A method, equipment, medium, and product for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system.

Abstract

This application discloses a method, device, medium, and product for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system. It relates to the fields of agricultural environmental pollution assessment, crop pollutant migration simulation, and ecological risk assessment. The method constructs a multi-compartment structure including an environmental compartment and a plant compartment, addressing the lack of coupled modeling of environmental compartments and plant multi-organ compartments. It also characterizes the absorption, migration, distribution, and loss processes of microplastics among various plant organs. Based on the ordinary differential equations governing the change of the total mass of microplastics in the plant compartment over time and the dynamic equations governing the change of the total mass of microplastics in the environmental compartment over time, a multi-compartment coupled dynamic model is established. Based on external exposure inputs and internal exposure responses, the initial parameters in the model are inverted and calibrated using measured data from various organs at different growth stages of the plant, improving the accuracy of the model's output microplastic concentration.

Description

Technical Field

[0001] This application relates to the fields of agricultural environmental pollution assessment, crop pollutant migration simulation and ecological risk assessment, and in particular to a method, equipment, medium and product for predicting the concentration of microplastics migrating and accumulating in plants in farmland systems. Background Technology

[0002] Mulching technology is widely used in dryland and high-efficiency agriculture, serving functions such as moisture retention, temperature increase, weed control, and crop yield promotion. However, with the continuous use, aging, breakage, and accumulation of residual film, microplastic pollution in farmland soil is gradually worsening. Due to the long-term retention of microplastics in the soil, they enter the near-surface air under the influence of tillage disturbance, wind erosion and resuspension, and atmospheric input. Microplastics then enter crops through processes such as root absorption, transpiration, air-plant interface exchange, and particle deposition.

[0003] The roots, stems, leaves, and fruits of plants exhibit significant differences in structural characteristics, interfacial area, transpiration intensity, and migration capacity. This leads to a marked organ-specific migration and accumulation of microplastics in different organs. Current research on microplastics in farmland mainly focuses on the detection and analysis of microplastic occurrence characteristics in a single environmental medium, without simultaneously considering the coupling relationship between the environment and multiple plant organs. It also lacks a unified description of the process of external exposure and internal accumulation. Alternatively, it focuses on monitoring the pollution level of microplastics in a single plant organ, making it difficult to simultaneously characterize processes such as root absorption, transpiration-driven migration, atmospheric absorption, and organ loss. Furthermore, it lacks measured data from different plant organs at different growth stages to calibrate model parameters, resulting in an inability to accurately obtain the concentration of microplastics in plants. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium, and product for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system, in order to solve the problem of difficulty in accurately obtaining the concentration of microplastics in a single environmental medium or a specific organ of a plant.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system, comprising: Based on the absorption, migration, distribution, and loss pathways of microplastics in the farmland system, and in conjunction with plant organs, a multi-compartment structure for the migration and accumulation of microplastics in farmland is constructed; the multi-compartment structure includes an environmental compartment and a plant compartment. Based on the principle of mass conservation, a set of ordinary differential equations for the change of the total mass of microplastics in the plant compartment with time and a dynamic equation for the change of the total mass of microplastics in the environmental compartment with time are established. Based on the aforementioned set of ordinary differential equations and the aforementioned environmental compartment dynamic equations, a multi-compartment coupled dynamic model is established. The total mass of microplastics in the plant compartment and the environment compartment is converted into microplastic concentration, and the microplastic concentration in the environment compartment is used as the external exposure input, while the microplastic concentration in the plant compartment is used as the internal exposure response. Based on the external exposure input and the internal exposure response, the initial parameters in the multi-compartment coupled dynamics model are inverted and calibrated using measured data of various organs at different growth stages of the plant. Based on the calibrated multi-compartment coupled dynamics model, the cumulative microplastic concentrations in the environment and various organs of crops under different mulching years and different growth stages were obtained.

[0006] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for predicting the concentration of microplastics migrating and accumulating in plants in an agricultural system as described above.

