High-resolution Model Research Articles

Baroclinic responses of Qiongzhou Strait throughflow to different forcings

The Qiongzhou Strait (QS) throughflow is affected by the seasonal circulation in both the northern South China Sea (SCS) shelf and the Beibu Gulf. The water masses with distinct properties between these two regions signify the baroclinic processes in the QS. In this study, a high-resolution unstructured-grid Finite Volume Community Ocean Model (FVCOM) was used to investigate the features and dynamic mechanisms of baroclinic-induced currents (BCC) in the QS. Different forcings were considered such as tides, river discharges, winds, and remote forcings (low-frequency sea surface level and current variations). The model results indicate that the volume transport of the BCC contributes to 31.26% of the total in the QS, determining the transport direction in summer. The baroclinic response to low-frequency remote forcings is most pronounced during the summer-autumn season, but less than winds during the winter-spring season. This finding underscores the importance of low-frequency remote forcings on the QS throughflow, which has often been overlooked in previous studies. Momentum balance suggests that the BCCs are predominantly driven by both the barotropic and baroclinic pressure gradient forces. Decrease (increase) in either of these two forces will lead to an intensified eastward (westward) flow. The contribution of the horizontal advection is relatively weak but cannot be ignored. This study highlights the baroclinic responses of QS throughflow to different forcings, which is important when investigating the circulation in the northern SCS, especially in the Beibu Gulf.

High-resolution reconstruction of a C. elegans ribosome sheds light on evolutionary dynamics and tissue specificity.

Caenorhabditis elegans is an important model organism for human health and disease, with foundational contributions to the understanding of gene expression and tissue patterning in animals. An invaluable tool in modern gene expression research is the presence of a high-resolution ribosome structure, though no such structure exists for C. elegans Here, we present a high-resolution single-particle cryogenic electron microscopy (cryo-EM) reconstruction and molecular model of a C. elegans ribosome, revealing a significantly streamlined animal ribosome. Many facets of ribosome structure are conserved in C. elegans, including overall ribosomal architecture and the mechanism of cycloheximide, whereas other facets, such as expansion segments and eL28, are rapidly evolving. We identify uL5 and uL23 as two instances of tissue-specific ribosomal protein paralog expression conserved in Caenorhabditis, suggesting that C. elegans ribosomes vary across tissues. The C. elegans ribosome structure will provide a basis for future structural, biochemical, and genetic studies of translation in this important animal system.

A high resolution hydrodynamic-biogeochemical-oyster-filtration model predicts that the presence of oysters (Crassostrea virginica) can improve, or reduce, water quality depending upon oyster abundance and location

The eastern oyster (Crassostrea virginica) provides numerous ecosystem services such as building reef habitat, clarifying the water by filtering seston, and reducing excess nitrogen when their biodeposits are denitrified. Some of these ecosystem services have been extensively studied using two-dimensional and three-dimensional (3D) models. Yet, the relationship between oyster abundance, their filtration and biodeposition rates, and associated water quality metrics has not been estimated with high-resolution models that include biodeposit resuspension as well as simulation of 3D processes in the sub-tributaries where oysters are abundant. To undertake these estimates, a 3D Regional Ocean Modeling System (ROMS) framework, comprised of a coupled hydrodynamic-water quality model with an oyster filtration and biodeposition sub-model, was implemented over a fine-resolution model grid (120–150 m) of the Choptank River on the eastern shore of Chesapeake Bay, U.S.A. After validation with data from 20 cruises between May and September 2010, the model was used to predict seven variables associated with water quality in simulations with zero, recent, and 50x recent abundances of oysters. Results indicated that improvement in the seven water quality variables differed in response to the abundance of oysters and between regions in the Choptank River. In line with expectations, the water quality metrics improved with increasing oyster abundance in the shallow and retentive sub-tributaries of Broad and Harris Creeks. In contrast, in the deeper and more flushed mainstem of the Choptank River, some water quality metrics deteriorated when recent abundances of oysters were added to the model compared to the simulation with zero oysters and the same metrics improved when oyster abundances were increased by 50x. Overall, model results indicated that the many complex physical and biogeochemical processes that influence the effect of oysters on water quality resulted in differing responses to oysters across different systems. In addition, this study introduces a high-resolution, predictive tool that could be used to estimate the number of oysters needed to meet water quality thresholds in specific systems in support of ecosystem management.

Big Data-driven MLOps workflow for annual high-resolution land cover classification models

Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the maintenance of these models, a feature particularly beneficial in systems that require generating new models each year alongside the continuous integration of validated data. This article details the development of an end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning.

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 μg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.

Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions-Part II: Sensitivity to Study Parameters and Assumptions.

