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cje ›› 2012, Vol. 31 ›› Issue (06): 1425-1431.

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Effects of lakeside plant spatial allocation in plateau wetlands of Yunnan, Southwest China on the removal of sewage nitrogen and phosphorus.

LI Yin1, TIAN Kun1,2**, XIAO De-rong2, YANG Qian1, XIE Wen-ying1   

  1. (1Department of Environmental Science and Engineering, Southwest Forestry University, Kunming 650224, China; 2National Plateau Wetlands Research Center, Kunming 650224, China)
  • Online:2012-06-09 Published:2012-06-09

Abstract: Field investigation in combining with laboratory simulation test was conducted to study the effects of 7 lakeside plant communities commonly found in plateau wetlands on the purification of different concentration urban sewage, aimed to provide reference to the selection and spatial allocation of plants for the recovery of degraded wetlands and the treatment of polluted lakes in Yunna Plateau of Southwest China. The test lakeside plant communities had definite limitations in enduring and purifying urban sewage. Only under the conditions of suitable sewage concentration (total nitrogen 8.39-22.95 mg·L-1, total phosphorous 0.61-1.96 mg·L-1, and NH4+-N 6.42-19.80 mg·L-1), the plant communities could have effective and obvious purification effects, with the removal rate of total phosphorous and NH4+-N reached over 30% and that of total nitrogen nearly 20%. Under high sewage concentration, the plant communities composed of the species in favor of nitrogen and phosphorus, such as Zizana caduciflora, Potamogeton pectinatus and Ceratophyllum demersum, showed better purification effects. There existed significant differences in the purification effects among the plant communities composed of one same species but different other species (P<0.05), indicating the interactions among the plant species. It was suggested that plant species selection and their spatial allocation would have great significance in the biotreatment of polluted lakes and the ecological recovery of degraded wetlands.

Key words: Pb, barley, toxicity threshold, leaching factor, predicted model.