Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment

IF 3.4 Q2 ENVIRONMENTAL SCIENCES
M. Manousakas , J. Rausch , D. Jaramillo-Vogel , K.S. Schneider-Beltran , A. Alastuey , J-L. Jaffrezo , G. Uzu , S. Perseguers , N. Schnidrig , A.S.H. Prevot , K.R. Daellenbach
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Abstract

This study examines the consistency between the chemical composition of source profiles retrieved by positive matrix factorization (PMF), which is based on bulk chemical analysis, and the composition of a large data set of individual particles from real-world environmental samples. Since PMF derives source profiles from the average chemical composition of many particles, it is crucial to assess how well these profiles reflect the actual composition of particles originating from individual sources. To address this, we compare PMF-based source apportionment of coarse particulate matter (PMcoarse) with Automated Single-Particle Analysis (ASPA) using Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDX) and a machine-learning based particle classification. Both methods identified at least four major PMcoarse sources—mineral dust, non-exhaust vehicle emissions, biological particles, and road salt—across urban and rural environments in Switzerland. The elemental composition of these sources determined by PMF was compared with ASPA-derived compositions of analogous particle types. The results indicate that while PMF effectively captures key source characteristics, single-particle analysis provides a more detailed representation of source-specific chemical compositions alongside morpho-textural features. ASPA also facilitated the identification and quantification of elements not detected in bulk analysis, such as oxygen and silica, improving overall PM characterization. A sensitivity test using a single-location subset demonstrated that incorporating ASPA-derived profiles into PMF enhances source differentiation, particularly for small data sets. These findings demonstrate the utility of single-particle analysis as an independent approach for constraining and validating the chemical composition of source profiles, thereby providing a means to enhance and validate source apportionment outcomes derived from bulk analysis methods such as PMF.
不同技术鉴定的PM源剖面的比较以及在源分配中利用单粒子分析数据的潜力
本研究考察了基于大量化学分析的正矩阵分解(PMF)提取的源剖面化学成分与来自真实环境样本的单个颗粒的大型数据集的组成之间的一致性。由于PMF从许多颗粒的平均化学成分中得出源剖面,因此评估这些剖面在多大程度上反映来自单个来源的颗粒的实际组成至关重要。为了解决这个问题,我们比较了基于pmf的粗颗粒物质(pm粗)源分配与使用扫描电子显微镜(SEM)结合能量色散x射线光谱(EDX)和基于机器学习的颗粒分类的自动单颗粒分析(ASPA)。两种方法都确定了瑞士城市和农村环境中至少四种主要的PMcoarse来源-矿物粉尘,非排气车辆排放,生物颗粒和道路盐。PMF测定的这些源的元素组成与aspa衍生的类似颗粒类型的组成进行了比较。结果表明,虽然PMF有效地捕获了关键的源特征,但单颗粒分析可以更详细地表示源特定的化学成分以及形态结构特征。ASPA还有助于鉴定和定量在整体分析中未检测到的元素,如氧和二氧化硅,提高整体PM表征。使用单一位置子集的灵敏度测试表明,将aspa衍生的剖面合并到PMF中可以增强源区分,特别是对于小数据集。这些发现证明了单颗粒分析作为约束和验证源剖面化学成分的独立方法的实用性,从而提供了一种增强和验证来自PMF等整体分析方法的源分配结果的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
CiteScore
8.00
自引率
0.00%
发文量
47
审稿时长
12 weeks
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