Particles size and composition in Mediterranean countries: geographical variability and short-term health effects MED-PARTICLES Project 2011-2013 Under the Grant Agreement EU LIFE+ ENV/IT/327 Particles size and composition in Mediterranean countries: geographical variability and short-term health effects MED-PARTICLES ACTION 8. Geographical comparison of different PM sizes and components across cities and countries, and according to Saharan dust/no dust days and forest fires days. Protocol on Source Apportionment Summary: Protocol for the source apportionment analysis to be performed in the monitoring stations that provided PM components data. -------------------------------------------------------------------------- 1 Source apportionment analysis Source apportionment analysis was conducted in the datasets of PM chemical components of Huelva, Barcelona, Madrid, Montelibretti and Villa Ada. The dataset from Bologna was not appropriate for such an analysis since only 8 components had 75% of the measured concentrations above the detection limit, LOD. The investigation of the emission sources was carried out by means of a Positive Matrix Factorization (PMF, Paatero and Tapper, 1994). PMF is a least squares factor analysis based on mass conservation principle (1) to assist in identification of sources and their contributions to observed pollutant loadings. In equation (1), xij is the jth species loading measured in the ith sample, gik is the contribution of the kth source to the ith sample, fkj is the loading of the jth species in the kth source and eij is the residual associated with the jth species loading measured in the ith sample. The number of elements to use within the PMF model and their uncertainties were determined taking into account the possible sources of uncertainty as the method analytical uncertainty, the error introduced and the blank values Karanasiou et al., (2009). The calculated uncertainty of these components was in the range of 5-25% of the measured concentrations. The robust mode was used to reduce the influence of extreme values. After several trials with different Fpeak values (-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2) the Fpeak value was set to zero. To determine the number of sources, different numbers of factors were tested. Preliminary runs of PMF were performed to investigate if the resulting factors make sense with respect to known source profiles provided in the literature. Key parameters given in the PMF analysis output, such as the Explained Variation of the matrix F were used to select the number of factors and assisted in interpreting the solution. The global optima of the PMF solution was also tested using multiple starting values. Finally, we selected the number of factors that both adequately fit the data and provided the most physically meaningful results. The resolved solution of PMF produced a good fit to the data as indicated by the Q-value and the scaled residuals. Furthermore, the comparison of the reconstructed PM10 mass contributions from all sources with the observed PM10 mass concentrations provided a squared correlation coefficient of >0.75 (Figure 2) with the only exception PM2.5 in Huelva where the correlation between measured and predicted mass was lower (0.54). This indicates that the resolved sources effectively account for most of the variation in the PM mass concentration. Figure 2. Regression of the PM mass determined gravimetrically and the PM mass predicted by PMF model for the four cities. Conclusions: the protocol provides the methodology adopted to determine sources of PM concentrations in the cities of Huelva, Barcelona, Madrid, Montelibretti and Villa Ada. Source apportionment analysis is conducted in the datasets of PM chemical components and Bologna isn’t included since only 8 components have 75% of the measured concentrations above the detection limit.