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Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

机译:得克萨斯州休斯敦的细颗粒物源分配:对二次有机气溶胶的见解

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Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM sub1/sub (PM ≤ 1 μ m) indicated major contributions from sulfate (averaging 50?% by mass), organic aerosol (OA, 40?%), and ammonium (14?%). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22?% hydrocarbon-like organic aerosol (HOA), 29?% cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48?% more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM sub2.5/sub (PM ≤ 2.5 μ m) filter samples agreed that organic matter (35?%) and sulfate (21?%) were the most abundant components. Organic speciation of PM sub2.5/sub organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM sub2.5/sub OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5?%), gasoline engines (24.3?%), nontailpipe vehicle emissions (11.1?%), ship emissions (2.2?%), cooking (1.0?%), biomass burning (BB, 10.6?%), isoprene SOA (11.0?%), high- NOsubx/sub anthropogenic SOA (6.6?%), and low- NOsubx/sub anthropogenic SOA (21.7?%). Using available source profiles, CMB apportioned 41?% of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5?% to BB, 15?% to SOA (including 7.4?% biogenic and 7.6?% anthropogenic), and 39?% to other sources that were not included in the model and are expected to be secondary. This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that ~ 50?% of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11?% of OC, with 5?% primary emissions and 6?% BB-SOA. SOA was dominantly anthropogenic (28?%) rather than biogenic (11?%) or BB-derived. The three-model approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.
机译:2015年5月,在美国得克萨斯州休斯顿的城市和工业休斯顿船舶航道附近,通过在线和离线测量环境颗粒物(PM)来表征其化学成分,并评估主要,次要,生物成因和人为来源。非难降解PM 1 (PM≤1μm)的气溶胶质谱(AMS)表明,硫酸盐(平均质量分数为50%),有机气溶胶(OA,浓度为40%)和铵的主要贡献(14%)。 AMS数据的正矩阵分解(PMF)将OA平均分为22%的类烃有机气溶胶(HOA),29%的受烹饪影响的氧化程度较低的氧化有机气溶胶(CI-LO-OOA)和48%氧化程度更高的氧化有机气溶胶(MO-OOA),后两种来源表示二次有机气溶胶(SOA)。对PM 2.5 (PM≤2.5μm)过滤器样品的化学分析表明,有机物(35%)和硫酸盐(21%)是最丰富的成分。 PM 2.5 有机碳(OC)的有机形态集中于主要来源的分子标记和源自生物和人为挥发性有机化合物(VOC)的SOA示踪剂。 PM 2.5 OC的来源使用基于分子标记的正矩阵因式分解(MM-PMF)和化学物质平衡(CMB)模型进行估算。 MM-PMF解决了九个因素,分别是柴油机(11.5%),汽油机(24.3%),非尾气排放(11.1%),船舶排放(2.2%),烹饪(1.0%),生物质燃烧(BB,10.6%),异戊二烯SOA(11.0%),高NO x 人为SOA(6.6 %%)和低NO x 人为SOA(21.7%)。使用可用的源配置文件,CMB将41%的OC分配给主要化石源(汽油发动机,柴油发动机和船舶排放物),将5%的OC分配给BB,将15%的SOA分配给SOA(包括7.4 %%的生物源性和7.6 %%的人为源性) ),占模型中未包括的其他来源的39%。本研究介绍了原位AMS-PMF,MM-PMF和CMB在OC源分配中的首次应用,以及这些方法的集成以评估生物源,人为源和BB-SOA的相对作用。三种来源分配模型都认为,约50%的OC与化石燃料尤其是机动车的化石燃料使用产生的主要排放有关。这些模型之间的差异反映了它们根据输入的化学测量值解析源的能力,基于分子标记的方法为较小的源提供了更大的源特异性和分辨率。结合MM-PMF和CMB的结果,估计BB贡献了OC的11%,主要排放量为5%,BB-SOA为6%。 SOA主要是人为因素(28%),而不是生物因素(11%)或BB来源的。三种模型方法证明了人为SOA对精细PM的重大贡献。更广泛地讲,本文介绍的发现和方法可用于促进对人为SOA贡献的本地和区域理解。

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