Rolling vs. Seasonal PMF: Real-world multi-site and synthetic dataset comparison
- 1Institute of Environmental Assessment and Water Research, Barcelona, 08034, Spain
- 2Department of Applied Physics, University of Barcelona, Barcelona, 08028, Spain
- 3Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland
- 4Datalystica Ltd., Park innovAARE, 5234 Villigen, Switzerland
- 5Aix Marseille Univ., CNRS, LCE, Marseille, France
- 6IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Energy and Environment, 59000 Lille, France
- 7Institut National de l’Environnement Industriel et des Risques, Parc Technologique ALATA, 60550, Verneuilen-Halatte, France
- 8Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, 200241 Shanghai, China
- 9Air Quality and Climate Department, Estonian Environmental Research Centre, Marja 4d, 10617 Tallinn, Estonia
- 10Department of Software Science, Tallinn University of Technology, 19086 Tallinn, Estonia
- 11School of Physics and Centre for Climate and Air Pollution Studies, Ryan Institute, National University of Ireland Galway, University Road, H91CF50 Galway, Ireland
- 12National Institute of Research and Development for Optoelectronics INOE2000, Atomistilor 409, RO77125 Magurele, Romania
- 13Department of Physics, Politehnica University of Bucharest, 313 Spl. Independentei Str., Bucuresti, Romania
- 14Laboratoire des Sciences du Climat et de l’Environnement, Orme des Merisiers, 91190 Gif-sur-Yvette, France
- 15Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, 2121, Cyprus
Abstract. Particulate Matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM1) has gained relevance as an atmospheric research field due to the diversity and complexity of its primary sources and secondary formation processes. Moreover, relatively simple but robust instruments such as the Aerosol Chemical Speciation Monitor (ACSM) are now widely available for the near real-time online determination of the composition of the non-refractory PM1. One of the most used tools for SA purposes is the source-receptor Positive Matrix Factorization (PMF) model. Even though the recently developed rolling PMF technique has already been used for OA SA on ACSM datasets, no study has assessed its added value concerning the more common seasonal PMF method from a practical approach yet. In this paper, both techniques were applied to a synthetic dataset and to nine European ACSM datasets in order to spot the main output discrepancies between methods. The main advantage of the synthetic dataset approach was that the methods’ outputs could be compared to the expected ‘true’ values, i.e. the original synthetic dataset values. This approach revealed similar apportionment results amongst methods, but differing with respect to the truth, although the rolling PMF profile adaptability feature has been proven advantageous. Also, these results highlighted the impact of the profile anchor on the solution. In the multi-site study, while differences were generally not significant when considering year-long periods, their importance grew towards shorter time spans, as in intra-month or intra-day cycles. Rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here as far correlation with external measurements is concerned, especially in periods between seasons. The results of this comparison also support rolling PMF benefits even though output discrepancies with seasonal PMF were scarce. Altogether, the results of this study provide solid evidence of the robustness of both methods and on the overall efficiency of the recently-proposed rolling PMF approach.
Marta Via et al.
Marta Via et al.
Marta Via et al.
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