Adjoint modeling, using U.S. EPA's Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM2.5 and PM2.5 precursors (NO x , SO2, and NH3) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO2 emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15-30% depending on the species, even when different shapes of concentration-response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log-linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.

Role of the Labrador Current in the Atlantic Meridional Overturning Circulation response to greenhouse warming

Anthropogenic warming is projected to enhance Arctic freshwater exportation into the Labrador Sea. This extra freshwater may weaken deep convection and contribute to the Atlantic Meridional Overturning Circulation (AMOC) decline. Here, by analyzing an unprecedented high-resolution climate model simulation for the 21st century, we show that the Labrador Current strongly restricts the lateral spread of freshwater from the Arctic Ocean into the open ocean such that the freshwater input has a limited role in weakening the overturning circulation. In contrast, in the absence of a strong Labrador Current in a climate model with lower resolution, the extra freshwater is allowed to spread into the interior region and eventually shut down deep convection in the Labrador Sea. Given that the Labrador Sea overturning makes a significant contribution to the AMOC in many climate models, our results suggest that the AMOC decline during the 21st century could be overestimated in these models due to the poorly resolved Labrador Current.

Exposure to Low-Level Air Pollution and Hyperglycemia Markers during Pregnancy: A Repeated Measure Analysis.

Epidemiologic evidence has emerged showing an association between exposure to air pollution and increased risks of gestational diabetes mellitus (GDM). This study examines the effect of low-level air pollution exposure on a subclinical biomarker of hyperglycemia (i.e., HbA1c) in pregnant people without diabetes before conception. We measured HbA1c in 577 samples repeatedly collected from 224 pregnant people in Rochester, NY, and estimated residential concentrations of PM2.5 and NO2 using high-resolution spatiotemporal models. We observed a U-shaped trajectory of HbA1c during pregnancy with average HbA1c levels of 5.13 (±0.52), 4.97 (±0.54), and 5.43 (±0.40)% in early-, mid-, and late pregnancy, respectively. After adjustment for the U-shaped trajectory and classic GDM risk factors, each interquartile range increase in 10 week NO2 concentration (8.0 ppb) was associated with 0.09% (95% CI: 0.02 to 0.16%) and 0.18% (95% CI: 0.08 to 0.28%) increases in HbA1c over the entire pregnancy and in late pregnancy, respectively. These associations remained robust among participants without GDM. Using separate distributed lag models, we identified a period between 8th and 14th gestational weeks as critical windows responsible for increased levels of HbA1c measured at 14th, 22nd, and 30th gestational weeks. Our results suggest that low-level air pollution contributes to hyperglycemia in medically low-risk pregnant people.

Numerical simulations of the interactions between counterflowing density currents

The collision and interactions between counterflowing density currents frequently occur in nature and engineering practices along the coastal regions. However, the mechanisms and implications of these interactions remain an active research area. In this study, we developed a high-resolution numerical model to simulate the hydrodynamic characteristics of symmetric and asymmetric colliding saline density currents. The aim was to understand the interaction mechanisms, quantify collision parameters, and provide baseline physical insights into the implications of such interactions. The direct numerical simulations reveal that when equal height counterflowing saline density currents collide, the dynamic characteristics of the resulting flow depend on the density difference between the colliding currents, collaborating experimental results. The interaction process initiated mixing and significant vertical motion in the resulting mixed fluid, which attained a maximum rise height of approximately twice that of the colliding currents, thereby enhancing the vertical motion of hypersaline fluid in the ambient column. While the extent of mixing at the collision boundary increases with increasing asymmetry between the colliding currents, a relationship is observed between the buoyancy ratio of the colliding currents and the maximum rise height of the vertically displaced fluid. Given that desalination operations require effective dispersion of brine into the marine environment to minimize local impacts, understanding the collision dynamics of counterflowing saline currents would facilitate improved engineering design and implementation of environmentally compliant coastal management strategies.

Improved Subsidence Assessment for More Reliable Excavation Activity in Tehran

This paper presents a particular tunneling method, the new Austrian tunneling method (NATM), which plays an important role in reducing subsidence of the surface and damage to structures in urban areas. It has a wide range of applications in shallow tunneling projects all over the world. In this study, numerical modeling of the third-line Metro tunnel in Tehran, which is designed and stabilized by the NATM, is under discussion. The foregoing tunnel is excavated manually with a one-meter advancing step. In this project, the constructors use a lattice girder and spray concrete with 31 cm thickness as the initial lining. A suitable numerical software for this modeling is PLAXIS 3D Tunnel, which allows high-resolution finite element modeling (FEM) of the studied object. The performance of this method is investigated and compared with that of other NATMs. The numerical modeling yielded a value of 30.01 mm for earth subsidence in the most damaged area of the settlement, which was confirmed with a dramatically low difference by earth surface monitoring. Moreover, this tunnel was drilled and excavated using various methods, among which the least settlement was obtained by the proposed method. The results are promising, and they indicate that tunneling with this method should continue to be used to expand the subway line in the city.

