Merge pull request #208 from OceanParcels/three-new-papers

Adding Cai, Saito and Giuseppe De Vita papers

Author: erikvansebille

src/data/papers-citing-parcels.ts

@@ -2485,10 +2485,38 @@ export const papersCitingParcels: Paper[] = [
   {
     title:
       'Modelling the spatial bound of an eDNA signal in the marine environment - the effect of local conditions',
-    published_info: 'Frontiers in marine Science, 12',
+    published_info: 'Frontiers in Marine Science, 12',
     authors: 'Silva, TAM, CPC Beraud, PD Lamb, W Rostant, HJ Tidbury (2025)',
     doi: 'https://doi.org/10.3389/fmars.2025.1613001',
     abstract:
       'Environmental DNA (eDNA) is a powerful technique for biological assessments and monitoring in aquatic environments. The accurate interpretation of the source of eDNA detected requires understanding of its spatial and temporal bound. Studies which estimate eDNA dispersal in the aquatic environment, in particular the marine environment, are scarce and seldom represent the effect of hydrodynamics and eDNA decay. This study modelled eDNA dispersal in a coastal environment under diverse environmental conditions to assess how these conditions influence dispersal patterns. A modelling experiment shows that under thermally stratified conditions sampling eDNA across this gradient reduces detectability. Statistical analysis shows that both median and extreme eDNA dispersal distances simulated by the model were primarily controlled by local tidal conditions (tidal excursion), followed by month (influencing the water temperature and thus eDNA decay rate). The median distance varies between 2.27 and 14.14 km which falls within the range of previously published model results, and is up to 10x greater than observed values. However this gap has been narrowing, and the present statistical model helps set limits on the distance to source as a function of regional oceanography and water temperature. The present method can also be used post-survey to help interpret the location and number of sources. This study constitutes an advance in modelling eDNA dispersal in coastal areas and crucially provides much needed evidence to underpin robust interpretation of eDNA monitoring data and to inform the design of eDNA monitoring programmes that account for variable environmental conditions.',
   },
+  {
+    title:
+      'Seasonal effects of hydrodynamics and biofouling on the vertical transport of microplastics in the Vietnam coastal region, South China Sea',
+    published_info: 'Journal of Hazardous Materials, in press',
+    authors: 'Cai, C, B Hong, L Zhu, H Xu (2025)',
+    doi: 'https://doi.org/10.1016/j.jhazmat.2025.139744',
+    abstract:
+      'Microplastic (MP) pollution poses a significant threat to marine ecosystems, eliciting widespread concern. In the coastal upwelling region, both hydrodynamic and biological processes playing crucial roles in effecting MP transport. How biofouling impact the sinking characteristics of MPs in the coastal region remains largely unknown. In this study, a hydrodynamic model coupled with a biofouling module was employed to simulate the seasonal vertical transport and distribution of MPs, considering four polymer types and six particle size classes. Results showed that the vertical displacement of expanded polystyrene, polypropylene, and low-density polyethylene MPs (radii ≥1.0 mm) was initially driven by their buoyancy, but gradually became dominated by sinking velocity as biofouling increased their density. In contrast, the vertical displacement of rigid polyamide (RPA) MPs with the same sizes were primarily governed by vertical advection during all seasons except winter. In summer and spring, over 95% of the MPs remained within the upper 0–5 m surface layer. In late seasons, especially winter, a substantial portion of MPs sank to deeper layers, with over 52% of RPA MPs (radii ≥ 1.0 mm) accumulating at depths of 50–100 m. These findings provide a scientific basis for assessing the environmental impacts of MPs in marine systems and developing policies for the mitigation of MPs.',
+  },
+  {
+    title:
+      'Internal tides drive spatial variation in impact areas of deep-sea mining plumes at seamounts',
+    published_info: 'Frontiers in Marine Science, 12',
+    authors: 'Saito, N, TW Washburn, M Nagao, H Kamoshida, A Suzuki (2025)',
+    doi: 'https://doi.org/10.3389/fmars.2025.1603902',
+    abstract:
+      'Deep-sea mining at seamounts can generate large amounts of suspended particles, or sediment plumes, which have the potential to cause environmental impacts. The physical oceanography at seamounts, including internal tides, is expected to complicate plume behavior. However, research incorporating numerical simulations to evaluate this influence is virtually nonexistent. In this study, we conducted hydrodynamic modeling and simulated dispersal and deposition of plumes across the entire seamount summit. The simulations were based on a crust excavation test conducted in 2020 and targeted suspended particles of ≥30 μm, which accounted for the majority of the plume volume. The modeled near-bottom tidal currents at the summit were ≤7 times stronger than those outside the seamount, indicating the occurrence of internal tides, with tidal current strength varying spatially across the summit. The deposition distances of plumes varied by a factor of ≤6.5 (~120–800 m), depending on the discharge location. Plumes tended to be deposited farther and in a thinner layer around sites with stronger tidal currents, whereas they were deposited closer and thicker around sites with weaker tidal currents. This study suggests that the spatial variability in tidal current strength, driven by internal tides, can alter the extent of plume dispersal and deposition by several-fold depending on the mining site. Understanding oceanographic heterogeneity within seamount summits can be crucial for assessing and mitigating the environmental impacts of mining.',
+  },
+  {
+    title:
+      'A coupled Lagrangian-AI hierarchical and heterogeneous model for predicting bacteria contamination in farmed mussels',
+    published_info: 'Future Generation Computer Systems, in press',
+    authors:
+      'Giuseppe De Vita, C, G Mellone, D Di Luccio, J Garcia-Blas, F Barchiesi, R Montella (2025)',
+    doi: 'https://doi.org/10.1016/j.future.2025.108108',
+    abstract:
+      'The quality of coastal waters, particularly aquaculture zones, is crucial to sustainable development and human health. Traditional monitoring methods based on scheduled in-situ sampling are often too slow, costly, and limited in spatial and temporal coverage to meet the needs of large-scale aquaculture management. To overcome these constraints, we introduce the Artificial Intelligence-based Water QUAlity Plus Plus model (AIQUAM++), an AI-based modeling framework designed to predict E. coli contamination directly within farmed mussels. We evaluated and compared a suite of recent and high-performing machine learning architectures, such as K-Nearest Neighbors (KNN), and several Transformer-based architectures (Transformer, Informer, Reformer, TimesNet), to address the complex temporal dependencies within this time series classification (TSC) task. AIQUAM++ was trained with historical microbiological measures of E. coli levels in the mussels provided by the local authorities involved in food safety monitoring. The system architecture, featuring an inference engine completely written in C++ for high performance, leverages hierarchical parallelism to ensure scalability and computational efficiency, incorporating Message Passing Interface (MPI) for inter-process communication on multi-core architectures, OpenMP for multithreaded processing, and CUDA-based acceleration for GPU-optimized computations. This design enables high-throughput inference that is suitable for operational deployment in aquaculture monitoring networks. A test case application of AIQUAM++ was conducted in the Gulf of Naples (Campania, Italy). Empirical results demonstrated that the proposed system achieves classification accuracies that exceed 90%, supporting its efficacy as a real-time data-driven decision support tool for aquaculture water quality management, minimizing health risks and contributing to sustainable marine resource governance.',
+  },
 ]