Dipartimento di Fisica, Università Degli Studi di Milano and INFN-Milan, Milan, Italy.
Dipartimento di Ingegneria "Enzo Ferrari", Università Degli Studi di Modena e Reggio Emilia, Modena, Italy.
Environ Pollut. 2017 Dec;231(Pt 1):601-611. doi: 10.1016/j.envpol.2017.08.040. Epub 2017 Aug 29.
In this work, a comprehensive characterisation and source apportionment of size-segregated aerosol collected using a multistage cascade impactor was performed. The samples were collected during wintertime in Milan (Italy), which is located in the Po Valley, one of the main pollution hot-spot areas in Europe. For every sampling, size-segregated mass concentration, elemental and ionic composition, and levoglucosan concentration were determined. Size-segregated data were inverted using the program MICRON to identify and quantify modal contributions of all the measured components. The detailed chemical characterisation allowed the application of a three-way (3-D) receptor model (implemented using Multilinear Engine) for size-segregated source apportionment and chemical profiles identification. It is noteworthy that - as far as we know - this is the first time that three-way source apportionment is attempted using data of aerosol collected by traditional cascade impactors. Seven factors were identified: wood burning, industry, resuspended dust, regional aerosol, construction works, traffic 1, and traffic 2. Further insights into size-segregated factor profiles suggested that the traffic 1 factor can be associated to diesel vehicles and traffic 2 to gasoline vehicles. The regional aerosol factor resulted to be the main contributor (nearly 50%) to the droplet mode (accumulation sub-mode with modal diameter in the range 0.5-1 μm), whereas the overall contribution from the two factors related to traffic was the most important one in the other size modes (34-41%). The results showed that applying a 3-D receptor model to size-segregated samples allows identifying factors of local and regional origin while receptor modelling on integrated PM fractions usually singles out factors characterised by primary (e.g. industry, traffic, soil dust) and secondary (e.g. ammonium sulphate and nitrate) origin. Furthermore, the results suggested that the information on size-segregated chemical composition in different size classes was exploited by the model to relate primary emissions to rapidly-formed secondary compounds.
在这项工作中，我们对使用多级级联冲击器收集的按粒径分级的气溶胶进行了全面的特征描述和源解析。样品采集于意大利米兰的冬季，该地位于欧洲主要污染热点地区之一的波河谷。对每一次采样，我们都测定了按粒径分级的质量浓度、元素和离子组成以及左旋葡聚糖的浓度。使用 MICRON 程序对按粒径分级的数据进行反演，以确定和量化所有测量成分的模态贡献。详细的化学特征分析使我们能够应用三向（3-D）受体模型（使用 Multilinear Engine 实现）对按粒径分级的源解析和化学特征进行识别。值得注意的是——据我们所知——这是首次尝试使用传统级联冲击器收集的气溶胶数据进行三向源解析。共确定了七个因子：木材燃烧、工业、扬尘、区域气溶胶、建筑工程、交通 1 和交通 2。进一步分析按粒径分级的因子特征表明，交通 1 因子可能与柴油车辆有关，交通 2 因子可能与汽油车辆有关。区域气溶胶因子是液滴模态（模态直径在 0.5-1μm 范围内的积聚亚模态）的主要贡献者（近 50%），而与交通相关的两个因子在其他粒径模式下的总贡献是最重要的（34-41%）。结果表明，将 3-D 受体模型应用于按粒径分级的样品可以识别出本地和区域来源的因子，而对整体 PM 分数进行受体建模通常只能识别出以初级（如工业、交通、土壤尘）和次级（如硫酸铵和硝酸盐）来源为特征的因子。此外，结果表明，模型利用不同粒径分级的按粒径分级化学成分信息，将初级排放物与快速形成的次级化合物联系起来。
