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DOI10.5194/acp-17-3165-2017
Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events
Aijala, Mikko1; Heikkinen, Liine1; Frohlich, Roman2; Canonaco, Francesco2; Prevot, Andre S. H.2; Junninen, Heikki1; Petaja, Tuukka1; Kulmala, Markku1; Worsnop, Douglas1,3; Ehn, Mikael1
2017-03-01
发表期刊ATMOSPHERIC CHEMISTRY AND PHYSICS
ISSN1680-7316
EISSN1680-7324
出版年2017
卷号17期号:4
文章类型Article
语种英语
国家Finland; Switzerland; USA
英文摘要

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statisticsbased data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k -means C C, for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral simi-larity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.


领域地球科学
收录类别SCI-E
WOS记录号WOS:000395149400002
WOS关键词POSITIVE MATRIX FACTORIZATION ; QUALITY INTERACTIONS EUCAARI ; EUROPEAN INTEGRATED PROJECT ; FINE-PARTICLE COMPOSITION ; MASS-SPECTROMETER DATA ; VOLATILITY BASIS-SET ; NEW-YORK-CITY ; ORGANIC-AEROSOL ; SOURCE APPORTIONMENT ; HIGH-RESOLUTION
WOS类目Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/16228
专题地球科学
作者单位1.Univ Helsinki, Dept Phys, Helsinki, Finland;
2.Paul Scherrer Inst, Lab Atmospher Chem, Villigen, Switzerland;
3.Aerodyne Res Inc, Billerica, MA USA
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GB/T 7714
Aijala, Mikko,Heikkinen, Liine,Frohlich, Roman,et al. Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(4).
APA Aijala, Mikko.,Heikkinen, Liine.,Frohlich, Roman.,Canonaco, Francesco.,Prevot, Andre S. H..,...&Ehn, Mikael.(2017).Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(4).
MLA Aijala, Mikko,et al."Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.4(2017).
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