## 3 Results and Discussion
### Combined Analysis of HD/HW Occurrence and Pollutants Concentrations

Table 5: Mean values (\(m_{\text{tot}}\) expressed in \(\mu\)g/m\({}^{3}\)) of pollutants concentrations measured in SS3, sampling site and the corresponding percentage variation (\(\Delta\)m%) calculated with respect to mean values in Hot Days submatrices \((M_{\text{HD}})_{\text{SS3}}^{y}\) and to mean values in Heat Waves submatrices \((M_{\text{HW}})_{\text{SS3}}^{y}\) (\(N\), number of sampling days). Figure 2: Dendrograms of sampling sites. In last step, we applied PCA to submatrices \(\left(M_{HD}\right)_{SS3}^{y}\) and \(\left(M_{noHD}\right)_{SS3}^{y}\) (the submatrices \(\left(M_{HW}\right)_{SS3}^{y}\) and \(\left(M_{noHW}\right)_{SS3}^{y}\) are a subset of the previous matrices, and we obtained similar results). For the entire investigated period 2015-2017, in \(\left(M_{HD}\right)_{SS3}^{y}\), the first three factors explain more than 80% of data variance (Table 6), and the first factor is characterized by PM concentrations, whereas for \(\left(M_{noHD}\right)_{SS3}^{y}\) submatrices (Table 7), all the examined pollutants and meteorological parameters have a similar weight (about 20%) in the first factor, confirming the dominant role of particulate during Hot Days independent from relative humidity, which often plays an isolated role in the correlation structure. These results are comparable with what is described in Athene, where a worsening of the air-quality index is described in correspondence with HDs and HWs [19]. In our case, the pollutants that most increase their concentration in correspondence with heat waves are PM\({}_{10}\) and PM\({}_{25}\). Our method allowed us to study the behavior of pollutants during extreme temperature events even if the concentrations of pollutants were not particularly high, while generally in the literature, we find comparisons between extreme events of concentration of pollutants and extreme events of temperature [23]. Moreover, our statistical procedure allowed us to make hypothesis also without a large database and on a local scale. \begin{table}
\begin{tabular}{c c c c} \hline
**2015** & _PCI_ & _PC2_ & _PC3_ \\ \hline \(p\%\) & 56.0 & 26.4 & 8.1 \\ \(P_{cum}\%\) & & 82.4 & 90.5 \\ \(w(PM_{10})\%\) & 26.2 & 0.3 & 12.1 \\ \(w(PM_{25})\%\) & 24.9 & 1.1 & 12.0 \\ \(w(NO_{3})\%\) & 14.9 & 9.7 & 66.3 \\ \(w(O_{3})\%\) & 22.9 & 0.6 & 45.2 \\ \(w(T_{max})\%\) & 10.9 & 34.2 & 0.1 \\ \(w(RH_{min})\%\) & 0.1 & 54.0 & 7.9 \\ \hline
**2016** & _PCI_ & _PC2_ & _PC3_ \\ \hline \(P\%\) & 45.0 & 26.2 & 14.7 \\ \(P_{cum}\%\) & & 71.2 & 85.9 \\ \(w(PM_{10})\%\) & 29.3 & 1.7 & 9.0 \\ \(w(PM_{2.5})\%\) & 30.7 & 0.6 & 6.5 \\ \(w(NO_{3})\%\) & 8.9 & 10.9 & 57.0 \\ \(w(O_{3})\%\) & 12.1 & 19.6 & 23.3 \\ \(w(T_{max})\%\) & 0.6 & 56.1 & 0.1 \\ \(w(RH_{min})\%\) & 18.3 & 11.1 & 4.1 \\ \hline
**2017** & _PCI_ & _PC2_ & _PC3_ \\ \hline \(P\%\) & 41.8 & 23.7 & 18.0 \\ \(P_{cum}\%\) & & 65.5 & 83.5 \\ \(w(PM_{10})\%\) & 33.6 & 3.4 & 0.4 \\ \(w(PM_{2.5})\%\) & 29.5 & 3.4 & 3.8 \\ \(w(NO_{3})\%\) & 8.0 & 20.5 & 21.1 \\ \(w(O_{3})\%\) & 5.1 & 5.9 & 56.0 \\ \(w(T_{max})\%\) & 21.9 & 15.1 & 8.3 \\ \(w(RH_{min})\%\) & 1.9 & 51.7 & 10.3 \\ \hline \end{tabular}
\end{table} 
Table 6: PCA results for HDs detected in SS3 sampling site. In the upper part of the table, the percentage of explained variance (\(P\%\)) and the cumulative percentage (\(P_{cum}\%\)) for the first three Principal Components (_PC_); in the lower part, the percentage weights (\(w\%\)) of the original descriptors in the new factors.