基于主成分分析的空气质量综合评价研究--以四川省21个城市为例  

Comprehensive Evaluation of Air Quality Based on Principal Component Analysis--Take 21 Cities in Sichuan Province as an Example

作　　者：卜兴兵 方自力 俸强 廖翀 王会镇 BU Xing-bing;FANG Zi-li;FENG Qiang;LIAO Chong;WANG Hui-zhen(Sichuan Ecological Enwvironment Monitoring Station,Chengdu 610041,China;School of Science,Xihua University,Chengdu 610039,China)

机构地区：[1]四川省生态环境监测总站,成都610041 [2]西华大学理学院,成都610039

出　　处：《四川环境》2023年第3期51-56,共6页Sichuan Environment

基　　金：教育部春晖计划(191630);四川省教育厅项目(18ZB0571)。

摘　　要：为探究四川省6种大气污染物(PM_(2.5)、PM_(10)、SO_(2)、NO_(2)、CO和O_(3))间相互关系及影响大气质量的关键指标,利用2017年四川省21个城市6种大气污染物浓度监测数据,结合Pearson相关性分析、主成分分析和复合污染特征分析方法,对21个城市空气质量状况和影响大气质量的关键指标进行了综合评价与分析。结果表明,以PM_(10)、PM_(2.5)、O_(3)和NO_(2)4个变量为主的第1主成分方差贡献率(53.353%)远超以CO和SO_(2)为主的第2主成分方差贡献率(26.615%),主成分分析结果和空气质量综合指数排名前5位和后3位完全一致,其余不完全一致。因此,采用主成分分析法对空气质量状况和影响大气质量的关键指标进行分析可为大气污染防治提供理论依据。In order to explore the relationship between six atmospheric pollutants(PM_(2.5)、PM_(10)、SO_(2)、NO_(2)、CO and O_(3))and the key indicators affecting atmospheric quality in Sichuan Province,the concentration monitoring data of six atmospheric pollutants in 21 cities of Sichuan Province in 2017.Combined with Pearson correlation analysis,principal component analysis and composite pollution characteristics analysis method,the air quality status and key indicators affecting atmospheric quality in 21 cities were comprehensively evaluated and analyzed.The results showed that the variance contribution rate of the first principal component(53.353%)dominated by PM_(10),PM_(2.5),O_(2) and NO_(2),was much higher than that of the second principal component(26.615%)dominated by CO and SO_(2).The results of principal component analysis were completely consistent with the top five and the bottom three positions of the air quality comprehensive index,while the rest were not Therefore,the principal component analysis method can provide a theoretical basis for the prevention and control of air pollution.

关 键 词：大气评价 空气质量 主成分分析 大气污染防治 

分 类 号：X823[环境科学与工程—环境工程]