Rolling vs. Seasonal PMF: Real-world multi-site and synthetic dataset comparison

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.