MP migration path and sedimentation velocity monitoring system and method based on numerical modeling

Abstract

This paper proposes a system and method for monitoring microplastic migration paths and sedimentation velocities based on numerical modeling. The system comprises a sensor module, offshore sampling equipment, a preprocessing module, a numerical model module, a discretization solution module, a data analysis module, and a visualization module. The proposed system utilizes numerical modeling techniques to optimize the prediction process of microplastic migration and sedimentation velocities, significantly improving the accuracy and efficiency of simulating microplastic behavior in complex marine environments. The system is particularly suitable for effectively tracking and quantifying microplastics under dynamically changing marine conditions, creating a more efficient data processing platform for monitoring microplastic behavior, thereby optimizing environmental protection measures and improving marine management efficiency.

Description

Technical Field

[0001] The present invention relates to a system and method for monitoring MP migration path and sedimentation velocity based on numerical modeling, and belongs to the technical fields of environmental monitoring technology and water pollution control technology. Background Art

[0002] Currently, monitoring of microplastics relies primarily on manual sampling and laboratory analysis. These methods are not only costly and inefficient, but also lack real-time data, limiting the ability to broadly and rapidly respond to the dynamics of microplastic pollution. While these traditional methods remain important in microplastic pollution research, their limitations are evident, making them inadequate for large-scale, real-time monitoring. Advances in numerical modeling have provided new solutions for environmental monitoring of microplastics. Numerical modeling can efficiently and accurately simulate and predict the migration pathways and accumulation areas of microplastics in the environment. By integrating real-world environmental data with the physical properties of microplastics, this technology can provide detailed simulations of the dynamic behavior and ultimate distribution of microplastics in water. However, current numerical models used to track microplastic migration are primarily based on experimental data and often fail to fully capture the physical and environmental factors that influence microplastic behavior. For example, some models rely heavily on precise characterizations of microplastics, such as size, shape, and density, which are difficult to accurately and consistently measure in a dynamic marine environment.

[0003] Furthermore, existing numerical models have limitations when dealing with the interactions of complex environmental variables, making it difficult to fully reflect the dynamic changes in the marine environment. The behavior of microplastics in the ocean is influenced not only by fluid dynamics but also by multiple factors such as temperature, salinity, and sedimentation velocity. The interactions of these factors increase the complexity and uncertainty of simulations. Therefore, a numerical modeling system that can comprehensively consider multiple environmental factors and dynamically adjust them is urgently needed to improve the accuracy and practicality of microplastic monitoring.

[0004] Based on this, the present invention proposes a numerically modeled system and method for monitoring the migration and settling velocity of microplastics. By integrating marine environmental data and the physical properties of microplastics, this system simulates the migration and settling paths of microplastics in the ocean. Employing the Regional Ocean Model System (ROMS) and Lagrangian particle tracking technology, this system not only simulates the motion paths of microplastic particles but also accurately calculates their settling velocities, significantly improving the efficiency and accuracy of microplastic pollution monitoring. This technical solution provides scientific and technological support for addressing the problem of microplastic pollution and has significant practical significance and application value for environmental protection and marine management. Summary of the Invention

[0005] The present invention aims to solve the technical problem that existing numerical models have limitations in dealing with the interactions of complex environmental variables and are difficult to fully reflect the dynamic changes of the marine environment. In addition, a system and method for monitoring MP migration paths and sedimentation velocities based on numerical modeling are proposed.

[0006] The technical solution adopted by the present invention to solve the above problems is: the present invention proposes a MP migration path and sedimentation velocity monitoring system based on numerical modeling, comprising:

[0007] Sensor module, offshore sampling equipment, preprocessing module, numerical model module, discretization solution module, data analysis module and visualization module;

[0008] The sensor module is used to collect marine environment data in real time;

[0009] At-sea sampling equipment was used to collect microplastic samples and record the initial location and physical characteristics of each microplastic sample;

[0010] The preprocessing module is used to preprocess the marine environment data collected in real time;

[0011] The numerical model module is used to simulate the migration path and sedimentation velocity of microplastics in the marine environment, and obtain the microplastic particle motion equation and sedimentation velocity formula;

[0012] The discretization solution module is used to discretize and solve the motion equations of microplastic particles and adjust the simulation parameters of the numerical model module according to changes in marine environmental parameters;

[0013] The data analysis module is used to analyze the consistency between the data simulated by the numerical model module and the real data, and adjust the numerical model module according to the analysis results;

[0014] The visualization module is used to display the distribution and migration path of microplastics in real time.

[0015] Optionally, the sensor module includes but is not limited to a current meter, a thermometer, a salinity meter and a wave sensor.

