Transport Stream Research Articles

Critical revision of check dams hydraulic design criteria

This paper addresses the problem of sizing the spillway of a closed check dam. Check dams are structures designed to control the solid transport and morphology of mountain streams. Paradoxically, however, their spillways are typically sized in clear-water conditions. The paper first analyses the behaviour of a check dam under clear water and then under live-bed conditions which is by far the most frequent operating condition. The theoretical approach is based on the conservation equations of solid mass, total mass, momentum and energy of the flow. The results of the theory are then compared with the results of some experiments in a laboratory channel equipped with a closed circuit for solid and liquid flow rates. The comparison between experiments and theory shows the validity of the approach. As expected, in some conditions that are far from rare, the design made in the clear-water condition is not safe.

Quantifying Surface Shelf Water Export in the Southern Middle Atlantic Bight Using a Lagrangian Particle Tracking Approach

AbstractShelf water is influenced by atmospheric forcing, river outflows, and the open ocean. Studying its variability is crucial for understanding anthropogenic impacts on coastal oceans and their transport to the open ocean. In the Middle Atlantic Bight (MAB), the interaction of the Gulf Stream with shelf/slope circulation leads to some of the complex exchanges between the shelf and open ocean along the U.S. East Coast. This study employs a Lagrangian particle tracking approach, grounded in a high‐resolution, data‐assimilative ocean reanalysis, to examine the export pathways of surface shelf water in the MAB. We analyzed over 700 daily images of simulated particle distributions using image clustering techniques. This revealed three distinct export patterns: abrupt entrainment to the Gulf Stream, gradual entrainment, and southern transport. Each pattern was observed roughly equally during the study period from January 2017 to December 2018. The observed export patterns are closely linked to the coastal circulation dynamics near Cape Hatteras. Understanding the timing and duration of these patterns is vital for assessing water quality and predicting the settlement of species that spawn in the region. Our study further underscores the influence of tropical cyclones, including Hurricanes Jose, Maria, and Chris, on these export patterns. These extreme weather events lead to significant shifts in coastal circulation near Cape Hatteras.

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

دور تيارات النقل في إبراز المركزية الحضرية وتنظيم المجال الجهوي للمدن حالة مدينة القنيطرة، المغرب

This article aims to highlight the role of transportation streams in enhancing the urban centralization of Kenitra. The study will analyze how the city’s regional area is organized, starting with the first-class taxi transport streams and other forms of transportation such as dual and unrevealed transportation. We will be able to get precise data regarding the number of daily trips made between the city of Kenitra and other locations due to this study. Additionally, it will provide us with a clear understanding of the relationships that exist between the city and the territorial areas and surrounding regions, which enables us to calculate the difference in movement density between the city of Kenitra and the various regions that fall under its area of impact. This study is contemporary because it is one of the few that examined how transportation organized Kenitra's regional area and increased its urban centrality. It is also contemporary because field research was used, which lends scientific validity to the conclusions drawn.

Insight into the forced convective radiative Stefan flow of nanofluid over an unsteady stretched sheet

Water-based ‘nanofluid’ flow owing to an unsteady stretched surface is inspected in this paper considering Stefan blowing and thermal radiation. ‘Similarity transformations’ are applied to reduce the governing ‘partial differential equations’ (PDEs) for momentum, energy and concentration into ‘nonlinear’ ‘ordinary differential equations’ (ODEs). By using a shooting technique, those equations are solved numerically with the help of fourth-order ‘Runge–Kutta method’. ‘Wall shear stress’ rises but ‘heat transfer’ as well as ‘mass transfer coefficients’ reduce for the augmentation in ‘Stefan blowing/suction parameter’. ‘Temperature’ and ‘concentration’ of nanoliquid are found to rise but liquid’s ‘velocity’ reduces for the growing of ‘nanoparticle’s volume fraction’. Liquid’s ‘velocity’ and ‘concentration’ are observed to decrease for enhanced ‘Lewis number’. Based on the results presented here as well as their anatomical analysis, the relevant parameters significantly affect the stream, warmth and mass transports.

Stability and deformation of biomolecular condensates under the action of shear flow.

