Positive matrix factorization (PMF) analysis of molecular marker measurements to quantify the sources of organic aerosols
Positive matrix factorization (PMF) analysis of molecular marker measurements to quantify the sources of organic aerosols
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DOI：
10.1021/es062536b
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发表时间：
2007-08-15
影响因子：
11.4
通讯作者：
Schauer, James J.
中科院分区：
文献类型：
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作者：
Jaeckels, Jeffrey M.;Bae, Min-Suk;Schauer, James J.
对在圣路易斯中西部Supersite 2年期间收集的125个颗粒物样品进行了24小时平均有机碳（OC）、元素碳（EC）和颗粒相有机化合物（分子标记物）浓度分析。超过100种有机化合物沿着硅和铝的测量进行了分析，使用基于因子分析的源解析模型，正矩阵因子分解（PMF），这在过去已被广泛使用的元素数据，但没有有机分子标记。四种不同的解决方案（7，8，9，10因子解决方案）的PMF模型进行了探索，考虑源解析结果的稳定性，这是合理的稳定。进一步探讨了八因素的解决方案，并与使用PMF数据子集的并行化学质量平衡（CMB）源解析建模结果进行了比较。一个基本的情况下，八因素PMF解决方案解决了两个点源因素，两个冬季燃烧因素，生物质燃烧因素，一个移动的源因素，二次有机气溶胶因素，和再悬浮土壤因素。一个优化的八个因素的情况下，也进行了检查，这是制定通过删除三个极端的点源的影响观察到的基础情况下，以更好地了解非点源。在优化的情况下，由生物质燃烧解释的每日OC与相应的CMB源显示出良好的一致性，斜率为0.93 +/- 0.03。同样，平均有机碳解释的优化PMF再悬浮土壤因子表现出良好的相关性与中巴道路灰尘分配，但有一个显着的偏差之间的两个结果。从冬季燃烧因素之一的优化PMF OC表现出良好的相关性与CMB天然气燃烧分配，但也有显着的偏差。在这两种情况下，PMF分析因素的一个移动的源控制的Hopanes和streranes，这并没有相关性以及与任何三个CMB移动的源。虽然大多数的分子标记物与PMF模型聚类的方式与这些有机化合物的先验知识一致，观察到一个显着的偏差。胆固醇，过去被用作肉类烟雾的示踪剂，被发现在很大程度上与道路灰尘有关，这对胆固醇作为美国中西部肉类烟雾示踪剂的适用性提出了质疑。
One hundred and twenty five particulate matter samples that were collected over a 2 year period at the St. Louis Midwest Supersite were analyzed for 24 hour average organic carbon (OC), elemental carbon (EC), and particle-phase organic compound (molecular markers) concentrations. Over 100 organic compounds along with measurements of silicon and aluminum were analyzed using a factor analysis based source apportionment model, positive matrix factorization (PMF), which has been widely used in the past with elemental data but not organic molecular markers. Four different solutions (7, 8, 9, and 10 factor solutions) to the PMF model were explored to consider the stability of the source apportionment results,,which were found to be reasonably stable. The eight-factor solution was further explored and compared to a parallel chemical mass balance (CMB) source apportionment modeling result that used a subset of the PMF data. A base case eight-factor PMF solution resolved two point source factors, two winter combustion factors, a biomass-burning factor, a mobile source factor, a secondary organic aerosol factor, and a resuspended soil factor. An optimized eight-factor case was also examined, which was formulated by removing three extreme point source impacts observed in the base case, to better understand the nonpoint sources. In the optimized case, the daily OC explained by the biomass burning shows good agreement with the corresponding CMB source, with a slope of 0.93 +/- 0.03. Likewise, the average OC explained by the optimized PMF resuspended soil factor showed good correlation with the CMB road dust apportionment, but there was a significant bias between the two results. The optimized PMF OC from one of the winter combustion factors showed good correlation with the CMB natural gas combustion apportionment but also has a significant bias. In both cases, PMF analysis factored one mobile source controlled by hopanes and streranes, which did not correlate well with any of the three CMB mobile sources. Although the most of the molecular markers were clustered with the PMF model in a manner consistent with prior knowledge of these organic compounds, one significant deviation was observed. Cholesterol, used in the past as a tracer for meat smoke, was found to largely associate with road dust, which raises questions on the suitability of cholesterol as a tracer for meat smoke in the midwestern U.S.