[0016] Numerical modeling-based monitoring methods for MP migration paths and sedimentation velocities include:

[0017] Step 1: Collect ocean environment data in real time based on the sensor module, and transmit the collected ocean environment data to the data processing center;

[0018] Step 2: Collect microplastic samples using offshore sampling equipment and record the initial location and physical characteristics of the microplastic samples; physical characteristics include but are not limited to the size, shape, and density of the microplastics;

[0019] Step 3: Preprocess the collected data of the sensor module and the offshore sampling equipment based on the preprocessing module;

[0020] Step 4: Build a data model based on the regional ocean model and the Lagrangian particle tracking method to obtain the microplastic particle motion equation and sedimentation velocity formula;

[0021] Step 5: Based on the discretization solution module, the motion equations of microplastic particles are discretized and solved in combination with the finite difference method. The velocity and position of microplastic particles are iteratively calculated, and the simulation parameters of the ocean dynamics model are adjusted according to the changes in the marine environmental parameters.

[0022] The numerical model of this invention comprehensively considers the physical properties of microplastics (such as size, shape, density) and environmental factors (such as water velocity, temperature, and salinity), and describes the dynamic behavior of microplastics through precise mathematical calculation models. This model can not only describe the sedimentation behavior of microplastics in still water, but also simulate their drift, suspension, and bottom-moving states in dynamic water environments, providing a quantitative description and prediction of the behavior of microplastics.

[0023] Step 6: Perform data analysis on the simulation data after discretization solution and iterative calculation based on the data analysis module, check the consistency between the simulation data and the real data, adjust the numerical model module according to the analysis results, and conduct a new round of verification on the adjusted numerical model;

[0024] Step 7: Use the visualization module to display the distribution and migration path of verified microplastics in real time.

[0025] Optionally, the preprocessing of the collected data from the sensor module and the offshore sampling equipment in step 3 specifically includes:

[0026] The filtering algorithm is applied to remove the noise in the collected data, and the collected data is cleaned and normalized, and the preprocessed collected data is stored in the database.

[0027] Optionally, the step of obtaining the microplastic particle motion equation and sedimentation velocity formula of the microplastic in step 4 includes:

[0028] Step 4.1: Initialize the numerical model based on the marine environmental data, the initial position and physical properties of the microplastic samples, and establish the boundary conditions and initial conditions of the numerical model; the boundary conditions of the numerical model include but are not limited to the initial position, velocity and density of the microplastic particles, and the initial conditions of the numerical model include but are not limited to the ocean current velocity field. ,temperature and salinity ;

[0029] Step 4.2: Use a regional ocean model, combined with three-dimensional non-hydrostatic equilibrium equations, to accurately describe the initialized ocean dynamics. By integrating and analyzing environmental variables that affect the suspension, sedimentation, and dispersion of microplastics, a comprehensive dynamic description of the behavior of microplastics in the marine environment is provided. Ocean dynamics include the speed, direction, and intensity of currents, and environmental variables that affect the suspension, sedimentation, and dispersion of microplastics include, but are not limited to, temperature, salinity, water depth, and seafloor topography.

[0030] Step 4.3: Use the Lagrangian method to establish the motion equations of microplastic particles and introduce a random diffusion model to simulate the random movement and diffusion process of microplastics in the marine environment;

[0031] Step 4.4: Calculate the sedimentation velocity of microplastics using the Stokes sedimentation equation based on the characteristics of the microplastics, and adjust the Stokes equation based on an analysis of the water resistance and turbulence encountered by the microplastics. The characteristics of the microplastics include, but are not limited to, density, shape, and size.

[0032] Stokes' formula is:

[0033] (1);

[0034] In formula (1), is the sedimentation velocity of microplastic particles, in m / s, The radius of the microplastic particle, in m, Density of microplastic particles, in kg / m 3 , The density of seawater, in kg / m 3 , Gravitational acceleration, in m / s 2 , usually 9.81 m / , The dynamic viscosity of seawater, in Pa·s.

[0035] Optionally, the steps of establishing the motion equations of microplastic particles and simulating the random motion and diffusion process of microplastics in the marine environment in step 4.3 include:

[0036] Step 4.3.1: Set For microplastic particles in time location, For microplastic particles in time The improved Lagrange equation is constructed based on the total force on the particles to calculate the position and velocity of microplastic particles at any time; the total force on microplastic particles includes dynamic force, gravity, buoyancy, and viscous resistance;

[0037] Step 4.3.2: Set the initial velocity of all microplastic particles to 0, calculate the position and velocity of the microplastic particles within a specific time according to the improved equation, set the microplastic particles as passively tracked particles, and obtain the motion equation of the microplastic particles;

[0038] Step 4.3.3: Introduce a random diffusion model and use the Monte Carlo method to generate random diffusion paths of microplastic particles, simulating the random movement of microplastic particles in the marine environment based on random sampling;

[0039] Step 4.3.4: Calculate the drift velocity of microplastic particles based on the motion equation of microplastic particles and the influence of local temperature and salinity on seawater density in the preprocessed collected data. ;

