Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).
Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).
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DOI：
10.1016/j.atmosenv.2010.04.038
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发表时间：
2010-07-01
影响因子：
5
通讯作者：
Hannigan, Michael P.
中科院分区：
文献类型：
--
作者：
Dutton, Steven J.;Vedal, Sverre;Piedrahita, Ricardo;Milford, Jana B.;Miller, Shelly L.;Hannigan, Michael P.
关键词：
直径小于2.5微米的颗粒物（PM2.5）与一系列不利的健康影响有关。确定对这些健康影响最重要的PM2.5来源，可以提高对这些影响机制的理解，并进行更有针对性的调控。这为丹佛气溶胶源与健康（DASH）研究提供了动力，这是一项多年的来源分配和健康影响研究，依赖于详细的无机和有机PM2.5形态测量。在本研究中，PM2.5源的解析是通过将正矩阵分解（PMF）与PM2.5的日常测量数据（包括无机离子、元素碳（EC）和有机碳（OC）以及有机分子标记物）耦合进行的。对PMF问题常用的两种模型PMF2和ME2进行了定性比较。之前的许多研究都将化学质量平衡（CMB）用于有限数据集上的有机分子标记源分配，但DASH数据集足够大，可以使用多因素分析技术，如PMF。深入研究了PMF2和ME2模型对特定PM2.5组分和模型输入参数选择的敏感性。结合诊断方法，选择一个最佳的7因素模型，使用一整年的每日数据与点测量不确定性。因子包括1)冬季/甲氧基酚因子，2)EC/甾烷因子，3)硝酸盐/多环芳烃（PAH）因子，4)夏季/选择性脂肪因子，5)正构烷烃因子，6)中间氧合PAH/烷烃酸因子和7)无机离子因子。这七个因素与已知的PM2.5排放源有不同程度的定性联系。采用7因子模型进行质量分配，揭示了各因子对OC、EC、硝酸盐和硫酸盐质量的贡献。从年尺度上看，有机酸和有机酸质量主要与夏季/选择性脂肪因子和有机酸/甾烷因子有关，而硝酸盐和硫酸盐质量主要与无机离子因子有关。这种分配发现因季节而有很大差异。在这项研究中确定的几个因素与在密苏里州圣路易斯和宾夕法尼亚州匹兹堡使用PMF和有机分子标记进行的类似评估非常一致。
Particulate matter less than 2.5 microns in diameter (PM2.5) has been linked with a wide range of adverse health effects. Determination of the sources of PM2.5 most responsible for these health effects could lead to improved understanding of the mechanisms of such effects and more targeted regulation. This has provided the impetus for the Denver Aerosol Sources and Health (DASH) study, a multi-year source apportionment and health effects study relying on detailed inorganic and organic PM2.5 speciation measurements. In this study, PM2.5 source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM2.5 measurements including inorganic ions, elemental carbon (EC) and organic carbon (OC), and organic molecular markers. A qualitative comparison is made between two models, PMF2 and ME2, commonly used for solving the PMF problem. Many previous studies have incorporated chemical mass balance (CMB) for organic molecular marker source apportionment on limited data sets, but the DASH data set is large enough to use multivariate factor analysis techniques such as PMF. Sensitivity of the PMF2 and ME2 models to the selection of speciated PM2.5 components and model input parameters was investigated in depth. A combination of diagnostics was used to select an optimum, 7-factor model using one complete year of daily data with pointwise measurement uncertainties. The factors included 1) a wintertime/methoxyphenol factor, 2) an EC/sterane factor, 3) a nitrate/polycyclic aromatic hydrocarbon (PAH) factor, 4) a summertime/selective aliphatic factor, 5) an n-alkane factor, 6) a middle oxygenated PAH/alkanoic acid factor and 7) an inorganic ion factor. These seven factors were qualitatively linked with known PM2.5 emission sources with varying degrees of confidence. Mass apportionment using the 7-factor model revealed the contribution of each factor to the mass of OC, EC, nitrate and sulfate. On an annual basis, the majority of OC and EC mass was associated with the summertime/selective aliphatic factor and the EC/sterane factor, respectively, while nitrate and sulfate mass were both dominated by the inorganic ion factor. This apportionment was found to vary substantially by season. Several of the factors identified in this study agree well with similar assessments conducted in St. Louis, MO and Pittsburgh, PA using PMF and organic molecular markers.
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影响因子：
11.4
作者：
Harley, RA;Marr, LC;Giddings, SN
通讯作者：
Giddings, SN
影响因子：
11.4
作者：
Jaeckels, Jeffrey M.;Bae, Min-Suk;Schauer, James J.
通讯作者：
Schauer, James J.
影响因子：
4.4
作者：
Polissar, AV;Hopke, PK;Paatero, P
通讯作者：
Paatero, P
DOI：
10.1016/s0169-7439(01)00200-3
发表时间：
2002-01-28
影响因子：
3.9
作者：
Paatero, P;Hopke, PK;Ramadan, Z
通讯作者：
Ramadan, Z
影响因子：
5
作者：
Dutton, Steven J.;Schauer, James J.;Vedal, Sverre;Hannigan, Michael P.
通讯作者：
Hannigan, Michael P.