[0007] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting the concentration of microplastics migrating and accumulating in plants in an agricultural system as described above.

[0008] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for predicting the concentration of microplastics migrating and accumulating in plants in an agricultural system as described above.

[0009] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system. By constructing a multi-compartment structure including environmental compartments and plant compartments, it solves the problem of lacking coupled modeling of environmental compartments and plant multi-organ compartments. The absorption, migration, distribution, and loss processes of microplastics among various organs are characterized by absorption pathways, migration pathways, distribution pathways, and loss pathways between organs in the plant compartments. Based on the set of ordinary differential equations for the change of the total mass of microplastics in the plant compartments over time and the dynamic equations of the environmental compartments for the change of the total mass of microplastics in the environmental compartments over time, a multi-compartment coupled dynamic model is established. Based on external exposure inputs and internal exposure responses, the initial parameters in the model are inverted and calibrated using measured data of various organs at different growth stages of the plant, thereby improving the accuracy of the model's output microplastic concentration. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of a multi-compartment structure of a farmland plant-environment system provided in an embodiment of this application; Figure 2 A schematic flowchart illustrating the kinetics of microplastic migration and accumulation in plants within a farmland system, provided as an embodiment of this application; Figure 3 A schematic diagram of the internal structure of the computer device provided in this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The existing technology has at least the following shortcomings: (1) It lacks a unified modeling framework that couples environmental compartments with plant multi-organ compartments; (2) It is difficult to simultaneously characterize multiple exposure pathways such as root absorption, transpiration-driven migration, atmospheric absorption, particle deposition and organ loss; (3) It is difficult to combine measured data of crop organs at different growth stages to systematically invert and calibrate model parameters; (4) It lacks a systematic evaluation of model prediction uncertainty and key control parameters, which affects the interpretability and generalizability of the model.

[0014] This application establishes a multi-compartment dynamic model for microplastic migration and accumulation in a plant-environment coupled system in farmland by constructing a multi-compartment structure consisting of soil compartments, air compartments, and compartments in wheat roots, stems, leaves, and grains. The model uses the concentration of microplastics in soil and air as external exposure inputs and the concentration of microplastics in different organs of wheat at different growth stages as internal exposure responses, thus uniformly describing the external exposure of microplastics and the internal accumulation process of multiple wheat organs. At the same time, it considers key pathways such as root absorption, transpiration-driven migration, atmospheric absorption, particle deposition, organ redistribution, and loss. The model describes the migration, distribution, and loss of microplastics between the environmental medium and plant organs through a set of ordinary differential equations, providing a more comprehensive expression of the mechanism.

[0015] Furthermore, through dynamic simulation in an environmental chamber, parameter inversion and calibration are performed using measured data from various organs at different growth stages of wheat to improve the model's prediction accuracy and achieve quantitative prediction of the migration, distribution, and accumulation processes of microplastics in the soil-air-wheat system. Through uncertainty propagation analysis and sensitivity analysis, key migration pathways and key control parameters are identified, enabling a systematic assessment of the model's applicability and predictive capabilities. This provides technical support for microplastic pollution assessment in farmland, crop safety evaluation, and agricultural pollution risk prevention and control. It overcomes the shortcomings of existing technologies, has strong scalability, and can be extended to other crop systems and particulate pollutant migration simulation scenarios.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] like Figure 1 As shown in the embodiment of this application, a method for predicting the concentration of microplastic migration and accumulation in a farmland system includes the following steps.

[0018] Step 1: Based on the microplastic absorption, migration, distribution, and loss pathways in the farmland system, and in conjunction with crop organs, construct a multi-compartment structure for microplastic migration and accumulation in the farmland; the multi-compartment structure includes environmental compartments and plant compartments.

[0019] Step 2: Based on the principle of mass conservation, establish a set of ordinary differential equations for the change of the total mass of microplastics in the environmental compartment with time, as well as the dynamic equations for the change of the total mass of microplastics in the plant compartment with time.

[0020] Step 3: Based on the set of ordinary differential equations and the dynamic equations of the environmental compartment, establish a multi-compartment coupled dynamic model.

