基于空间自相关的水污染空间聚类研究  

Spatial Clustering of Water Pollution Based on Spatial Autocorrelation Analysis

作　　者：王新成 王斌之 黄建毅[2] 

机构地区：[1]潍坊市环境监测中心站,山东潍坊261041 [2]北京联合大学应用文理学院,北京100191

出　　处：《环境工程技术学报》2014年第4期293-298,共6页Journal of Environmental Engineering Technology

基　　金：山东省科技发展计划项目(2011SJGZ03);北京联合大学人才强校计划人才资助项目(BPHR2012E01)

摘　　要：运用空间自相关分析方法对《中国环境质量报告2012》中全国地表水监测点位的溶解氧、高锰酸钾指数、五日生化需氧量、氨氮、石油类、挥发酚、汞和铅8项主要污染物水质常规监测指标进行了空间分布特征的定量研究。结果表明,各污染物的Moran指数都大于0,说明我国主要污染物的空间分布具有明显的空间自相关性;局部自相关分析的Moran散点图表明,各类污染物形成了多种空间分布格局。总体而言,我国污染物的扩散分布与地区社会经济有着密切关系,人口活动密集地区的水污染程度明显较高,如京津冀地区。从宏观格局看,我国水污染可分为东西、南北两大格局,东部沿海地区的水污染高集聚区明显高于中西部地区;北方地区的水环境污染状况比南方地区严重,且各种污染物指标在全国范围内有不同的集聚地域。The spatial autocorrelation analysis was performed to quantitatively analyze the spatial distribution characteristics of water pollution monitoring indices , based on China Status of Environment in 2012.Eight water pollution indices , including dissolved oxygen , potassium permanganate index , BOD5 , ammonia nitrogen , petroleum, volatile phenol, mercury and lead.The Moran’s I is greater than 0 for all pollution indices, indicating that the spatial distribution of the main pollutants in China has significant spatial autocorrelation .The result of Moran scatter diagram of LISA shows that the pollutants have developed several kinds of spatial patterns .In general , the distribution of the water pollutants has close relationship with regional socio-economic situations , with obviously higher pollution in densely populated areas , such as Beijing-Tianjin-Hebei region .The water pollution in China could be divided into two major patterns ‘east-west’ and ‘north-south’, namely, the water pollution in China’s eastern coastal areas were significantly higher than that in the midwest , and the water pollution in northern China was worse than that in southern region .Different water pollution indices had different agglomeration areas .

关 键 词：水污染 空间自相关 空间聚类 

分 类 号：X824[环境科学与工程—环境工程]