[0040] Step 4.3.5: Calculate the diffusion coefficient D, which is the drift velocity of the plastic particle. Substitute the diffusion coefficient D into the motion equation of microplastic particles to simulate the random movement and diffusion process of microplastics in the marine environment;

[0041] Fluid Power The calculation formula is:

[0042] (2);

[0043] In formula (2), is the fluid pressure, is the viscosity coefficient of the fluid, is the Laplace operator;

[0044] gravity The calculation formula is:

[0045] (3);

[0046] In formula (3), is the mass of the particle, is the acceleration due to gravity;

[0047] buoyancy The calculation formula is:

[0048] (4);

[0049] In formula (4), is the fluid density, is the volume of fluid displaced by the particles, is the upward unit vector;

[0050] Viscous resistance The calculation formula is:

[0051] (5);

[0052] In formula (5), is the drag coefficient, is the cross-sectional area of ​​the particle, is the particle velocity, is the velocity vector;

[0053] The equation of motion for microplastic particles is:

[0054] (6);

[0055] The calculation formula of the diffusion coefficient D is:

[0056] (7);

[0057] In formula (7), is the baseline diffusion coefficient, is a tuning parameter, is the local temperature of seawater, indicating that the diffusion coefficient changes with temperature;

[0058] The random movement and diffusion process of plastics in the marine environment can be expressed as:

[0059] (8);

[0060] In formula (8), For time The location of microplastic particles, The drift velocity of microplastic particles driven by fluid dynamics, including the velocity caused by factors such as water currents and wind; is the diffusion coefficient, which can be dynamically adjusted according to seawater temperature, salinity and other environmental factors; It is a multidimensional Brownian motion with random perturbations, representing the impact of environmental randomness on the path of microplastic particles.

[0061] The numerical simulation model presented in this paper uses a modified Lagrangian equation and a random diffusion model to more comprehensively simulate the movement of microplastics in the ocean. By fitting model parameters to experimental data, the present invention improves the accuracy of model predictions, enabling the model to accurately reflect the migration and sedimentation patterns of microplastics under different ocean dynamic conditions.

[0062] Optionally, the step of discretizing and solving the motion equation of the microplastic particles using the finite difference method in step 5 includes:

[0063] Based on physical principles, all forces acting on microplastic particles are quantified, and the continuous differential equation of microplastic motion is converted into discrete form through the finite difference method. The central difference method is used to accurately process the spatial derivative in space, and the forward difference method is used for discretization in time.

[0064] Optionally, a new round of verification of the adjusted numerical model in step 6 specifically includes:

[0065] Analyze the consistency between the simulated data and the real data, adjust the model according to the analysis results, and conduct a new round of verification on the adjusted numerical model until the error between the simulated data and the real data output by the adjusted numerical model is less than the preset value; model adjustment includes but is not limited to adjusting the model's time step, spatial discretization method, and model boundary condition settings.

[0066] Optionally, the step of using a visualization module in step 7 to display the verified distribution and migration path of microplastics in real time includes:

[0067] Step 7.1: Use the 3D visualization tool MATLAB to display the distribution and migration path of verified microplastics in real time;

[0068] Step 7.2: Create a three-dimensional view based on the preprocessed data, generate a visualization report of microplastics, and analyze and understand the migration patterns of microplastics; the visualization report of microplastics includes but is not limited to a microplastic distribution map, a migration path map, and a sedimentation velocity map.

[0069] The beneficial effects of the present invention are:

[0070] 1. Accurately simulate the movement of microplastics: The numerical model of this invention comprehensively considers the physical properties of microplastics (such as size, shape, and density) and environmental factors (such as water velocity, temperature, and salinity), describing the dynamic behavior of microplastics through precise mathematical calculations. This model not only describes the settling behavior of microplastics in still water but also simulates their drift, suspension, and bottom-moving behavior in moving water, providing a quantitative description and prediction of microplastic behavior.

[0071] 2. Efficient prediction of dynamic migration trajectories: Compared to traditional methods, the numerical simulation model presented here more comprehensively simulates the movement of microplastics in the ocean by incorporating a modified Lagrangian equation and a random diffusion model. By fitting model parameters to experimental data, the present invention improves the accuracy of model predictions, enabling the model to accurately reflect the migration and sedimentation patterns of microplastics under different ocean dynamic conditions.

