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基于层次分析法的北运河流域栖境得分与BMWP值相关性分析
引用本文:谢阳村,郭亚坤,高世凯,王备新.基于层次分析法的北运河流域栖境得分与BMWP值相关性分析[J].水生态学杂志,2021,42(4):26-31.
作者姓名:谢阳村  郭亚坤  高世凯  王备新
作者单位:生态环境部环境规划院,天津大学,华北水利水电大学,南京农业大学昆虫系
基金项目:国家水体污染控制与治理科技重大专项“北运河流域水质目标综合管理示范研 究”课题(2018ZX07111003)
摘    要:准确描述北运河流域水生态状况,为北运河流域水生态管理决策提供支持。在北运河流域26个采样点进行采样,对河流栖境打分,采用层次分析法对河流栖境各参数得分进行加权,计算大型底栖动物BMWP值,分析北运河栖境得分与BMWP值相关性。结果表明,加权前栖境得分与BMWP值的Pearson相关系数为0.346,相伴概率为0.086,两者无显著相关性;层次分析法将河流栖境一级指标河道水量状况、河道变化状况、河滨带状况、河堤岸稳定状况分别加权为0.1466、0.1623、0.6040、0.0872,加权后栖境得分与BMWP值的Pearson相关系数为0.413,相伴概率为0.036,两者显著相关。后续研究需要进一步优化一级、二级指标的加权值,以使栖境得分值既可以有效指示不同河段的生物状况,也可以有效反映水生生物栖息地质量。

关 键 词:河流栖境得分  大型底栖动物生物监测工作组指数  层次分析法  相关性分析
收稿时间:2020/8/20 0:00:00
修稿时间:2021/7/27 0:00:00

Correlation of Habitat Scores with BMWP Values in Beiyun River Basin
XIE Yang-cun,GUO Ya-kun,GAO Shi-kai,WANG Bei-xin.Correlation of Habitat Scores with BMWP Values in Beiyun River Basin[J].Journal of Hydroecology,2021,42(4):26-31.
Authors:XIE Yang-cun  GUO Ya-kun  GAO Shi-kai  WANG Bei-xin
Institution:Chinese Academy of Environmental Planning,Tianjin University,North China University of Water Resources and Electric Power,Department of Entomology, Nanjing Agricultural University
Abstract:Beiyun River basin is an important source of water for the Beijing-Tianjin-Hebei?region and the primary area of water pollution control and treatment. To effectively manage the resource, the status of aquatic ecology in the Beiyun River basin must be comprehensively assessed. In October 2019, river habitat was investigated and macrobenthos sampling was carried out at 26 sampling points in Beiyun River basin. Based on this investigation, river habitat quality was described by the scores of several habitat parameters after being weighted by the analytic hierarchy process. The biological monitoring working party (BMWP) score for macrobenthos in Beiyun River basin was calculated and, finally, the relationship of river habitat scores and the BMWP scores were analyzed by Pearson correlation analysis. The Pearson correlation coefficient between unweighted river habitat scores and the BMWP scores was 0.346 and the associated probability was 0.086, indicating no significant correlation. The weights given to first-level river habitat indicators of water volume, channel alteration, shoreline, and river bank stability were 0.1466, 0.1623, 0.6040, 0.0872, respectively. The Pearson correlation coefficient between weighted habitat scores and the BMPP value was 0.413 and the associated probability was 0.036, indicating a significant correlation. Thus, river habitat indicators optimized by weighting gave an internally consistent system for evaluating aquatic ecosystems. However, additional research is still needed to further optimize the weighting values of primary and secondary indicators of river habitat, so that habitat scores effectively indicate the biological conditions in different river sections and accurately reflect the quality of aquatic habitats.
Keywords:
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