Biomolecular condensates play a key role in cytoplasmic compartmentalization and cell functioning. Despite extensive research on the physico-chemical, thermodynamic, or crowding aspects of the formation and stabilization of the condensates, one less studied feature is the role of external perturbative fluid flow. In fact, in living cells, shear stress may arise from streaming or active transport processes. Here, we investigate how biomolecular condensates are deformed under different types of shear flows. We first model Couette flow perturbations via two-way coupling between the condensate dynamics and fluid flow by deploying Lattice Boltzmann Molecular Dynamics. We then show that a simplified approach where the shear flow acts as a static perturbation (one-way coupling) reproduces the main features of the condensate deformation and dynamics as a function of the shear rate. With this approach, which can be easily implemented in molecular dynamics simulations, we analyze the behavior of biomolecular condensates described through residue-based coarse-grained models, including intrinsically disordered proteins and protein/RNA mixtures. At lower shear rates, the fluid triggers the deformation of the condensate (spherical to oblated object), while at higher shear rates, it becomes extremely deformed (oblated or elongated object). At very high shear rates, the condensates are fragmented. We also compare how condensates of different sizes and composition respond to shear perturbation, and how their internal structure is altered by external flow. Finally, we consider the Poiseuille flow that realistically models the behavior in microfluidic devices in order to suggest potential experimental designs for investigating fluid perturbations in vitro.

Illuminating the “Invisible”: Substantial Deep Respiration and Lateral Export of Dissolved Carbon From Beneath Soil

AbstractDissolved organic and inorganic carbon (DOC and DIC) influence water quality, ecosystem health, and carbon cycling. Dissolved carbon species are produced by biogeochemical reactions and laterally exported to streams via distinct shallow and deep subsurface flow paths. These processes are arduous to measure and challenge the quantification of global carbon cycles. Here we ask: when, where, and how much is dissolved carbon produced in and laterally exported from the subsurface to streams? We used a catchment‐scale reactive transport model, BioRT‐HBV, with hydrometeorology and stream carbon data to illuminate the “invisible” subsurface processes at Sleepers River, a carbonate‐based catchment in Vermont, United States. Results depict a conceptual model where DOC is produced mostly in shallow soils (3.7 ± 0.6 g/m2/yr) and in summer at peak root and microbial respiration. DOC is flushed from soils to the stream (1.0 ± 0.2 g/m2/yr) especially during snowmelt and storms. A large fraction of DOC (2.5 ± 0.2 g/m2/yr) percolates to the deeper subsurface, fueling deep respiration to generate DIC. DIC is exported predominantly from the deeper subsurface (7.1 ± 0.4 g/m2/yr, compared to 1.3 ± 0.3 g/m2/yr from shallow soils). Deep respiration reduces DOC and increases DIC concentrations at depth, leading to commonly observed DOC flushing (increasing concentrations with discharge) and DIC dilution patterns (decreasing concentrations with discharge). Surprisingly, respiration processes generate more DIC than weathering in this carbonate‐based catchment. These findings underscore the importance of vertical connectivity between the shallow and deep subsurface, highlighting the overlooked role of deep carbon processing and export.

Drifting along: using diatoms to track the contribution of microbial mats to particulate organic matter transport in a glacial meltwater stream in the McMurdo Dry Valleys, Antarctica.

