Microplastic transport simulation and risk identification method based on multi-factor coupling

Abstract

The application discloses a kind of based on multi-factor coupling microplastic transport simulation and risk identification method, by coupling hydrodynamic model and microplastic transport model, multiple environmental factors such as tide, water level, runoff, wind speed, wind direction are comprehensively considered, wave-current coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions in Pearl River Estuary is constructed;The model is calibrated and verified by measured data, the precision of the model is evaluated using Nash efficiency coefficient, root mean square error and other indicators;Combined with the spatial distribution characteristics of the simulation results and the typical sensitive areas, representative sections and sites are selected to analyze the annual scale microplastic concentration variation;And further introduce ecological risk index to identify the regional ecological risk grade, clear microplastic high-risk area and impact main control factor.The method of the application has strong regional adaptability and expansibility, and can provide scientific support and technical reference for microplastic pollution prevention and control in estuary and coastal water.

Description

Technical Field

[0001] This invention relates to the field of environmental dynamics and ecological risk assessment technology, specifically to a method for microplastic transport simulation and risk identification based on multi-factor coupling. Background Technology

[0002] With the widespread use of plastic products, microplastics have become ubiquitous in global water bodies, posing a significant emerging pollutant in estuarine-oceanic systems. Microplastics are characterized by their small particle size, high mobility, reluctance to degrade, and high ecological risk. Existing research largely focuses on the static distribution of microplastics, while simulations often rely on hypothetical source terms and simplified models, lacking dynamic migration simulations and risk assessment mechanisms based on measured data. Especially in estuarine areas with complex hydrodynamics and diverse pollution sources, the multi-path migration, repeated deposition, and resuspension of microplastics further complicate their transport mechanisms. Therefore, there is an urgent need to establish a coupled simulation method that integrates measured data and multiple physical factors to accurately reproduce the migration and diffusion process of microplastics from estuaries into the ocean, supporting pollution control and risk management decisions. This invention proposes a microplastic transport simulation and risk identification method based on multi-factor coupling. Summary of the Invention

[0003] This invention provides a method for simulating and identifying microplastic transport based on multi-factor coupling, in order to solve the problems existing in current microplastic pollution simulation studies, such as unrealistic pollution source settings, incomplete dynamic simulation, and inaccurate risk identification.

[0004] According to the first aspect, one embodiment provides a method for microplastic transport simulation and risk identification based on multi-factor coupling, the method comprising:

[0005] Water samples were collected and pretreated from the target water area, and the spatial distribution and physical properties of microplastics in the water samples were obtained through detection and analysis.

[0006] A hydrodynamic model and a microplastic transport model based on the Lagrange particle tracking method were established. By coupling the hydrodynamic model and the microplastic transport model, multiple environmental factors including wind speed, tide, water level change and runoff change were comprehensively considered. The measured microplastic data were used as the model input to construct a wave-current coupled microplastic migration and diffusion model suitable for simulating the migration and diffusion behavior of microplastics under complex hydrodynamic environments.

[0007] The wave-current coupled microplastic migration and diffusion model was calibrated and validated based on measured microplastic data. The model accuracy was evaluated using multiple indicators. The spatiotemporal evolution characteristics of microplastics in the target water area were analyzed based on the model simulation results.

[0008] Based on the obtained wave-current coupled microplastic migration and diffusion model, various climate change scenarios were designed for simulation, and the simulation results under various climate change scenarios were compared and analyzed to obtain the spatiotemporal distribution characteristics of regional microplastics under climate change conditions.

[0009] The ecological risk of microplastics in the region was assessed using the ecological risk entropy method, and the spatial distribution of the ecological risk level of microplastics in the region was obtained by combining spatial analysis.

[0010] Furthermore, water samples were collected and pretreated from the target water area. Through detection and analysis, data on the measured distribution and physical properties of microplastic particles in the water samples were obtained, specifically including:

[0011] Multiple sampling points were set up, and surface water and bottom water samples were collected at each sampling point. The sampling period covered at least one year of dry and wet seasons.

[0012] Water samples were vacuum filtered using glass fiber membranes to remove the vast majority of microplastic particles from the water; the collected membrane samples were then used... The solution is oxidized to remove organic matter, and then HCl solution is used to remove inorganic salt deposits in order to remove impurities to the maximum extent.

[0013] Furthermore, water samples are collected and pretreated from the target water area. Through detection and analysis, data on the measured distribution and physical properties of microplastic particles in the water samples are obtained. Specifically, this also includes:

[0014] The pretreated samples were analyzed using a Fourier transform infrared spectrometer to obtain information on the polymer type and particle size distribution of each particle.

[0015] Statistical analysis was performed on the microplastic particles obtained from each sampling point, and their color, shape, particle size and polymer type were recorded and classified, so as to provide quantitative and differentiated source term boundary conditions for subsequent migration and diffusion models;

[0016] The microplastic particle count at each sampling point is converted into abundance per unit volume, and the calculation unit is "particles / 100 mL" or "particles / m³".

[0017] The sampling results are compiled into a database to establish a spatial distribution database of microplastics covering the entire region.

[0018] Furthermore, a wave-current coupled microplastic migration and diffusion model is constructed, specifically including:

[0019] Construct a hydrodynamic model by inputting hydrological and meteorological data of the target water body, including topography, tides, water level, runoff, and wind field, to simulate the changes in the water flow field, including:

[0020] The hydrodynamic model is solved using alternating explicit and implicit numerical integration based on the finite difference method. It is suitable for incompressible free surface fluids and can simulate the complex hydrodynamic characteristics of water dynamic processes. The hydrodynamic model is calculated using a curved mesh, and a flexible mesh version can be selected as needed.

[0021] The hydrodynamic model can accurately simulate fluid dynamics processes affected by time-varying tidal forces, variable wind fields, and density gradients caused by temperature and salinity variations. Flow field and water level changes are described by three-dimensional hydrodynamics (for simulating two-dimensional cases, vertical velocity and stratification effects can be ignored), with the specific formulas as follows:

[0022] Continuity equation:

[0023]

[0024] The x-direction of the momentum equation can be expressed as:

[0025]

[0026] The y-direction can be represented as:

[0027]

[0028] The z-direction can be represented as:

[0029]

[0030] In the formula, , , These are the velocity components in the x, y, and z directions, respectively. For water density, g It is the acceleration due to gravity; P Water pressure; The turbulent viscosity coefficient is provided by the k−ε turbulence model; Fx, Fy, Fz These are external force terms (such as wind stress, Coriolis force, etc.).

