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

Abstract

The invention provides an MP migration path and settling velocity monitoring system and method based on numerical modeling. The system comprises a sensor module, an offshore sampling device, a preprocessing module, a numerical model module, a discretization solving module, a data analysis module and a visualization module. According to the micro-plastic migration path and settling velocity monitoring system provided by the invention, the prediction process of micro-plastic migration and settling velocity is optimized by utilizing a numerical modeling technology, and the simulation accuracy and efficiency of micro-plastic behaviors in a complex marine environment are remarkably improved. The method is especially suitable for effective tracking and quantitative analysis of the microplastics under dynamically changing ocean conditions, and a more efficient data processing platform is created to monitor behaviors of the microplastics, so that environmental protection measures are optimized, and ocean management efficiency is improved.

Description

Technical Field

[0001] The 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. Background Art

[0002] Microplastic pollution has become a hot topic of global environmental issues, especially in marine ecosystems. These tiny plastic particles pose a potential threat to marine life and even human health due to their widespread distribution and small size. At present, the monitoring of microplastics mainly relies on manual sampling and laboratory analysis, which are not only costly and inefficient, but also unable to provide instant data, limiting the widespread and rapid response to the dynamic changes of microplastic pollution. Although these traditional methods still play an important role in the study of microplastic pollution, their limitations are obvious and it is difficult to meet the needs of large-scale, real-time monitoring. With the development of numerical modeling technology, new solutions have been provided for the environmental monitoring of microplastics. By using numerical modeling, the migration paths and aggregation areas of microplastics in the environment can be simulated and predicted efficiently and accurately. This technology can simulate the dynamic behavior and final distribution of microplastics in water bodies in detail by integrating actual environmental data with the physical properties of microplastics. However, the current numerical models used to track the migration of microplastics are mainly based on experimental data, and these models often fail to fully capture the physical and environmental factors that affect the behavior of microplastics. For example, some models rely heavily on accurate descriptions of microplastic characteristics, such as size, shape, and density, which are difficult to measure continuously and accurately in a dynamic marine environment.

[0003] In addition, existing numerical models have limitations in dealing with the interaction 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 not only affected by fluid dynamics, but also involves multiple factors such as temperature, salinity, and sedimentation velocity. The interaction of these factors increases the complexity and uncertainty of the simulation. Therefore, there is an urgent need for a numerical modeling system that can comprehensively consider multiple environmental factors and make dynamic adjustments to improve the accuracy and practicality of microplastic monitoring.

[0004] Based on this, the present invention proposes a system and method for monitoring the migration path and sedimentation velocity of MP based on numerical modeling, which simulates the migration and sedimentation path of microplastics in the ocean by integrating marine environmental data and the physical properties of microplastics. The system uses the Regional Ocean Model System (ROMS) and Lagrangian particle tracking technology, which can not only simulate the motion trajectory of microplastic particles, but also accurately calculate their sedimentation velocity, thereby significantly improving the efficiency and accuracy of microplastic pollution monitoring. The technical solution of the present invention provides scientific and technical support for solving the problem of microplastic pollution, and has important 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 the existing numerical models have limitations in dealing with the interaction of complex environmental variables and are difficult to fully reflect the dynamic changes of the marine environment, and further proposes a system and method for monitoring the MP migration path and sedimentation velocity based on numerical modeling.

[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] The offshore 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 the 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] MP migration path and sedimentation velocity monitoring methods based on numerical modeling, including:

[0017] Step 1: Collect ocean environment data in real time based on the sensor module, and transmit the collected ocean environment data in real time 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; the physical characteristics include but are not limited to the size, shape and density of the microplastics;

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

[0020] Step 4: Construct 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 of microplastics;

[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 speed 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 the present invention comprehensively considers the physical properties of microplastics (such as size, shape, density) and environmental factors (such as water flow velocity, temperature, salinity), and describes the dynamic behavior of microplastics through an accurate mathematical calculation model. This model can not only describe the sedimentation behavior of microplastics in static water, but also simulate their drift, suspension and bottom push state in a dynamic water environment, providing a quantitative description and prediction of the behavior of microplastics.