Flow pulses mobilize particulate organic matter (POM) in streams from the surrounding landscape and streambed. This POM serves as a source of energy and nutrients, as well as a means for organismal dispersal, to downstream communities. In the barren terrestrial landscape of the McMurdo Dry Valleys (MDV) of Antarctica, benthic microbial mats occupying different in-stream habitat types are the dominant POM source in the many glacier-fed streams. Many of these streams experience daily flow peaks that mobilize POM, and diatoms recovered from underlying stream sediments suggest that mat-derived diatoms in the POM are retained there through hyporheic exchange. Yet, 'how much' and 'when' different in-stream habitat types contribute to POM diatom assemblages is unknown. To quantify the contribution of different in-stream habitat types to POM diatom assemblages, we collected time-integrated POM samples over four diel experiments, which spanned a gradient of flow conditions over three summers. Diatoms from POM samples were identified, quantified, and compared with dominant habitat types (i.e., benthic 'orange' mats, marginal 'black' mats, and bare sediments). Like bulk POM, diatom cell concentrations followed a clockwise hysteresis pattern with stream discharge over the daily flow cycles, indicating supply limitation. Diatom community analyses showed that different habitat types harbor distinct diatom communities, and mixing models revealed that a substantial proportion of POM diatoms originated from bare sediments during baseflow conditions. Meanwhile, orange and black mats contribute diatoms to POM primarily during daily flow peaks when both cell concentrations and discharge are highest, making mats the most important contributors to POM diatom assemblages at high flows. These observations may help explain the presence of mat-derived diatoms in hyporheic sediments. Our results thus indicate a varying importance of different in-stream habitats to POM generation and export on daily to seasonal timescales, with implications for biogeochemical cycling and the local diatom metacommunity.

Automatic Detection of Ballast Unevenness Using Deep Neural Network

The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity.

Check dam impact on sediment loads: example of the Guerbe River in the Swiss Alps – a catchment scale experiment

Abstract. The construction of check dams is a common practice around the world where the aim is to reduce the damage by flooding events through mountain streams. However, quantifying the effectiveness of such engineering structures has remained very challenging and requires well-selected case studies, since the outcome of such an evaluation depends on site-specific geometric, geologic and climatic conditions. Conventionally, the check dams' effectiveness has been estimated using information about how the bedload sediment flux in the stream changes after the check dams are constructed. A permanent lowering of the bedload flux not only points to a success in reducing the probability of sediment transport occurrence but also implies that the sediment input through the system is likely to decrease. Here, we applied a method for data acquisition and two different equations (Meyer-Peter–Müller and Recking approach) to estimate and compare the sediment transport in a mountain stream in Switzerland under engineered and non-engineered conditions. Whereas the first equation is derived from a classical approach that is based on flume experiment data with a slope of less than 0.02 m m−1, the second equation (Recking) has been derived based on a bedload field dataset comprising active mountain streams under steeper conditions. We selected the Guerbe (Gürbe) River situated in the Swiss Alps as a case study, which has been engineered since the end of the 19th century. This has resulted in more than 110 check dams along a ca. 5 km reach where sediment has continuously been supplied from adjacent hillslopes, primarily by landsliding. We measured the riverbed grain size, topographic gradients and river widths within selected segments along this reach. Additionally, a gauging station downstream of the reach engineered with check dams yielded information to calibrate the hydroclimatic situation for the study reach, thus offering ideal conditions for our catchment-scale experiment. Using the acquired data and the dataset about historical runoff covering the time interval between 2009 and 2021 and considering the current engineered conditions, we estimated a mean annual volume of transported bedload which ranges from 900 to 6000 m3 yr−1. We then envisaged possible channel geometries before the check dams were constructed. We inferred (1) higher energy gradients which we averaged over the length of several check dams and which we considered a proxy for the steeper river slope under natural conditions; (2) channel widths that are smaller than those measured today, thereby anticipating that the channel was more confined in the past; and (3) larger grain size percentiles, which we consider to be similar to the values measured from preserved landslides in the region. Using such potential non-engineered scenarios as constraints, the two equations both point towards a larger sediment flux compared to the engineered state, although the results of these equations differed significantly in magnitude. Whereas the Recking approach returned estimates where the bedload sediment flux is ca. 10 times larger in comparison with the current situation, the use of the Meyer-Peter–Müller equation predicts an increase of ca. 100 times in bedload fluxes for a state without check dams. These results suggest that the check dams in the Guerbe River are highly efficient not only in regulating sediment transport by decreasing the probability of high sediment flux occurrence during torrential conditions but also in stabilizing the channel bed by avoiding incision. The most likely consequence is a stabilization of the terrain around such structures by reducing the activation of landslides.