[0072] 3. Improving the efficiency and accuracy of microplastic monitoring: The implementation of this invention not only significantly improves the accuracy and efficiency of simulating microplastic behavior in complex marine environments, but also expands the model's scope of application. Compared to traditional qualitative description methods, this invention is applicable to a variety of environments, from coastal areas to the open ocean, providing strong scientific and technological support for marine environmental protection and microplastic pollution control. By accurately predicting the migration and sedimentation patterns of microplastics through this system, environmental scientists and managers can more effectively track the sources and transmission pathways of microplastic pollution, optimize environmental protection measures, and thus better protect marine ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A block diagram of the MP migration path and sedimentation velocity monitoring system based on numerical modeling provided by the present invention;

[0074] Figure 2 A flow chart of the method for monitoring MP migration path and sedimentation velocity based on numerical modeling provided by the present invention;

[0075] Figure 3 A three-dimensional visualization of the simulated microplastic migration path provided by the present invention;

[0076] Figure 4 A three-dimensional visualization of the simulated microplastic sedimentation and aggregation area provided by the present invention;

[0077] Figure 5 A three-dimensional trajectory diagram of the migration path and sedimentation of a single microplastic particle in the ocean provided by the present invention;

[0078] Figure 6 This is a diagram of the position changes of a single microplastic particle on the xyz axes during migration and sedimentation provided by the present invention. DETAILED DESCRIPTION

[0079] Specific embodiment 1: In this embodiment, MP is microplastic, FDM is finite difference method, and ROMS is regional ocean model system.

[0080] Combine Figure 1 To illustrate this embodiment, the structure of the MP migration path and sedimentation velocity monitoring system based on numerical modeling described in this embodiment includes:

[0081] Sensor module, offshore sampling equipment, preprocessing module, numerical model module, discretization solution module, data analysis module and visualization module;

[0082] Sensor modules include but are not limited to current meters, thermometers, salinity meters, and wave sensors.

[0083] The sensor module is used to collect marine environment data in real time;

[0084] The offshore sampling equipment was used to collect microplastic samples and record the initial location and physical characteristics of each microplastic sample;

[0085] The preprocessing module is used to preprocess the marine environment data collected in real time;

[0086] The numerical model module is used to simulate the migration path and sedimentation velocity of microplastics in the marine environment, and obtain the microplastic particle motion equation and sedimentation velocity formula;

[0087] The discretization solution module is used to discretize and solve the motion equations of microplastic particles and adjust the simulation parameters of the numerical model module according to changes in marine environmental parameters;

[0088] The data analysis module is used to analyze the consistency between the data simulated by the numerical model module and the real data, and adjust the numerical model module according to the analysis results;

[0089] The visualization module is used to display the distribution and migration path of microplastics in real time.

[0090] Specific implementation method 2: Combination Figure 2-6 This embodiment is described as follows. Figure 2 As shown, the steps of the MP migration path and sedimentation velocity monitoring method based on numerical modeling described in this embodiment include:

[0091] S1: Real-time ocean environment data collection and transmission;

[0092] This implementation method monitors and collects data on microplastics by deploying and utilizing a sensor network in the ocean. Current meters, thermometers, salinometers, and wave sensors are used to collect real-time data on the marine environment, ensuring the capture of key environmental data such as currents, temperature gradients, salinity changes, and wave activity. All sensors are equipped with real-time data transmission capabilities, transmitting the collected data to a remote data processing center via a wireless network.

[0093] S2: Microplastic sample collection and recording;

[0094] Using specialized marine sampling equipment, such as trawls and underwater drones, we systematically collected microplastic samples from the ocean surface and deep layers. The initial location and physical properties of each sample were recorded for subsequent model initialization and validation.

[0095] S3: data preprocessing and storage;

[0096] S301: Apply complex filtering algorithms, such as Kalman filtering or Fourier transform methods, to remove background noise and random fluctuations in the data collected in S1 and S2. These algorithms can effectively separate the interference between microplastic signals and natural fluctuations in the marine environment, and improve the signal-to-noise ratio of the data.

[0097] S302: To ensure the validity and comparability of the data collected in S1 and S2 within the model, all sensor data is normalized to convert the data into a unified metric. This step uses normalization techniques such as min-max normalization (scaling the data to between 0 and 1) or z-score normalization (subtracting the mean and dividing by the standard deviation) to ensure comparability of data from different sources and over time. The preprocessed data is stored in a structured database to facilitate data retrieval and analysis in subsequent steps.

[0098] S4: Construct a numerical model based on ocean dynamics model and Lagrangian particle tracking method;

[0099] S5: setting of model initial conditions and boundary conditions;

[0100] The initial and boundary conditions of the numerical model are set based on the collected environmental data and microplastic sample data. The characteristics of the microplastic samples obtained from the ocean sampling data (including particle size, shape, density and initial position) are used to set the initial conditions of the model. These characteristics directly affect the behavior of particles in the water, such as sedimentation rate and drift direction. Real-time monitored marine environmental data (such as current speed, temperature, and salinity) are integrated into the boundary conditions of the model. These marine environmental data reflect the dynamic changes in the marine environment and have a direct impact on the movement trajectory of microplastic particles. At the same time, the spatial boundaries of the numerical model should be set according to the geographical characteristics of the sea area, such as the location of islands and coastlines and depth changes, to ensure that the spatial scope of the model is consistent with the actual situation.

