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| DOI | 10.5194/acp-20-2489-2020 |
| A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements | |
| Li Z.; D'Ambro E.L.; Schobesberger S.; Gaston C.J.; Lopez-Hilfiker F.D.; Liu J.; Shilling J.E.; Thornton J.A.; Cappa C.D. | |
| 发表日期 | 2020 |
| ISSN | 16807316 |
| 起始页码 | 2489 |
| 结束页码 | 2512 |
| 卷号 | 20期号:4 |
| 英文摘要 | One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral dataset helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed an algorithm for clustering mass spectra, the noise-sorted scanning clustering (NSSC), appropriate for application to thermal desorption measurements of collected OA particles from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO-CIMS). NSSC, which extends the common density-based special clustering of applications with noise (DBSCAN) algorithm, provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for the determination of thermal profiles for compositionally distinct clusters of mass spectra, increasing the accessibility and enhancing the interpretation of FIGAERO data. Applications of NSSC to several laboratory biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of NSSC to distinguish different types of thermal behaviors for the components comprising the particles along with the relative mass contributions and chemical properties (e.g., average molecular formula) of each mass spectral cluster. For each of the systems examined, more than 80% of the total mass is clustered into 9-13 mass spectral clusters. Comparison of the average thermograms of the mass spectral clusters between systems indicates some commonality in terms of the thermal properties of different BSOA, although with some system-specific behavior. Application of NSSC to sets of experiments in which one experimental parameter, such as the concentration of NO, is varied demonstrates the potential for mass spectral clustering to elucidate the chemical factors that drive changes in the thermal properties of OA particles. Further quantitative interpretation of the thermograms of the mass spectral clusters will allow for a more comprehensive understanding of the thermochemical properties of OA particles. © 2020 Author(s). |
| 关键词 | aerosol compositionalgorithmcluster analysisconcentration (composition)data interpretationmass spectrometrymeasurement methodthermodynamic property |
| 语种 | 英语 |
| 来源机构 | Atmospheric Chemistry and Physics |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/132197 |
| 推荐引用方式 GB/T 7714 | Li Z.,D'Ambro E.L.,Schobesberger S.,et al. A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements[J]. Atmospheric Chemistry and Physics,2020,20(4). |
| APA | Li Z..,D'Ambro E.L..,Schobesberger S..,Gaston C.J..,Lopez-Hilfiker F.D..,...&Cappa C.D..(2020).A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements.,20(4). |
| MLA | Li Z.,et al."A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements".20.4(2020). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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