection pursuit cluster (PPC)  Potential ecological risk index  Comprehensive evaluation
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