A comprehensive assessment and comparison of the impacts of storage parameters on solute transport in streams using a novel framework

Researchers must determine the storage parameter values to understand the storage processes and compare them across streams. These parameters are highly variable in time and space, making them challenging to measure. Individual studies of a single stream cannot characterize the interrelations among the processes involved in solute transport, hydraulic variables, and physical stream characteristics. Despite decades of research, a comprehensive framework is lacking. This study uses a simple and effective Monte Carlo approach to propose a general framework within which most solute transport models can undergo sensitivity analysis. This framework provides a comprehensive depiction of model performance under various scenarios, such as flow properties, stream geomorphology, and solute status. The relative and absolute sensitivities of three solute transport models, namely, the modified advection–dispersion equation (MADE), transient storage model (TSM), and variable residence time (VART), were evaluated and compared within the proposed framework. The main findings can be summarized as follows. In most conditions, storage models are more sensitive to the ratio of the storage zone cross-section to the stream cross-section than other parameters. Increasing the exchange rate between the stream and storage zones reduces the sensitivity to its indicative parameter and, consequently, its impact on the concentration time series. MADE's capability to identify modification parameters is higher for streams where dispersion dominates over advection, and their estimates have lower uncertainty. On the other hand, as advection becomes more dominant over dispersion in TSM and VART, estimated storage parameters have lower uncertainty.“

Deep learning cosmic ray transport from density maps of simulated, turbulent gas

The coarse-grained propagation of galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines—for instance, diffusive vs streaming transport models—is an unsolved challenge. Leveraging a recent training set of magnetohydrodynamic turbulent box simulations, with CRs spanning a range of transport parameters, we use convolutional neural networks (CNNs) trained solely on gas density maps to classify CR transport regimes. We find that even relatively simple CNNs can quite effectively classify density slices to corresponding CR transport parameters, distinguishing between streaming and diffusive transport, as well as magnitude of diffusivity, with class accuracies between 92% and 99%. As we show, the transport-dependent imprints that CRs leave on the gas are not all tied to the resulting density power spectra: classification accuracies are still high even when image spectra are flattened (85%–98% accuracy), highlighting CR transport-dependent changes to turbulent phase information. We interpret our results with saliency maps and image modifications, and we discuss physical insights and future applications.

Deep convolutional neural networks with Bee Collecting Pollen Algorithm (BCPA)-based landslide data balancing and spatial prediction

Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models.

MODELING CITY TRAFFIC MANAGEMENT AND IDENTIFICATION OF COMPETITIVE ROUTES USING INTELLIGENT TRANSPORTATION SYSTEMS

This article presents a conceptual approach to modelling city traffic management and identifying competitive routesusing intelligent transportation systems. It argues that modelling traffic in transport streams, considering the behaviour ofeach individual vehicle, yields the most reliable results for real-time modelling of specific intersections in city trafficmanagement. The proposed methodology for developing an algorithm to detect competitive routes allows users to choosethe optimum route. The method is based on the statistical relationship between route indicators and helps to accuratelycalculate the demand for the implementation and improvement of the transportation network with subsequent changes inits operation. Redistribution of traffic flows for the identification of competitive routes in transport corridors and the impactof random variable values on route indicators are discussed. It is proven that the probability of describing the distributionof intervals between vehicles entering the network is of utmost importance for describing processes in a transport corridor,with the exponential distribution being typically used for these purposes. The conclusion is made that the micro-levelmodel for calculating vehicle movement modes should meet the requirements necessary for solving the task of modelingcity traffic management and identifying competitive routes using ITS. Such conditions are characterized by significantfluctuations in vehicle movement modes, stops at regulated intersections and in traffic jams, lane and trajectory changes,which significantly affect the algorithm for finding a competitive route in the transport corridor.

To hear or not to hear: selective tidal stream transport can interfere with the detectability of migrating silver eels in a Tidal River

