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Naslov:Rolling vs. seasonal PMF : real-world multi-site and synthetic dataset comparison
Avtorji:ID Via, Marta (Avtor)
ID Chen, Gang (Avtor)
ID Canonaco, Francesco (Avtor)
ID Daellenbach, Kaspar Rudolf (Avtor)
ID Chazeau, Benjamin (Avtor)
ID Chebaicheb, Hasna (Avtor)
ID Jiang, Jianhui (Avtor)
ID Keernik, Hannes (Avtor)
ID Lin, Chunshui (Avtor)
ID Marchand, Nicolas (Avtor), et al.
Datoteke:URL https://amt.copernicus.org/articles/15/5479/2022/
 
.pdf Via_2022_Rolling_vs_seasonal_PMF_Suppl.pdf (2,03 MB)
MD5: D088A73454B726144759E658C941CAA6
 
.pdf Via_2022_Rolling_vs_seasonal_PMF.pdf (1,43 MB)
MD5: B662DC6A713E805F7A3F00F5FFBD6482
 
To gradivo ima še več datotek. Celoten seznam je na voljo spodaj.
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:UNG - Univerza v Novi Gorici
Opis: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.
Ključne besede:particulate matter, synthetic dataset comparison, source apportionment, organic aerosols
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Datum objave:01.01.2022
Leto izida:2022
Št. strani:str. 5479-5495
Številčenje:Vol. 15, issue 18
PID:20.500.12556/RUNG-9023 Novo okno
COBISS.SI-ID:195009027 Novo okno
UDK:53
ISSN pri članku:1867-8548
eISSN:1867-8548
DOI:10.5194/amt-15-5479-2022 Novo okno
NUK URN:URN:SI:UNG:REP:QXFXMPBS
Datum objave v RUNG:10.05.2024
Število ogledov:258
Število prenosov:4
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Atmospheric measurement techniques
Skrajšan naslov:Atmos. meas. tech.
Založnik:Copernicus Publications
ISSN:1867-8548
COBISS.SI-ID:522351897 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:27.09.2022

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