## 3 Results
### Hierarchical Clustering on Principal Components  
For better understanding similarities in collected biomonitoring data, hierarchical clustering was used to identify respective groups of corresponding sites. Hierarchical Clustering on Principal Component (HCPC) performed on the results of the June sample collections (clr-transformed concentrations) revealed four clusters of sampling sites which were plotted in the map (Figure 10a). Cluster 1 was characterised by higher values of Al and V and lower values of H, C and N in comparison to the dataset means. Cluster 2 was not distinct by any lower or higher values. Cluster 3 was distinct by higher H, N, Mn, Zn, Cd and C values while having lower values of Fe, Cr, V, As and Al. Cluster 3 was characterised by higher values of Pb and Cu. According to the characterisation and location of respective sites, it can be assumed that Cluster 1 gathered sites influenced by soil particles; Cluster 2 sites had dominant pollution from the factory; the site representing Cluster 3 was directly affected by sintering plant, and the last cluster comprised sites in a more natural environment with a lower impact of the factory.  
Figure 10: Clusters resulting from HCPC: (**a**) June sampling, (**b**) October sampling.  
In the case of October samplings, HCPC revealed three clusters (Figure 10b). Cluster 1 was characterised by higher values of As, Al, V, Pb and Cr and lower values of C, H, N, Sb and Zn. Cluster 2 was distinct only by the lower values of Mn, while Cluster 3 was distinct by higher values of C, Sb, Zn, H, Mn and N and lower values of Fe, Al, V, Cr and As. The clustering of sampling point in October can suggest the similar origin of the pollution for Clusters 2 and 3 as in June, except that Cluster 2 comprises the site of June Cluster 4. Samples grouped in Cluster 1 for this season had no direct connection to possible soil contamination and thus also represented the pollution from the factory.  
Visualisation of the correlation matrix of the clr-transformed concentrations of both the June and October samplings are presented in Figure 11a,b, respectively. In both cases, modelled PM\({}_{10}\) concentrations of the previous season (winter for June and summer for October sampling) were also assessed. As is apparent, the modelled PM\({}_{10}\) concentrations were, in both campaigns, closely positively correlated with recorded Fe concentrations, and in the case of the October sampling with the concentrations of As. This confirms Fe as the strongest biomonitoring marker for assessing the impact of metallurgical processes on the environment as it is predominantly carried by the coarse particles and consequently deposited closer to the facility [23; 25; 27; 28; 30]. On the other hand, a significant negative correlation was found for Mn and Cd concentrations in the case of June samplings, while in the October samplings, the modelled PM\({}_{10}\) negatively correlated with the concentrations of Zn, C, H and N. This can suggest that Mn, Zn and Cd are not good markers to study the pollution load in the vicinity of an industrial source as they are carried by fine particles and their spatial distribution depends more on atmospheric dispersion and long-range transport.