中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Metal-resistant microorganisms and metal chelators synergistically enhance the phytoremediation efficiency of Solanum nigrum L. in Cd- and Pb-contaminated soil

文献类型:SCI/SSCI论文

作者Gao Y.; Xia J.
发表日期2012
关键词heavy metals phytoremediation microorganism chelate combined pollution mustard brassica-juncea indian mustard citric-acid phytoextraction bacteria cadmium lead amendments deaminase stress
英文摘要The effects of metal-resistant microorganisms and metal chelators on the ability of Solanum nigrum L. to accumulate heavy metals were investigated. In the presence of multiple metal contaminants (Cd and Pb), citric acid (CA) significantly enhanced the biomass and Cd accumulation of S. nigrum, but these conditions decreased the accumulation of Pb. Application of Cd- or Pb-resistant microorganisms improved the ability of S. nigrum to accumulate heavy metals and increased plant yield, but the effects of microorganisms on phytoextraction were smaller than the effects of CA. When plants were grown in the presence of Cd contamination, the co-application of CA and metal-resistant strains enhanced biomass by 30-50% and increased Cd accumulation by 25-35%. However, these conditions decreased Pb accumulation in the presence of Pb pollution. S. nigrum could tolerate a combination of Cd and Pb pollution. In the presence of CA and the metal-resistant microorganisms, the plants were able to acquire 15-25% more Cd and 10-15% more Pb than control plants. We propose that the synergistic combination of plants, microorganisms and chelators can enhance phytoremediation efficiency in the presence of multiple metal contaminants.
出处Environmental Technology
33
12
1383-1389
收录类别SCI
语种英语
ISSN号0959-3330
源URL[http://ir.igsnrr.ac.cn/handle/311030/26931]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Gao Y.,Xia J.. Metal-resistant microorganisms and metal chelators synergistically enhance the phytoremediation efficiency of Solanum nigrum L. in Cd- and Pb-contaminated soil. 2012.

入库方式: OAI收割

来源:地理科学与资源研究所

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