## 3 Materials and Methods
0.5-2.0 g of bulk sample digested after application of modified aqua regia (1:1 HNO\({}_{3}\): HCl) solution for low to ultralow determination of soil and analysed with an inductively coupled plasma mass/emission spectrometer (ICP-MS/ES). In this study, we mostly analysed trace elements that might originate from the anthropogenic activities common in coastal systems as fossil fuel burning, sewage discharge, metals common in ship ports, etc. and monitored at the national monitoring program [65]. We also selected macroelements that could help to describe the origin of sediments. The results are given in ppm for elements As, Cu, Cr, Co, Mn, Ni, Pb, and Zn; in ppb for Hg; and in percentage for Ca, Mg, Fe, Al, and Ti. Analytical quality was monitored in each batch of samples by repeated analyses, recovery of spiked samples, and analysis of a certified reference material (OREAS45 EA and DS11), and duplicates and blanks were used to assure the quality of the analysis. The reaction mixture was chosen to evaluate the labile trace elements dissolving sulphide/oxide type minerals to exclude the elements incorporated in the silicate lattice as metals from anthropogenic sources tend to be more mobile than those from pedogenic or lithogenic sources [43; 66]. Descriptive and multivariate statistical methods were applied to analyse the results. Correlation analysis using Pearson's coefficient (linear relation, significant \(p>0.01\) or \(0.05\)) and principal component analysis (PCA) were applied to transform the correlation matrix to identify the relationship between the metals [31]. For the PCA analysis, we used the Varimax rotation method to derive more reliable information on the distribution of the weights of the variables on a factor, and loadings higher than \(0.5\) were considered. Statistical programs IBM SPSS Statistics \(22.0\) and PAST \(3.24\) were used. The concentration of trace metal in each site was compared with the median estimated of all samples at that year \(K_{k}=(K_{n})/(M_{n})\), where \(K_{n}\)--concentration of the element \(n\) and \(M_{n}\)--the median concentration of the element \(n\). Median presents the central or typical value in a set of data and is weakly dependent on minimal and maximal values and outliers, this approach has been used and developed in a few studies [48; 52; 67]. This analysis helps to compare the loading of analysed elements among sites; the lower limit of the anomaly is considered as \(1.5\)[52]. Following a multi-element index, the integrating and averaging data were estimated, \(K_{d}=\Sigma K_{k}/n\), where \(K_{k}\)--is the concentration ratio for a specific element and \(n\)--number of elements. We assumed that \(K_{d}\) values higher than \(2\) indicate an anomaly concentration in the site [68]. Other studies use concentration ratios to determine trace element accumulation in sediments and focus on anthropogenic pollution [43; 53; 69; 70; 71], the determination of background concentrations in such studies is essential [71]. The main concept to identify the anthropogenic impact is to compare the concentrations of elements measured in uncontaminated sites--to establish a local baseline or, as in most studies, compare sediment element concentrations with preindustrial levels such as average shale [72] or average crustal value [73]. Using the average shale or earth's crust concentrations, the local geo chemical framework is being ignored, which might lead to misinterpretation of anomaly concentrations in the analysed region. Second, the shale concentration mostly represents fine grain sediments and earth's crust--coarse sediments, meanwhile the background concentration represents sites in similar mineralogical and textural environments [71]. In this study, we analysed beach sand, dynamic environment and usage of shale or earth's crust as the background concentration might miss an important pattern in the distribution of trace elements. For that reason, we chose to use the values calculated during our study.