[0101] S6: Regional ocean model simulations;

[0102] S601: This embodiment uses the Regional Ocean Model System (ROMS) to simulate the fluid dynamics behavior in the ocean. This model can accurately describe the ocean dynamics process based on three-dimensional non-hydrostatic equilibrium equations. In a specific embodiment of the present invention, the ROMS configuration must first be performed by adjusting the grid density, time step, and total simulation duration to ensure the precision and coverage of the simulation. When initializing the parameter settings, the number of microplastic particles is defined as 1000, the time step is 800, the time step interval is 1s, and the simulation area size is 100 meters (x direction), 100 meters (y direction), and 50 meters (z direction).

[0103] S602: The core of the ROMS model used in this embodiment is to solve the three-dimensional non-hydrostatic equilibrium dynamic equations, including the continuity equation, momentum equation, salinity and temperature equations. The continuity equation ensures the conservation of mass and numerically solves the continuity in the flow field; the momentum equation is used to calculate the speed and direction of the ocean current, which is a key factor in simulating the movement of microplastics; the salinity and temperature equations are used to simulate the effects of salinity and temperature on ocean current density and stratification, which indirectly affect the sedimentation and suspension state of microplastics. The specific calculation formulas are as follows:

[0104] The continuity equation is expressed as:

[0105] (1);

[0106] In formula (1), is the density of seawater, is the ocean velocity field vector, is the divergence of seawater density and ocean current field, For time;

[0107] The momentum equation is expressed as:

[0108] (2);

[0109] in, is the seawater pressure, is the dynamic viscosity of seawater, It is the external force acting on seawater, including gravity, Coriolis force and other external forces.

[0110] The expressions for the temperature and salinity equations are:

[0111] (3);

[0112] (4);

[0113] In formulas (3) and (4), and represent seawater temperature and seawater salinity, respectively. is the diffusion coefficient, which affects the diffusion rate with temperature and salinity.

[0114] S7: Lagrangian method to establish the equation of motion of microplastic particles;

[0115] S701: In this embodiment, one of the key steps in numerical modeling is to use the Lagrangian method to establish the motion equation of microplastic particles and introduce a random diffusion model to simulate the random movement and diffusion process of microplastics in the marine environment.

[0116] S702: In a specific embodiment of the present invention, taking into account the complexity of fluid dynamics and the diversity of environmental factors, in order to more accurately simulate the movement of microplastic particles in the ocean, the present invention makes an improvement based on the Lagrange equation. This improved equation comprehensively considers factors such as the dynamics, gravity, buoyancy, and viscous resistance of microplastic particles to describe the movement trajectory of microplastics in the ocean. Among them, the fluid dynamics takes into account the influence of seawater pressure and viscosity coefficient; buoyancy is calculated according to Archimedes' principle; and viscous resistance depends on the shape of the microplastic particles and the properties of the seawater.

[0117] Fluid Power The calculation formula is:

[0118] (5);

[0119] In formula (5), is the fluid pressure, is the viscosity coefficient of the fluid, is the Laplace operator;

[0120] gravity The calculation formula is:

[0121] (6);

[0122] In formula (6), is the mass of the particle, is the acceleration due to gravity;

[0123] buoyancy The calculation formula is:

[0124] (7);

[0125] In formula (7), is the fluid density, is the volume of fluid displaced by the particles, is the upward unit vector;

[0126] Viscous resistance The calculation formula is:

[0127] (8);

[0128] In formula (8), is the drag coefficient, is the cross-sectional area of ​​the particle, is the particle velocity, is the velocity vector;

[0129] S703: After calculating the actual forces in the above-mentioned ocean environment, the improved Lagrange equation can be established. Indicates that microplastic particles location, For microplastic particles in time In a specific embodiment of the present invention, the initial positions of microplastic particles are randomly generated to ensure that the microplastic particles are evenly distributed in the simulation area to obtain comprehensive migration path information; to simplify the model, the initial velocities of all microplastic particles are set to zero. The position and velocity of microplastic particles within a specific time are calculated based on the improved Lagrange equation. In this step, microplastic particles are regarded as passively tracked particles.

[0130] The equation of motion for microplastic particles is:

[0131] (9).

