## 4 Discussion  
We found many significant correlations between the initial set of 38 metals, most of which were positive. No significant correlation between Cu and Pb was found in our study. In contrast, a significant positive correlation between Cu and Pb was found in four other studies [27; 29; 32; 39], and one study found a significant negative correlation [60]. Deposit and accumulation processes are site-specific, and Cu and Pb may primarily stem from different sources in the research area [61; 62]. We found a significant positive correlation between Cu and Zn, which agrees with the six studies' results [27; 29; 32; 59; 60; 63]. Similarly, our study found a significant positive correlation between Pb and Zn, as did four other studies [27; 29; 32; 59]. Na was found to have a very large number of significant negative correlations (with 20 metals), and most of the higher Na values are concentrated in the southeast of the floodplain where most other metals have low values. It is possible that there is a natural or anthropogenic Na source in that region of the study area [64; 65]. Similarly, Ba, Cd, and Sn were generally found to have only a few significant correlations in our study; this is probably caused by their relatively homogeneous distribution over the study area, without a clear concentration pattern. The high number of significant correlations highlighted the fact that close relationships exist between many metals, which potentially hold valuable information about their distributions. Additionally, the high number of significant correlations for less commonly studied metals such as Ga (31), La (25), Sc (27), and V (28) showed that the research of these metals can be supported by studying metal relationships. The underlying reasons for correlations and relationships in any direction are similarities in the source and the processes of transport and accumulation of the metals. The results show the need for a reduction in complexity. In our study, RF was used as a first step in identifying 13 individual metal distribution patterns named after the respective metal describing them. These IMDP were the most important variables for describing the distribution of 1 or more of the remaining 22 metals. Here, the IMDP may not necessarily be that of the metal with the strongest correlation in earlier results, as seen in Figure 3, since  
Figure 7: The six most important variables for each of the three MMDP RF models. The colours of the bars indicate the group to which each variable belongs. the relationship between the IMDP and the respective metal may be non-linear. Model variable importance can be a powerful tool in finding relationships between variables, especially if these relationships may be non-linear [66, 67]. The IMDP Ni and V described the distribution of three metals each. Ga, La, Li, Rb, and Sc described that of two metals each. This means that the IMDP are representative of several metals' distribution. There are several sources of Ni in the environment. Among the natural sources is the release from the weathering of rocks and minerals and volcanic activities. Anthropogenic sources include fuel combustion, mining and smelting, industrial and domestic wastewater, and effluent from landfills [40, 68, 69, 70]. The origin of V is similar. It can be released from natural sources, such as the weathering of rocks, but also by human activities, such as from metal-related industries, fuel combustion, and due to its use as a catalyst in different production techniques [71, 72]. Generally, the sources of Ga, La, Li, Rb, and Sc sources are also similar. They are either released from weathering minerals and rocks, as by-products in industrial processes, or from waste products [73, 74, 75, 76]. The results of our study reflect the same close relationship between these metals. The allocation to each of the sources is site-specific, and similar patterns are formed for different metals.