Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

• Published in: Atmospheric chemistry and physics 2021-10, Vol.21 (19), p.15081-15101
• Main Authors: Chen, Gang, Sosedova, Yulia, Canonaco, Francesco, Fröhlich, Roman, Tobler, Anna, Vlachou, Athanasia, Daellenbach, Kaspar R, Bozzetti, Carlo, Hueglin, Christoph, Graf, Peter, Baltensperger, Urs, Slowik, Jay G, El Haddad, Imad, Prévôt, André S. H
• Format: Article
• Language: English
• Subjects: Aerosols; Air pollution; Air quality; Algorithms; Alpine regions; Analysis; Apportionment; Atmospheric gases; Biomass; Biomass burning; Burning; Chemical speciation; Correlation; Discriminant analysis; Environmental aspects; Environmental policy; Factor analysis; Factorization; Hydrocarbons; Inorganic salts; Mass; Mass spectra; Mass spectrometry; Measurement; Mitigation; Oxidation; Oxygenation; Rural areas; Salts; Seasonal variation; Seasonal variations; Speciation; Statistical methods; Summer; Temporal variations; Time dependence; Tracers; Traffic congestion; Uncertainty; Winter
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-21-15081-2021

We collected 1 year of aerosol chemical speciation monitor (ACSM) data in Magadino, a village located in the south of the Swiss Alpine region, one of Switzerland's most polluted areas. We analysed the mass spectra of organic aerosol (OA) by positive matrix factorisation (PMF) using Source Finder Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed a rolling algorithm, which is closer to the measurement, to account for the temporal changes in the source profiles. As the first-ever application of rolling PMF with multilinear engine (ME-2) analysis on a yearlong dataset that was collected from a rural site, we resolved two primary OA factors (traffic-related hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one mass-to-charge ratio (m/z) 58-related OA (58-OA) factor, a less oxidised oxygenated OA (LO-OOA) factor, and a more oxidised oxygenated OA (MO-OOA) factor. HOA showed stable contributions to the total OA through the whole year ranging from 8.1 % to 10.1 %, while the contribution of BBOA showed an apparent seasonal variation with a range of 8.3 %–27.4 % (highest during winter, lowest during summer) and a yearly average of 17.1 %. OOA (sum of LO-OOA and MO-OOA) contributed 71.6 % of the OA mass, varying from 62.5 % (in winter) to 78 % (in spring and summer). The 58-OA factor mainly contained nitrogen-related variables which appeared to be pronounced only after the filament switched. However, since the contribution of this factor was insignificant (2.1 %), we did not attempt to interpolate its potential source in this work. The uncertainties (σ) for the modelled OA factors (i.e. rotational uncertainty and statistical variability in the sources) varied from ±4 % (58-OA) to a maximum of ±40 % (LO-OOA). Considering that BBOA and LO-OOA (showing influences of biomass burning in winter) had significant contributions to the total OA mass, we suggest reducing and controlling biomass-burning-related residential heating as a mitigation strategy for better air quality and lower PM levels in this region or similar locations. In Appendix A, we conduct a head-to-head comparison between the conventional seasonal PMF analysis and the rolling mechanism. We find similar or slightly improved results in terms of mass concentrations, correlations with external tracers, and factor profiles of the constrained POA factors. The rolling results show smaller scaled residuals and enhanced correlations between OOA factors and corresponding inorganic sal