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No abstract is provided for this article.

Sediments store the majority of the microplastic legacy in the aquatic environment. To predict microplastic distribution in sediments, models have been developed focussing on coarse ocean basin scales or suited to relatively simple geomorphological and hydrodynamic systems. However, such models reach their limits in more complex estuarine systems, leaving a significant lack of modelling capability for microplastic pollution in key ecosystems. We address this gap by employing machine learning techniques to predict spatial MP inventories deposited in the Schlei, Northern Germany. Notably, the complex hydrodynamic regime, influenced by narrowings and braided embayments, freshwater tributaries, wind-driven mixing, and brackish inflows, creates an interplay of fluvial and marine sedimentary processes, which poses non-trivial challenges for reliable modelling of sedimentary MP transportation. Our approach, termed NIXVEGS (Nested Iterative X-Validation-to-Ensemble-modelling through GridSearches), leverages machine learning, integrating model selection, rigorous validation, and ensemble techniques tailored for small datasets. We estimated 20 trillion MP particles or 14.5 tonnes (50-5000 µm) residing in upper sediments of the Schlei proper, representing the highest resolution picture of an MP inventory for an entire geomorphological region to date. Our modelling concept is founded on the idea of applying granulometric proxies to account for the hydrodynamic regime bias in observed MP concentrations. Estuarine sediments store some of the highest MP concentrations among all sediment types. As depositional systems at the terrestrial-aquatic and fluvial-marine interface, they are key indicator ecosystems for monitoring the evolution of (micro)plastic pollution. Optimising sampling strategies for sediment grain size coverage and spatio-temporal hydrodynamics, and using models such as NIXVEGS, will be important to understand the contribution of complex estuarine systems to the global microplastic budget. Also see: https://micro2024.sciencesconf.org/559755/document

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We present a compilation and analysis of 1099 Holocene relative shore-level (RSL) indicators located around the Baltic Sea including 867 relative sea-level data points and 232 data points from the Ancylus Lake and the following transitional phase. The spatial distribution covers the Baltic Sea and near-coastal areas fairly well, but some gaps remain mainly in Sweden. RSL data follow the standardized HOLSEA format and, thus, are ready for spatially comprehensive applications in, e.g., glacial isostatic adjustment (GIA) modelling. We apply a SQL database system to store the nationally provided data sets in their individual form and to map the different input into the HOLSEA format as the information content of the individual data sets from the Baltic Sea area differs. About 80% of the RSL data is related to the last marine stage in Baltic Sea history after 8.5 ka BP (thousand years before present). These samples are grouped according to their dominant RSL tendencies into three clusters: regions with negative, positive and complex (transitional) RSL tendencies. Overall, regions with isostatic uplift driven negative tendencies dominate and show regression in the Baltic Sea basin during the last marine stage. Shifts from positive to negative tendencies in RSL data from transitional regions show a mid-Holocene highstand around 7.5–6.5 ka BP which is consistent with the end of the final melting of the Laurentide Ice Sheet. Comparisons of RSL data with GIA predictions including global ICE-5G and ICE-6G_C ice histories show good fit with RSL data from the regions with negative tendencies, whereas in the transitional areas in the eastern Baltic, predictions for the mid-Holocene clearly overestimate the RSL and fail to recover the mid-Holocene RSL highstand derived from the proxy reconstructions. These results motivate improvements of ice-sheet and Earth-structure models and show the potential and benefits of the new compilation for future studies.

No abstract is provided for this article.

Over the past few years, several attempts have been performed to find alternative “chemical proxies” in order to discriminate “tsunami backwash deposits” from “typical marine sediments”. A wide range of statistical tools has been selected in order to investigate the sediments and/or terrestrial soils transportation mechanism during the tsunami inundation period by using several types of chemical tracers. To relate the physical and chemical characteristics of Typical Marine Sediments (TMS), Tsunami Backwash Deposits (TBD), Onshore Tsunami Deposits (OTD) and Coastal Zone Soils (CZS) with their synchrotron radiation based micro-X-ray Fluorescence (μ-SXRF) spectra, the μ-SXRF spectra were built in the appropriate selected spectra range from 3,000 eV to 8,000 eV. Further challenges were considered by using the first-order derivative μ-SXRF spectra coupled with Probability Distribution Function (PDF), Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) in order to investigate the elemental distribution characteristics in various types of terrestrial soils and marine sediments. Dendrographic classifications and multi-dimensional plots of principal components (i.e. bi-polar and three dimensional plots) could indicate the impacts of terrestrial soils and/or marine sediments transport on onshore and/or offshore during the tsunami inundation period. Obviously, these advanced statistical analyses are quite useful and provide valuable information and thus shed new light on the study of paleotsunami.

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The persistence of microplastics (MP) in aquatic environments presents a pressing concern, with sediments serving as substantial repositories for these anthropogenic particles. Estuarine depositional systems are sensitive indicator environments for MP monitoring as they: (1) represent aquatic-terrestrial interfaces acting as a bottleneck for MP accumulation coming from land (capable of capturing riverine run-off as well as the often highly populated coastal areas), (2) comprise a confined area, favourable for sampling initiatives as compared to other systems such as long river courses or disperse inland waters. Hence, understanding MP distribution in intricate estuarine systems is essential, yet current models often falter in capturing complexities to sufficiently describe the present pollution patterns for an entire geomorphological region.We address this gap by employing machine learning techniques to predict spatial MP inventories deposited in the Schlei, Northern Germany. Notably, the complex hydrodynamic regime, influenced by narrowings and braided embayments, freshwater tributaries, wind-driven mixing, and brackish inflows, creates an interplay of fluvial and marine sedimentary processes, which poses non-trivial challenges for reliable modelling of sedimentary MP transportation. Our approach, termed NIXVEGS (Nested Iterative X-Validation-to-Ensemble-modelling through Grid Searches), leverages machine learning, integrating model selection, rigorous validation, and ensemble techniques tailored for small datasets.We estimated ~20 trillion MP particles or ~14.5 tonnes (50-5000 µm) residing in upper sediments of the Schlei proper, emphasizing the pivotal role of sediments as primary MP reservoirs. Our modelling concept is founded on the idea of applying granulometric proxies to account for the hydrodynamic regime bias in observed MP concentrations. We found that the high complexity of the geomorphology and extreme input events – both are predominant conditions in our study system – produce major spatio-temporal discontinuities in MP data which are not alleviated by a granulometric normalisation. Here we use hydrodynamic tracer simulations to derive variables which incorporate these discontinuities in empirical predictive modelling, but discuss simpler possibilities to enable modelling studies in systems for which such simulation data might not be feasible to acquire.This study provides a novel framework for geospatial prediction of MP inventories in complex aquatic systems. The integration of granulometric proxies and hydrodynamic discontinuities elucidates MP distribution patterns, offering a pathway for robust predictions and informed mitigation strategies. Our findings underscore the critical role of sediments in storing and reflecting the contemporary plastic legacy, crucial for comprehensive environmental management.

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