South China Institute of Environmental Sciences, MEP, Guangzhou 510655, China.
South China Institute of Environmental Sciences, MEP, Guangzhou 510655, China.
Sci Total Environ. 2017 Sep 1;593-594:310-318. doi: 10.1016/j.scitotenv.2017.03.099. Epub 2017 Mar 27.
In order to accurately apportion the many distinct types of individual particles observed, it is necessary to characterize fingerprints of individual particles emitted directly from known sources. In this study, single particle mass spectral signatures from vehicle exhaust particles in a tunnel were performed. These data were used to evaluate particle signatures in a real-world PM apportionment study. The dominant chemical type originating from average positive and negative mass spectra for vehicle exhaust particles are EC species. Four distinct particle types describe the majority of particles emitted by vehicle exhaust particles in this tunnel. Each particle class is labeled according to the most significant chemical features in both average positive and negative mass spectral signatures, including ECOC, NaK, Metal and PAHs species. A single particle aerosol mass spectrometry (SPAMS) was also employed during the winter of 2013 in Guangzhou to determine both the size and chemical composition of individual atmospheric particles, with vacuum aerodynamic diameter (d) in the size range of 0.2-2μm. A total of 487,570 particles were chemically analyzed with positive and negative ion mass spectra and a large set of single particle mass spectra was collected and analyzed in order to identify the speciation. According to the typical tracer ions from different source types and classification by the ART-2a algorithm which uses source fingerprints for apportioning ambient particles, the major sources of single particles were simulated. Coal combustion, vehicle exhaust, and secondary ion were the most abundant particle sources, contributing 28.5%, 17.8%, and 18.2%, respectively. The fraction with vehicle exhaust species particles decreased slightly with particle size in the condensation mode particles.
为了准确分配所观察到的许多不同类型的个体颗粒，有必要对直接来自已知源的个体颗粒的指纹进行特征描述。在本研究中，在隧道内对车辆排放颗粒的单颗粒质谱特征进行了研究。这些数据用于评估实际 PM 分配研究中的颗粒特征。来源于车辆排放颗粒正、负质谱平均的主要化学类型是 EC 物质。四种不同的颗粒类型描述了该隧道中车辆排放颗粒中大部分颗粒的排放情况。根据正、负质谱平均中最显著的化学特征，为每个颗粒类别进行了标记，包括 ECOC、NaK、金属和多环芳烃物质。2013 年冬季，还在广州使用单颗粒气溶胶质谱法（SPAMS）来确定单个大气颗粒的大小和化学成分，真空空气动力学直径（d）在 0.2-2μm 的范围内。总共对 487,570 个颗粒进行了正、负离子质谱分析，并收集和分析了大量单颗粒质谱，以确定其分类。根据不同源类型的典型示踪离子和使用源指纹进行环境颗粒分配的 ART-2a 算法分类，模拟了单个颗粒的主要来源。煤燃烧、车辆尾气和二次离子是最丰富的颗粒源，分别贡献了 28.5%、17.8%和 18.2%。随着冷凝模式颗粒粒径的增加，车辆尾气物质颗粒的比例略有下降。