[0031] Furthermore, a wave-current coupled microplastic migration and diffusion model is constructed, specifically including:

[0032] Coupled with the hydrodynamic field, a microplastic transport model based on the Lagrange particle tracking method is constructed. Measured microplastic data are used as model input, considering physical properties of microplastics including particle size, density, morphology, and settling velocity. The model simulates the migration, diffusion, settling, and resuspension behavior of microplastics on spatial and temporal scales, including:

[0033] The Lagrange particle tracing method was used to simulate the migration paths of microplastics from different sources and with different particle properties.

[0034] In the microplastic transport model, microplastics are represented as a group of particles, each of which wanders randomly and is tracked independently. The particle diffusion process is not affected by the hydrodynamic spatial resolution, and particle diffusion can be simulated even in larger-scale grid regions.

[0035] The model uses the number of particles to represent the concentration of the simulated substance, and the target substance is uniformly distributed on each grid; during the continuous release simulation, a specified number of particles will be released continuously throughout the simulation time;

[0036] Define the physical properties of microplastic particles, including: density ( ),diameter( ), initial position ( );

[0037] The Stokes formula is used to estimate the vertical velocity of microplastic particles under the combined effects of gravity and buoyancy:

[0038]

[0039] In the formula, >0 indicates settlement. <0 indicates upward movement; dynamic viscosity , That is, the water density multiplied by the turbulent viscosity coefficient.

[0040] Using turbulent diffusion theory, the diffusion coefficients of microplastic particles in the horizontal and vertical directions are calculated. The horizontal diffusion coefficient is:

[0041]

[0042] In the formula, The frictional velocity is calculated from the near-bottom shear stress. The total depth of the water body; This is an empirical coefficient (generally ranging from 1 to 10).

[0043] The diffusion coefficient in the vertical direction is:

[0044]

[0045] In the formula, k is the Kármán constant, with a value of 0.41; z This represents the height of the particle above the bottom of the water.

[0046] Within each time step Δt, the trajectory of the microplastic particles is updated by the following equation:

[0047]

[0048] In the formula, , , Let be an independent random variable that follows a standard normal distribution, representing turbulent disturbance (dimensionless). This represents the perturbation term in the random walk diffusion model; , , These are the velocity components in the x, y, and z directions, respectively.

[0049] Furthermore, the constructed wave-current coupled microplastic migration and diffusion model was calibrated and validated based on measured data of microplastics. Multiple indicators were used to evaluate the model's accuracy, including:

[0050] The accuracy of the model was verified by comparing and analyzing the model prediction results with the measured data. To ensure the reliability of the model, several typical cross sections and stations were selected for actual measurement verification.

[0051] Using the coefficient of determination R 2 Evaluation metrics for model prediction accuracy include percentage bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the ratio of root mean square error to standard deviation of observations (RSR).

[0052] Model parameters were optimized through experiments, and model performance was verified by comparing with benchmark models.

[0053] The model simulation results include the generation of spatiotemporal distribution maps of microplastic concentration, transport path maps, and ecological risk level distribution maps.

[0054] Furthermore, based on the obtained wave-current coupled microplastic migration and diffusion model, various climate change scenarios were designed for simulation, and the simulation results under various climate change scenarios were compared and analyzed to obtain the spatiotemporal distribution characteristics of regional microplastics under climate change conditions, specifically including:

[0055] Design a variety of typical climate change scenarios, including runoff change scenario, sea level rise scenario, wind speed change scenario, and combined scenario;

[0056] Climate change parameters were introduced into the wave-current coupled microplastic migration and diffusion model to adjust the boundary conditions, including: modifying the offshore boundary water level to reflect the future sea level rise trend; adjusting the wind field input data to represent the frequent occurrence of typhoons and strong winds; setting the upstream runoff variation curve to reflect the response of watershed precipitation and flood runoff; and setting the simulation time to a typical year, with each scenario running for one year.

[0057] Compare and analyze the migration paths, enrichment area distributions, average residence times, and changes in microplastic concentrations under different climate scenarios. The key identification contents include: whether the enrichment hotspots shift to the open sea or retention areas; whether extreme weather enhances microplastic resuspension; whether enhanced hydrodynamic forces prompt microplastics to cross tidal flats or ecological protection zones; and whether sea-level changes expand the risk of microplastic invasion into sensitive ecosystems.

[0058] Furthermore, use the ecological risk entropy method to evaluate the ecological risks of regional microplastics, and combine spatial analysis to obtain the spatial distribution results of the ecological risk levels of regional microplastics. Specifically, it includes:

[0059] The ecological risk entropy method calculates the pollution degree of each sampling point based on the ratio between the actual detected environmental concentration MEC of microplastics and the predicted no-effect concentration PNEC to evaluate the potential risks of pollutants to the ecosystem; among which, PNEC is obtained through the species sensitivity distribution method SSD; the specific calculation formula is as follows:

[0060]

[0061]

[0062] Among them, RQ is the risk entropy value; RCR is the risk characterization ratio; MEC is the microplastic concentration of the sampling point, with the unit of ng / L; PNEC is expressed as the no-observed-effect concentration NOEC in toxicological data. When NOEC data is lacking, LC50 and EC50 are used for estimation; AF is the prediction factor;

[0063] Calculate the RQ values of each monitoring point in the target water area, and divide the ecological risk levels according to the magnitudes of the RQ values, including RQ ≤ 0.1 being risk-free, 0.1 < RQ ≤ 1 being low-risk, and RQ > 1 being high-risk.

[0064] Furthermore, use the ecological risk entropy method to evaluate the ecological risks of regional microplastics, and combine spatial analysis to obtain the spatial distribution results of the ecological risk levels of regional microplastics. Specifically, it also includes:

[0065] Based on the calculation results of the ecological risk entropy values, use the GIS spatial analysis method to identify high-risk areas. Overlay and analyze the RQ value spatial distribution map with the ecological sensitive area layer of the target water area. Through spatial overlay analysis, identify the overlapping areas of high microplastic concentration areas and high ecological sensitivity areas, delineate the high-exposure zones of ecological risks, and output the spatial distribution map of microplastic risk levels;

[0066] Based on the results, a quantitative analysis was conducted on each high-risk area, including: the proportion of high-risk areas and their changing trends under various scenarios; the differences in RQ response of microplastics of different polymer types; the dynamic changes of high-risk areas under seasonal or tidal conditions; and the potential impact of enrichment time and risk retention time on the ecosystem.

