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Fifty-four coal clast samples in the siliciclastic rocks (e.g., sandstone and conglomerate) were collected from cores of two-deep research wells (K20H and K20K) drilled at the Kozlu coalfield in Zonguldak Basin, and for the first time, they were evaluated using mineralogy by XRD and SEM-EDX and random vitrinite reflectance (%Rr) measurements in order to find out their origin and timing. Petrographic observations on polish surfaces show that the coal clasts are either entirely xylitic/vitrinitic particle or coals including a broader range of macerals. The detected minerals in the samples are mostly derived from the parental coal seams and, to lesser extent, precipitated from penetrated pore-water in the cleats/fractures of clasts. The %Rr values of coal clasts in Carboniferous sediments are generally relatively higher than measured in the coal seams due to weak oxidation during transportation. Furthermore, similar mineralogical and maceral compositions between coal clasts and coal seams imply that these clasts were mainly eroded during the peatification and/or early coalification of parental seams and display similar coalification patterns. The close %Rr value of a coal clast sample in the Early Aptian Zonguldak Formation and Carboniferous coal seams could suggest that this coal clast sample is presumably derived from the coal seams eroded during Early Aptian.

Coal, as most common fossil-fuel, has been subjected to several geochemical and mineralogical studies due to presence of potentially toxic elements. In these studies, statistical methods (e.g., correlation coefficient, cluster and factor analyses) are mainly used for determination of toxic elements affinities in coal. In environmental concerns, the statistical methods are recently topic of discussion due to correlations between some elements with minerals that cannot be chemically affiliated. Nevertheless, microanalyses methods as like scanning electron microscopy-energy dispersive spectrometer (SEM-EDX) or electron microprobe (EPMA) and machine learning algorithms more commonly applied in determination of affinities of some toxic elements in coal. This study aims to correlate geochemical and mineralogical data of lower (kM2) coal seam in the Soma coalfield with Bray-Curtis, Cosine and Tanimoto similarities and different similarity measures like Pearson correlation co-efficiencies. The results of similarity measures evaluated using agglomerative hierarchical clustering algorithm (average linkage) and elements grouped in several clusters. Most of identified elemental groups, expected a few of them, based on Canberra, Chebyshev, Bray-Curtis and Tanimoto measures do not appear to be in agreement with mineralogical and geochemical data. Nevertheless, elements affiliated with aluminosilicate elements (e.g., Al, K, B, and Cs) are grouped in together, and elements (e.g., S, As, Mo and U) related with redox conditions in coal formation environment are located in the same group according to Pearson correlation co-efficiencies and cosine similarity. In addition, this data is in agreement with SEM-EDX and XRD data of studied coal samples. These results imply that cosine similarity could be an alternative for Pearson correlation coefficiency’s method in coal studies. As a result, more detailed studies using both similarity measures should be conducted in future, and these similarity measures should always be correlated with SEM-EDX and XRD data.

The hierarchical clustering algorithm, especially Pearson correlation coefficient, along with other statistical approach, is used statistical approach for determining the toxic elements affinities in coal, and this method is one of the common approaches due to the correlations between some elements with minerals that cannot be chemically affiliated. This study aims to correlate geochemical and mineralogical and data of the lower seam (up to 30 m) in the Soma coalfield with Cosine and Bray-Curtis similarity measures, Chebyshev and Canberra distance metrics, and also Pearson correlation and Tanimoto coefficients. The results have been evaluated using agglomerative hierarchical clustering algorithm with average linkage, and elements are grouped in several clusters. Some of the similarity measures and distance metrics do not seem to agree with mineralogical and geochemical data. However, elements affiliated with aluminosilicate elements (e.g., Al, K, and Cs) are grouped, and elements (e.g., As, Mo and U) related to redox conditions in coal depositional environment are located in the same group according to Pearson correlation coefficient and Cosine similarity. In addition, this data has been observed in agreement with SEM-EDX and XRD data of studied coal samples. The present study indicates that Cosine similarity could be an alternative for the Pearson correlation coefficient in coal studies.