## 3 Results and Discussion
### Granulometric Distribution of the Enmitted PM  
The comparison of daily PM\({}_{2.5}\) patterns of ELPI+ stages showed wide differences. In the same day, the pattern of particles accumulated in near stages were very similar (see Supplementary Materials Figure S4), suggesting their relation, depending obviously on the emission source. To ascertain the possible correlation of fractions, linear correlation plots were used. An example of statistical analysis by linear correlation of data corresponding to Stage 4, highlighting the event of a truck entering the warehouse on the indoor busy day, is reported in the Supplementary Materials (see Figure S5). The linear correlation of data corresponding to the event was clear among the first lower size fractions, which were more representative of the specific emission source, and was progressively lost as the fraction size increased. Linear correlation matrices were built with all the daily patterns (see Table S3), and the PM correlation plots for the most representative environmental condition (garage) are reported in Figure 4.  
A unit slope correlation is mostly present, whereas different slopes characterize car exhaust emissions. Indeed, it is known that vehicle emissions depend on the categories, age, and fuel type, but most of them belong to the inhalable fractions of PM\({}_{2.5}\)-PM\({}_{10}\)[39]. When only ultrafine emissions were present the correlation was progressively lost from the lowest to the highest size fractions. Similar considerations could be done for the indoor busy environment (see Table S3 22 March 2018). The correlation here was always present among different fraction size ranges. In particular, the coarse fractions were well correlated, which corresponded to the typical emissions of a construction site where regulations indicate to test coarse PM [40]. This information is fundamental for source apportionment studies.  
Figure 3: SEM images of the Teflon membrane filter where PM\({}_{2.5}\) was collected at different magnifications.  
To determine people exposure to PM for health impact assessments it is important to know the size distribution of particles collected over time. ELPI+ daily patterns in the different stages may be used for that evaluation in the monitoring area. However, daily patterns are not very representative of the contribution of the different emission sources, being mediated over the whole day. For this reason, the portion of patterns corresponding to the exact time of the day when recorded emissions occurred were selected and concentrations over the corresponding hour were summed up. These data were used to build size distribution curves related to five identified emission sources: outdoors with and without the presence of smokers, indoor quiet, during construction work (busy), and garage in the early morning when cars passed by. Normalized particle and volume size distributions are reported in Figure 5, clearly showing a polydisperse curve.  
The comparison of normalized data shows that the potential exposure was higher in the outdoor environment, mostly due to the background values and even increased by the presence of smokers, and particularly high in the confined area of the garage where car exhaust emissions occur [41; 42; 43]. A meaningful increase of inhalable particles was evident in the indoor quiet environment and the contribution of coarse particles was higher when construction work was ongoing.  
Figure 4: Linear correlation plots of PM concentrations (expressed in \(\upmu\)g/m\({}^{3}\)) among all the ELPI+ fraction daily patterns derived from data monitored in the garage on 27 March 2018. Time as well as PM values associated with the ELPI+ stages (1–14) are reported on the \(x\) and \(y\) axis.