文献类型：期刊文献

中文题名：洞庭湖退田还湖区不同土地利用方式下土壤重金属分布特征

英文题名：Distribution and pollution of soil heavy metals of different land-use patterns under converting polder back into wetlands at Dongting Lake Area

作者：王月容[1,2] 卢琦[1] 周金星[1] 周志翔[2]

第一作者：王月容

机构：[1]中国林业科学研究院荒漠化研究所;[2]华中农业大学园艺林学学院

年份：2011

卷号：30

期号：6

起止页码：734-739

中文期刊名：华中农业大学学报

外文期刊名：Journal of Huazhong Agricultural University

收录：CSTPCD;;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金项目(41071334);国家"十一五"科技支撑项目(2006BAD03A15)

语种：中文

中文关键词：土壤重金属;污染评价;土地利用方式;洞庭湖;退田还湖

外文关键词：soil heavy metal; pollution evaluation; land use pattern; Dongting Lake; converting polder back into wetlands

分类号：S714.2;X53

摘要：以洞庭湖退田还湖区杨树林地、园地、旱地(包括棉花地、甘蔗地及玉米地)、水田、荒地等为研究对象,考察不同土地利用方式下的土壤重金属(Cu、Zn、Mn、Pb、Cd、As、Hg)的分布、污染及其影响因子。结果表明:0～25cm土层中Cu、Zn、Pb、As和Hg含量以9a杨树人工林地最高,Mn、Cd含量分别以2a杨树人工林地和水田最高;25～50cm土层中Cu、Mn、Hg含量以2a杨树人工林地最高,Zn、As含量以水田最高,Pb、Cd含量分别以甘蔗地、4a杨树人工林地最高;4a和6a杨树人工林地、甘蔗地、水田土壤已受到Cd污染;不同土地利用方式综合污染指数以水田最高,荒地最低;主成分分析及双重筛选逐步回归分析表明,土壤重金属分布与土壤理化性质密切相关。
Based on plot data of poplar forestland soil（Ⅰ1,9 a;Ⅰ2,6 a;Ⅰ3,4 a;Ⅰ4,2 a）,garden-land soil（vegetable soil and fruit soil）（Ⅱ）,nonirrigated farmland soil（Ⅲ1,cotton soil;Ⅲ2,sugar cane soil;Ⅲ3,maize soil）,irrigated farmland soil（rice soil）（Ⅳ） and wasteland soil（Ⅴ） located at Qianlianghu polder,Dongting Lake Area,a research was carried out to investigate the distribution and pollution of soil heavy metals（Cu,Zn,Mn,Pb,Cd,As and Hg） in five typical land-use patterns mentioned above.The results showed that in the soil layer of 0 to 25 cm,the highest contents of Cu,Zn,Pb,As and Hg all occurred in Ⅰ1,and the highest contents of Mn and Cd in Ⅰ4 and Ⅳ,respectively.In the soil layer of 25 to 50 cm,the highest contents of Cu,Mn and Hg occurred inⅠ1,Zn and As in Ⅳ,Pb and Cd in Ⅲ2 and Ⅰ3,respectively.Soils of pattern Ⅰ2,Ⅰ3,Ⅲ2 and Ⅳ were polluted with Cd,and the highest integrated pollution index occurred in Ⅳ and the lowest one occurred in Ⅴ.The interrelate regularities between heavy metals and soil physical and chemical properties were found through principal component analysis and double sieving stepwise regression analysis.