西安南郊夏季颗粒物组成分类、理化特征及潜在来源  

Composition classification,physicochemical characteristics and potential sources of particulate matter in the southern suburbs of Xi'an in summer

作　　者：王浩飞 刘立忠[1,2] 刘焕武 杨毅[1,2] 高冉冉 李凡 陈庆奎 王若愚 WANG Haofei;LIU Lizhong;LIU Huanwu;YANG Yi;GAO Ranran;LI Fan;CHEN Qingkui;WANG Ruoyu(School of Environmental and Municipal Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China;Shaanxi Province Key Laboratory of Environmental Engineering,Xi'an University of Architectural Technology,Xi'an 710055,China;Xi'an Environmental Monitoring Station,Xi'an 710055,China)

机构地区：[1]西安建筑科技大学环境与市政工程学院,西安710055 [2]西安建筑科技大学陕西省环境工程重点实验室,西安710055 [3]西安市环境监测站,西安710119

出　　处：《环境工程学报》2025年第1期188-200,共13页Chinese Journal of Environmental Engineering

基　　金：陕西省自然科学基础研究计划项目(2021JM-375);西安建筑科技大学自然科学专项(ZR21019)。

摘　　要：利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS),采用ART-2a自适应神经网格分类法和后向轨迹模型,探究西安市夏季颗粒物的组分特征、粒径分布和潜在来源。结果表明:监测期间SPAMS采样得到的颗粒数和PM2.5日平均浓度具有一定相关性(r=0.41,P<0.05)。根据颗粒物质谱特征相似度,将颗粒物分为6大类18小类:富钾类颗粒(K-EC、K-EC-SEC、K-NO_(3)-PO_(3)、K-NO_(3)-SiO_(3)、K-SEC,37.41%)、碳质颗粒(EC、EC-SEC、HEC、HEC-SEC、OC-SEC、ECOC-SEC、PAH-SEC,33.62%)、扬尘颗粒(Dust-HEC、Dust-SEC,13.23%)、富钠类颗粒(Na-Cl-NO_(3)、Na-SEC,3.47%)、重金属(HM,1.36%)及生物质(LEV,4.30%)。K-SEC、K-EC-SEC、ECOC-SEC颗粒数浓度在早晚交通高峰期出现峰值,主要由机动车尾气排放贡献;HEC-SEC、EC-SEC、Na-Cl-NO_(3)和Na-SEC颗粒数在午间出现峰值,发生非均相光化学反应加速其生成,HEC-SEC、EC-SEC粒径比HEC、EC较大;LEV颗粒数在夜间达到峰值,与生物质燃烧源和燃煤源有关。各气团轨迹中K-Rich和HM平均颗粒数占比分别最大(40.49%±2.43%)和最低(1.72%±0.61%)。来自重庆北部和河南西部的气团会带来高浓度的颗粒物污染,其中K-Rich和EC类颗粒含量较高,分别主要来自与生物质燃烧有关过程和一次污染源;来自蒙古国的气团轨迹颗粒数浓度较低,含有较多的OC颗粒,与其以燃煤和生物质燃烧为主要能源供应方式有关。SPAMS测定的颗粒数浓度可以反映当地细颗粒物污染状况,6类颗粒中绝大部分包含NO_(2)^(−)、NO_(3)^(−)、SO_(4)^(−)等二次离子组分,NO_(2)^(−)、NO_(3)^(−)由氮氧化物在大气中经过光化学反应和氧化反应与其他气态物质结合生成,SO_(4)^(−)由二氧化硫在大气中与氧气反应,并在气溶胶水滴中转化成硫酸盐,表明采集到的颗粒物大都经历了不同的老化,或与二次组分进行了不同程度的混合。西安市夏季颗粒物受本地排放和河南、�The composition characteristics,particle size distributions,and potential sources of particulate matter of Xi'an city in summer were investigated using a single particle aerosol mass spectrometer(SPAMS),the ART-2a adaptive neural network classification method and backward trajectory model.Results showed that there was a certain correlation(r=0.41,P<0.05)between the number of particles and the daily average concentration of PM2.5 during the monitoring period.According to the similarity of particle mass spectrometry characteristics,particles were divided into 6 major categories and 18 subcategories,namely 37.41%of potassium rich particles(K-EC,K-EC-SEC,K-NO_(3)-PO_(3),K-NO_(3)-SiO_(3),K-SEC),33.62%of carbonaceous particles(EC,EC-SEC,HEC,HEC-SEC,OC-SEC,ECOC-SEC,PAH-SEC),13.23%of dust particles(Dust-HEC,Dust-SEC),3.47%of sodium rich particles(Na-Cl-NO_(3),Na-SEC),1.36%of heavy metals(HM),and 4.30%of biomass(LEV).The particles including K-SEC,K-EC-SEC and ECOC-SEC were mainly contributed by motor vehicle exhaust emissions,and their concentrations achieved peak during morning and evening traffic rush periods;The HEC-SEC,EC-SEC,Na-Cl-NO_(3),and Na-SEC particles were accelerated by heterogeneous photochemical reactions,which number achieved peak at noon,and the particle sizes of HEC-SEC and EC-SEC were larger than those of HEC and EC;the LEV particle was related to biomass combustion and coal-fired sources,which number reached its peak at night.The average particle proportion of the K-Rich was the highest(40.49%±2.43%)and that of the HM was the lowest(1.72%±0.61%)in each air mass trajectory,respectively.High concentrations of particulate matter pollution referring to K-Rich and EC type particles were produced by the air masses from northern Chongqing and western Henan,which mainly derived from biomass combustion and primary pollution sources.The particle concentrations in the trajectory of air masses from Outer Mongolia were relatively low,which contained more OC particles due to their energy supply of mainly coal and

关 键 词：夏季 大气颗粒物 SPAMS ART-2a 理化特征 后向轨迹 

分 类 号：X513[环境科学与工程—环境工程]