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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|
|Authors:||BELIS CLAUDIO; KARAGULIAN FEDERICO; AMATO Fulvio; ALMEIDA M.; ARTAXO Paulo; BEDDOWS David; BERNARDONI Vera; BOVE Maria Chiara; CARBONE Samara; CESARI Daniela; CONTINI Daniele; CUCCIA Eleonora; DIAPOULI E.; ELEFTHERIADIS K.; FAVEZ Olivier; HADDAD Imad; HARRISON Roy; HOVORKA Jan; HELLEBUST Stig; JANG Eunhwa; KAMMERMEIER Tom; JORQUERA Hector; KARL Matthias; LUCARELLI Franco; MOOIBROEK Dennis; NAVA Silvia; NOJGAARD Jakob Kleno; PAATERO Pentti; PANDOLFI Marco; PERRONE Maria Grazia; PETIT Jean Eudes; PIETRODANGELO Adriana; POKORNA Petra; PRATI Paolo; PREVOT Andre S. H.; QUASS Ulrich; QUEROL Xavier; SARAGA Dikaia E.; SCIARE Jean; SFETSOS A.; VALLI Gianluigi; VECCHI Roberta; VESTENIUS Mika; YUBERO Eduardo; HOPKE Philip|
|Citation:||ATMOSPHERIC ENVIRONMENT vol. 123 no. Part A p. 240-250|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Type:||Articles in periodicals and books|
|Abstract:||The 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.|
|JRC Directorate:||Sustainable Resources|
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