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Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

机译:使用正矩阵分解(PMF)中央洛杉矶环境粒子数集中的源分摊(PMF)

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摘要

In this study, the positive matrix factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 μm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (with a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60% (60.8-68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 to 24 %), with a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12% (7.4-17.1), 2.1% (1.5-2.5 %),
机译:在该研究中,使用阳性矩阵分解(PMF)受体模型(5.0版)来识别和量化有助于颗粒物质(PM)数量浓度的主要来源,使用13nm至10μm的组合范围内的PM数尺寸分布具有几种辅助变量,包括黑碳(BC),元素和有机碳(EC / OC),PM质量浓度,气态污染物,气象和流量计数数据,在2014年8月至2015年期间在洛杉矶中部约9个月收集,加利福尼亚州。几个参数,包括粒子数和体积大小分布配置文件,辅助变量的简档,不同季节不同因素的贡献到总数浓度,每个分辨因子的昼夜变化在寒冷和温暖的阶段,平日/周末分析用于识别PM源的每个分辨因子和辅助变量与每个已解决因子的相对贡献之间的每个分辨因子和相关性。将六因素解决方案鉴定为上述输入数据的最佳选择。解决因子包括成核,交通1,交通2(具有比交通1因素的更大的模式直径),城市背景气溶胶,二次气溶胶和土壤/道路灰尘。交通资源(1和2)是PM数量浓度的主要贡献者,在研究期间总数浓度的总数(60.8-68.4%的主要贡献者。与温度相比,它们在冷相中的贡献也明显高。成核是另一个主要因素,显着贡献总数浓度(总贡献17%,范围为11.7至24%),在温暖阶段贡献较大,而不是冷阶段。其他鉴定的因素是城市背景气溶胶,二次气溶胶和土壤/道路粉尘,相对贡献约为12%(7.4-17.1),2.1%(1.5-2.5%),

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