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海河流域河流生态系统健康评价
摘要点击 3109  全文点击 1763  投稿时间:2013-02-21  修订日期:2014-05-18
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中文关键词  海河流域  河流生态系统健康  水质  营养盐  底栖动物
英文关键词  Haihe River Basin  health of river ecosystem  water quality  nutrient  benthic macroinvertebrates
作者单位E-mail
郝利霞 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085 lxhao@rcees.ac.cn 
孙然好 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085  
陈利顶 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085 liding@rcees.ac.cn 
中文摘要
      随着经济发展,人类活动对河流生态系统的胁迫日益强烈,生态系统健康状况受到严重威胁. 本研究以海河流域2010年73个采样点的水质、营养盐和底栖动物指标为例,采用指标体系法,从化学完整性和生物完整性两方面评价了流域内河流生态系统健康. 结果表明,海河流域河流生态系统健康状况整体较差,有72.6%的样点处于“极差”健康状态,同时表现出明显的地区集聚效应;河流水质与人类活动强度密切相关;海河流域水体富营养化趋势明显;流域内底栖动物多样性低,清洁物种较少. 氨氮、总氮、总磷等营养盐指标是影响河流生态系统健康的关键因子,应从控制上述指标入手,遏制海河流域河流生态系统健康恶化. 对于河流生态系统健康评价,多因子的综合评价法优于单因子评价法.
英文摘要
      With the development of economy, the health of river ecosystem is severely threatened because of the increasing effects of human activities on river ecosystem. In this paper, the authors assessed the river ecosystem health in aspects of chemical integrity and biological integrity, using the criterion in water quality, nutrient, and benthic macroinvertebrates of 73 samples in Haihe River Basin. The research showed that the health condition of river ecosystem in Haihe River Basin was bad overall since the health situation of 72.6% of the samples was "extremely bad". At the same time, the health situation in Haihe River Basin exhibited obvious regional gathering effect. We also found that the river water quality was closely related to human activities, and the eutrophication trend of water body was evident in Haihe River Basin. The biodiversity of the benthic animal was low and lack of clean species in the basin. The indicators such as ammonia nitrogen, total nitrogen and total phosphorus were the key factors that affected the river ecosystem health in Haihe River Basin, so the government should start to curb the deterioration of river ecosystem health by controlling these nutrients indicators. For river ecosystem health assessment, the multi-factors comprehensive evaluation method was superior to single-factor method.

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