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基于加权主成分距离聚类的江苏省环境质量评价 被引量:4

Evaluation of Jiangsu Environmental Quality Based on Clustering Analysis Method of Weighted Principal Component Distance
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摘要 基于污染物排放和环境保护视角,构建包含水、空气、固体废物、声环境的环境质量评价指标体系,采用因子分析对指标进行降维,再采用加权主成分距离聚类分析方法对江苏省2012—2016年的环境质量进行分类评价。结果表明:2012—2016年徐州、苏州的环境质量状况最差,环境污染最严重,南京、盐城其次,其他城市环境质量较乐观稳定;江苏省环境质量整体不均衡,呈现区域性特征。根据因子分析和加权主成分距离聚类分析结果,分析了江苏环境质量存在的问题,认为该省环境质量与经济发展方式关系密切。根据江苏经济发展特点,对该省的环境治理提出了改进建议。 Considering the view of pollution emission and environmental protection,this paper constructed the environmental quality evaluation index system which included water environment,air environment,solid waste and sound environment. Factor analysis was used to reduce the index and then the weighted principal component distance clustering analysis was used to classify the environmental quality of Jiangsu Province from 2012-2016. The results showed that the environmental quality of Suzhou,Xuzhou was most poor in the past 2012-2016 years,and the environmental pollution was most serious,followed by Nanjing,Yancheng,and the environmental quality of other 9 cities were more optimistic and stable. The environmental quality of Jiangsu was unbalanced as a whole and the environmental quality showing regional characteristics. Based on the results of factor analysis and weighted principal component distance clustering analysis,this paper analyzed the existing problems of environmental quality in Jiangsu Province. It was considered that the environmental quality of Jiangsu Province was closely related to the pattern of economic development. The improvement suggestions were proposed according to the characteristics of Jiangsu's economic development.
作者 陈军飞 陈琳 CHEN Jun-fei;CHEN Lin(Hohai University 1.State Key Laboratory of Hydrology-water Resources and Hydraulic Engineering;Business School,Nanjing 211100,China)
出处 《资源开发与市场》 CAS CSSCI 2018年第10期1383-1388,共6页 Resource Development & Market
基金 国家重点研发计划项目(编号:2017YFC0404600) 国家自然科学基金重点项目(编号:71433003) 江苏省"青蓝工程"中青年学术带头人培养计划项目(编号:2014)
关键词 环境质量评价 因子分析 加权主成分距离 聚类分析 environmental quality evaluation factor analysis weighted principle component distance clustering analysis
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