European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets

Chen, Gang; Canonaco, Francesco; Tobler, Anna; Aas, Wenche; Alástuey, Andrés; Allan, J. D.; Atabakhsh, Samira; Aurela, Minna; Baltensperger, Urs; Bougiatioti, A.; Brito, Joël; Čeburnis, Darius; Chazeau, Benjamin; Chebaicheb, Hasna; Daellenbach, Kaspar R.; Ehn, Mikael; Haddad, Imad El; Eleftheriadis, Konstantinos; Favez, Olivier; Flentje, H.; Font, Anna; Fossum, Kirsten N.; Freney, Evelyn; Gini, Maria I.; Green, David C.; Heikkinen, Liine; Herrmann, Hartmut; Kalogridis, Athina-Cerise; Keernik, Hannes; Lhotka, Radek; Lin, Chunshui; Lunder, Chris Rene; Maasikmets, Marek; Manousakas, M.; Marchand, Nicolas; Marin, Cristina; Mărmureanu, Luminiţa; Mihalopoulos, Nikolaos; Močnik, Griša; Nęcki, Jarosław; O’Dowd, Colin; Ovadnevaitė, Jurgita; Peter, Thomas; Petit, Jean-Eudes; Pikridas, Michael; Platt, Stephen Matthew; Pokorná, Petra; Poulain, Laurent; Priestman, Max; Riffault, Véronique; Rinaldi, Matteo; Różański, Kazimierz; Schwarz, Jaroslav; Sciare, Jean; Simon, Leïla; Skiba, Alicja; Slowik, Jay G.; Sosedova, Yulia; Stavroulas, Iasonas; Styszko, Katarzyna; Teinemaa, Erik; Timonen, Hilkka; Tremper, Anja H.; Vasilescu, Jeni; Via, Marta; Vodička, Petr; Wiedensohler, Alfred; Zografou, Olga; Minguillón, María Cruz; Prévôt, Andrê S. H.

doi:10.1016/j.envint.2022.107325

Cited by 81 publications

(86 citation statements)

References 72 publications

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“…This study is one of the outcomes of the Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL) project (https://cost-colossal.eu/, last access: 14 September 2022) supported by the COST programme and based on measurements performed within the ACTRIS network. It is closely related to the overview study of Chen et al (2022), in which 22 more-than-one-year-long PMF datasets were joined for a rolling PMF intercomparison. Participants of the WG2 of the COST COLOSSAL Action contributed to the preparation of a protocol for SA, with the purpose of homogenising the PMF application (Chen et al, 2022).…”

Section: Instrumentation and Datasetsmentioning

confidence: 99%

“…It is closely related to the overview study of Chen et al (2022), in which 22 more-than-one-year-long PMF datasets were joined for a rolling PMF intercomparison. Participants of the WG2 of the COST COLOSSAL Action contributed to the preparation of a protocol for SA, with the purpose of homogenising the PMF application (Chen et al, 2022). A total of 9 of the 22 datasets from that study, whose main characteristics can be found in Table 1, were also provided for this rolling-seasonal comparison.…”

Section: Instrumentation and Datasetsmentioning

confidence: 99%

“…Air pollution is one of the biggest current and future environmental threats to human health and climate change. Results from Chen and Hoek (2020) notably relate an increased risk for all-cause mortality due to fine aerosol (PM 2.5 , particulate matter with an aerodynamic particle diameter below 2.5 mm) exposure. Also, even for concentrations below the WHO guidelines threshold (annual means of 5 µg m −3 for PM 2.5 at the time that this article was published), the life expectancy of the population of Europe has been reduced by an average of about 8.6 months.…”

Section: Introductionmentioning

confidence: 99%

“…Since organic species account for 20-90 % of the total submicron aerosol mass (Chen et al, 2022;Jimenez et al, 2009), scientific interest has been set on the characterisation of these pollutants by offline and online techniques. The use of ACSM (Aerosol Chemical Speciation Monitor, Aerodyne Research Inc., Billerica, MA, USA) for continuous monitoring and quantification of submicron non-refractory compounds has become a key approach for air quality (AQ) assessment.…”

Section: Introductionmentioning

confidence: 99%

“…The current state of the art supports that rolling PMF should be more accurate and/or suitable than seasonal PMF due to its profileadaptation feature and its lower computational and evaluation time, which will be the base hypothesis of this study. Nevertheless, only a few individual studies (Chazeau et al, 2021;Chen et al, 2021; and an intercomparison (Chen et al, 2022) using this technique have been published so far, and no thorough seasonal vs. rolling comparison has been conducted thus far to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

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

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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.

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Section: Instrumentation and Datasetsmentioning

confidence: 99%

Section: Instrumentation and Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%