[0067] Furthermore, the method also includes:

[0068] Based on the results of the ecological risk analysis, we propose a tiered and zoned management approach for microplastic pollution in target water areas, including: source control: strengthening the management of key areas, industrial emission points, and urban stormwater overflow outlets to restrict single-use plastic sources from entering water bodies; ecological restoration: deploying ecological buffer zones or artificial wetlands in high-risk areas to enhance the pollution filtering and buffering capacity of the ecosystem; and refined monitoring: optimizing the layout of existing monitoring points, tilting key observation areas towards high RQ areas, and improving monitoring and early warning capabilities.

[0069] This invention provides a method for simulating and identifying the risks of microplastic transport based on multi-factor coupling, which has the following advantages:

[0070] (1) Integrate measured data to improve the realism of the simulation.

[0071] This invention uses data such as microplastic abundance, particle size, and polymer type obtained from on-site sampling as model inputs to replace traditional assumed source terms, significantly improving the accuracy and reliability of simulated source strength settings and enhancing the practical guiding significance of simulation results.

[0072] (2) Construct a water-wind coupled driving field to fully simulate the migration mechanism.

[0073] Considering the coupling effects of multiple factors such as tidal current, wind speed and wind direction, a three-dimensional high-resolution hydrodynamic-wind field model is constructed to accurately invert the sedimentation, suspension, resuspension and diffusion processes of microplastics under complex hydrodynamic environment conditions, and to more accurately reflect the actual migration path and spatiotemporal distribution characteristics of microplastics in the estuarine environment.

[0074] (3) Supports quantitative identification of multi-source release and pollution flux.

[0075] By constructing source terms using measured data from different inlets and outlets, and combining this with the particle tracking frequency method, the pollution contribution rate of each input channel is quantitatively assessed, key pollution sources are identified, and the needs of refined watershed management are met.

[0076] (4) Achieve accurate overlay identification of pollution hotspots and risk areas

[0077] By combining simulation results with data from ecologically sensitive areas and functional zones for spatial overlay analysis, high-risk microplastic exposure areas can be identified, providing visualized decision support for environmental supervision and resource protection.

[0078] (5) The method is highly versatile and has a wide range of applications.

[0079] This technology is applicable to various estuaries, deltas, coastal cities, and other areas with complex hydrodynamic conditions and diverse pollution sources. It has good promotion value and transplantability, and can provide a technical paradigm for the prevention and control of microplastic pollution in typical estuaries. Attached Figure Description

[0080] Figure 1 A flowchart illustrating a method for simulating and identifying the transport of microplastics based on multi-factor coupling, as provided in one embodiment of the present invention;

[0081] Figure 2 A flowchart illustrating a specific implementation of a microplastic transport simulation and risk identification method based on multi-factor coupling, as provided in one embodiment of the present invention;

[0082] Figure 3 Microplastic occurrence feature map of the eight major estuaries of the Pearl River Estuary in a microplastic transport simulation and risk identification method based on multi-factor coupling, as provided in an embodiment of the present invention;

[0083] Figure 4 A hydrodynamic model verification diagram is provided in a microplastic transport simulation and risk identification method based on multi-factor coupling, according to an embodiment of the present invention.

[0084] Figure 5 This is a verification diagram of the microplastic particle migration model in a microplastic transport simulation and risk identification method based on multi-factor coupling, provided in an embodiment of the present invention.

[0085] Figure 6 The figure shows the simulation results of the hydrodynamic model in a microplastic transport simulation and risk identification method based on multi-factor coupling, which is provided in an embodiment of the present invention.

[0086] Figure 7 A spatiotemporal distribution map of microplastics in a microplastic transport simulation and risk identification method based on multi-factor coupling provided in an embodiment of the present invention;

[0087] Figure 8 The microplastic particle migration path diagram is provided in a microplastic transport simulation and risk identification method based on multi-factor coupling, as an embodiment of the present invention.

[0088] Figure 9 A graph showing the change in microplastic concentration in a microplastic transport simulation and risk identification method based on multi-factor coupling, provided as an embodiment of the present invention;

[0089] Figure 10 An ecological risk assessment diagram is provided in an embodiment of the present invention for a microplastic transport simulation and risk identification method based on multi-factor coupling. Detailed Implementation

[0090] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0091] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0092] The first embodiment of this invention provides a microplastic transport simulation and risk identification method based on multi-factor coupling. By using characteristic data such as microplastic abundance, particle size and polymer type obtained from field sampling as model input, combined with a high-resolution tidal current and wind field coupled simulation system, and employing the Lagrange particle tracking algorithm, the method dynamically simulates the spatiotemporal migration process of microplastics in the estuary area, and constructs a hot zone identification mechanism and a pollution source contribution rate analysis method to identify potential high-risk exposure areas and key pollution input paths.

[0093] This invention can realize a complete chain from "data acquisition - source identification - path simulation - risk assessment", and has the advantages of high simulation accuracy, wide applicability and high degree of result visualization. It provides scientific basis and technical support for the prevention and control of microplastic pollution in typical estuaries, the management of input sources and ecological protection strategies.

[0094] The following is combined with Figure 1 and Figure 2 Please provide a detailed explanation.

[0095] S100 collects and pre-processes water samples from the target water area, and obtains data on the measured spatial distribution and physical properties of microplastics in the water samples through detection and analysis.

[0096] Microplastic field sampling and data acquisition: Field data on microplastics were collected at the eight outlets of the Pearl River Estuary, including particle size, morphology, polymer type, and abundance distribution. The microplastic data included surface and subsurface samples from multiple observation stations, covering the eight outlet areas. The sampling period covered at least one year of both dry and wet seasons to ensure representativeness.

[0097] In this embodiment, to obtain the distribution and basic characteristic parameters of microplastics in the Pearl River Estuary, the present invention sets up a systematic field sampling plan in the study area, constructs a microplastic spatial distribution database, and establishes a pollution source spectrum through characteristic parameter statistics and cluster analysis, providing a high-precision source term input basis for subsequent simulations.

