Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window
- 1Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland
- 2Datalystica Ltd., Park innovAARE, CH-5234 Villigen, Switzerland
- 3Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Air Pollution and Environmental Technology, CH-8600 Dübendorf, Switzerland
- These authors contributed equally to this work.
Abstract. We have collected one year of aerosol chemical speciation monitor (ACSM) data in Magadino, a village located in the south of the Swiss Alpine region, which is one of the most polluted areas in Switzerland. We analysed the mass spectra of organic aerosol (OA) by positive matrix factorization (PMF) using Source Finder Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed the rolling algorithm to account for the temporal changes of the source profiles, which is closer to the real world. As the first ever application of rolling PMF analysis for a rural cite, we resolved two primary OA factors (traffic-related hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one local OA (LOA) factor, a less oxidized oxygenated OA (LO-OOA) factor, and a more oxidized oxygenated OA (MO-OOA) factor. HOA showed stable contributions to the total OA through the whole year ranging from 8.1–10.1 %, while the contribution of BBOA showed a clear seasonal variation with a range of 8.3–27.4 % (highest during winter, lowest during summer) and a yearly average of 17.1 %. The OOA was represented by two factors (LO-OOA and MO-OOA) throughout the year. OOA contributed 71.6 % of the OA mass, varying from 62.5 % (in winter) to 78 % (in spring and summer). The uncertainties (σ) for the modelled OA factors (i.e., rotational uncertainty and statistical variability of the sources) varied from ±4 % (LOA) to a maximum of ±40 % (LO-OOA). Considering the fact that BBOA and LO-OOA (showing influences of biomass burning in winter) had significant contributions to the total OA mass, we suggest a reduction and control of the residential heating as a mitigation strategy for better air quality and lower PM levels in this region. In Appendix A, we conducted a head-to-head comparison between the conventional seasonal PMF analysis and the rolling mechanism. It showed 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 salts than those of the seasonal solutions, was most likely because the rolling PMF analysis can capture the temporal variations of the oxidation processes for OOA sources. Specifically, the time dependent factor profiles of MO-OOA and LO-OOA can well explain the temporal viabilities of two main ions for OOA factors, m/z 44 (CO2+) and m/z 43 (mostly C2H3O+). This rolling PMF analysis therefore provides a more realistic source apportionment (SA) solution, with time-dependent OA sources. The rolling results show also good agreement with offline Aerodyne aerosol mass spectrometer (AMS) SA results from filter samples, except for winter. This is likely because the online measurement is capable of capturing the fast oxidation processes of biomass burning sources. This study demonstrates the strengths of the rolling mechanism and provides a comprehensive criterion list for ACSM users to obtain reproducible SA results and is a role model for similar analyses of such world-wide available data.
Gang Chen et al.
Gang Chen et al.
Gang Chen et al.
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