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首页> 外文期刊>Environmental Science and Pollution Research >Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM_(2.5) in Guangzhou, southern China, using positive matrix factorization (PMF)
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Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM_(2.5) in Guangzhou, southern China, using positive matrix factorization (PMF)

机译:基于正矩阵分解(PMF)的广州市PM_(2.5)中基于示踪剂的多环芳烃源分配

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From 28 November to 23 December 2009, 24-h PM_(2.5) samples were collected simultaneously at six sites in Guangzhou. Concentrations of 18 polycyclic aromatic hydrocarbons (PAHs) together with certain molecular tracers for vehicular emissions (i.e., hopanes and elemental carbon), coal combustion (i.e., picene), and biomass burning (i.e., levoglucosan) were determined. Positive matrix factorization (PMF) receptor model combined with tracer data was applied to explore the source contributions to PAHs. Three sources were identified by both inspecting the dominant tracer(s) in each factor and comparing source profiles derived from PMF with determined profiles in Guangzhou or in the Pearl River Delta region. The three sources identified were vehicular emissions (VE), biomass burning (BB), and coal combustion (CC), accounting for 11 ±2 %, 31 ±4 %, and 58± 4 % of the total PAHs, respectively. CC replaced VE to become the most important source of PAHs in Guangzhou, reflecting the effective control of VE in recent years. The three sources had different contributions to PAHs with different ring sizes, with higher BB contributions (75 ±3 %) to four-ring PAHs such as pyrene and higher CC contributions (57±4 %) to six-ring PAHs such as benzo[ghi]perylene. Temporal variations of VE and CC contributions were probably caused by the change of weather conditions, while temporal variations of BB contributions were additionally influenced by the fluctuation of BB emissions. Source contributions also showed some spatial variations, probably due to the source emission variations near the sampling sites.
机译:从2009年11月28日至12月23日,在广州的六个地点同时采集了24小时的PM_(2.5)样品。测定了18种多环芳烃(PAHs)的浓度以及某些分子示踪剂,这些元素用于车辆排放(例如,hop烷和元素碳),燃煤(例如,pic烯)和生物质燃烧(即,左葡萄糖葡萄糖)。正矩阵分解(PMF)受体模型与示踪数据相结合,用于探讨源对PAHs的贡献。通过检查每个因素中的显性示踪剂,并将PMF的源剖面与广州或珠江三角洲地区确定的剖面进行比较,确定了三个源。确定的三个排放源是车辆排放量(VE),生物质燃烧(BB)和煤燃烧(CC),分别占总PAHs的11±2%,31±4%和58±4%。 CC取代VE成为广州最重要的PAHs来源,这反映了近年来对VE的有效控制。这三种来源对不同环尺寸的PAH的贡献不同,对to等四环PAH的BB贡献较高(75±3%),对六环PAH如苯并[6]的CC贡献较高(57±4%)。 hi。 VE和CC贡献的时间变化可能是由于天气条件的变化引起的,而BB贡献的时间变化还受到BB排放量波动的影响。源的贡献也显示出一些空间变化,可能是由于采样地点附近的源排放变化所致。

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