## 3 Results and Discussion
### Principal Component Analysis  
Before applying PCA, a preliminary analysis of data set was performed: 8 samples were excluded from it (6 samples characterized by Saharan Dust episode and 2 samples characterized by biomass burning) and the distribution of the variables was checked: some of them showed logarithmic distributions. For these variables logarithmic transformation was therefore applied to bring them closer to a normal distribution. Since PCA is sensible to the presence of outliers in the data matrix, Q index and T\({}^{2}\) Hoteling tests were applied to evaluate this presence. By the tests, one sample was identified as outlier and it was removed from the data matrix. Variables redundancy influences the efficiency of PCA method and complicates the results interpretation. For this reason, redundant variables such as TC, PM\({}_{2.5}\), K, Na, Ca, Mg, C\({}_{2}\)O\({}_{4}\)\({}^{-}\), etc. have been deleted from the data set before applying the PCA, and the final data set used was composed of 91 samples and 19 variables (Fe, V, Ni, Cu, Cr, Cd, Mn, Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{-}\), Na\({}^{+}\), NH\({}_{4}\)\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\), WSOC, WSIC, OC, EC). Figure 3 shows the score plot in the plane of the first and second Principal Components (auto-scaled data), accounting for 45% of the total variance. The separation of samples between two prevalent wind directions is not totally clear. Blue samples (collected when the prevalent wind direction was from urban and industrial area) showed a slightly larger dispersion than the red ones. This is since different sources affected the sampling site [21] and the collected samples which are daily averages were affected by all sources. Perhaps in future campaigns wind selective sampling could be utilized to reduce meteorology related uncertainties. Table 2 shows the factor loading values of the considered parameters. All the factors which showed eigenvalue greater or equal to 1 have been considered. Seven factors (PCs) were evaluated, explaining up to 81% of the total variance. Factor loadings of the variables were used to identify source profiles affecting the investigated sampling site. Figure 3: Score plot in the plane of the first and second Principal Components. Blue numbers indicate samples collected when the prevalent wind directions were from urban and industrial area; red numbers indicate samples collected when the prevalent wind directions were from the port area. The first Principal Component (PC1) accounting for 29% of the total variance showed larger loading values for Fe, V, Ni and Mn, suggesting a mixed source, composed of ship emissions and crustal resuspension. The second PC, accounting for 17% of the total variance, showed high loading values for Cl\({}^{-}\), Na\({}^{+}\), and Mg\({}^{2+}\), suggesting the presence of sea salt source. The third PC, accounting for 10% of the total variance, is characterized by higher loading values of SO\({}_{4}\)\({}^{2-}\) and NH\({}_{4}\)\({}^{+}\) and is associated to ammonium sulphate of secondary origin. The fourth PC, characterized by Cu, EC, and NO\({}_{3}\)\({}^{-}\), represents the contribution of vehicular traffic emissions accounting for 8% of the total variance. The fifth PC, characterized by K\({}^{+}\), EC, and NO\({}_{3}\)\({}^{-}\), indicates a possible contribution from biomass combustions or fires to PM\({}_{2.5}\) concentration, with an explained total variance of 7%. The sixth PC is characterised by the species WSIC and Ca\({}^{2+}\) (5% of the total variance) and it represents the contribution of calcium carbonate which is present in the crustal matter, re-suspended dust and particles from construction work. The seventh PC presents higher loadings of Pb, Cd, and EC with an explained variance of 5%, this factor suggests an industrial origin. It is well known that both PCA and LDA are linear transformation techniques used for dimensionality reduction.