[0021] Step 4: Convert the total mass of microplastics in the plant compartment and the environment compartment into microplastic concentration, and use the microplastic concentration in the environment compartment as the external exposure input and the microplastic concentration in the plant compartment as the internal exposure response.

[0022] Step 5: Based on the external exposure input and the internal exposure response, the initial parameters in the multi-compartment coupled dynamics model are inverted and calibrated using measured data of various organs at different growth stages of the crop.

[0023] Step 6: Based on the calibrated multi-compartment coupled dynamics model, obtain the cumulative microplastic concentration in the environment and various organs of the crop under different mulching years and different growth stages.

[0024] In an exemplary embodiment, the environmental compartment includes a soil compartment and an air compartment, wherein the soil compartment is used to characterize the microplastic storage and exposure level in the rhizosphere environment; the air compartment is used to characterize the microplastic exposure level in the air medium of the plant growth environment; the plant compartment includes a root compartment, a stem compartment, a leaf compartment, and a grain compartment; the root compartment, stem compartment, leaf compartment, and grain compartment are used to characterize the absorption pathway, migration pathway, distribution pathway, and loss pathway of microplastics in different organs of the crop, respectively.

[0025] As one feasible implementation, the absorption pathway includes: a root absorption pathway from the soil compartment to the root compartment, and an atmospheric absorption pathway from the air compartment to the stem compartment, leaf compartment, and grain compartment; the migration pathway includes: transpiration-driven transport pathways from the root compartment to the stem compartment and from the stem compartment to the leaf compartment and grain compartment; the distribution pathway includes: redistribution pathways between the compartments within the plant compartment; and the loss pathway includes: backdiffusion, growth dilution, degradation, or removal loss pathways between the compartments within the plant compartment.

[0026] In an exemplary embodiment, step 2 specifically includes: based on the principle of mass conservation, establishing a set of ordinary differential equations for the change of the total mass of microplastics in the plant compartment over time.

[0027] The dynamic equation for the soil compartment is as follows.

[0028] .

[0029] in, This indicates the total mass (mg) of microplastics in the soil compartment. This indicates the input flux generated by the coating residue (mg·d). -1 ), This represents the input flux generated by agricultural activities (mg·d). -1 ), This represents the flux (mg·d) that migrates or leaches into deeper soil layers. -1 ), Resuspension loss flux (mg·d) -1 ), This represents the soil apparent loss coefficient.

[0030] The dynamic equation for the air compartment is as follows.

[0031] .

[0032] in, This indicates the total mass (mg) of microplastics in the air compartment. This represents the input flux from soil resuspension (mg·d). -1 ), Background atmospheric input flux (mg·d) -1 ), Atmospheric diffusion loss flux (mg·d) -1 ), This represents the flux lost through sedimentation into the plant or soil (mg·d). -1 ).

[0033] The set of ordinary differential equations comprehensively considers processes such as residual film breakage input, soil accumulation, tillage disturbance, wind erosion resuspension, atmospheric deposition, irrigation or rainfall leaching, and environmental cleanup. It is used to characterize the input, output, migration, accumulation, and loss processes of microplastics in different organs of plant compartments. The Runge-Kutta method, variable step size ordinary differential equation solvers, or other numerical integration methods for ordinary differential equations are employed.

[0034] Based on the principle of mass conservation, a dynamic equation for the environmental compartment is established to reflect the change of the total mass of microplastics in the compartment over time.

[0035] The dynamic equation of the root compartment is as follows.

[0036] .

[0037] Where dmR / dt describes the mass balance of diffusion in and out at the root. This indicates the mass (mg) of microplastics in the root compartment. Represents the root surface area (m²) 2 ), Represents root interface permeability (m·d) -1 ), The water-soil distribution coefficient (kg·L) -1 ), The root-water partition coefficient (L·kg) -1 ), Indicates soil microplastic concentration (mg·kg) -1 ), Indicates the concentration of microplastics in the root (mg·kg) -1 ).

