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基于无机和有机特定PM(2.5)每日测量值,使用正定矩阵因子分解法进行源解析。

Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).

作者信息

Dutton Steven J, Vedal Sverre, Piedrahita Ricardo, Milford Jana B, Miller Shelly L, Hannigan Michael P

机构信息

National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.

出版信息

Atmos Environ (1994). 2010 Jul 1;44(23):2731-2741. doi: 10.1016/j.atmosenv.2010.04.038.

Abstract

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been linked with a wide range of adverse health effects. Determination of the sources of PM(2.5) 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 PM(2.5) speciation measurements.In this study, PM(2.5) source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM(2.5) 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 PM(2.5) 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 PM(2.5) 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.

摘要

直径小于2.5微米的颗粒物(PM2.5)与多种不良健康影响相关联。确定对这些健康影响负有最大责任的PM2.5来源,有助于增进对此类影响机制的理解,并实现更具针对性的监管。这为丹佛气溶胶来源与健康(DASH)研究提供了动力,该研究是一项为期多年的来源解析与健康影响研究,依赖于详细的无机和有机PM2.5成分测量。在本研究中,通过将正定矩阵因子分解法(PMF)与每日特定的PM2.5测量结果相结合来进行PM2.5来源解析,这些测量结果包括无机离子、元素碳(EC)、有机碳(OC)以及有机分子标志物。对常用于解决PMF问题的两种模型PMF2和ME2进行了定性比较。许多先前的研究在有限数据集上采用化学质量平衡法(CMB)进行有机分子标志物来源解析,但DASH数据集规模足够大,可使用诸如PMF等多变量因子分析技术。深入研究了PMF2和ME2模型对特定PM2.5成分选择和模型输入参数的敏感性。使用了一系列诊断方法,利用一整年的每日数据及逐点测量不确定度,选择了一个最优的七因子模型。这些因子包括:1)冬季/甲氧基苯酚因子;2)EC/甾烷因子;3)硝酸盐/多环芳烃(PAH)因子;4)夏季/选择性脂肪族因子;5)正构烷烃因子;6)中等氧化态PAH/链烷酸因子;7)无机离子因子。这七个因子在不同程度上与已知的PM2.5排放源存在定性关联。使用七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。就年度而言,大部分OC和EC质量分别与夏季/选择性脂肪族因子和EC/甾烷因子相关,而硝酸盐和硫酸盐质量则主要由无机离子因子主导。研究发现,这种分配在不同季节有很大差异。本研究中确定的几个因子与在密苏里州圣路易斯市和宾夕法尼亚州匹兹堡市使用PMF和有机分子标志物进行的类似评估结果高度一致。

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