[0098] Specifically, eight sampling points were set up at the eight typical estuaries of the Pearl River Estuary (Humen, Jiaomen, Hongqili, Hengmen, Modaomen, Jitimen, Hutiaomen, and Yamen). Surface water (0–0.5 m depth) and bottom water samples were collected at each monitoring point, with a sample volume of 10–20 L each time to ensure representativeness and efficient particle acquisition. The water samples were vacuum filtered using 0.45 μm pore size glass fiber membranes to remove the vast majority of microplastic particles from the water. 30% of the collected filter membrane samples were used. Organic matter was removed by solution oxidation, followed by removal of inorganic salt deposits using 10% HCl to maximize impurity removal and improve the purity of microplastic identification. The pretreated samples were analyzed using Fourier Transform Infrared Spectroscopy (FTIR) to obtain the polymer type and particle size distribution information for each particle. Statistical analysis was performed on the microplastic particles obtained from each sampling point, recording and classifying their color (e.g., transparent, blue, black), shape (e.g., fiber, film, fragment, spherical), particle size (μm level), and polymer type (e.g., polyethylene PE, polypropylene PP, polystyrene PS). This provides quantitative and differentiated source term boundary conditions for subsequent migration models. The microplastic particle count at each sampling point was converted to abundance per unit volume, calculated in units of "particles / 100 mL" or "particles / m³". Sampling results are shown below. Figure 3 As shown in the figure. The sampling results are uniformly summarized into the database to establish a microplastic spatial distribution database covering the entire region. This database serves as one of the input bases for the microplastic transport simulation model, providing high-quality supporting data for subsequent migration and diffusion simulations, hotspot identification, and ecological risk assessment.

[0099] S200 establishes a hydrodynamic model and a microplastic transport model based on the Lagrange particle tracking method. By coupling the hydrodynamic model and the microplastic transport model, and comprehensively considering multiple environmental factors including wind speed, tide, water level change, and runoff change, and using measured microplastic data as model input, a wave-current coupled microplastic migration and diffusion model suitable for simulating the migration and diffusion behavior of microplastics under complex hydrodynamic environments is constructed.

[0100] Construction of a wave-current coupled microplastic migration and diffusion model in the Pearl River Estuary: First, a hydrodynamic model is constructed, inputting hydrological and meteorological data such as topography, tides, water levels, runoff, and wind fields in the Pearl River Estuary region to simulate the changes in the water flow field; then, a Lagrange microplastic transport model is constructed by coupling the hydrodynamic field, introducing measured data as microplastic input data, and considering the physical properties of microplastics such as particle size, density, morphology, and settling velocity to simulate their migration, diffusion, settling, and resuspension behavior on spatial and temporal scales.

[0101] In this embodiment, the wave-current coupled Pearl River Estuary microplastic migration and diffusion model is constructed by dividing the model into a hydrodynamic model and a microplastic particle migration module. The hydrodynamic calculation employs an explicit and implicit alternating numerical integration method based on the finite difference method to solve the hydrodynamic model. Derived from the Navier-Stokes equations, it can simulate the complex hydrodynamic characteristics of estuarine dynamic processes. The model uses a curved mesh for calculation, and a flexible mesh version can be selected as needed. The hydrodynamic model can accurately simulate fluid dynamic processes influenced by time-varying tidal forces, variable wind fields, and density gradients caused by temperature and salinity variations. The flow field and water level changes are described by three-dimensional hydrodynamics (for simulating two-dimensional cases, vertical velocity and stratification effects can be ignored), with the specific formulas as follows:

[0102] Continuity equation:

[0103]

[0104] The x-direction of the momentum equation can be expressed as:

[0105]

[0106] The y-direction can be represented as:

[0107]

[0108] The z-direction can be represented as:

[0109]

[0110] In the formula, , , These are the velocity components in the x, y, and z directions, respectively. For water density, g It is the acceleration due to gravity; P Water pressure; The turbulent viscosity coefficient is provided by the k−ε turbulence model; Fx, Fy, Fz These are external force terms (such as wind stress, Coriolis force, etc.).

[0111] The microplastic transport model employs the Lagrange particle tracking method to simulate the migration paths of microplastics from different sources and with varying particle properties, improving simulation accuracy at the particle level. The Lagrange method has been widely used to simulate the transport and diffusion of plumes (such as oil spills), as well as salts, rhodamine-like dyes, BOD, or chemicals exhibiting first-order decay kinetics. Furthermore, microplastics are chemically stable and difficult to degrade, classifying them as conservative substances. Therefore, by combining a hydrodynamic model with the Lagrange particle tracking module, hydrodynamic and pollutant transport and diffusion models in estuaries, nearshore areas, and the ocean can be successfully established. This model can describe the transport process of microplastics in water bodies and predict their distribution patterns under different climate scenarios. In this model, microplastics are represented as a group of particles, each randomly wandering and independently tracked. The particle diffusion process is unaffected by the hydrodynamic spatial resolution, allowing for particle diffusion simulation even in larger grid areas. The model uses particle count to represent the concentration of the simulated substance, and the target substance is uniformly distributed across each grid cell. For example, if the release of 10,000 particles represents 100g of tracer, then each particle represents 0.01g of tracer. In a continuous release simulation, a specified number of particles will be released continuously throughout the simulation time; that is, if 10,000 particles are released in one day, this is equivalent to 0.116 particles per second.

[0112] To simulate the initial moment, it is first necessary to define the physical properties of the microplastic particles, including: density ( ),diameter( ), initial position ( );

[0113] The Stokes formula is used to estimate the vertical velocity of microplastic particles under the combined effects of gravity and buoyancy:

[0114]

[0115] In the formula, >0 indicates settlement. <0 indicates upward movement; dynamic viscosity , That is, the water density multiplied by the turbulent viscosity coefficient.

[0116] Using turbulent diffusion theory, the diffusion coefficients of microplastic particles in the horizontal and vertical directions are calculated. The horizontal diffusion coefficient is:

[0117]

[0118] In the formula, The frictional velocity is calculated from the near-bottom shear stress. The total depth of the water body; This is an empirical coefficient (generally ranging from 1 to 10).

[0119] The diffusion coefficient in the vertical direction is:

[0120]

[0121] In the formula, k is the Kármán constant, with a value of 0.41; z This represents the height of the particle above the bottom of the water.