[0038] The kinetic equation for the stem septum is as follows.

[0039] .

[0040] in, This indicates the mass (mg) of microplastics in the stem compartment. This represents the transpiration rate from the root septum to the stem septum (L·d). -1 ), Represents the stem surface area (m²) 2 ), The stem-to-interface permeability (m·d)-1 ), The stem-water partition coefficient (L·kg) -1 ), The gas-water distribution coefficient (L·L) -1 ), Indicates the concentration of microplastics in the air (mg / m³) 3 ), This indicates the proportion of particulate microplastics in the air (dimensionless). The settling velocity of particulate microplastics (m·d) -1 ), The first-order degradation rate constant (d) in the stem compartment represents the rate constant of degradation in the stem compartment. -1 ).

[0041] The dynamic equation of the blade compartment is as follows.

[0042] .

[0043] in, This indicates the mass (mg) of microplastics in the leaf compartment. This indicates the transport flow rate from the stem septum to the leaf septum (L·d). -1 ), Represents leaf surface area (m²) 2 ), Permeability at the leaf interface (m·d) -1 ), The leaf-water distribution coefficient (L·kg) -1 ), The first-order degradation rate constant (d) in the leaf compartment represents the rate constant of degradation. -1 ), This indicates the concentration of microplastics in the air (mg·m³). -3 ).

[0044] The kinetic equation for the grain compartment is as follows.

[0045] .

[0046] in, This indicates the mass (mg) of microplastics in the grain compartment. This indicates the flow rate (L·d) from the stem compartment to the grain compartment. -1 ), Represents the surface area of ​​the grain (m²) 2 ), Indicates the interfacial permeability of grains (m·d) -1 ), The grain-water partition coefficient (L·kg) -1 ), The first-order degradation rate constant (d) in the grain compartment represents the rate constant of degradation in the grain compartment. -1 ).

[0047] In one exemplary embodiment, step 4 specifically includes the following steps.

[0048] Step 401: Measure the total mass of microplastics in the soil compartment. Soil microplastic concentration can be calculated by converting soil mass, soil volume, or soil bulk density parameters. .

[0049] Step 402: Measure the total mass of microplastics in the air compartment. Airborne microplastic concentration can be calculated by converting air volume or near-surface mixing layer parameters. .

[0050] Step 403: The soil microplastic concentration and airborne microplastic concentration As an external exposure input concentration.

[0051] Step 404: Convert the total mass of microplastics in the root compartment, stem compartment, leaf compartment, and grain compartment into the microplastic concentration in each compartment; use the microplastic concentration in each compartment of the plant as the internal exposure response concentration.

[0052] In one exemplary embodiment, step 5 specifically includes the following steps.

[0053] Step 501: Set the initial parameters of the multi-compartment coupled kinetic model based on the microplastic particle size, morphology, polymer type, aging degree, crop growth stage and environmental conditions.

[0054] Step 502: Input the external exposure concentration for different years of mulching, and the microplastic concentration in various organs of the crop at different growth stages.

[0055] Step 503: By calculating the error between the measured concentration of microplastics in each organ at different growth stages of the crop and the microplastic concentration predicted by the multi-compartment coupled kinetic model, the initial parameters are inverted and calibrated to determine the calibrated multi-compartment coupled kinetic model.

[0056] As an feasible approach, the initial parameters include: transpiration rate, organ surface area, organ interface permeability, water-soil partition coefficient, air-water partition coefficient, organ-water partition coefficient, proportion of particulate microplastics in the air, settling velocity of particulate microplastics, and primary degradation rate constant of microplastics in the organ.

[0057] As a feasible approach, initial parameters can be calibrated using methods such as least squares, maximum likelihood estimation, Bayesian inversion, Markov chain Monte Carlo, particle swarm optimization, or genetic algorithms.