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

Novel physics-informed optimization framework for complex multi-physics problems: Implementation for a sweeping gas membrane distillation module

The application of advanced machine learning algorithms such as deep learning is limited for the surrogate-based process optimization of complex multi-physics problems because high-quality experimental and numerical training samples required for deep-learning model training are expensive and scarce. To address this issue, in this study, a novel physics-informed process optimization (PIPO) framework was introduced. In the first step, surrogate models based on conventional neural networks (NN) were trained using a few available high-quality training samples. In the second step, the trained NN models were coupled to the process optimization solver in which the loss of physical laws was added to the optimizer’s objective function to find optimal design points that satisfy the laws of physics. As a result, the generalization performance of the framework was greatly improved for design targets outside the training range of NN models. PIPO is substantially different from the physics-informed neural networks where the loss of physics is added to the loss function used during NN model training. The PIPO framework was used to optimize a sweeping gas membrane distillation (SGMD) module. Eight input design variables, including process and geometrical parameters, were optimized for different challenging targets to achieve the best SGMD performance in terms of ammonia recovery ratio and concentration. It was shown that for noticeably few training samples of 68 experiments, the proposed framework was able to achieve the optimization targets within a reasonable computational cost. The optimum designs were verified and analyzed in detail by high-resolution computational fluid dynamics models.

Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities

Climate change is expected to influence urban living conditions, challenging cities to adopt mitigation and adaptation measures. This paper assesses climate change projections for different urban areas in Europe –Eindhoven (The Netherlands), Genova (Italy) and Tampere (Finland)—and discusses how nature-based solutions (NBS) can help climate change adaptation in these cities. The Weather Research and Forecasting Model was used to simulate the climate of the recent past and the medium-term future, considering the RCP4.5 scenario, using nesting capabilities and high spatial resolution (1 km2). Climate indices focusing on temperature-related metrics are calculated for each city: Daily Temperature Range, Summer Days, Tropical Nights, Icing Days, and Frost Days. Despite the uncertainties of this modelling study, it was possible to identify some potential trends for the future. The strongest temperature increase was found during winter, whereas warming is less distinct in summer, except for Tampere, which could experience warmer summers and colder winters. The warming in Genova is predicted mainly outside of the main urban areas. Results indicate that on average the temperature in Eindhoven will increase more than in Genova, while in Tampere a small reduction in annual average temperature was estimated. NBS could help mitigate the increase in Summer Days and Tropical Nights projected for Genova and Eindhoven in the warmer months, and the increase in the number of Frost Days and Icing Days in Eindhoven (in winter) and Tampere (in autumn). To avoid undesirable impacts of NBS, proper planning concerning the location and type of NBS, vegetation characteristics and seasonality, is needed.

Utilizing high-resolution 3D Voronoi meshing to analyze field data from the Brine Availability Test in Salt (BATS)

Salt is an attractive disposal medium for radioactive waste because intact salt is essentially impermeable and non-porous. However, upon drift or borehole excavation a damaged region develops surrounding the excavation which causes increased permeability and porosity creating potential flow paths for brine. Brine leads to corrosion of waste forms and waste packages and is a possible transport vector for radionuclides, so it is important to better understand the early-time behavior and evolution of brine flow in a salt. As a result, this study is part of Task E of DECOVALEX-2023 which focuses on understanding the evolution of thermal, two-phase hydrological, and mechanical processes in the excavation damaged zone in salt. Field measurements from The Brine Availability Test in Salt (BATS) 1a heater experiment are analyzed by implementing a high-resolution three-dimensional numerical model. This salt heater experiment consists of 28 days of heating and 13 days of cooling in a central borehole within bedded salt at the Waste Isolation Pilot Plant (WIPP). Here, the flow simulator PFLOTRAN is utilized; simulations are run on a Voronoi mesh, with temperature-dependent thermal conductivity, permeability and porosity decay away from excavations. The temperature-dependency of permeability is done to match field measurements. Results from the simulation match temperature measured in the field within + /- 0.1 °C and the total brine inflow over the 41-day experiment. This study illustrates that the accuracy of the temperature evolution within salt is critically important when analyzing and modeling experimental data by simulating three heating scenarios of the BATS 1a experiment showing that temperature has a direct effect on total brine inflow.

Phylogenetic biogeography inference using dynamic paleogeography models and explicit geographic ranges.