[0122] Within each time step Δt, the trajectory of the microplastic particles is updated by the following equation:

[0123]

[0124] In the formula, , , Let be an independent random variable that follows a standard normal distribution, representing turbulent disturbance (dimensionless). This represents the perturbation term in the random walk diffusion model; , , Velocity components in the x, y, and z directions.

[0125] The coupled model can effectively characterize the diffusion behavior of microplastics in complex hydrodynamic environments and provide theoretical support for the study of the spatiotemporal distribution of microplastics in the Pearl River Estuary.

[0126] Specifically, this embodiment constructs a hydrodynamic simulation model of the Pearl River Estuary, which includes hydrodynamic elements such as tides, waves, and current velocity. First, based on the hydrodynamic module, underwater topographic data, tidal boundary conditions, measured tide levels, runoff boundary discharges, and wind speed and direction data (such as meteorological data from ECMWF, with a spatial resolution of 0.125°×0.125°) of the Pearl River Estuary region are input to establish a regional hydrodynamic model, simulating key hydrodynamic elements such as regional tidal fields, water level changes, and current velocity fields. Then, a microplastic particle tracking model is constructed, using the Lagrange method to describe the trajectory of microplastic particles in the flow field. Combining the measured data from step S100, the microplastic concentration distribution is used as the pollution source input, and different particle size, morphology, and density parameters are set to simulate their migration, diffusion, sedimentation, and resuspension processes driven by tidal currents and wind fields. The model simulation time is set to one year, with a time step of 1 minute. The simulation time range should cover typical dry and wet seasons to ensure that the model can respond to seasonal hydrological changes. During the simulation, it is essential to ensure good coupling between the hydrodynamic model and the wind field to accurately reflect the migration of microplastics under different environmental conditions. Simulation results output include particle trajectories, microplastic particle migration paths and spatial distribution maps, and microplastic concentration variation data.

[0127] S300 calibrates and validates the constructed wave-current coupled microplastic migration and diffusion model based on measured microplastic data, evaluates the model accuracy using multiple indicators, and analyzes the spatiotemporal evolution characteristics of microplastics in the target water area based on the model simulation results.

[0128] Spatiotemporal Evolution Characteristics of Microplastics in the Pearl River Estuary: A multi-factor coupling of a hydrodynamic model and a microplastic transport model was performed, integrating the effects of wind, tidal currents, water level changes, and runoff. Measured data was incorporated into the model calibration, combining measured tidal level and flow velocity data to optimize model parameters and verify accuracy. MAE, RMSE, and NSE were used as evaluation parameters to assess the model's predictive performance. Parameter optimization was conducted experimentally, and the performance was validated by comparing the model with a benchmark model. The hydrodynamic model validation results are as follows: Figure 4 As shown, the microplastic transport model is as follows: Figure 5 As shown.

[0129] The simulation results include the generation of spatiotemporal distribution maps of microplastic concentration, transport path maps, and ecological risk level distribution maps, enabling the identification of microplastic migration patterns and high-risk areas in the Pearl River Estuary, and providing technical support for pollution control and management. Figure 6 The diagram shows the hydrodynamic results. Figure 7 This is a simulated spatiotemporal distribution map of microplastics. Figure 8 This is a simulated migration path diagram of microplastic particles. Figure 9 This is a graph showing the changes in microplastic concentration.

[0130] Specifically, this embodiment uses the coefficient of determination (R²). 2 The percentage bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the ratio of root mean square error to the standard deviation of observed values ​​(RSR) are used as evaluation indicators of model prediction accuracy. R² measures the degree of fit between predicted and observed values, indicating the proportion of variance explained by the model; a value close to 1 indicates good model performance. PBIAS, also known as relative error, reflects the cumulative deviation between simulated and observed values. The better the simulated hydrological process fits the actual trend, the closer PBIAS is to 0, indicating a better model performance. A positive value indicates that the simulated value underestimates the observed value, while a negative value indicates that the simulated value overestimates the observed value. NSE, proposed by Nash and Sutcliffe, is commonly used to evaluate the performance of hydrological models and reflects the degree of fit between simulated and observed values. It can be characterized by how close the simulated and observed values ​​are to the fitted straight line (1:1). RSR ranges from an optimal value of 0 to a larger positive value; the smaller the value, the better the model performance. The specific calculation formula is as follows:

[0131]

[0132]

[0133]

[0134]

[0135] The model performance evaluation levels are as follows:

[0136] Table 2 Model Performance Evaluation Levels

[0137]

[0138] This embodiment achieves integrated multiphysics simulation by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple factors such as wind speed, tides, water level changes, and runoff changes. The model's accuracy is verified through comparative analysis of measured data. To ensure model reliability, several typical cross-sections and stations are selected for field verification. Based on model calibration and verification, the spatiotemporal distribution characteristics and migration and diffusion mechanisms of microplastics in the Pearl River Estuary are further analyzed. The Lagrange particle tracing method is used to simulate the transport process of microplastics, analyzing their spatial distribution patterns and diffusion trends under the influence of tidal currents, runoff, and monsoons. Furthermore, the main accumulation areas of microplastics in the Pearl River Estuary are identified based on the output results, laying the foundation for subsequent ecological risk analysis. These results are combined to analyze the diffusion trends, main migration channels, enrichment areas, and seasonal response patterns of microplastics in the Pearl River Estuary.

[0139] Based on the obtained wave-current coupled microplastic migration and diffusion model, S400 designs various climate change scenarios for simulation, and compares and analyzes the simulation results under various climate change scenarios to obtain the spatiotemporal distribution characteristics of regional microplastics under climate change conditions.

[0140] Prediction of Microplastic Distribution Trends in the Pearl River Estuary under Climate Change: Eight typical climate change scenarios were designed, considering runoff change, sea-level rise, wind speed change, and combined scenarios. The aforementioned climate change parameters were incorporated into the coupled hydrodynamic and microplastic migration model, and boundary conditions (such as offshore sea level, wind intensity, and upstream runoff) were adjusted while maintaining the other model structures unchanged. The simulation period was set to typical years (e.g., 2035, 2050, and 2080), and each scenario was run for one year, outputting key variables such as microplastic concentration distribution, particle trajectory, and residence time. The migration paths, enrichment zone distribution, average residence time, and microplastic concentration changes of microplastics under different climate scenarios were compared and analyzed, focusing on identifying: whether enrichment hotspots shift to the open sea or retention areas; whether extreme weather enhances microplastic resuspension; whether enhanced hydrodynamics promotes microplastic crossings of tidal flats or ecological protection zones; and whether sea-level changes increase the risk of microplastic invasion into sensitive ecosystems.

