Abstract
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.
Highlights
Source Apportionment (SA) is the practice of deriving information about the pollution sources and the amount they contribute to measured concentrations
The results of the assessment indicate that Receptor models (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
Modelling), intercomparison exercises aimed at quantitatively assessing the performance and the uncertainty of RMs by comparing the results reported from different practitioners on the same dataset using different RM techniques
Summary
Source Apportionment (SA) is the practice of deriving information about the pollution sources and the amount they contribute to measured concentrations. RMs derive information from measurements including estimations of their uncertainty and have been extensively used in Europe to estimate the contribution of emission sources to atmospheric pollution at a given site or area (Belis et al, 2013; Viana et al, 2008a). In the Chemical Mass Balance (CMB) approach, both chemical concentrations of pollutants, including their uncertainties, and chemical fingerprints of the sources (source profiles) are used as input. In the multivariate factor analytical approach (MFA), only environmental concentrations and uncertainties of pollutants are used as input data and the model computes the factor profiles and the mass contributed by the factors. Modelling), intercomparison exercises aimed at quantitatively assessing the performance and the uncertainty of RMs by comparing the results reported from different practitioners on the same dataset using different RM techniques
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