[0058] In practical applications, uncertainty analysis is performed on the model output to obtain the confidence range of the model prediction results (such as microplastic concentration in plant organs); and sensitivity analysis is performed to obtain the quantitative ranking of the influence of the initial parameters after inversion on the output results, so as to evaluate the ability of the multi-compartment coupled kinetic model to predict the cumulative concentration of microplastics in various organs of wheat, effectively improve the model's prediction ability, and accurately identify the key control parameters and exposure paths that affect the prediction results of microplastic concentrations in soil, air and various organs of wheat.

[0059] The model outputs include the concentrations of microplastics in soil and air under different mulching years, as well as the concentrations of microplastics in wheat roots, stems, leaves, and grains at different growth stages. Uncertainty analysis employs Monte Carlo sampling or Latin hypercube sampling to perform uncertainty propagation analysis on the model outputs and obtain prediction intervals. Sensitivity analysis uses Sobol global sensitivity analysis, Morris screening, or local sensitivity analysis. Key control parameters include microplastic input flux, soil resuspension coefficient, transpiration flow, interorgan transport flow, organ surface area, interfacial permeability, and partition coefficient. Microplastics are classified and modeled according to at least one of particle size, morphology, polymer type, aging degree, or particulate and non-particulate states, and corresponding migration, partition, and loss parameters are set for different categories of microplastics.

[0060] Compared with the prior art, the present invention has at least the following beneficial effects.

[0061] A plant-soil-air coupled multi-compartment dynamic model for farmland was constructed to uniformly describe the external exposure and internal accumulation processes. It simultaneously considers key pathways such as root absorption, transpiration-driven migration, atmospheric absorption, particulate deposition, and organ loss, resulting in a more comprehensive mechanistic expression. It can utilize monitoring data from various organs at different wheat growth stages to perform parameter inversion, improving model prediction accuracy. Through uncertainty analysis and sensitivity analysis, key parameters and key exposure pathways are identified, providing a basis for farmland management and risk control. It has strong scalability and can be extended to other plant systems and particulate pollutant migration simulation scenarios.

[0062] This application also provides an application scenario that utilizes the aforementioned method for predicting the concentration of microplastic migration and accumulation in a farmland system. Specifically, this embodiment provides a system applied to the coupling of wheat crops and the environment.

[0063] 1. Experimental system construction.

[0064] A soil-air-wheat coupled simulation system was constructed in the laboratory. A transparent plexiglass simulation box was used as the experimental setup, measuring 60 cm in length, 40 cm in width, and 80 cm in height. The box was filled with 25.0 kg of air-dried topsoil from farmland, with a soil layer thickness of 20 cm. Above the soil was an air chamber with an effective volume of 0.144 m³. 3 The simulation chamber is equipped with an air inlet and an air outlet at the top. The air inlet is fitted with a 0.22 μm filter membrane to reduce the entry of exogenous particles; the air outlet is connected to an air sampling filter membrane for collecting suspended microplastic particles in the air compartment. The tested microplastics are polyethylene microplastics in the form of thin film fragments with a particle size of 5 μm. Two treatments were set up with initial soil microplastic concentrations of 0 and 100 mg / kg, with four replicates for each treatment. The amount of microplastic added to the soil in each chamber was: 100 mg / kg × 25.0 kg = 2500 mg.

[0065] Wheat was used as the test crop, with 20 seeds sown per box. After emergence, the seedlings were thinned to 12 plants per box, and the cultivation period was 100 days. During the cultivation period, the daytime temperature was 25±2 ℃, the nighttime temperature was 18±2 ℃, the relative humidity was 60±5%, and the soil moisture content was controlled between 18.5% and 21.5%. A miniature fan was used to disturb the soil surface for 10 minutes daily at a speed of 1.2 m / s to simulate the soil particle resuspension process.

[0066] 2. Sample collection and testing.

[0067] Using the day of sowing as day 0, soil, air, and wheat root, stem, leaf, and grain samples were collected at 20, 40, 60, 80, and 100 days. Microplastics were extracted from soil samples using density flotation; plant samples were digested with 30% hydrogen peroxide followed by density separation; and air samples were collected through a filter membrane. Extracted microplastics were identified using fluorescence microscopy, and polymer types were verified using Fourier transform infrared spectroscopy. The results were converted into soil microplastic concentrations, air microplastic concentrations, and microplastic concentrations in wheat roots, stems, leaves, and grains, respectively.

