<i>Rolling</i> vs. <i>seasonal</i> PMF: real-world multi-site and synthetic dataset comparison - Archive ouverte HAL Access content directly
Journal Articles Atmospheric Measurement Techniques Year : 2022

Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison

Marta Via
Gang Chen
Francesco Canonaco
  • Function : Author
Kaspar Daellenbach
  • Function : Author
Benjamin Chazeau
Hasna Chebaicheb
  • Function : Author
Jianhui Jiang
  • Function : Author
Hannes Keernik
  • Function : Author
Chunshui Lin
  • Function : Author
Nicolas Marchand
Cristina Marin
  • Function : Author
Colin O'Dowd
  • Function : Author
Jurgita Ovadnevaite
  • Function : Author
Jean-Eudes Petit
Michael Pikridas
Véronique Riffault
Jean Sciare
  • Function : Author
Jay Slowik
  • Function : Author
Jeni Vasilescu
  • Function : Author
André Prévôt
  • Function : Author
Andrés Alastuey
María Cruz Minguillón


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 factorisation (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 compared to the more common seasonal PMF method using 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, although the rolling PMF profile's adaptability feature proved to be advantageous, as it generated output profiles that moved nearer to the truth points. Nevertheless, these results highlighted the impact of the profile anchor on the solution, as the use of a different anchor with respect to the truth led to significantly different results in both methods. 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. As far as correlation with external measurements is concerned, rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here, especially in periods between seasons. The results of this multi-site comparison coincide with the synthetic dataset in terms of rolling–seasonal similarity and rolling PMF reporting moderate improvements. Altogether, the results of this study provide solid evidence of the robustness of both methods and of the overall efficiency of the recently proposed rolling PMF approach.
Fichier principal
Vignette du fichier
2022-117.pdf (1.39 Mo) Télécharger le fichier
Origin : Publication funded by an institution
Licence : CC BY - Attribution

Dates and versions

hal-03800466 , version 1 (21-11-2022)



Marta Via, Gang Chen, Francesco Canonaco, Kaspar Daellenbach, Benjamin Chazeau, et al.. Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison. Atmospheric Measurement Techniques, 2022, 15 (18), pp.5479-5495. ⟨10.5194/amt-15-5479-2022⟩. ⟨hal-03800466⟩
20 View
10 Download



Gmail Facebook Twitter LinkedIn More