Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity.

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive when R 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

Comparative analysis of tracer studies in the determination of longitudinal dispersion coefficient.

The research presented in this paper is to determine the best tracer studies that will give acceptable estimates of longitudinal dispersion coefficient for Orashi river using rhodamine WT dye and sodium chloride as water tracer. Estimated results obtained for longitudinal dispersion coefficient for the case of rhodamine WT experiment ranges between 71 and 104.4 m2s-1 while that of sodium chloride experiment ranges between 20.1 and 34.71 m2s-1. These results revealed lower dispersion coefficient using sodium chloride as water tracer (WT) indicating that for larger rivers, sodium chloride should not be used as water tracer. The usage of sodium chloride as water tracer in the estimation of longitudinal dispersion coefficient is recommended in smaller streams as NaCl is relatively conservative. The established equations for both cases of investigation are proving satisfactory upon validation as degree of accuracy of 100.0% was obtained using discrepancy ratio (Dr). Standard error (SE), normal mean error (NME) and mean multiplication error (MME) of the developed equations is better when compared with other existing equations. However, Equation (17) is satisfactorily recommended.

Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction.

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

Application of multi-criteria decision-making on low-impact development practice selection for the Kinyerezi River sub-catchments in Dar es Salaam, Tanzania.

The impermeable areas in catchments are proportional to peak flows that result in floods in river reaches where the flow-carrying capacity is inadequate. The high rate of urbanization witnessed in the Kinyerezi River catchment in Dar es Salaam city has been noted to contribute to floods and siltation in the Msimbazi River. The Low-Impact Development (LID) practices that includes bio-retention (BR) ponds, rain barrels (RBs), green roofs (GRs), etc. can be utilized to mitigate portion of the surface runoff. This study aims to propose suitable LID practices and their sizes for mitigating runoff floods in the Kinyerezi River catchment using the Multi-Criteria Decision-Making (MCDM) approach. The results indicated that the BR and RBs were ranked high in capturing the surface runoff while the sediment control fences were observed to be the best in reducing sediments flowing into the BR. The proposed BR ponds were greater than 800 m2 with 1.2 m depth while RB sizes for Kinyerezi and Kisungu secondary schools and Kinyerezi and Kifuru primary schools were 2,730; 2,748; 1,385; and 1,020 m3, respectively. The BR ponds and RBs are capable of promoting water-demanding economic activities such as horticulture, gardening, car washing while reducing the school expenses and runoff generation.

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.

A dynamic one-dimensional model for simulating unsteady air-water stratified flow in sewer pipes.

Ventilation is paramount in sanitary and stormwater sewer systems to mitigate odor problems and avert pressure surges. Existing numerical models have constraints in practical applications in actual sewer systems due to insufficient airflow modeling or suitability only for steady-state conditions. This research endeavors to formulate a mathematical model capable of accurately simulating various operational conditions of sewer systems under the natural ventilation condition. The dynamic water flow is modeled using a shock-capturing MacCormack scheme. The dynamic airflow model amalgamates energy and momentum equations, circumventing laborious pressure iteration computations. This model utilizes friction coefficients at interfaces to enhance the description of the momentum exchange in the airflow and provide a logical explanation for air pressure. A systematic analysis indicates that this model can be easily adapted to include complex boundary conditions, facilitating its use for modeling airflow in real sewer networks. Furthermore, this research uncovers a direct correlation between the air-to-water flow rate ratio and the filling ratio under natural ventilation conditions, and an empirical formula encapsulating this relationship is derived. This finding offers insights for practical engineering applications.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

Comparison of the performance of SWAT and hybrid M5P tree models in rainfall-runoff simulation.

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.

Stream macroinvertebrate communities in restored and impacted catchments respond differently to climate, land-use, and runoff over a decade.

