1. Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison
- Author
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Marta Via, Gang Chen, Francesco Canonaco, Kaspar R. Daellenbach, Benjamin Chazeau, Hasna Chebaicheb, Jianhui Jiang, Hannes Keernik, Chunshui Lin, Nicolas Marchand, Cristina Marin, Colin O'Dowd, Jurgita Ovadnevaite, Jean-Eudes Petit, Michael Pikridas, Véronique Riffault, Jean Sciare, Jay G. Slowik, Leïla Simon, Jeni Vasilescu, Yunjiang Zhang, Olivier Favez, André S. H. Prévôt, Andrés Alastuey, María Cruz Minguillón, Laboratoire Chimie de l'environnement (LCE), Aix Marseille Université (AMU)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut National de l'Environnement Industriel et des Risques (INERIS), and Tang, M
- Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,Atmospheric Science ,[SDE]Environmental Sciences ,Meteorology & Atmospheric Sciences ,Source apportionment (SA) ,0401 Atmospheric Sciences ,Organic aerosols ,Particulate matter (PM) - 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., Acknowledgements IDAEA-CSIC is a Centre of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S). The authors gratefully acknowledge the Romanian National Air Quality Monitoring Network (NAQMN, https://www.calitateaer.ro/public/home-page/?__locale=ro, last access: September 2022) for providing NOx data. Financial support This research has been supported by the Generalitat de Catalunya (grant no. AGAUR 2017 SGR41), the European Cooperation in Science and Technology (grant no. COST Action CA16109 COLOSSAL), the Ministerio de Ciencia, Innovación y Universidades (CAIAC, grant no. PID2019-108990RB-I00 and FEDER, grant no. EQC2018-004598-P.), the Horizon 2020, the Ministry of Education and Research, Romania (grant nos. PN-III-P1-1.1-TE-2019-0340 and 18PFE/30.12.2021, 18N/2019), the Agence Nationale de la Recherche (grant no. PIA, ANR-11_LABX-0005-01), the Conseil Régional Hauts-de-France (CLIMIBIO grant), the Ministère de l'Enseignement Supérieur et de la Recherche (CARA grant), the Environmental Protection Agency (AEROSOURCE, grant no. 2016-CCRP-MS-31), the Department of the Environment, Climate and Communications (AC3 network grant), and the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SAMSAM, grant nos. IZCOZ9_177063 and PZPGP2_201992). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).
- Published
- 2022