牛栏江污染物源解析与空间差异性分析  

Source Apportionment and Spatial Pattern Analysis of River Niulanjiang

作　　者：李发荣[1] 李玉照[2] 刘永[2,3] 李晓铭[1] 郭怀成[2] 

机构地区：[1]昆明市环境监测中心,云南昆明650028 [2]北京大学环境科学与工程学院、教育部水沙科学重点实验室,北京100871 [3]云南省高原湖泊流域污染过程与管理重点实验室,云南昆明650034

出　　处：《环境科学研究》2013年第12期1356-1363,共8页Research of Environmental Sciences

基　　金：国家水体污染控制与治理科技重大专项(2013ZX07102-006);国家自然科学基金项目(51279001)

摘　　要：牛栏江-滇池补水是缓解滇池生态用水短缺的重要工程,对牛栏江流域主要污染源与空间差异性的识别分析,将有助于进一步改善牛栏江的水质.采用CFA(对应分析)对牛栏江流域污染源进行解析识别,结果表明流域内TN、NH3-N(氨氮)等污染物主要来源于嵩明县境内,TP、氟化物等污染物主要来源于寻甸县境内.在CFA分析的基础上,采用HCA(层次聚类分析)和SOM(自组织映射神经网络)对4个监测点、10种污染物指标进行分析,以识别空间分布的差异性和相似性,并且评价各指标的空间分布特征及监测点代表性.结果表明:4个监测点中,TP、氟化物、砷化物、Vph(挥发酚)在寻甸县境内的七星桥污染最严重;TN、NH3-N在牛栏江上游嵩明县境内的四营污染最重.结合流域污染负荷调查可知,寻甸县的磷负荷最大,占流域总负荷的58.73%,七星桥的TP污染贡献大于其他3个监测点,与七星桥TP污染最为严重相符合.该研究结果可为牛栏江流域实施进一步的分段治理提供决策支撑.Lake Dianchi has been severely polluted with various types of wastewater in the past 3 decades, and has been suffering from extensive algal blooms and serious eutrophication. Many efforts on pollution control have been taken to alleviate pollution and reverse considerable economic losses in the lake caused by eutrophication, since it has been in the list of Chinese government＇s key lake restoration priorities. The Chinese State Council approved the water diversion project by transferring water from the River Niulangjing to the Lake Dianchi watershed for lake water diversion/dilution purposes in 2008. However, since the quality of water from the River Niulanjiang was deteriorated from 2010, it is extremely necessary to identify the sources of water pollution in the basin of River Niulanjiang. The characteristics of spatial distribution patterns of polluted sources should be also recognized in order to understand their potential impacts on Lake Dianchi. An integrated approach based on the correspondence factor analysis （CFA） , hierarchical cluster analysis （HCA） and self-organizing feature mapping （SOM） was proposed for the pollution source apportionment and spatial pattern analysis along the River Niulanjiang. The water quality data used for the CFA-HCA-SOM analysis include 10 different water quality parameters measured from four monitoring stations from 2001 to 2010 along the mainstream of the river. The results indicated that： （1）the pollution distribution at Qixingqiao and Siying Monitoring Stations are most typical in term of spatial pattern characteristics ： the most severe section polluted with total phosphorus （TP）, fluoride （F）, arsenic （As） and volatile phenol is around the Qixingqiao Station in Xundian County; the most severe section polluted with total nitrogen （TN） and ammonia nitrogen is nearby Siying Station in the Niulanjiang upstream in Songming County;and also （2） pollution load surveys conducted for the administrative jurisdictions along me ruver muia,,jl

关 键 词：对应分析 自组织映射神经网络 来源识别 空间差异 

分 类 号：X32[环境科学与工程—环境工程]