Identifying which environmental drivers underlie degradation and improvements of ecological communities is a fundamental goal of ecology. Achieving this goal is a challenge due to diverse trends in both environmental conditions and ecological communities across regions, and it is constrained by the lack of long-term parallel monitoring of environmental and community data needed to study causal relationships. Here, we identify key environmental drivers using a high-resolution environmental - ecological dataset, an ensemble of the Soil and Water Assessment Tool (SWAT+) model, and ecological models to investigate effects of climate, land-use, and runoff on the decadal trend (2012-2021) of stream macroinvertebrate communities in a restored urban catchment and an impacted catchment with mixed land-uses in Germany. The decadal trends showed decreased precipitation, increased temperature, and reduced anthropogenic land-uses, which led to opposing runoff trends - with decreased runoff in the restored catchment and increased runoff in the impacted catchment. The two catchments also varied in decadal trends of taxonomic and trait composition and metrics. The most significant improvements over time were recorded in communities of the restored catchment sites, which have become wastewater free since 2007 to 2009. Within the restored catchment sites, community metric trends were primarily explained by land-use and evaporation trends, while community composition trends were mostly associated with precipitation and runoff trends. Meanwhile, the communities in the impacted catchment did not undergo significant changes between 2012 and 2021, likely influenced by the effects of prolonged droughts following floods after 2018. The results of our study confirm the significance of restoration and land-use management in fostering long-term improvements in stream communities, while climate change remains a prodigious threat. The coupling of long-term biodiversity monitoring with concurrent sampling of relevant environmental drivers is critical for preventative and restorative management in ecology.

Modeling subsurface contaminant transport from a former open-pit uranium mine in fractured granites (La Ribière, France): Reducing uncertainties with geophysics.

The long-term management of tailings from former uranium (U) mines requires an in-depth understanding of the hydrogeological processes and water flow paths. In France, most of the legacy U mines are located in fractured crystalline (plutonic) rocks, where the intrinsic subsurface heterogeneity adds to the uncertainties about the former extraction and milling activities and the state of the mine when production was ceased. U ores were mainly processed by sulfuric acid leaching, leading to high-sulfate-content mill tailings now contained in several tailing storage facilities (TSFs). The La Ribière site, located in western central France, is a former open-pit and underground U mine, closed in 1992 and used to store mill tailings. This site is being used as a test case to establish a workflow in order to explain and predict water flow and subsurface contaminant transport. A conceptual model of water flow and sulfate transport, at the scale of the La Ribière watershed, is first developed based on available information and hydrogeochemical monitoring. Recent geophysical investigations allows refining this model. Electrical Resistivity Tomography (ERT) proves to be efficient at localizing the extent of the highly conductive sulfate plume inherited from the U-mill tailings, but also at imaging the weathering profile. Magnetic Resonance Sounding (MRS), despite the limited signal intensity due to the low porosity in crystalline rocks, gives some insight into the porosity values, the depth of the fractured layer and the location of the low-porosity ore-processing muds. Based on this conceptual model, a 3D flow and non-reactive transport model with the METIS code is developed and calibrated. This model allows predicting the evolution of the sulfate plume, but will also be used in future investigations, to build reactive transport models with simplified hydrogeology for U and other reactive contaminants.

Responses of runoff and sediment yield to slope length and gravel content of Lou soil engineering accumulation slope in Guanzhong region, Northwest China.

With the economic development, a large number of engineering accumulation bodies with Lou soil as the main soil type were produced in Guanzhong area, Northwest China. We examined the characteristics of runoff and sediment yield of Lou soil accumulation bodies with earth (gravel content 0%) and earth-rock (gravel content 30%) under different rainfall intensities (1.0, 1.5, 2.0, 2.5 mm·min-1) and different slope lengths (3, 5, 6.5, 12 m) by the simulating rainfall method. The results showed that runoff rate was relatively stable when rainfall intensity was 1.0-1.5 mm·min-1, while runoff rate fluctuated obviously when rainfall intensity was 2.0-2.5 mm·min-1. The average runoff rate varied significantly across different rainfall intensities on the same slopes, and the difference of average runoff rate of the two slopes was significantly increased with rainfall intensity. Under the same rainfall intensity, the difference in runoff rate between the slope lengths of the earth-rock slope was more obvious than that of the earth slope. When the slope length was 3-6.5 m, flow velocity increased rapidly at first and then increased slowly or tended to be stable. When the slope length was 12 m, flow velocity increased significantly. In general, with the increases of rainfall intensity, inhibition effect of gravel on the average flow velocity was enhanced. When rainfall intensity was 2.5 mm·min-1, the maximum reduction in the average flow velocity of earth-rock slope was 61.5% lower than that of earth slope. When rainfall intensity was less than 2.0 mm·min-1, sediment yield rate showed a trend of gradual decline or stable change, while that under the other rainfall intensities showed a trend of rapid decline and then fluctuated sharply. The greater the rainfall intensity, the more obvious the fluctuation. There was a significant positive correlation between the average sediment yield rate and runoff parameters, with the runoff rate showing the best fitting effect. Among the factors, slope length had the highest contribution to runoff velocity and rainfall erosion, which was 51.8% and 35.5%, respectively. This study can provide scientific basis for soil and water erosion control of engineering accumulation in Lou soil areas.

A machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties.

As a basic infrastructure, sewers play an important role in the innards of every city and town to remove unsanitary water from all kinds of livable and functional spaces. Sewer pipe failures (SPFs) are unwanted and unsafe in many ways, as the disturbance that they cause is undeniable. Sewer pipes meet manholes frequently, unlike water distribution systems, as in sewers, water movement is due to gravity and manholes are needed in every intersection as well as through pipe length. Many studies have been focused on sewer pipe failures and so on, but few investigations have been done to show the effect of manhole proximity on pipe failure. Predicting and localizing the sewer pipe failures is affected by different parameters of sewer pipe properties, such as material, age, slope, and depth of the sewer pipes. This study investigates the applicability of a support vector machine (SVM), a supervised machine learning (ML) algorithm, for the development of a prediction model to predict sewer pipe failures and the effects of manhole proximity. The results show that SVM with an accuracy of 84% can properly approximate the manhole effects on sewer pipe failures.

Do non-linearity and non-Gaussianity truly matter in streamflow forecasting? A comparative study between PAR(p) and vine copula for Brazilian streamflow time series.

This study evaluates the joint impact of non-linearity and non-Gaussianity on predictive performance in 23 Brazilian monthly streamflow time series from 1931 to 2022. We consider point and interval forecasting, employing a PAR(p) model and comparing it with the periodic vine copula model. Results indicate that the Gaussian hypothesis assumed by PAR(p) is unsuitable; gamma and log-normal distributions prove more appropriate and crucial for constructing accurate confidence intervals. This is primarily due to the assumption of the Gaussian distribution, which can lead to the generation of confidence intervals with negative values. Analyzing the estimated copula models, we observed a prevalence of the bivariate Normal copula, indicating that linear dynamic dependence is frequent, and the Rotated Gumbel 180°, which exhibits lower tail dependence. Overall, the temporal dynamics are predominantly shaped by combining these two types of effects. In point forecasting, both models show similar behavior in the estimation set, with slight advantages for the copula model. The copula model performs better during the out-of-sample analysis, particularly for certain power plants. In interval forecasting, the copula model exhibits pronounced superiority, offering a better estimation of quantiles. Consistently demonstrating proficiency in constructing reliable and accurate intervals, the copula model reveals a notable advantage over the PAR(p) model in interval forecasting.

Pollutant transport model with large time-scale and estimation of land-use export coefficients at a watershed level.

The pollutant transport equilibrium in a watershed can be analyzed on a large time scale, and land-use export coefficients can be calculated directly under certain hydrologic and transport conditions, by ignoring hydrologic and transport processes at small space and time scales on hydrologic response units. In this study, the water environment system of a watershed was deconstructed into three parts (source, source-sink, and runoff transport) to construct a pollutant transportation equilibrium model on a large time scale. A watershed with an annual source-sink accumulation of zero was defined as a completely transported watershed; therefore, we derived a completely transported equilibrium equation. The problem of seeking the land export coefficient was converted into a problem of seeking the optimal solution of linear programming, which can be estimated according to the variation in pollutant output processes. The feasibility of the solution can be analyzed using multi-year stochastic rainfall processes. The model was used to analyze the transport equilibrium of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) upstream of the monitored cross-sections in a watershed, which covered 3145.66 km2. The land export coefficients were calculated according to the model. The model calculations indicated that the watershed was completely transported during perennial years. The calculated export coefficients of COD, TN, and TP for farmland, primary vegetation, and urban land were within the range of general empirical values. The calculated maximum accumulations of COD, TN, and TP were 0.19 × 107, 0.063 × 107, and 0.049 × 106 kg, respectively, for perennial rainfall. PRACTITIONER POINTS: A completely transported watershed was defined, and a model of pollutant transportation equilibrium with large time-scale was constructed. A problem of seeking the optimal solution of a linear programming was designed to estimate the land export coefficient of COD, TN, and TP. The runoff transport and accumulation processes of COD, TN, and TP in a watershed was analyzed.

Evidence of deviations between experimental and empirical mixing lengths: Multi-discharge field tests in an arid river system.

