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, which 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 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 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, ion-balance-based
inorganics apportionment schemes 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- or semi-supervised machine-learning applications in future analyses of aerosol
mass spectrometric data.
MS2DB: A Mass-Based Hashing Algorithm for the Identification of Disulfide Linkage Patterns in Protein Utilizing Mass Spectrometric Data
