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利用多点 PMF 通过先验信息约束 G 值来改进 PM 的分配。

Improving apportionment of PM using multisite PMF by constraining G-values with a priori information.

机构信息

State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, USA; Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA.

出版信息

Sci Total Environ. 2020 Sep 20;736:139657. doi: 10.1016/j.scitotenv.2020.139657. Epub 2020 May 25.

Abstract

Rotational ambiguity in factor analyses prevents users from obtaining accurate source apportionment results. The rotational space in positive matrix factorization (PMF) can be reduced by constraining the solution with a prioriinformation such as source profiles. However, the only prior report on constraints using information on the source contributions was their use to ensure compatibility in the simultaneous analyses of PM and PM data. By combining data from three monitoring sites affected by a gear casting plant in Xi'an as an example, a methodology for improving the accuracy of PMF results by constraining source contributions using wind information was explored. Seven common factors derived from individual PMF analyses for each of the three sites (INDUS, URBAN, and RURAL) with different location characteristics, were then combined in a multisite PMF analysis. The factors were interpreted as nitrate with all site average contributions of 28.7%, sulfate (22.5%), coal combustion (19.3%), road traffic (12.8%), biomass burning (6.4%), soil (5.4%), and metallurgical industry (4.9%). Except for the INDUS site, contributions of metallurgical industry to the URBAN and RURAL sites were pulled down maximally to reduce the rotational space. The constrained solution substantially improved the results over the base run. The local and regional nature of the sources were identified by coefficient of divergence combined with Pearson correlation analysis, and further quantitatively estimated using Lenschow approach. On average, local sources contributed for 52.4% and 47.7% of the PM mass concentrations at the INDUS and URBAN site respectively. The metallurgical industry showed the highest local contributions while sulfate was primarily regional. For the multisite analysis where there are considerable point source emissions, this methodology highlights the role of local wind directions to inform constraints on the results and obtaining more reliable solutions.

摘要

旋转模糊性会妨碍因子分析用户获得准确的源解析结果。正矩阵因子分解(PMF)中的旋转空间可以通过使用先验信息(如源谱)来约束解来缩小。然而,唯一一份关于使用源贡献信息进行约束的先验报告是将其用于确保 PM 和 PM 数据的同时分析具有兼容性。通过结合西安一家齿轮铸造厂附近三个监测点的数据作为示例,探索了一种通过使用风向信息约束源贡献来提高 PMF 结果准确性的方法。然后,在多站点 PMF 分析中,将具有不同位置特征的三个站点(INDUS、URBAN 和 RURAL)的单个 PMF 分析得出的七个常见因子进行了组合。这些因子被解释为硝酸盐,所有站点的平均贡献为 28.7%,硫酸盐(22.5%),煤炭燃烧(19.3%),道路交通(12.8%),生物质燃烧(6.4%),土壤(5.4%)和冶金工业(4.9%)。除了 INDUS 站点之外,冶金工业对 URBAN 和 RURAL 站点的贡献被最大程度地拉低,以缩小旋转空间。约束解大大改善了基础运行的结果。通过发散系数与 Pearson 相关分析相结合,并进一步使用 Lenschow 方法进行定量估计,确定了源的局部和区域性质。平均而言,本地源对 INDUS 和 URBAN 站点的 PM 质量浓度的贡献分别为 52.4%和 47.7%。冶金工业显示出最高的本地贡献,而硫酸盐主要是区域性的。对于存在大量点源排放的多站点分析,该方法强调了本地风向的作用,以告知结果的约束,并获得更可靠的解决方案。

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