Description: The fate of plastic debris entering the oceans is largely unconstrained. Currently, intensified research is devoted to the abiotic and microbial degradation of plastic floating near the ocean surface for an extended period of time. In contrast, the impacts of environmental conditions in the deep sea on polymer properties and rigidity are virtually unknown. Here, we present unique results of plastic items identified to have been introduced into deep-sea sediments at a water depth of 4150 m in the eastern equatorial Pacific Ocean more than two decades ago. The results, including optical, spectroscopic, physical and microbial analyses, clearly demonstrate that the bulk polymer materials show no apparent sign of physical or chemical degradation. Solely the polymer surface layers showed reduced hydrophobicity, presumably caused by microbial colonization. The bacterial community present on the plastic items differed significantly (p 〈 0.1%) from those of the adjacent natural environment by a dominant presence of groups requiring steep redox gradients (Mesorhizobium, Sulfurimonas) and a remarkable decrease in diversity. The establishment of chemical gradients across the polymer surfaces presumably caused these conditions. Our findings suggest that plastic is stable over extended times under deep-sea conditions and that prolonged deposition of polymer items at the seafloor may induce local oxygen depletion at the sediment-water interface.

Description: Concentrations of methane (headspace gas) in gravity cores and mini cores from the Kerch Seep area, Black Sea.

Description: Source data of the North Sea well inventory: United Kingdom (UK)- Oil and Gas Authority (Dec. 2018) - https://data-ogauthority.opendata.arcgis.com/datasets/oga-wells-ed50 Contains information provided by the OGA. Wells are extracted for the area of the PGS data set PGS Mega Survey Plus. We measured the distance between all wells of the test group (n = 43) and all those who are within the seismic data set (n = 1,792; presented here) and their closest bright spot with polarity reversal. Furthermore, we calculated the mean RMS amplitudes and RMS amplitude standard deviation for a buffer radius of 300 m around the well paths for all wells inside the seismic data set and the visited wells as 300 m is the distance below which all of the visited wells of the test group showed gas release in form of flares from the seafloor. We test, if the propensity of a well to leak can be identified by using a logistic regression, which includes regressors such as well activity data and/or derived parameters such as mean RMS amplitude and mean RMS amplitude standard deviation, the distance towards the most proximal bright spot with polarity reversal and age (spud date). In order to identify the most suitable regressor combination best subset selection is employed. The main selection criterion chosen was the prediction accuracy from randomly and repeatedly splitting the visited wells into a training and a test set and then using the fitted logistic regression to predict the test data. The most suitable subset turns out to only employ the distance to polarity reversal, producing a prediction accuracy of 89% and the following logistic regression results: In order to obtain confidence intervals using the normal distribution the distance to bright spot with polarity reversal has to be normally distributed, which it is not. Yet it can be transformed to normality by adding 100 meters to the original distance and then taking the natural logarithm: Logistic regression fit for leakage of all visited wells using distance to bright spot with polarity reversal in meters as a regressor. Please find further information on the applied statistical analyses in the supplementary material. EstimateStd. Errorz valuePr(〉|z|)Significance Intercept4,853.9461,735.1282.7970.005150.01 Distance−0.0073610.002700−2.7260.006400.01 The transformed logistic regression model is then used to predict the probabilities of leakage for the wells within our seismic data set in the Central North Sea (here presented data). In order to obtain confidence bands this logistic regression is performed subtracting and adding two standard deviations from the calculated probability. The point estimate predicts leakage from 926 of the 1,792 wells, where the 95% confidence interval ranges from 719 to 1,058.

Description: This dataset consists of the results particulate matter geochemical analyses of sediment cores that were taken during the cruise AL534/2 in March 2020. The research areas were the coastal waters close to major river mounds off the coasts of Spain, Portugal, Belgium, Great Britain, the Netherlands and Germany. During this cruise, samples were collected for the JPI Oceans project HOTMIC. The research objective of HOTMIC is the investigation of distribution and transport of microplastic particles from the European coast to the garbage accumulations in the North Atlantic gyre. Sediment samples were taken with a mini multi corer and, in one case, with a mini box corer. Particulate organic carbon and nitrogen, total sulfur and CaCO3 contents of the solid phase were determined with an elemental analyzer.

Description: This dataset consists of the results particulate matter geochemical analyses of sediment cores that were taken during the cruise AL534/2 in March 2020. The research areas were the coastal waters close to major river mounds off the coasts of Spain, Portugal, Belgium, Great Britain, the Netherlands and Germany. During this cruise, samples were collected for the JPI Oceans project HOTMIC. The research objective of HOTMIC is the investigation of distribution and transport of microplastic particles from the European coast to the garbage accumulations in the North Atlantic gyre. Sediment samples were taken with a mini multi corer and, in one case, with a mini box corer. Particulate organic carbon and nitrogen, total sulfur and CaCO3 contents of the solid phase were determined with an elemental analyzer.