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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Oct 2018

Research article | 19 Oct 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

Constructing a data-driven receptor model for organic and inorganic aerosol – a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

Mikko Äijälä1, Kaspar R. Daellenbach1, Francesco Canonaco2, Liine Heikkinen1, Heikki Junninen1,3, Tuukka Petäjä1, Markku Kulmala1, André S. H. Prévôt2, and Mikael Ehn1 Mikko Äijälä et al.
  • 1Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, Helsinki, Finland
  • 2Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland
  • 3Laboratory of Environmental Physics, University of Tartu, Tartu, Estonia

Abstract. The interactions between organic and inorganic aerosol chemical components are integral to understanding and modelling climate and health-relevant aerosol physicochemical properties, such as volatility, hygroscopicity, light scattering and toxicity. This study presents a synthesis analysis for eight data sets, of non-refractory aerosol composition, measured at a boreal forest site. The measurements, performed with an aerosol mass spectrometer, cover in total around 9 months over the course of 3 years. In our statistical analysis, we use the complete organic and inorganic unit-resolution mass spectra, as opposed to the more common approach of only including the organic fraction. The analysis is based on iterative, combined use of (1) data reduction, (2) classification and (3) scaling tools, producing a data-driven chemical mass balance type of model capable of describing site-specific aerosol composition. The receptor model we constructed was able to explain 83±8% of variation in data, increased to 96±3% when signals from low signal-to-noise variables were not considered. The resulting interpretation of an extensive set of aerosol mass spectrometric data infers seven distinct aerosol chemical components for a rural boreal forest site: ammonium sulphate (35% of mass), low and semi-volatile oxidised organic aerosols (27 and 12%), biomass burning organic aerosol (11%), a nitrate containing organic aerosol type (7%), ammonium nitrate (5%), and hydrocarbon-like organic aerosol (3%). Some of the additionally observed, rare outlier aerosol types likely emerge due to surface ionisation effects, and likely represent amine compounds from an unknown source and alkaline metals from emissions of a nearby district heating plant. Compared to traditional, simplistic inorganics apportionment methods for aerosol mass spectrometer data, our statistics-based method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron atmospheric aerosols physical and chemical properties. The results also shed light on the division between organic and inorganic aerosol types and dynamics of salt formation in aerosol. Equally importantly, the combined methodology exemplifies an iterative analysis, using consequent analysis steps by a combination of statistical methods. Such an approach offers new ways to home in on physicochemically sensible solutions with minimal need for a priori information or analyst interference. We therefore suggest that similar statistics-based approaches offer significant potential for un/semi supervised machine-learning applications in future analyses of aerosol mass spectrometric data.

Mikko Äijälä et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
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Mikko Äijälä et al.
Mikko Äijälä et al.
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Publications Copernicus
Short summary
Aerosol mass spectrometry produces large amounts of complex data, the analysis of which necessitates chemometrics – the application of advanced statistical and mathematical tools on chemical data. Here, we perform a data-driven analysis of multiple aerosol mass spectrometric data sets, to show that the traditional separation of organics and inorganics is not necessary. The resulting 7-component aerosol speciation explains 83 % to 96 % of observed variability at our boreal forest experiment site.
Aerosol mass spectrometry produces large amounts of complex data, the analysis of which...