To model distribution ranges, the most popular methods of phylogenetic biogeography divide Earth into a handful of predefined areas. Other methods use explicit geographic ranges, but unfortunately, these methods assume a static Earth, ignoring the effects of plate tectonics and the changes in the landscape. To address this limitation, I propose a method that uses explicit geographic ranges and incorporates a plate motion model and a paleolandscape model directly derived from the models used by geologists in their tectonic and paleogeographic reconstructions. The underlying geographic model is a high-resolution pixelation of a spherical Earth. Biogeographic inference is based on diffusion, approximates the effects of the landscape, uses a time-stratified model to take into account the geographic changes, and directly integrates over all probable histories. By using a simplified stochastic mapping algorithm, it is possible to infer the ancestral locations as well as the distance traveled by the ancestral lineages. For illustration, I applied the method to an empirical phylogeny of the Sapindaceae plants. This example shows that methods based on explicit geographic data, coupled with high-resolution paleogeographic models, can provide detailed reconstructions of the ancestral areas but also include inferences about the probable dispersal paths and diffusion speed across the taxon history. The method is implemented in the program PhyGeo.

Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

Representative residual transport pathways in a mixed-energy open tidal system

The North Frisian Wadden Sea (NFWS), an open tidal basin within the UNESCO World Heritage Wadden Sea, is characterized by its barrier systems, tidal inlets, intertidal flats, and estuaries. Unlike its East Frisian and Dutch counterparts, the NFWS is underexplored in terms of residual sediment and flow transport pathways—knowledge crucial for coastal conservation and nature-based protection. These pathways are a product of complex interplay between tides, winds and waves that together shape the morphology of the NFWS. This study investigates these interactions using a high-resolution process-based model to perform a representative period simulation derived through a novel unfiltered-reduction technique. Our results reveal an anticlockwise residual flow circulation in the back-barrier region, which was not discernible in the residual sediment pathways. Waves primarily dictated sediment transport over intertidal flats, showing high variability in transport direction under energetic conditions. The coastline orientation and fetch size favored southward wavedriven sediment transport, opposing the northward residual flow transport driven by tidal propagation. A dynamic residual divergence pattern in the nearshore region of the barrier islands is also revealed for both sediment and flow, which moves alongshore during energetic events. The discussion compares these patterns and their implications with earlier local measurements, conceptual pathways, and different systems globally to provide a comprehensive overview of the transport dynamics in the NFWS.

Mapping pedestrian network level outdoor heat hazard distributions in Philadelphia

With the rise of global temperature, many cities are suffering from more and more frequent extreme heat in hot summers. Quantitative information on the spatial distributions of urban heat has become more and more important for extreme heat mitigation and adaptation in cities. This study first investigated the fine-level heat hazard distributions at the sidewalk and building block level from the pedestrian perspective in Philadelphia, Pennsylvania. The urban microclimate modeling based on a high-resolution urban geometrical model was used to generate the 1m resolution outdoor heat hazard map in the study area. The sidewalk map was overlaid on the generated high-resolution heat hazard map to estimate the sidewalk level heat hazard. Based on the sidewalk level heat hazard map, this study further calculated the heat hazard level in the 400m walkshed along sidewalks for each building block. The building level hazard data were then aggregated at the census tract level to compare with the socioeconomic and racial/ethnic variables. The result shows that neighborhoods with higher proportion of African Americans have a higher heat hazard level in Philadelphia. This study would provide new insights for developing more thermally comfortable and pedestrian-friendly neighborhoods in the context of climate change.

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.

From source to sink: part 1-characterization and Lagrangian tracking of riverine microplastics in the Mediterranean Basin.

The Mediterranean Sea is one of the most critically polluted areas due to its semi-enclosed structure and its highly anthropized shoreline. Rivers are significant vectors for pollutant transfers from the continental to the marine environment. In this context, a 3D Lagrangian simulation of the dispersion of riverine microplastics (MPs) was performed, which included the application of a recently developed model that reassessed the MP fluxes discharged by rivers. MP physical properties from river samples were further investigated to approximate vertical displacement in modeled ocean currents. The use of a high-resolution circulation model, integrating Stokes drift, turbulent diffusion, and MP sinking and rising velocities, enabled us to establish stock balances. Our simulation suggested that 65% of river inputs may be made of floating MPs drifting in the surface layer and 35% of dense MPs sinking to deeper layers. The Eastern Mediterranean tends to accumulate floating MPs, primarily originating from the Western Mediterranean Basin, where major river sources are concentrated. After 2 years of simulation, modeled stranding sequestered 90% of the MP inputs, indicating relatively short average residence times from a few days to months at most for particles at sea. Although spatial distribution patterns stabilized after this period and a steady state may have been approached, the surface concentrations we modeled generally remained below field observations. This suggested either an underestimation of sources (rivers and unaccounted sources), by a factor of 6 at most, or an overestimation of MP withdrawal through stranding, to be reduced from 90 to around 60% or less if unaccounted sinks were considered.