Acoustic telemetry provides valuable insights into behavioural patterns of aquatic animals such as downstream migrating European eels (Anguilla anguilla), so called silver eels. The behaviour of silver eels during the migration is known to be influenced by environmental factors, yet so is the performance of acoustic telemetry networks. This study quantifies the impact of these environmental factors on both, migration behaviour and receiver performance to determine possible limiting conditions for detecting tagged eels in tidal areas. A dominance analysis of the selected models describing migration speed, activity and receiver performance was conducted following 234 silver eels that were tagged with acoustic transmitters and observed by a receiver network in the Ems River during two subsequent migration seasons. The results suggest a passive locomotion of silver eels during their downstream migration by taking advantage of selective tidal stream transport (STST). It is further shown that water temperature, salinity, turbidity, precipitation, and especially current velocity were major parameters influencing migration activity and speed. At the same time, analyses of the detection probability of tagged eels under varying environmental conditions indicated a decreased receiver performance during increased current velocities, meaning that high migration activity and -speed coincides with reduced detection probability. Consequently, there is a risk that particularly during phases of increased activity, migration activity may be underestimated due to reduced acoustic telemetry performance. To avoid bias in telemetry studies, it is, therefore, crucial to conduct range tests and adjust the receiver placement in areas and conditions of high current velocities.

Maneuverability and Movement Stability of Buses of the Especially Large Size Class

One of the main problems of modern big city is the global crisis of normal urban environment operation as a result of structural growing of automobilization level, oversaturation of road-street network by the transport streams. Buses of the especially large size class occupy, at the least, 20% in the structure of a modern bus park. The buses of especially large size classes, with the aim of necessary maneuverability, as a rule are made articulated.One of the ways of improving the maneuverability of articulated buses is the usage of guided towed sections. Solving the mentioned problem usually needs the mathematic models for predicting the vehicle behavior.The aim of introducing the trailer direction is to increase the overall traffic lane of an articulated bus that ensures a necessary maneuverability. The aim is gained by a choice of a rational transmitting ratio of trailer axle gear and necessary value of braking moment on one of its wheels.The solving of the problem concerning the stability of an articulated bus with a guided trailer axle was made by the way of analyzing the linear approaching of stationary vehicle movement.An influence of the construction parameters of the bus and the trailer, a transmitting ratio of control gear and a value of braking moment on the parameters of bus maneuverability were analyzed. It was proved that for an articulated bus, the normal maneuverability parameters can be ensured at the trailer direction by a way of braking of one of the wheels.

Developing a method of multi-lane roundabouts capacity

Introduction. In recent years a number of methodological and regulatory documents have been developed for public road roundabouts in the Russian Federation. In particular it was proposed to evaluate the quality of traffic management at roundabouts by the level of service measured by the average delay at roundabout entrances. The extension of the average delay criterion to the design of roundabouts requires the development of a methodology for estimating the capacity of traffic lanes at multi-lane roundabout entrances.The aim of the study. Is to develop a methodology for estimating the capacity of multi-lane roundabouts.The object of the study. Is the functioning of multilane roundabouts. Subject of the research. Are the regularities of influence of traffic volume on the values of characteristics of transport streams interaction in the conflict points of multilane roundabouts.Theoretical foundations of the research. The model for estimation of carrying capacity of multilane roundabouts based on the use of conflict points and function of acceptance of intervals is offered.Research Methodology. The video footage of multi-lane roundabouts in the cities of Bratsk, Vladivostok, Irkutsk, Lipetsk, Nakhodka, Orenburg, Petrozavodsk and Pskov was processed. On each lane of the circular carriageway, intervals in the flow, measured between the front bumpers of vehicles, were recorded. The accepted and rejected headways were registered, as well as the number of vehicles using accepted intervals. The determination of the values of critical headways and follow-up time was performed using the Siegloch’s method based on linear regression.Results. A model for estimating capacity at the entrance to a multi-lane roundabout was validated. The values of critical intervals, queuing intervals of each of the entrance lanes to 2-lane and 3-lane roundabouts were determined. The values of the minimum headways in the flows moving on the circular carriageway are determined, also the dependences of the influence of traffic intensity on the free share of the traffic flow are established.

Appraisal of heavy metal contents, spatial-temporal variation, toxic metal pollution, and health risk in water and sediment of Uluabat Lake (Ramsar Site, Turkey).

