## 4 Discussion
### Limitations and Considerations  
Several limitations can be observed in the presented methods: (1) The input data are a limiting factor, and more data should theoretically make the models more robust. R.F. can handle large datasets just as easily as smaller datasets. However, some potentiallyimportant data were not available in the utilized datasets. For example, there were no hydro-chemical parameters stored in the database. Parameters such as stream pH, oxygen content, hardness, or alkalinity were not available to the same degree as the other data. Another type of data not included was the extent of leveee construction in the respective areas. The effects of levees on the connectivity between floodplain and river may play an important role in a part of the studied areas. In these areas, the absence of data describing lateral connectivity might lead to miscalculations of the importance of factors. However, most of the studied sites in this research were in areas of the US with few or no levees. (2) There is a bias of the methodology towards F.G. with more variables, which results in a focus on the bigger factor groups such as Dams, Geology, and Land Cover. This affects the grouping of the metals into the meta groups. Still, the variables from all F.G. contributed to the final estimation of metal pollution potential. (3) Several R.F. models created from the same data will show slightly different results. That is why a relatively large number of MIV was selected, because the most important variable in one model may be the second- or fourth-most important variable in the next model created from the same data.  
Furthermore, several issues should be considered: (1) The features included in the study are spatially not homogeneous. The number of dams in the NID database is much higher in the eastern parts of the USA [87], and patterns are visible in which dams are associated with a more pronounced terrain. Climate is spatially heterogeneous, showing large-scale differences between wetter and dryer regions of the country. There are differences in the established vegetation and associated vegetation indices in connection with these climate patterns. Finally, the distribution of the sediment samples (Figure 2) shows that these are not evenly scattered over the country. The effects of the spatial coincidence of different attributes and the consequences for determining basin-scale factors affecting metal pollution would pose an interesting research question. (2) The scope of this research was the basin-scale factors of metal concentrations in sediments. This means that often important point sources of metals and other chemicals were not represented in the datasets. Among these point-sources are ports and other water transport facilities and metallurgical and industrial enterprises. The spatially heterogeneous distribution of these sources makes it challenging to represent them adequately at the basin scale. (3) As shown in Supplementary Table S3, the majority of drainage areas in the study dataset was smaller than 1000 km\({}^{2}\). Therefore, the validity of our results for drainage areas larger than 1000 km\({}^{2}\) may be limited.