[0023] 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 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 of the sensor module and the offshore sampling device 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 environment 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 the regional ocean model in combination with the three-dimensional non-hydrostatic equilibrium equation to accurately describe the initialized ocean dynamics process, and provide a comprehensive dynamic description of the behavior of microplastics in the marine environment by integrating and analyzing the environmental variables that affect the suspension, sedimentation and diffusion of microplastics. The ocean dynamics process includes the speed, direction and intensity of the water flow, and the environmental variables that affect the suspension, sedimentation and diffusion of microplastics include but are not limited to temperature, salinity, water depth and seabed topography.

[0030] Step 4.3: 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;

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

[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 equation of microplastic particles and simulating the random movement 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 speed of the microplastic particles is calculated by constructing an improved Lagrange equation based on the total force on the particles to calculate the position and speed of the microplastic particles at any time; the total force on the microplastic particles includes power, 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 the random diffusion model and use the Monte Carlo method to generate the random diffusion path of microplastic particles, and simulate the random movement of microplastic particles in the marine environment based on random sampling;

[0039] Step 4.3.4: Obtain the position and time 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 to calculate the drift speed of microplastic particles ;

[0040] Step 4.3.5: Calculate the diffusion coefficient D and convert the drift velocity of the plastic particle into 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 particle, 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 is 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 adjusted dynamically 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 of the present invention more comprehensively simulates the movement trajectory of microplastics in the ocean by introducing the improved Lagrangian equation and random diffusion model. By fitting the model parameters with experimental data, the present invention improves the accuracy of model prediction, enabling the model to accurately reflect the migration and sedimentation laws of microplastics under different ocean dynamic conditions.

[0062] Optionally, the step of discretizing and solving the motion equation of microplastic particles in step 5 by combining the finite difference method 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 setting.

[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 three-dimensional 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 the present invention comprehensively considers the physical properties of microplastics (such as size, shape, density) and environmental factors (such as water flow velocity, temperature, salinity), and describes the dynamic behavior of microplastics through an accurate mathematical calculation model. This model can not only describe the sedimentation behavior of microplastics in still water, but also simulate its drift, suspension and bottom push state in a moving water environment, providing a quantitative description and prediction of the behavior of microplastics.

[0071] 2. Efficient prediction of dynamic migration trajectory: Compared with traditional methods, the numerical simulation model of the present invention more comprehensively simulates the movement trajectory of microplastics in the ocean by introducing the improved Lagrangian equation and random diffusion model. By fitting the model parameters with experimental data, the present invention improves the accuracy of model prediction, enabling the model to accurately reflect the migration and sedimentation laws of microplastics under different ocean dynamic conditions.

[0072] 3. Improve the efficiency and accuracy of microplastic monitoring: The implementation of this invention not only significantly improves the simulation accuracy and efficiency of microplastic behavior in complex marine environments, but also expands the scope of application of the model. Compared with traditional qualitative description methods, this invention is applicable to various environments from coastal areas to open oceans, providing strong scientific and technological support for marine environmental protection and microplastic pollution control. Through this system, environmental scientists and managers can more effectively track the sources and transmission pathways of microplastic pollution and optimize environmental protection measures, thereby better protecting marine ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A structural 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 MP migration path and sedimentation velocity monitoring method based on numerical modeling provided by the present invention;

[0075] Figure 3 A three-dimensional visualization diagram 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 A diagram showing the position change of a single microplastic particle on the xyz axes during migration and sedimentation provided by the present invention. DETAILED DESCRIPTION

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

[0080] Combination 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 the 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 marine 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, salinity meters, and wave sensors are used to collect real-time data about the marine environment, ensuring that key environmental data such as ocean currents, temperature gradients, salinity changes, and wave activity are captured. All sensors are equipped with real-time data transmission capabilities, and the collected data is sent to a remote data processing center via a wireless network.

[0093] S2: Microplastic sample collection and recording;

[0094] Use specialized marine sampling equipment, such as trawls, underwater drones, etc., to systematically collect microplastic samples from the surface and deep layers of the ocean. And record the initial position and physical properties of each sample for subsequent model initialization and verification.

