## 4 Results
Running NbClust For the Features series in Zone 1, we saw that one algorithm found 1 as the best number of clusters, 10--2 clusters, 6--3 groups, 1--4 clusters, and 5--6 clusters, respectively. Based on the maximum principle, we selected \(k=2\). The validation measures (Table 5, rows 3-6, columns 5-7) indicated the highest stability for two clusters and various values for \(k\) for the highest internal connectivity (Table 5, lines 3-6, column 4). The AJ got the values 0.8716 for cluster 1 and 0.8749 for cluster 2, indicating very high stability of each cluster. For the features series in Zone 2, the procedures implemented in NbClust returned the best number of clusters: 2 (3 algorithms), 3 (6 algorithms), 4 (4 algorithms), 5 (2 times), and 6 (7 times), respectively. The Dunn index, AD, and FOM indicate that \(k=6\) is the best choice from the viewpoint of stability and connectivity (Table 5, rows 7-10). According to the AJ values, two clusters are unstable when \(k=6\). The internal measures Silhouette and ADM indicated \(k=5\) as the optimal number of clusters from the viewpoint of stability and connectivity, contradicting the results found running NbClust. Based on the majority principle and the above analysis, we kept \(k=6\) to balance the connectivity and stability criteria. For the Features series in Zone 3, one algorithm found \(k=1\) as the best number of clusters, 5 found \(k=2\), 7 found \(k=3\) and \(k=6\), and 2 found \(k=4\), respectively. Considering the stability and connectivity, most indices found \(k=2\) and \(k=6\) as the best choice (Table 5, rows 11-14). When \(k=6\), the AJ has the values 0.4833, 0.8000, 0.7250, 0.7000, 0.4000, and 0.9000, so the first and fifth clusters are unstable, the second, third, and fourth are stable, and the last one is highly stable. For \(k=3\), AJ values are between 0.6608 and 0.8068, indicating stable clusters. The clusters of the series of features in Zone 1 (\(k=2\)), Zone 2 (\(k=6\)), and Zone 3 (\(k=3\)) are presented in Figure 12.  
In Figure 12a, the first cluster contains the series recorded at Z1M1, Z1M2, Z1M3, Z1M7, Z1M8, Z1M10, and Z1M11, the rest belonging to the second cluster. Most points from the first cluster are situated on the border of Zone 1, and the other is on the perpendicular bisector of one of the sides. Therefore, the series of emissions from the tanks \"bordered\" by these points seems to have a similar behavior. The points from the second cluster are situated at a higher distance from the emission points. The AJ (0.876, 0.870) coefficients indicate a high stability of both clusters. Moreover, note that all of them are in the zone with the maximum concentrations of VOCs (Figure 3c). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Internal Clustering Connectivity**} & \multicolumn{3}{c}{**Clusters’ Stability**} \\ \hline
**Zone** & **Index** & **Score** & \(k\) & **Index** & **Score** & \(k\) \\ \hline  & Connectivity & 10.7210 & 2 & APN & 0.0000 & 2 \\
1 & Dunn & 0.8003 & 5 & ADM & 0.0000 & 2 \\  & Silhouette & 0.2882 & 3 & AD & 1.4794 & 6 \\  & & & & & FOM & 0.1832 & 2 \\ \hline  & Connectivity & 6.2607 & 2 & APN & 0.0198 & 4 \\  & Dunn & 1.0088 & 6 & ADM & 0.1018 & 5 \\
2 & Silhouette & 0.3854 & 5 & AD & 0.9774 & 6 \\  & & & & FOM & 0.4616 & 6 \\ \hline  & Connectivity & 10.6345 & 2 & APN & 0.0248 & 2 \\
3 & Dunn & 0.8368 & 6 & ADM & 0.1513 & 6 \\  & Silhouette & 0.2385 & 2 & AD & 1.5051 & 2 \\  & & & & FOM & 0.6955 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Internal connectivity and stability of the clusters obtained using the Feature sets. The same remark applies to clustering the series in Zone 2 based on the features series after comparing the results with the charts in Figure S2c. The stations grouped in the same clusters in Figure 12c are characterized by low average concentrations (cluster 1) and moderate to high maxima (cluster 2). By comparison, Figure 11 does not provide a clear understanding of the series classification.