[0141] Specifically, under the background of global climate change, changes in environmental factors such as altered runoff intensity and sea-level rise significantly affect the hydrodynamic patterns of estuaries, thereby potentially altering the migration pathways, spatial distribution, and ecological risk characteristics of microplastics. To further investigate the transport mechanisms and pollution trends of microplastics in the Pearl River Estuary under future climate change scenarios, this invention designs climate scenario simulation experiments based on a constructed wave-current coupled microplastic migration model, and conducts trend identification and risk analysis.

[0142] This invention combines the latest climate prediction pathways released by the IPCC (Intergovernmental Panel on Climate Change) to construct eight typical climate change scenarios, including runoff change, sea level rise, wind speed reduction, and combined change scenarios. The specific scenario settings are shown in Table 3.

[0143] Table 3 Model Scenario Settings

[0144]

[0145] Based on the above scenario parameters, boundary conditions were adjusted using the constructed coupled hydrodynamic and Lagrange particle transport model. Specifically, this included: modifying the offshore boundary water level to reflect the future sea level rise trend; adjusting the wind field input data (wind speed and direction) to represent typhoon and strong wind frequency conditions; setting an upstream runoff variation curve to reflect the response of basin precipitation and flood runoff; and uniformly setting the simulation time for each scenario to one year to explore the evolutionary impact of medium- and long-term climate change on microplastic migration.

[0146] By comparing the simulation results under eight climate change scenarios, the spatiotemporal distribution characteristics of microplastics were extracted, and the following contents were analyzed in detail: (1) the phenomenon of enrichment area transfer: to determine whether the high concentration of microplastics has been transferred from the inner bay and estuary areas to the open sea or nearshore open waters; (2) the risk of resuspension enhancement: to assess the resuspension probability of bottom-deposited microplastics under strong wind and wave conditions, and to identify short-term high-risk pollution events; (3) the trend of transport across ecological red lines: to analyze whether the enhancement of hydrodynamics has caused microplastics to cross ecological buffer zones, such as mangrove areas and tidal flats; (4) the degree of invasion of ecologically sensitive areas: to assess whether sea level rise has aggravated the invasion and enrichment of microplastics in aquaculture areas and ecological protection areas; based on the simulation output data of each climate scenario, the statistical trend analysis method was used to identify the changing trends of microplastic concentration, migration distance and risk index. Through multi-scenario overlay analysis, a "future microplastic pollution evolution pattern map" was constructed to clarify the potential pollution hotspots and microplastic accumulation core areas of the Pearl River Estuary under different climate paths.

[0147] S500 uses the ecological risk entropy method to assess the regional microplastic ecological risk and combines it with spatial analysis to obtain the spatial distribution results of the regional microplastic ecological risk level.

[0148] Ecological risk analysis of microplastic pollution in the Pearl River Estuary: Based on the fusion of simulation results and measured data, high-concentration areas were extracted. Combined with regional ecological sensitivity and toxicological data, the ecological risk index RQ value was calculated to spatially classify and identify the ecological risk of microplastics.

[0149] This embodiment uses the ecological risk entropy method to assess the ecological risk of microplastics in the environment. The ecological risk entropy method (RQ) is a widely used risk assessment method in environmental science to measure the potential impact of pollutants on ecosystems. This method calculates the pollution level at each sampling point based on the ratio between the measured environment concentration (MEC) of microplastics and the predicted no-effect concentration (PNEC), thus assessing the potential risk of pollutants to the ecosystem. PNEC is obtained using the species sensitivity distribution (SSD) method. The specific calculation formula is as follows:

[0150]

[0151]

[0152] In the formula, RQ is the risk entropy value, RCR is the risk characterization ratio, and PNEC is the microplastic concentration of the MEC sample point, in ng / L; PNEC is generally expressed as the no-observable-effect concentration NOEC in toxicological data, in ng / L, using the literature reference value of 6650 particles / m 3 When NOEC data is scarce, LC50 and EC50 are used for estimation, with AF as a predictor.

[0153] The specific evaluation criteria are shown in the table below:

[0154] Table 1 Ecological Risk Assessment Standards

[0155]

[0156] The key to using the RQ method is determining the PNEC parameters. Previous studies, combining extensive toxicological data, have derived predicted PNEC using the Species Sensitivity Distribution (SSD) method. The basic principle of SSD is that the sensitivity of different species to a specific pollutant can be described by a statistical distribution. By fitting the pollutant's toxicological data to the cumulative distribution function (CDF), an SSD curve is established, and corresponding PNEC baseline values ​​are determined based on different conservation targets. However, comprehensive toxicological data for specific species is often difficult to obtain. Related studies have derived the PNEC of microplastics in the ocean to be 6,650 particles / m³. -3 The Pearl River Estuary encompasses the upstream estuary and a large marine area of ​​the South China Sea; therefore, this PNEC is applicable to the risk assessment of the impacts on the Pearl River Estuary ecosystem in this study.

[0157] The Relative Risk Assessment (RQ) method is simple to calculate and can quantitatively assess the risk level of pollutants. It only requires obtaining the MEC (Mean Energy Concentration) of the pollutant and the corresponding ecological safety threshold PNEC (Persistent Ecological Risk Concentration), which allows for the calculation of the RQ within the study area. The ecological risk can then be categorized by numerical range, effectively identifying the potential threat to the environment. Furthermore, the RQ method is applicable to various types of pollutants, including heavy metals, organic pollutants, and microplastics. In addition, the RQ method can be combined with GIS technology for spatial distribution analysis, visually displaying the risk distribution characteristics of pollutants within the study area, providing a scientific basis for pollution control and environmental management. Therefore, this study selected the RQ method as the ecological risk assessment method for microplastic pollution in the Pearl River Estuary. First, preliminary data processing and analysis were performed to obtain the RQ values ​​at key locations. Then, Kriging interpolation was used to visually quantify the risk level, revealing the pollution degree and potential ecological risks in different areas of the Pearl River Estuary, providing a scientific basis for the control of microplastic pollution in the Pearl River Estuary, and further providing important references for environmental management and decision-making.

