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. The interactions between organic andinorganic aerosol chemical components are integral to understanding andmodelling climate and health-relevant aerosol physicochemical properties,such as volatility, hygroscopicity, light scattering and toxicity. This studypresents a synthesis analysis for eight data sets, of non-refractory aerosolcomposition, measured at a boreal forest site. The measurements, performedwith an aerosol mass spectrometer, cover in total around 9 months over thecourse of 3 years. In our statistical analysis, we use the complete organicand inorganic unit-resolution mass spectra, as opposed to the more commonapproach of only including the organic fraction. The analysis is based oniterative, combined use of (1) data reduction, (2) classification and(3) scaling tools, producing a data-driven chemical mass balance type ofmodel capable of describing site-specific aerosol composition. The receptormodel we constructed was able to explain 83±8  % of variation indata, which increased to 96±3  % when signals from lowsignal-to-noise variables were not considered. The resulting interpretationof an extensive set of aerosol mass spectrometric data infers seven distinctaerosol chemical components for a rural boreal forest site: ammonium sulfate( 35±7  % of mass), low and semi-volatile oxidised organic aerosols( 27±8  % and 12±7  %), biomass burning organic aerosol ( 11±7  %), a nitrate-containing organic aerosol type ( 7±2  %),ammonium nitrate ( 5±2  %), and hydrocarbon-like organic aerosol ( 3±1  %). Some of the additionally observed, rare outlier aerosol typeslikely emerge due to surface ionisation effects and likely represent aminecompounds from an unknown source and alkaline metals from emissions of anearby district heating plant. Compared to traditional, ion-balance-basedinorganics apportionment schemes for aerosol mass spectrometer data, ourstatistics-based method provides an improved, more robust approach, yieldingreadily useful information for the modelling of submicron atmosphericaerosols physical and chemical properties. The results also shed light on thedivision between organic and inorganic aerosol types and dynamics of saltformation in aerosol. Equally importantly, the combined methodologyexemplifies an iterative analysis, using consequent analysis steps by acombination of statistical methods. Such an approach offers new ways to homein on physicochemically sensible solutions with minimal need for a prioriinformation or analyst interference. We therefore suggest that similarstatistics-based approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosolmass spectrometric data.

atmospheric chemistry and physics

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