# Sediment Dynamics of the Neretva Channel (Croatia Coast) Inferred by Chemical and Physical Proxies
## 3 Materials and Methods  
During the oceanographic cruise NERES06 onboard the R/V Bios DVA in May 2006, twelve box cores (BC) and five grab samples (G) were collected in the study area (Figure 1). Sampling sites were selected along three transects parallel to the NW-SE-oriented axis of the channel in order to obtain an exhaustive coverage of the investigated area. The BC short cores (\(\approx\)20 cm long) were radiographed via a directional X-ray tube (M60 Gilardoni) using an aluminum and PVC filter, allowing the identification of the sedimentary structures before sub-sampling. Once the cores were opened, sediments were described for visual characteristics, and then, one-half of each was stored for the historical archive and the other half was sub-sampled with a frequency of 2 cm for sediment grain size and tracer determinations. Before analyses, sediments were dried at 60 \({}^{\circ}\)C in order to calculate the sediment porosity assuming a particle density of 2.5 g cm\({}^{-3}\)[26]. Total carbon (TC) and total nitrogen (TN) contents, and the stable isotopic composition, were determined on the surficial samples by using a FINNIGAN Delta Plus mass spectrometer, directly coupled to the FISONS NA2000 EA (for further details see [27]). The total organic carbon (TOC) was measured by a pre-treatment with 1.5 M HCl to remove the carbonates. TOC contents are reported as weight percent (wt%) on dry weight. Moreover, C/N was calculated as molar ratio between TOC% and TN%. Grain size analysis was carried out on separates following wet sieving on a 63 \(\upmu\)m mesh-size sieve in order to separate sand from finer fractions, after a pre-treatment with H\({}_{2}\)O\({}_{2}\) to remove the organic matter and to favor disaggregation between sediment particles. Salt and clay fractions were determined with an X-ray Sedigraph Malvern Mastersizer 2000s. The time framework was based on \({}^{137}\)Cs and \({}^{210}\)Pb radionuclides. \({}^{137}\)Cs activity was measured by non-destructive gamma spectrometry (see [28] and references therein) using coaxial intrinsic germanium detectors (Ortec HPGe GMX-20195P and GEM-20200), while \({}^{210}\)Pb was determined by chemical extraction of its daughter \({}^{210}\)Pb, assuming secular equilibrium between the two isotopes [11; 29]. In order to determine the metal fraction of surficial sediment particles and/or dissolved in the interstitial water, 0.5 g of de-frozen wet sediment was leached with HNO\({}_{3}\) and H\({}_{2}\)O\({}_{2}\) (10:3) under reflux [30]. Cr, Ni and Pb concentrations were determined by furnace atomic absorption spectrophotometers and the results were normalized to the sediment dry weight. Accuracy and precision were tested through repeated analysis of certified reference material NIST 2709, and in comparison with the reported values for the determination of labile or extractable elements. Results fell within the range of certified values. Accuracies (as %RDS), estimated by replicate analyses of NIST 2709, were 4% (for Ni and Cr) and 3% (for Pb). The leaching recoveries, calculated on the basis of certified values for total concentrations, were 66%, 63% and 93% for Pb, Cr and Ni, respectively. Conductivity and temperature (CTD) data collections were carried out during the oceanographic cruise by correspondence with sampling stations with a Sea-Bird SBE 25 CTD equipped with temperature and conductivity sensors. The CTD data were processed according to UNESCO standards [31], and pressure values were averaged at 0.5 dbar intervals. The Ocean Data View software was used to interpolate spatially the CTD vertical profiles [32]. The areal distributions of metal contents, sand fraction and sediment accumulation rate (SAR) were computed by universal kriging interpolation, a method for which the values are modelled by a Gaussian process governed by prior covariance. Under suitable assumptions on the spatial continuity of the variable to interpolate, kriging gives the best linear unbiased prediction of the interpolated values [33].