Despite advances in wastewater treatment plant (WWTP) efficiencies, multiple contaminants of concern, such as microplastics, pharmaceuticals, and per- and poly-fluoroalkyl substances (PFAS) remain largely untreated near discharge points and can be highly concentrated before they are fully mixed within the receiving river. Environmental agencies enforce mixing zone permits for the temporary exceedance of water quality parameters beyond targeted control levels under the assumption that contaminants are well-mixed and diluted downstream of mixing lengths, which are typically quantified using empirical equations derived from one-dimensional transport models. Most of these equations were developed in the 1970s and have been assumed to be standard practice since then. However, their development and validation lacked the technological advances required to test them in the field and under changing flow conditions. While new monitoring techniques such as remote sensing and infrared imaging have been employed to visualize mixing lengths and test the validity of empirical equations, those methods cannot be easily repeated due to high costs or flight restrictions. We investigated the application of Lagrangian and Eulerian monitoring approaches to experimentally quantify mixing lengths downstream of a WWTP discharging into the Rio Grande near Albuquerque, New Mexico (USA). Our data spans river to WWTP discharges ranging between 2-22x, thus providing a unique dataset to test long-standing empirical equations in the field. Our results consistently show empirical equations could not describe our experimental mixing lengths. Specifically, while our experimental data revealed "bell-shaped" mixing lengths as a function of increasing river discharges, all empirical equations predicted monotonically increasing mixing lengths. Those mismatches between experimental and empirical mixing lengths are likely due to the existence of threshold processes defining mixing at different flow regimes, i.e., jet diffusion at low flows, the Coanda effect at intermediate flows, and turbulent mixing at higher flows, which are unaccounted for by the one-dimensional empirical formulas. Our results call for a review of the use of empirical mixing lengths in streams and rivers to avoid widespread exposures to emerging contaminants.

Back to the future: Comparing yeast as an outmoded artificial tracer for simulating microbial transport in karst aquifer systems to more modern approaches.

Bacterial contamination of karst groundwater is a major concern for public health. Artificial tracing studies are crucial for establishing links between locations where pollutants can rapidly reach the aquifer systems and subsequent receptors, as well as for enhanced understanding of pollutant transport. However, widely used solute artificial tracers do not always move through the subsurface in the same manner as particles and microorganisms, hence may not be ideal proxies for predicting movement of bacterial contaminants. This study evaluates whether a historically used microbial tracer (yeast) which is readily available, inexpensive, and environmentally friendly, but usually overlooked in modern karst hydrogeological studies due to challenges associated with its detection and quantification in the past, can reemerge as a valuable tracer using the latest technology for its detection. Two field-based studies on separate karst systems were carried out during low-flow conditions using a portable particle counter along with flow cytometry measurements to monitor the recovery of the yeast at the springs. Soluble fluorescent dyes were also injected simultaneously with the yeast for comparison of transport dynamics. On one tracer test, through a karst conduit of much higher velocities, the injected yeast and fluorescent dye arrived at the same time at the spring, in comparison to the tracer test on a conduit system with lower groundwater velocities in which the yeast particles were detected before the dye at the sampling site. Both a portable particle counter and flow cytometry successfully detected yeast during both tests, thereby demonstrating the applicability of this tracer with contemporary instrumentation. Even though no significant advantages of flow cytometry over the portable counter system can be reported on the basis of the presented results, this study has shown that flow cytometry can be successfully used to detect and quantify introduced microbial tracers in karst environments with extremely high precision.

Effect of nanobubbles on the mobilization of microplastics in shorelines subject to seawater infiltration.

The widespread presence of microplastics (MPs) in the ocean has varying degrees of impact on ecosystems and even human health. Coastal tidal zones are crucial in controlling the movement of MPs, which are influenced by waves and tidal forces. Meanwhile, natural nanobubbles (NBs) in the ocean can affect the hydrodynamic properties of the tidal zone. The mobilization of MPs in coastal tidal zones under the effect of NBs has been less studied. In this study, we explored natural NBs' influence on the mobilization of MPs in shorelines subject to seawater infiltration. Using glass beads as a substrate, a coastal porous environment was constructed through column experiments, and the pump-controlled water flow was used to study the transport of MPs subject to seawater movement within the substrate. The infiltration of MPs under continuous and transient conditions, as well as the upward transport induced by flood tide, were considered. The role of salinity in the interactions between NBs, MPs, and substrates was evaluated. Salinity altered the energy barriers between particles, which in turn affected the movement of MPs within the substrate. In addition, hydrophilic MPs were more likely to infiltrate within the substrate and had different movement patterns under continuous and transient flow conditions. The motion of the MPs within the substrate varied with flow rate, and NBs limited the vertical movement of MPs in the tidal zone. It was also observed that NBs adsorbed readily onto substrates, altering the surface properties of substrates, particularly their ability to attach and detach from other substances.