基于快速聚类方法分析常州市区PM2.5的统计特性

运用统计方法研究常州市区2013~2014年6个国控点六项基本污染物(SO_2、NO_2、CO、O_3、PM_(2.5)和PM_(10))月平均浓度变化,结果表明,除O_3外,其它五项污染物月平均浓度夏季较低冬季较高.颗粒物与风速之间的关系为PM_(2.5)浓度随风速的升高一直降低,PM_(10)随风速的升高浓度先降低后升高.采用快速聚类分析(k-means)并运用SWV和DIV指数对六项基本污染物进行分类,得到4个样本分类.与依据颗粒物化学成分或粒径谱对PM进行源解析方法不同,本研究更多是从PM_(2.5)与其它污染物相关关系以及污染程度等角度按照欧式距离进行分类.不同类中PM_(2.5)来源明显不同,类1中PM_(2.5)与化石燃料燃烧排放密切相关,类2与O_3密切相关,类3与城市不完全燃烧排放、区域灰霾污染密切相关,类4可以归类于城市"背景"类.快速聚类分析结果也表明常州市区PM_(2.5)有着复杂的来源.

Statistical Characteristics of Urban Changzhou PM2.5 Based on k-means Analysis

Statistical analysis methods was utilized to investigate the variations of monthly average concentrations of the six basic pollutants (SO₂, NO₂, CO, O₃, PM_2.5 and PM₁₀) of six national standard monitoring sites from 2013 to 2014 in urban Changzhou. The results showed that, except for O₃, SO₂, NO₂, CO, PM_2.5 and PM₁₀ concentrations were all high in winter and low in summer. The relationship between particulate matter and wind speed showed, with increasing wind speed, the concentration of PM_2.5 reduced. However, the concentration variations of PM₁₀ were complicated and when wind speed increased, its concentration started to go down and then elevated. Fast-cluster analysis (k-means) and the index of SWV & DIV were used to classify the six basic pollutants into four clusters, and then the relationship between gaseous pollutants and PM_2.5in each cluster was emphatically discussed by statistical analysis method. Four clusters were assigned to fossil fuel combustion emissions (cluster1), O₃ and secondary aerosols (cluster2), incomplete combustion emissions and regional haze (cluster3), urban city "background" (cluster4). Incomplete combustion cluster accounted for the smallest percentage of urban Changzhou and city "background" was cluster of urban Changzhou with the largest contribution. k-means analysis results also showed that PM_2.5 had complex sources in urban Changzhou.