[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 in the model, all sensor data are normalized and converted into a unified measure. This step ensures the comparability of data from different sources and at different times through standardization techniques such as minimum-maximum standardization (scaling the data to between 0 and 1) or Z-score standardization (subtracting the mean from the data and dividing it by the standard deviation). 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 the ocean dynamics model and the 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 according to the collected environmental data and microplastic sample data. The characteristics of the microplastic samples (including particle size, shape, density and initial position) obtained from the ocean sampling data are used to set the initial conditions of the model. These characteristics directly affect the behavior of particles in the water, such as sedimentation velocity and drift direction. The real-time monitored marine environmental data (such as current velocity, temperature, and salinity) are integrated into the boundary conditions of the model. These marine environmental data reflect the dynamic changes of 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 and depth changes of islands and coastlines, 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. The model can accurately describe the ocean dynamic process based on the three-dimensional non-static equilibrium equation. In a specific embodiment of the present invention, 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-static equilibrium dynamic equations, including the continuity equation, momentum equation, salinity and temperature equation. 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 sea water 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 of 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, considering 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 is improved based on the Lagrange equation. The improved equation comprehensively considers the dynamics, gravity, buoyancy, viscous resistance and other factors of microplastic particles, and is used to describe the movement trajectory of microplastics in the ocean. Among them, 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 microplastic particles and the properties of 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 particle, 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 marine environment, the improved Lagrange equation can be established. Indicates that microplastic particles location, For microplastic particles in time In the specific embodiment of the present invention, the initial position of the microplastic particles is 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 velocity of all microplastic particles is set to zero. The position and velocity of the microplastic particles within a specific time are calculated according to the improved Lagrange equation. In this step, the 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 deterministic fluid dynamics and random diffusion processes. The present invention introduces the Monte Carlo method to simulate the random diffusion path of microplastic particles and the random disturbance of microplastic particles in the marine 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 flow rate. In this step, the drift velocity and diffusion coefficient must be calculated. 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 is 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 adjusted dynamically 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 rate of microplastics;

[0142] Based on the density, shape and size of microplastics, the sedimentation rate of microplastics is calculated using the Stokes sedimentation formula, 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 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 microplastic motion differential equation is converted into a 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 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, the simulation area size is 100 meters (x direction), 100 meters (y direction) and 50 meters (z direction), and 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), and the state of the particles is updated at each step according to the current velocity and force. During the simulation process, the simulation parameters are monitored and adjusted in real time to reflect the 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 iteration process, the microplastic particles are monitored in real time to ensure that each microplastic particle meets the termination conditions of the simulation, such as the microplastic particles move out of the simulation area or reach the predetermined simulation time. After the simulation, 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 motion 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 the 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 the comparison and analysis of the data, identify the model parameters that may need to be adjusted, such as the sedimentation rate and diffusion coefficient of microplastics. And by adjusting the time step, spatial discretization method and boundary condition settings, improve the responsiveness and prediction accuracy of the model. According to the deviation between the actual data and the simulated data, if the deviation is large, the model structure should be optimized as necessary, such as improving the kinetic equation or introducing new variables to more accurately simulate the environmental response of microplastics. After the optimization is completed, implement new verification operations to ensure that all modifications can effectively improve the overall performance and reliability of the model.

[0153] S12: Output 3D views 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, this implementation method visualizes the migration path and sedimentation position of microplastic particles in three dimensions at each time step, and generates a variety of visualization reports such as microplastic distribution map, migration path map and sedimentation velocity map to help analyze and understand the migration law 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 multiple time points to help researchers and policymakers better understand the behavior and impact of microplastics in the marine environment.

[0155] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A MP migration path and sedimentation velocity monitoring system 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 offshore sampling equipment is used to collect microplastic samples and record the initial position 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; 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; 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 MP migration path and sedimentation velocity monitoring system based on numerical modeling according to claim 1, characterized in that: The sensor modules include but are not limited to a current meter, a thermometer, a salinity meter and a wave sensor.

3. The MP migration path and sedimentation velocity monitoring method based on numerical modeling is applied to the MP migration path and sedimentation velocity monitoring system based on numerical modeling according to any one of claims 1 to 2, 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 based on the offshore sampling equipment, and record the initial position 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: preprocessing the collected data of the sensor module and the offshore sampling equipment based on the preprocessing module; Step 4: Construct 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 of microplastics; Step 5: Based on the discretization solution module, the motion equation of the microplastic particles is discretized and solved in combination with the finite difference method, the speed 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, the simulated data after discretization solution and iterative calculation are analyzed to check the consistency between the simulated data and the real data, the numerical model module is adjusted according to the analysis results, and a new round of verification is performed on the adjusted numerical model; Step 7: Use the visualization module to display the distribution and migration path of verified microplastics in real time.

4. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 3, characterized in that: In step 3, the preprocessing of the collected data of the sensor module and the offshore sampling equipment 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.

5. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 3, 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 environment 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 T and salinity S; Step 4.2: Use the regional ocean model in combination with the three-dimensional non-hydrostatic equilibrium equation to accurately describe the initialized ocean dynamics process, and provide a comprehensive dynamic description of the behavior of microplastics in the marine environment by integrating and analyzing the environmental variables that affect the suspension, sedimentation and diffusion of microplastics. The ocean dynamics process includes the speed, direction and intensity of the water flow, and the environmental variables that affect the suspension, sedimentation and diffusion of microplastics include but are not limited to temperature, salinity, water depth and seabed topography. Step 4.3: 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; Step 4.4: Calculate the sedimentation velocity of microplastics using the Stokes sedimentation formula based on the characteristics of microplastics, and adjust the calculation of the Stokes formula based on the analysis of the water flow resistance and turbulence encountered by microplastics; the characteristics of microplastics include but are not limited to the density, shape and size of microplastics; Stokes formula is: In formula (1), v is the sedimentation velocity of microplastic particles, in m / s, r is the radius of microplastic particles, in m, and ρ p is the density of microplastic particles, in kg / m 3 , ρ f is the density of seawater, in kg / m 3 , g is the acceleration due to gravity, unit is m / s 2 , usually 9.81m / s 2 , η is the dynamic viscosity of seawater, in Pa·s.

6. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 5, characterized in that: The steps in step 4.3 to establish the motion equation of microplastic particles and simulate the random movement and diffusion process of microplastics in the marine environment include: Step 4.3.1: Set is the position of the microplastic particle at time t, is the speed of the microplastic particles at time t. According to the total force on the particles, an improved Lagrange equation is constructed to calculate the position and speed of the microplastic particles at any time. The total force on the microplastic particles includes dynamic force, gravity, buoyancy, and viscous resistance. Step 4.3.2: The initial velocity of all microplastic particles is set to 0, the position and velocity of the microplastic particles within a specific time are calculated according to the improved equation, the microplastic particles are set as passively tracked particles, and the motion equation of the microplastic particles is obtained; Step 4.3.3: Introduce the random diffusion model and use the Monte Carlo method to generate the random diffusion path of microplastic particles, and simulate the random movement of microplastic particles in the marine environment based on random sampling; Step 4.3.4: Based on the motion equation of the microplastic particles, the position and time of the microplastic particles and the influence of the local temperature and salinity on the seawater density in the pre-processed collected data are obtained to calculate the drift speed of the microplastic particles Step 4.3.5: Calculate the diffusion coefficient D and convert the drift velocity of the plastic particle into 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: In formula (2), p is the fluid pressure, μ is the viscosity coefficient of the fluid, is the Laplace operator; gravity The calculation formula is: In formula (3), m is the mass of the particle, is the acceleration due to gravity; buoyancy The calculation formula is: In formula (4), ρ is the fluid density, V is the volume of the fluid displaced by the particles, is the upward unit vector; Viscous resistance The calculation formula is: In formula (5), C d is the drag coefficient, A is the particle cross-sectional area, is the particle velocity, is the velocity vector; The equation of motion for microplastic particles is: The calculation formula of the diffusion coefficient D is: D=D0e -αT (7); In formula (7), D0 is the baseline diffusion coefficient, α is an adjustment parameter, and T 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: In formula (8), is the position of the microplastic particles at time t, is 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 adjusted dynamically 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.

7. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 3, characterized in that: The steps of discretizing and solving the motion equations of microplastic particles in step 5 by combining 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.

8. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 3, 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 setting.

9. The method for monitoring MP migration path and sedimentation velocity based on numerical modeling according to claim 3, characterized in that: The steps of using the visualization module in step 7 to display the distribution and migration path of verified microplastics in real time include: Step 7.1: Use the three-dimensional 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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