Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
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...
| Autores: | , , , , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/399104 |
| Acesso em linha: | http://hdl.handle.net/10261/399104 https://api.elsevier.com/content/abstract/scopus_id/105014253603 |
| Access Level: | acceso abierto |
| Palavra-chave: | Source apportionment Automated single particle analysis Bulk analysis Machine-learning PMF http://metadata.un.org/sdg/9 http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/11 Ensure healthy lives and promote well-being for all at all ages Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation Make cities and human settlements inclusive, safe, resilient and sustainable |
| Resumo: | 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 (PM<inf>coarse</inf>) 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 PM<inf>coarse</inf> 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. |
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