[0132] S8: Introduction of random diffusion model;

[0133] S801: The movement of microplastic particles in the ocean is determined by both deterministic fluid dynamics and random diffusion processes. This paper introduces the Monte Carlo method to simulate the random diffusion path of microplastic particles and the random disturbance of microplastic particles in the ocean environment. The diffusion process is determined by the drift velocity. and diffusion coefficient D, which are dynamically adjusted according to the current ocean temperature, salinity, and current velocity. This step first requires the calculation of the drift velocity and diffusion coefficient. Depending on the location and timing of the microplastic particles, it is also necessary to consider the effects of local temperature and salinity on seawater density, which can be obtained from ocean current data or predicted using numerical weather prediction models;

[0134] The calculation formula of the diffusion coefficient D is:

[0135] (10);

[0136] In formula (10), is the baseline diffusion coefficient, is a tuning parameter, is the local temperature of seawater, indicating that the diffusion coefficient changes with temperature;

[0137] S802: The drift speed of plastic particles Substitute the diffusion coefficient D into the motion equation of microplastic particles to simulate the random movement and diffusion process of microplastics in the marine environment;

[0138] The random movement and diffusion process of plastics in the marine environment can be expressed as:

[0139] (11);

[0140] In formula (11), For time The location of microplastic particles, The drift velocity of microplastic particles driven by fluid dynamics, including the velocity caused by factors such as water currents and wind; is the diffusion coefficient, which can be dynamically adjusted according to seawater temperature, salinity and other environmental factors; It is a multidimensional Brownian motion with random perturbations, representing the impact of environmental randomness on the path of microplastic particles.

[0141] S9: Stokes formula to calculate the sedimentation velocity of microplastics;

[0142] The sedimentation rate of microplastics is calculated using the Stokes sedimentation formula based on the density, shape, and size of microplastics, taking into account the influence of environmental factors;

[0143] Stokes' formula is:

[0144] (12);

[0145] In formula (12), is the sedimentation velocity of microplastic particles, in m / s, The radius of the microplastic particle, in m, Density of microplastic particles, in kg / m 3 , The density of seawater, in kg / m 3 , Gravitational acceleration, in m / s 2 , usually 9.81 m / , The dynamic viscosity of seawater, in Pa·s.

[0146] S10: numerical solution and iterative calculation;

[0147] S1001: The finite difference method (FDM) is used to discretize and solve the motion equations of microplastic particles, thereby simulating the movement of microplastic particles in a dynamically changing marine environment. First, each force acting on the microplastic particles is quantified based on physical principles, including fluid dynamics, gravity, buoyancy, and viscous resistance. The specific values ​​have been solved in step S40301. Secondly, the continuous differential equation of microplastic motion is converted into a discrete form through the finite difference method. The central difference method is used in space to accurately process the spatial derivative, and the forward difference method is used for discretization in time to ensure numerical stability and accuracy.

[0148] S1002: In an embodiment of the present invention, before starting the simulation, the initial position and velocity of each microplastic particle are set. According to a series of initial and boundary conditions set in step S5, the number of microplastic particles is defined as 1000, the time step is 800, the time step interval is 1s, and the simulation area size is 100 meters (x direction), 100 meters (y direction) and 50 meters (z direction). Finally, numerical methods such as the Euler method or the higher-order Runge-Kutta method are used for numerical integration to gradually iterate and calculate the position and velocity of the microplastic particles at each time step. This process needs to be repeated until the termination condition of the simulation is reached, and the spatiotemporal changes of environmental parameters must be taken into account to ensure the high accuracy of the simulation results.

[0149] S1003: Starting from the initial state, the position and velocity of the microplastic particles are calculated step by step according to the set time step (Δt=1s). At each step, the particle state is updated according to the current velocity and force. During the simulation process, the simulation parameters are monitored and adjusted in real time to reflect changes in marine environmental parameters such as water flow and temperature. For example, if a significant change in flow rate or water temperature is detected, the relevant parameters in the simulation are adjusted accordingly to ensure the accuracy and practicality of the simulation results. At the same time, during the iterative process, the microplastic particles are monitored in real time to ensure that each microplastic particle meets the simulation termination conditions, such as the microplastic particle moves out of the simulation area or reaches the predetermined simulation time. After the simulation is completed, the final state of all particles is recorded for analysis of the migration path and sedimentation behavior of microplastics in the ocean. By comparing the movement trajectories and sedimentation locations of different particles, the diffusion characteristics and potential environmental impacts of microplastic pollution can be studied.

[0150] S11: Numerical validation and model adjustment;

[0151] S1101: After completing the dynamic simulation of microplastic particles, conduct a detailed analysis of the simulated data and compare it with marine field sampling data and laboratory test results to verify the accuracy and reliability of the numerical model;

[0152] S1102: Based on the results of data comparison and analysis, identify model parameters that may need adjustment, such as the sedimentation rate and diffusion coefficient of microplastics. Improve the model's responsiveness and prediction accuracy by adjusting the time step, spatial discretization method, and boundary condition settings. Based on the deviation between the actual data and the simulated data, if the deviation is large, optimize the model structure as necessary, such as improving the kinetic equations or introducing new variables to more accurately simulate the environmental response of microplastics. After optimization is completed, perform new verification operations to ensure that all modifications effectively improve the overall performance and reliability of the model.

