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
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
Received: 29 Apr 2022 – Discussion started: 30 May 2022
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.
The manuscript by Marta Via et al. performed a comprehensive comparison between the two methodologies of fine organic aerosol (OA) source apportionment through the Positive Matrix Factorization (PMF) model: rolling and seasonal PMF. They found that the rolling PMF can be considered more accurate and precise, globally, than the seasonal one, although both meet the standards of quality required by the source apportionment protocol. In addition, the results showed that the selection of anchor profiles is highly influencing the OA factors, so local reference profiles are encouraged to minimise this impact. The topic fits well within the scope of Atmospheric Measurement Techniques.
Overall, the data analysis is solid and the manuscript is clearly written. Before its publication, the following comments need to be addressed.
Specific comments:
1 Line 308: What are the contributions of SOA species to m/z 55? Looking into these datasets would be helpful to evaluate the uncertainty of using m/z 55 as a marker for HOA.
2 Are comparisons of Rolling vs. Seasonal PMF depended on the type of site (e.g., Urban Background, Suburban) and/or the instruments(i.e., Q-ACSM and ToF-ACSM). Please be specific.
3 Figure 3: I noticed that there are three distinguished lines in the triangle plot of f44 vs. f43 using seasonal PMF data, while this phenomenon does not appear using the rolling PMF and the truth PMF. Please describe and explain these differences in detail.
4 The sampling period that appears in Figure 2 is not in Table 1 (Participant sites). Please have a check.
5 It would be better to change the name of “truth PMF”, because no one knows what the truth is like, and our goal is to pursue infinite access to the truth
6 The figure captions for each panel should be clearly stated. Take Figure S4 for example, what is SHINDOA representing? In addition, "58-OOA" must be defined.
This work presents the differences resulting from two techniques (rolling and seasonal) for the Positive Matrix Factorization model that can be run for organic aerosol source apportionment. The current state-of-the-art suggests that the rolling technique is more accurate, but no proof of its effectiveness has been provided yet. This paper tackles this issue in the context of a synthetic dataset and a multi-site real-world comparison.
This work presents the differences resulting from two techniques (rolling and seasonal) for the...