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Title

A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

AuthorsBelis, C.A.; Karagulian, F.; Amato, Fulvio CSIC ORCID ; Almeida, M.; Artaxo, P.; Beddows, D.C.S.; Bernardoni, V.; Bove, M.C.; Carbone, S.; Cesari, D.; Contini, Daniele; Cuccia, E.; Diapouli, E.; Eleftheriadis, Konstantinos; Favez, O.; El Haddad, I.; Harrison, Roy M.; Hellebust, S.; Hovorka, J.; Jang, E.; Jorquera, H.; Kammermeier, T.; Karl, M.; Lucarelli, F.; Mooibroek, D.; Nava, S.; Nøjgaard, Jacob Klenø; Paatero, P.; Pandolfi, Marco CSIC ORCID; Perrone, M.G.; Petit, J.E.; Pietrodangelo, A.; Pokorná, Petra; Prati, P.; Prévôt, André S.H.; Quass, U.; Querol, Xavier CSIC ORCID ; Saraga, D.; Sciare, J.; Sfetsos, A.; Valli, G.; Vecchi, R.; Vestenius, M.; Yubero, Eduardo; Hopke, P.K.
Issue Date2015
CitationAtmospheric Environment 123: 240- 250 (2015)
AbstractThe performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management.
Publisher version (URL)http://dx.doi.org/10.1016/j.atmosenv.2015.10.068
URIhttp://hdl.handle.net/10261/218590
DOI10.1016/j.atmosenv.2015.10.068
Identifiersdoi: 10.1016/j.atmosenv.2015.10.068
issn: 1873-2844
Appears in Collections:(IDAEA) Artículos

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