西安城区冬季污染天单颗粒气溶胶的化学组分特征及混合状态

Chemical composition and Mixing states of single atmospheric particles in Xi'an during winter

本研究利用单颗粒气溶胶质谱仪(SPAMS)于2018年12月6—12日在西安市开展高时间分辨率的实时在线观测。观测期间颗粒物数浓度和PM_(2.5)质量浓度变化趋势一致,表明颗粒物数浓度可以反映大气污染状况。结合自适应共振神经网络算法和人工分类方法,将颗粒物共分为7类,分别是元素碳颗粒(EC)、有机碳与元素碳混合颗粒(OCEC)、有机碳颗粒(OC)、富钾颗粒(K-rich)、生物质颗粒(BB)、扬尘颗粒(Dust)、重金属颗粒(Metal),其中占比最大的是含碳颗粒(包括EC、OCEC和OC),总占比达到66.9%。颗粒物的粒径分布表明,88.6%的颗粒物粒径分布在200—500 nm之间。OCEC、EC和OC颗粒在整个测径范围内占比较大。在污染期间,EC颗粒和K-rich颗粒的占比升高较为明显,表明此次污染事件在很大程度上受到了燃烧源以及颗粒物老化的影响。混合状态结果表示,污染期颗粒物与无机离子组分的混合程度高于清洁期,表明污染期颗粒物的老化程度更深。

To investigate the chemical composition and possible sources of particulate matter,an intensive campaign was performed in Xi'an from December 6 to 12,2018 using a single particle aerosol mass spectrometer(SPAMS).A good correlation between the mass concentration of PM_(2.5) and SPAMS particle number indicated that the SPAMS particle numbers could reflect the trend of air pollution.The measured particles were clustered into seven groups based on the adaptive resonance theory-based neural network algorithm.They included particles of elemental carbon(EC),organic carbon mixed with elemental carbon(OCEC),organic carbon(OC),potassium-rich particles(K-rich),biomass-burning particles(BB),dust particles(Dust),and heavy metal particles(Metal).Carbon-containing particles accounted for the largest of the total particles(66.9%).Particles mixed with organic carbon and elemental carbon,elemental carbon particles,and organic carbon particles accounted for a large proportion in the measured diameter range.The proportion of elemental carbon particles and potassium-rich particles increased obviously in polluted period,which indicated that the pollution event was affected by the combustion sources and the aging of particulate matter.The results of mixing state showed that more particles mixed with secondary organic aerosols in polluted period than clean period,indicating stronger aging of particles in pollution event.