Pontificia Universidad Católica de Chile Pontificia Universidad Católica de Chile
Belis C.A., Karagulian F., Amato F., Almeida M., Artaxo P., Beddows D.C.S., Bernardoni V., Bove M.C., Carbone S., Cesari D., Contini D., Cuccia E., Diapouli E., Eleftheriadis K., Favez O., El Haddad I., Harrison R.M., Hellebust S., Hovorka J., et al. (2015)

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

Revista : Atmospheric Environment
Volumen : 123
Páginas : 240-250
Tipo de publicación : ISI Ir a publicación


The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercisesemploying real-world and synthetic input datasets. To that end, the results obtained by differentpractitioners using ten different RMs were compared with a reference. In order to explain the differencesin the performances and uncertainties of the different approaches, the apportioned mass, the number ofsources, the chemical profiles, the contribution-to-species and the time trends of the sources were allevaluated using the methodology described in Belis et al. (2015).In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47different source apportionment model results met the 50% standard uncertainty quality objectiveestablished for the performance test. In addition, 68% of the SCE uncertainties reported in the resultswere 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 performancesin the estimation of SCEs while unconstrained models, that do not account for the uncertainty inthe input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-definedchemical profiles and seasonal time trends, that make appreciable contributions (>10%), were thosebetter quantified by the models while those with contributions to the PM mass close to 1% represented achallenge.The results of the assessment indicate that RMs are capable of estimating the contribution of the majorpollution source categories over a given time window with a level of accuracy that is in line with theneeds of air quality management.