广州市空气污染物和气象要素的主成分与典型相关分析  

Principal component and canonical correlation analysis for air pollutants and meteorological data in Guangzhou

作　　者：沈家芬[1] 张凌 莫测辉[3] 冯建军 

机构地区：[1]华南农业大学资环学院,广东广州510642 [2]广州市环境监测中心站,广东广州510030 [3]暨南大学环境工程系,广东广州510632 [4]广州市林业科学研究所,广东广州510515

出　　处：《生态环境》2006年第5期1018-1023,共6页Ecology and Environmnet

基　　金：广东省自然科学基金项目(036716)

摘　　要：用主成分和典型相关分析方法分析广州市近4a(2001—2004年)的空气污染物与气象要素之间的关系,按夏半年、冬半年和全年3个时间尺度分别进行。结果表明:污染物数据所得到的主成分分别代表机动车污染源(汽油燃烧和扬尘)和工业污染源(工业燃煤和燃油),气象数据的主成分分析表明空气的温度、湿度及对流速度对空气污染作用明显,而污染物和气象要素的主成分分析表明气温高低和空气干湿程度对大气污染的影响较大。污染物与气象要素两组数据之间的典型相关分析表明污染物与气象要素之间存在显著的相关关系,其中温度和风速对气态污染物有显著影响。Four years data on SO2, NO2, CO and PMx0 concentrations recorded at air-pollution monitoring stations in the city of Guangzhou and meteorological data concerning temperature, relative humidity, wind velocity, duration of precipitation and atmospheric pressure were analyzed using principal component analysis （PCA）. Separate analyses were undertaken for summer, winter and year periods. It was found that the main principal components extracted from the air pollution data were related to gasoline combustion （automobiles） and coal or oil combustion （industrial pollution）. The most prominent principal components from the meteorological data were related to air temperature and air humidity. Finally, canonical correlation analysis determined relationships between the two different data sets. The air pollution data showed a remarkable correlation with the meteorological data, the main relationship was between gaseous pollutants with temperature and wind velocity.

关 键 词：空气污染物 气象要素 主成分分析 典型相关分析 广州 

分 类 号：X169[环境科学与工程—环境科学]