Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, Colorado 80309, USA.
Environ Sci Technol. 2012 Nov 6;46(21):11962-70. doi: 10.1021/es302358g. Epub 2012 Oct 24.
To evaluate the utility and consistency of different speciation data sets in source apportionment of PM(2.5), positive matrix factorization (PMF) coupled with a bootstrap technique for uncertainty assessment was applied to four different 1-year data sets composed of bulk species, bulk species and water-soluble elements (WSE), bulk species and organic molecular markers (OMM), and all species. The five factors resolved by using only the bulk species best reproduced the observed concentrations of PM(2.5) components. Combining WSE with bulk species as PMF inputs also produced five factors. Three of them were linked to soil, road dust, and processed dust, and together contributed 26.0% of reconstructed PM(2.5) mass. A 7-factor PMF solution was identified using speciated OMM and bulk species. The EC/sterane and summertime/selective aliphatic factors had the highest contributions to EC (39.0%) and OC (53.8%), respectively. The nine factors resolved by including all species as input data are consistent with those from the previous two solutions (WSE and bulk species, OMM and bulk species) in both factor profiles and contributions (r = 0.88-1.00). The comparisons across different solutions indicate that the selection of input data set may depend on the PM components or sources of interest for specific source-oriented health study.
为了评估不同形态数据集在 PM（2.5）源解析中的效用和一致性，应用正矩阵因子分解（PMF）结合 bootstrap 技术进行不确定性评估，对由大量物种、大量物种和水溶性元素（WSE）、大量物种和有机分子标志物（OMM）以及所有物种组成的四个不同的 1 年数据集进行了分析。仅使用大量物种解析的五个因子最能再现 PM（2.5）成分的观测浓度。将 WSE 与大量物种结合作为 PMF 输入，也产生了五个因子。其中三个因子与土壤、道路灰尘和加工灰尘有关，共占重建 PM（2.5）质量的 26.0%。使用特定形态的 OMM 和大量物种确定了 7 因子 PMF 解决方案。EC/甾烷和夏季/选择性脂肪因子对 EC（39.0%）和 OC（53.8%）的贡献最大。包含所有物种作为输入数据的九个因子在因子分布和贡献方面与前两种解决方案（WSE 和大量物种、OMM 和大量物种）一致（r = 0.88-1.00）。不同解决方案之间的比较表明，输入数据集的选择可能取决于特定面向源的健康研究中感兴趣的 PM 成分或源。
