Highly oxidized multifunctional compounds (HOMs) have been demonstrated to be important for atmospheric secondary organic aerosols (SOA) and new-particle formation (NPF), yet it remains unclear which ...the main atmospheric HOM formation pathways are. In this study, a nitrate-ion-based chemical ionization atmospheric-pressure-interface time-of-flight mass spectrometer (CI-APi-TOF) was deployed to measure HOMs in the boreal forest in Hyytiälä, southern Finland. Positive matrix factorization (PMF) was applied to separate the detected HOM species into several factors, relating these “factors” to plausible formation pathways. PMF was performed with a revised error estimation derived from laboratory data, which agrees well with an estimate based on ambient data. Three factors explained the majority (> 95 %) of the data variation, but the optimal solution found six factors, including two nighttime factors, three daytime factors, and a transport factor. One nighttime factor is almost identical to laboratory spectra generated from monoterpene ozonolysis, while the second likely represents monoterpene oxidation initiated by NO3. The exact chemical processes forming the different daytime factors remain unclear, but they all have clearly distinct diurnal profiles, very likely related to monoterpene oxidation with a strong influence from NO, presumably through its effect on peroxy radical (RO2) chemistry. Apart from these five “local” factors, the sixth factor is interpreted as a transport related factor. These findings improve our understanding of HOM production by confirming current knowledge and inspiring future research directions and provide new perspectives on using factorization methods to understand short-lived atmospheric species.
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, statistics-based 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+ + , 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 similarity 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.
The volatility distribution of secondary organic aerosols that formed and had undergone aging – i.e., the particle mass fractions of semi-volatile, low-volatility and extremely low volatility organic ...compounds in the particle phase – was characterized in a boreal forest environment of Hyytiälä, southern Finland. This was done by interpreting field measurements using a volatility tandem differential mobility analyzer (VTDMA) with a kinetic evaporation model. The field measurements were performed during April and May 2014. On average, 40 % of the organics in particles were semi-volatile, 34 % were low-volatility organics and 26 % were extremely low volatility organics. The model was, however, very sensitive to the vaporization enthalpies assumed for the organics (ΔHVAP). The best agreement between the observed and modeled temperature dependence of the evaporation was obtained when effective vaporization enthalpy values of 80 kJ mol−1 were assumed. There are several potential reasons for the low effective enthalpy value, including molecular decomposition or dissociation that might occur in the particle phase upon heating, mixture effects and compound-dependent uncertainties in the mass accommodation coefficient. In addition to the VTDMA-based analysis, semi-volatile and low-volatility organic mass fractions were independently determined by applying positive matrix factorization (PMF) to high-resolution aerosol mass spectrometer (HR-AMS) data. The factor separation was based on the oxygenation levels of organics, specifically the relative abundance of mass ions at m∕z 43 (f43) and m∕z 44 (f44). The mass fractions of these two organic groups were compared against the VTDMA-based results. In general, the best agreement between the VTDMA results and the PMF-derived mass fractions of organics was obtained when ΔHVAP = 80 kJ mol−1 was set for all organic groups in the model, with a linear correlation coefficient of around 0.4. However, this still indicates that only about 16 % (R2) of the variation can be explained by the linear regression between the results from these two methods. The prospect of determining of extremely low volatility organic aerosols (ELVOAs) from AMS data using the PMF analysis should be assessed in future studies.
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