Uluabat Lake holds a significant status as one of our country's essential Ramsar areas. Nevertheless, the lake faces considerable pressure from environmental pollutants. This study employed GIS-based index methods to examine the heavy metal pollution and water quality in Uluabat Lake. To achieve this, sediment and water samples were collected from 19 different locations during various seasons. The concentrations of As, Ni, Cr, Zn, Cu, Cd, Pb, Hg, Al, Fe, and Mn in these samples were analyzed. The average contents of the investigated heavy metals in the lake waters were found to be in the following order: Al > Fe > Mn > As > Zn > Ni > Pb > Cu > Cr > Hg > Cd. The As content in the lake waters exceeded the limit value of 10 μg/L, as defined by TS-266 (2005) and WHO (2017) guidelines. Consequently, the lake waters were deemed unsuitable for drinking. The health risk assessments revealed that consuming the lake waters could lead to both carcinogenic and non-carcinogenic health problems. However, all other metal concentrations were measured below the specified limit values. Based on index calculations, the heavy metal pollution index value indicated that the lake water samples were suitable for drinking water use. Additionally, all samples fell into the "low pollution" category according to the heavy metal evaluation index, and degree of contamination values. However, geoaccumulation index values indicated that As was moderately contaminated. Moreover, enrichment factors of As, Cr, Ni, Pb, and Cd exhibited significant enrichment in lake sediments, with pollution load index values of all sediment samples indicating the impact of anthropogenic pollutants and a decline in sediment quality. Considering all the obtained results together, it can be concluded that environmental pollutants, especially heavy metal pollution, primarily enter the lake water through stream transports, leading to pollution in Uluabat Lake.

Tidal and circadian patterns of European eel during their spawning migration in the North Sea and the English Channel

Technological advances in tracking methods enable the mapping of anguillid eel migration routes from continental habitats to their spawning sites in the ocean. However, the behaviour and orientation abilities of anguillids are still poorly understood, and have only rarely been studied on the continental shelf. Here we present the results of a study into the vertical and horizontal movement behaviour of 42 European eels (Anguilla anguilla L.) tagged with electronic tags that migrated through the North Sea and English Channel towards and into the Atlantic Ocean during their spawning migration. We used actograms, periodograms and linear mixed effects models to determine the periodicity and significance of the timing and pattern of vertical movement and activity. Overall, eels had a complex behavioural repertoire that included classical diel vertical migration (DVM), reverse DVM and vertical movement behaviours that synchronized with tidal patterns. All of the eels that were tracked showed one or more of these behaviours during their time at liberty, and many exhibited all of them. We also observed that the eels had a higher horizontal migration speed when the current in the favourable direction was stronger. This, together with the vertical movement synchronized with the tides, suggests the eels adopt selective tidal stream transport. Finally, tracked eels had a higher vertical movement range at night compared to daytime. We hypothesize that these behaviours are driven by bio-energetic efficient movement, navigation and predator avoidance.

Importance of Dense Aquatic Vegetation in Seasonal Phosphate and Particle Transport in an Agricultural Headwater Stream

AbstractAgricultural headwater streams and ditches commonly host dense stands of aquatic vegetation that grow and decay over seasons and exert physical and biological controls on the transport of nutrients from cropland to larger rivers. This study examined changes in the transport of phosphorus (P) in an agricultural drainage ditch in the Maumee River Basin (Ohio, USA) by conducting constant rate injections of a novel tracer mixture (conservative salt [Cl as NaCl], dissolved P [KH2PO4], and a fluorescent fine particle) in spring, summer, and fall. We quantified transport behavior for solutes and particles using a traditional transient storage modeling framework consisting of mobile and immobile storage zones connected by a first‐order exchange rate constant. Transient storage was greatest during the spring, when thicker vegetation caused more pooling and flow stagnation, and decreased through fall, as vegetation thinned. Soluble P uptake lengths were 8.7 times longer in fall than spring, likely due to declines in biological uptake rates with colder temperatures and immobile zone storage with thinning vegetation. Particle capture lengths also decreased by a factor of 4.3 from fall to spring. With the increasing eutrophication of Lake Erie and waterbodies around the world that lie downstream from agricultural landscapes, it is beneficial to understand nutrient transport across watersheds, including small agricultural streams. This study highlights the physical and biological roles that aquatic vegetation plays in small agricultural streams by creating seasonally variable immobile zones that slow the flow of nutrients, providing surface area for biofilms, and capturing particles that bind nutrients.