[0153] S12: Output a three-dimensional view of microplastic migration and sedimentation;

[0154] Use MATLAB to convert the processed data into an intuitive graphical representation to show the distribution and migration path of microplastics in real time. This step creates a three-dimensional view based on the processed data, such as Figure 3-Figure 6 As shown in the figure, this embodiment performs a three-dimensional visualization of the migration path and sedimentation location of microplastic particles at each time step, generating a variety of visualization reports such as microplastic distribution maps, migration path maps, and sedimentation velocity maps to help analyze and understand the migration patterns of microplastics. Among them, the time series animation shows the complete process of microplastics from release to sedimentation, providing analysis views from multiple angles and time points, helping researchers and policymakers better understand the behavior and impact of microplastics in the marine environment.

[0155] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement of the above embodiments made according to the technical essence of the present invention, within the spirit and principles of the present invention, without departing from the content of the technical solution of the present invention, shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A system for monitoring MP migration paths and sedimentation velocities based on numerical modeling, characterized in that: The structure of the MP migration path and sedimentation velocity monitoring system based on numerical modeling includes: Sensor module, offshore sampling equipment, preprocessing module, numerical model module, discretization solution module, data analysis module and visualization module; The sensor module is used to collect marine environment data in real time; The sensor module includes but is not limited to a current meter, a thermometer, a salinity meter and a wave sensor; The offshore sampling equipment is used to collect microplastic samples and record the initial location and physical characteristics of each microplastic sample; The preprocessing module is used to preprocess the marine environment data collected in real time; The numerical model module is used to simulate the migration path and sedimentation velocity of microplastics in the marine environment, and obtain the microplastic particle motion equation and sedimentation velocity formula; Specifically: a data model was constructed based on a regional ocean model and the Lagrangian particle tracking method to obtain the microplastic particle motion equation and sedimentation velocity formula; The discretization solution module is used to discretize and solve the microplastic particle motion equation and adjust the simulation parameters of the numerical model module according to changes in marine environmental parameters; Specifically, the finite difference method is used to discretize and solve the motion equations of microplastic particles, the velocity and position of microplastic particles are iteratively calculated, and the simulation parameters of the ocean dynamics model are adjusted according to changes in marine environmental parameters; The data analysis module is used to analyze the consistency between the data simulated by the numerical model module and the real data, and adjust the numerical model module according to the analysis results; The visualization module is used to display the distribution and migration path of microplastics in real time.

2. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling is applied to the system for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 1, characterized in that: include: Step 1: collecting ocean environment data in real time based on the sensor module, and transmitting the collected ocean environment data in real time to a data processing center; Step 2: Collect microplastic samples using the offshore sampling equipment and record the initial location and physical characteristics of the microplastic samples; wherein the physical characteristics include but are not limited to the size, shape, and density of the microplastics; Step 3: pre-processing the collected data of the sensor module and the offshore sampling equipment based on the pre-processing module; Step 4: Build a data model based on the regional ocean model and the Lagrangian particle tracking method to obtain the microplastic particle motion equation and sedimentation velocity formula; Step 5: Based on the discretization solution module, the motion equations of the microplastic particles are discretized and solved in combination with the finite difference method. The velocity and position of the microplastic particles are iteratively calculated, and the simulation parameters of the ocean dynamics model are adjusted according to the changes in the marine environmental parameters. Step 6: Based on the data analysis module, perform data analysis on the simulation data after discretization solution and iterative calculation, check the consistency between the simulation data and the real data, adjust the numerical model module according to the analysis results, and perform a new round of verification on the adjusted numerical model; Step 7: Use the visualization module to display the distribution and migration path of the verified microplastics in real time.

3. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 2, characterized in that: The pre-processing of the data collected by the sensor module and the offshore sampling equipment in step 3 specifically includes: The filtering algorithm is applied to remove the noise in the collected data, and the collected data is cleaned and normalized, and the preprocessed collected data is stored in the database.

4. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 2, characterized in that: The steps for obtaining the microplastic particle motion equation and sedimentation velocity formula of microplastics in step 4 include: Step 4.1: Initialize the numerical model based on the marine environmental data, the initial position and physical properties of the microplastic samples, and establish the boundary conditions and initial conditions of the numerical model; wherein the boundary conditions of the numerical model include but are not limited to the initial position, velocity and density of the microplastic particles, and the initial conditions of the numerical model include but are not limited to the ocean current velocity field. ,temperature and salinity ; Step 4.2: Use a regional ocean model, combined with three-dimensional non-hydrostatic equilibrium equations, to accurately describe the initialized ocean dynamics. By integrating and analyzing environmental variables that affect the suspension, sedimentation, and dispersion of microplastics, a comprehensive dynamic description of the behavior of microplastics in the marine environment is provided. Ocean dynamics include the speed, direction, and intensity of currents, and environmental variables that affect the suspension, sedimentation, and dispersion of microplastics include, but are not limited to, temperature, salinity, water depth, and seafloor topography. Step 4.3: Use the Lagrangian method to establish the motion equations of microplastic particles and introduce a random diffusion model to simulate the random movement and diffusion process of microplastics in the marine environment; Step 4.4: Calculate the sedimentation velocity of microplastics using the Stokes sedimentation equation based on the characteristics of the microplastics, and adjust the Stokes equation based on an analysis of the water resistance and turbulence encountered by the microplastics. The characteristics of the microplastics include, but are not limited to, density, shape, and size. Stokes' formula is: （1）； In formula (1), is the sedimentation velocity of microplastic particles, in m / s, The radius of the microplastic particle, in m, Density of microplastic particles, in kg / m 3 , The density of seawater, in kg / m 3 , Gravitational acceleration, in m / s 2 , which is 9.81 m / , The dynamic viscosity of seawater, in Pa·s.

5. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 4, characterized in that: The steps in step 4.3 to establish the motion equations of microplastic particles and simulate the random movement and diffusion process of microplastics in the marine environment include: Step 4.3.1: Set For microplastic particles in time location, For microplastic particles in time The improved Lagrange equation is constructed based on the total force on the particles to calculate the position and velocity of microplastic particles at any time; the total force on microplastic particles includes dynamic force, gravity, buoyancy, and viscous resistance; Step 4.3.2: Set the initial velocity of all microplastic particles to 0, calculate the position and velocity of the microplastic particles within a specific time according to the improved Lagrange equation, set the microplastic particles as passively tracked particles, and obtain the motion equation of the microplastic particles; Step 4.3.3: Introduce a random diffusion model and use the Monte Carlo method to generate random diffusion paths of microplastic particles, simulating the random movement of microplastic particles in the marine environment based on random sampling; Step 4.3.4: Calculate the drift velocity of microplastic particles by obtaining the position and time of microplastic particles and the influence of local temperature and salinity on seawater density in the pre-processed collected data based on the motion equation of microplastic particles. ; Step 4.3.5: Calculate the diffusion coefficient D, which is the drift velocity of the plastic particle. Substitute the diffusion coefficient D into the motion equation of microplastic particles to simulate the random movement and diffusion process of microplastics in the marine environment; Fluid Power The calculation formula is: （2）； In formula (2), is the fluid pressure, is the viscosity coefficient of the fluid, is the Laplace operator; gravity The calculation formula is: （3）； In formula (3), is the mass of the particle, is the acceleration due to gravity; buoyancy The calculation formula is: （4）； In formula (4), is the fluid density, is the volume of fluid displaced by the particles, is the upward unit vector; Viscous resistance The calculation formula is: （5）； In formula (5), is the drag coefficient, is the cross-sectional area of ​​the particle, is the particle velocity, is the velocity vector; The equation of motion for microplastic particles is: （6）； The calculation formula of the diffusion coefficient D is: （7）； In formula (7), is the baseline diffusion coefficient, is a tuning parameter, is the local temperature of seawater, indicating that the diffusion coefficient changes with temperature; The random movement and diffusion process of plastics in the marine environment can be expressed as: （8）； In formula (8), For time The location of microplastic particles, is the drift velocity of microplastic particles driven by fluid dynamics, including the velocity caused by water currents and wind factors; is the diffusion coefficient, which is dynamically adjusted according to seawater temperature, salinity, and other environmental factors; It is a multidimensional Brownian motion with random perturbations, representing the impact of environmental randomness on the path of microplastic particles.

6. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 2, characterized in that: In step 5, the steps of discretizing and solving the motion equations of microplastic particles by using the finite difference method include: Based on physical principles, all forces acting on microplastic particles are quantified, and the continuous differential equation of microplastic motion is converted into discrete form through the finite difference method. The central difference method is used to accurately process the spatial derivative in space, and the forward difference method is used for discretization in time.

7. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 2, characterized in that: The new round of verification of the adjusted numerical model in step 6 specifically includes: Analyze the consistency between the simulated data and the real data, adjust the model according to the analysis results, and conduct a new round of verification on the adjusted numerical model until the error between the simulated data and the real data output by the adjusted numerical model is less than the preset value; model adjustment includes but is not limited to adjusting the model's time step, spatial discretization method, and model boundary condition settings.

8. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 2, characterized in that: In step 7, the steps of using the visualization module to display the distribution and migration path of verified microplastics in real time include: Step 7.1: Use the 3D visualization tool MATLAB to display the distribution and migration path of verified microplastics in real time; Step 7.2: Create a three-dimensional view based on the preprocessed data, generate a visualization report of microplastics, and analyze and understand the migration patterns of microplastics; the visualization report of microplastics includes but is not limited to a microplastic distribution map, a migration path map, and a sedimentation velocity map.

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