[0158] Specifically, based on the simulation of microplastic migration paths and concentration distribution in the Pearl River Estuary, this invention further conducts ecological risk identification and analysis to assess the potential negative impacts of microplastics on the aquatic ecosystem and proposes corresponding pollution prevention and control recommendations, constructing an integrated pollution control framework of "simulation-assessment-management". This invention employs the internationally recognized Risk Quotient (RQ) method to assess the ecological risk of microplastic pollution in the Pearl River Estuary. Based on the research results, RQ values ​​at key locations in the Pearl River Estuary are calculated, and ecological risk levels are classified according to the RQ values. Then, GIS spatial analysis methods are used to identify high-risk areas. The spatial distribution map of RQ values ​​is overlaid with a layer of ecologically sensitive areas in the Pearl River Estuary. Through spatial overlay analysis, overlapping areas between high-concentration microplastic areas and ecologically sensitive areas are identified, delineating "high-exposure ecological risk zones," and outputting a spatial distribution map of microplastic risk levels. Figure 10This is an ecological risk assessment map.

[0159] Based on the results, further quantitative analysis was conducted on each high-risk area, including: the proportion of high-risk areas and their changing trends under various scenarios; the differences in RQ response of microplastics of different polymer types; the dynamic changes of high-risk areas under seasonal or tidal conditions; and the potential impact of enrichment time and risk retention time on the ecosystem.

[0160] Based on the risk identification results, this invention proposes a tiered and zoned management approach for microplastic pollution in the Pearl River Estuary, including: source control: strengthening the management of key outlets, industrial discharge points, and urban stormwater overflow outlets to restrict single-use plastic sources from entering water bodies; ecological restoration: deploying ecological buffer zones or artificial wetlands in high-risk areas to enhance the pollution filtration and buffering capacity of the ecosystem; and refined monitoring: optimizing the layout of existing monitoring points, tilting key observation areas towards high RQ areas to improve monitoring and early warning capabilities.

[0161] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for simulating and identifying risks in microplastic transport based on multi-factor coupling, characterized in that, The method includes: Water samples were collected and pretreated from the target water area, and the spatial distribution and physical properties of microplastics in the water samples were obtained through detection and analysis. A hydrodynamic model and a microplastic transport model based on the Lagrange particle tracking method were established. By coupling the hydrodynamic model and the microplastic transport model, multiple environmental factors including wind speed, tide, water level change and runoff change were comprehensively considered. The measured microplastic data were used as the model input to construct a wave-current coupled microplastic migration and diffusion model suitable for simulating the migration and diffusion behavior of microplastics under complex hydrodynamic environments. The wave-current coupled microplastic migration and diffusion model was calibrated and validated based on measured microplastic data. The model accuracy was evaluated using multiple indicators. The spatiotemporal evolution characteristics of microplastics in the target water area were analyzed based on the model simulation results. Based on the obtained wave-current coupled microplastic migration and diffusion model, various climate change scenarios were designed for simulation, and the simulation results under various climate change scenarios were compared and analyzed to obtain the spatiotemporal distribution characteristics of regional microplastics under climate change conditions. The ecological risk of microplastics in the region was assessed using the ecological risk entropy method, and the spatial distribution of the ecological risk level of microplastics in the region was obtained by combining spatial analysis. The construction of the wave-current coupled microplastic migration and diffusion model specifically includes: A hydrodynamic model is constructed by inputting hydrological and meteorological data of the target water body, including topography, tides, water level, runoff, and wind field, to simulate the changes in the water flow field. This includes: using explicit and implicit alternating numerical integration based on the finite difference method to solve the hydrodynamic model and simulate the complex hydrodynamic characteristics of the water body's dynamic processes; the hydrodynamic model is calculated using a curved mesh; the hydrodynamic model can accurately simulate the fluid dynamic processes affected by time-varying tidal forces, variable wind fields, and density gradients caused by temperature and salinity changes; the flow field and water level changes are described by three-dimensional hydrodynamics. A microplastic transport model based on the Lagrange particle tracking method is constructed by coupling the hydrodynamic field. Measured microplastic data are used as input data, considering physical properties of microplastics including particle size, density, morphology, and settling velocity. The model simulates the migration, diffusion, settling, and resuspension behavior of microplastics on spatial and temporal scales. This includes: using the Lagrange particle tracking method to simulate the migration paths of microplastics from different sources and with different particle properties; representing microplastics as a group of particles in the microplastic transport model, where each particle wanders randomly and is independently tracked, and particle diffusion is unaffected by the hydrodynamic spatial resolution; using particle number to represent the concentration of the simulated substance, with the target substance uniformly distributed across each grid; and releasing a specified number of particles continuously throughout the simulation time during the continuous release simulation.

2. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, Water samples were collected and pretreated from the target water area. Data on the measured distribution and physical properties of microplastic particles in the water samples were obtained through detection and analysis. Specifically, this included: Multiple sampling points were set up, and surface water and bottom water samples were collected at each sampling point. The sampling period covered at least one year of dry and wet seasons. Water samples were vacuum filtered using glass fiber membranes to remove the vast majority of microplastic particles from the water; the collected membrane samples were then used... The solution is oxidized to remove organic matter, and then HCl solution is used to remove inorganic salt deposits in order to remove impurities to the maximum extent.

3. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 2, characterized in that, Water samples were collected and pretreated from the target water area. The distribution and physical properties of microplastic particles in the water samples were obtained through detection and analysis. Specifically, this also included: The pretreated samples were analyzed using a Fourier transform infrared spectrometer to obtain information on the polymer type and particle size distribution of each particle. Statistical analysis was performed on the microplastic particles obtained from each sampling point, and their color, shape, particle size and polymer type were recorded and classified, so as to provide quantitative and differentiated source term boundary conditions for subsequent migration and diffusion models; The microplastic particle count at each sampling point is converted into abundance per unit volume, and the calculation unit is "particles / 100 mL" or "particles / m³". The sampling results are compiled into a database to establish a spatial distribution database of microplastics covering the entire region.

4. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, The flow field and water level changes are described by three-dimensional hydrodynamics, and the specific formulas are as follows: Continuity equation: The x-direction of the momentum equation is expressed as: The y-direction is represented as: The z-direction is represented as: In the formula, , , These are the velocity components in the x, y, and z directions, respectively. For water density, g It is the acceleration due to gravity; P Water pressure; Let k be the turbulent viscosity coefficient. The ε-turbulence model provides; Fx, Fy, Fz This is an external force term.

5. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, Coupled with the hydrodynamic field, a microplastic transport model based on the Lagrange particle tracking method is constructed. Measured microplastic data are used as model input, considering physical properties of microplastics including particle size, density, morphology, and settling velocity. The model simulates the migration, diffusion, settling, and resuspension behavior of microplastics on spatial and temporal scales. It also includes: Define the physical properties of microplastic particles, including: density ,diameter Initial position ; The Stokes formula is used to estimate the vertical velocity of microplastic particles under the combined effects of gravity and buoyancy. The formula is as follows: In the formula, >0 indicates settlement. <0 indicates upward movement; dynamic viscosity , That is, the water density multiplied by the turbulent viscosity coefficient; Using turbulent diffusion theory, the diffusion coefficients of microplastic particles in the horizontal and vertical directions are calculated. The horizontal diffusion coefficient is: In the formula, The frictional velocity is calculated from the near-bottom shear stress. The total depth of the water body; This is an empirical coefficient; The diffusion coefficient in the vertical direction is: In the formula, k Kármán's constant; z The height of the particle above the bottom of the water; Within each time step Δt, the trajectory of the microplastic particles is updated by the following equation: In the formula, , , Let be an independent random variable that follows a standard normal distribution, representing turbulent disturbance; This represents the perturbation term in the random walk diffusion model; , , These are the velocity components in the x, y, and z directions, respectively.

6. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, The constructed wave-current coupled microplastic migration and diffusion model was calibrated and validated based on experimental data of microplastics. Multiple metrics were used to evaluate the model's accuracy, including: The accuracy of the model was verified by comparing and analyzing the model prediction results with the measured data. To ensure the reliability of the model, several typical cross sections and stations were selected for actual measurement verification. Using the coefficient of determination R 2 Evaluation metrics for model prediction accuracy include percentage bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the ratio of root mean square error to standard deviation of observations (RSR). Model parameters were optimized through experiments, and model performance was verified by comparing with benchmark models. The model simulation results include the generation of spatiotemporal distribution maps of microplastic concentration, transport path maps, and ecological risk level distribution maps.

7. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, Based on the obtained wave-current coupled microplastic migration and diffusion model, various climate change scenarios were designed for simulation, and the simulation results under various climate change scenarios were compared and analyzed to obtain the spatiotemporal distribution characteristics of regional microplastics under climate change conditions, specifically including: Design a variety of typical climate change scenarios, including runoff change scenario, sea level rise scenario, wind speed change scenario, and combined scenario; Climate change parameters were introduced into the wave-current coupled microplastic migration and diffusion model to adjust the boundary conditions, including: modifying the offshore boundary water level to reflect the future sea level rise trend; adjusting the wind field input data to represent the frequent occurrence of typhoons and strong winds; setting the upstream runoff variation curve to reflect the response of watershed precipitation and flood runoff; and setting the simulation time to a typical year, with each scenario running for one year. Compare and analyze the migration paths, enrichment area distributions, average residence times, and changes in microplastic concentrations under different climate scenarios. The key identification contents include: whether the enrichment hotspots shift to the open sea or retention areas; whether extreme weather enhances microplastic resuspension; whether enhanced hydrodynamic forces prompt microplastics to cross tidal flats or ecological protection zones; and whether sea-level changes expand the risk of microplastic invasion into sensitive ecosystems.

8. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, Evaluate the ecological risks of regional microplastics using the ecological risk entropy method, and combine spatial analysis to obtain the spatial distribution results of the ecological risk levels of regional microplastics, specifically including: The ecological risk entropy method calculates the pollution degree of each sampling point based on the ratio between the actual detected environmental concentration MEC of microplastics and the predicted no-effect concentration PNEC to evaluate the potential risks of pollutants to the ecosystem; PNEC is obtained through the species sensitivity distribution method SSD; the specific calculation formula is as follows: Among them, RQ is the risk entropy value; RCR is the risk characterization ratio; MEC is the microplastic concentration of the sampling point, with the unit of ng / L; PNEC is expressed as the no-observed-effect concentration NOEC in toxicological data. When NOEC data is lacking, LC50 and EC50 are used for estimation; AF is the prediction factor; Calculate the RQ values of each monitoring point in the target water area, and divide the ecological risk levels according to the magnitudes of the RQ values, including RQ ≤ 0.1 being risk-free, 0.1 < RQ ≤ 1 being low-risk, and RQ > 1 being high-risk.

9. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 8, characterized in that, Evaluate the ecological risks of regional microplastics using the ecological risk entropy method, and combine spatial analysis to obtain the spatial distribution results of the ecological risk levels of regional microplastics. Specifically, it also includes: Based on the calculation results of the ecological risk entropy, use the GIS spatial analysis method to identify high-risk areas. Overlay the spatial distribution map of RQ values with the ecological sensitive area layer of the target water area. Through spatial overlay analysis, identify the overlapping areas of high microplastic concentration areas and high ecological sensitivity areas, delineate the high-exposure zones of ecological risks, and output the spatial distribution map of microplastic risk levels; And conduct quantitative analysis on each high-risk area according to the results, including: the proportion of high-risk areas and their changing trends under each scenario; the RQ response differences of different polymer types of microplastics; the dynamic change characteristics of high-risk areas at the seasonal scale or under the tidal background; and the potential impacts of enrichment time and risk retention time on the ecosystem.

10. The method for microplastic transport simulation and risk identification based on multi-factor coupling as described in claim 1, characterized in that, The method also includes: Based on the results of ecological risk analysis, put forward hierarchical and zonal control suggestions for microplastic pollution in the target water area, including: source control: strengthen the management of key areas, industrial emission points, and urban rainwater overflow outlets, and restrict the entry of single-use plastic sources into the water body; ecological restoration: layout ecological buffer zones or constructed wetlands in high-risk areas to enhance the pollution filtration and buffering capabilities of the ecosystem; refined monitoring: optimize the layout of existing monitoring points, tilt the key observation areas towards high-RQ areas, and enhance the monitoring and early warning capabilities.

Images