[0068] 3. Compartment division and migration cumulative path.

[0069] Wheat was selected as the research subject, and polyethylene microplastics were used as the test microplastics. The system was divided into soil compartments, air compartments, root compartments, stem compartments, leaf compartments, and grain compartments. Figure 2 ).like Figure 2As shown, soil compartments characterize microplastic exposure levels in the rhizosphere soil, air compartments characterize microplastic exposure levels in the air medium of the wheat growing environment, and root, stem, leaf, and grain compartments characterize the microplastic accumulation status in different wheat organs, respectively. The migration and accumulation pathways of microplastics in different compartments include: microplastics from soil compartments are absorbed into the wheat root compartments via root uptake, migrate to the stem and grain compartments via transpiration, and from air compartments are absorbed into the wheat leaf compartments via leaf uptake, then migrate to the wheat stem and grain compartments. Furthermore, microplastics from soil compartments enter the air compartments via suspension, and microplastics from air compartments enter the soil compartments via sedimentation.

[0070] 4. Initial condition settings.

[0071] Using the sowing period as the initial time, that is: .

[0072] The initial concentration of microplastics in the soil was set as follows: .

[0073] If the soil mass is 25.0 kg, then the initial total mass of microplastics in the soil compartment is .

[0074] .

[0075] The initial microplastic mass of the air compartment and the wheat root, stem, leaf and grain compartments was set to 0.

[0076] .

[0077] in, , , , , and These represent the mass of microplastics in soil, air, root compartments, stem compartments, leaf compartments, and grain compartments, respectively.

[0078] 5. Data collection.

[0079] Wheat root, stem, leaf, and grain samples were collected at 0, 20, 40, 60, 80, and 100 days, and the microplastic concentration in each organ was measured. The measured data are shown in Table 1.

[0080] Table 1. Measured concentrations of microplastics in various wheat organs at different time points.

[0081] The above data serves as the target response value for model parameter inversion and calibration.

[0082] 6. Multi-compartment dynamic equations.

[0083] Based on the principle of mass conservation, a set of ordinary differential equations was established to describe the changes in the mass of microplastics in soil, air, root compartments, stem compartments, leaf compartments, and grain compartments.

[0084] 7. Parameter inversion calibration.

[0085] In this embodiment, the transpiration flow rate, organ interface permeability, organ-water partition coefficient, particle settling velocity, and first-order loss or dilution coefficient in the model are all parameters to be identified and are not given in advance.

[0086] Let the first The sampling time of the first sampling moment The measured concentration in each organ was: The model simulates a concentration of ,in Given the set of parameters to be identified, a target function is constructed.

[0087] .

[0088] To avoid the high concentration data at the roots contributing too much to the objective function, a normalized objective function can also be used.

[0089] .

[0090] in, To prevent small constants with denominators of zero, we can take: .

[0091] The set of parameters to be identified can be represented as follows.

[0092] .

[0093] Using one or more of the following methods—least squares, particle swarm optimization, genetic algorithm, Bayesian inversion algorithm, or Markov chain Monte Carlo method—to process the parameter set... To optimize the objective function Minimum.

[0094] 8. Apparent parameter inversion.

[0095] When only root, stem, leaf and grain concentration data are available, but synchronous soil concentration, air concentration, organ biomass and organ surface area data are lacking, concentration-type apparent parameters can be obtained first through inversion.

[0096] The apparent dynamic equation is as follows.

[0097] .

[0098] .

[0099] .

[0100] .

[0101] in, This represents the apparent input rate from soil to roots; , and These represent the apparent transport coefficients from root to stem, stem to leaf, and stem to grain, respectively. , , and These represent the apparent loss or dilution coefficients in roots, stems, leaves, and grains, respectively.

[0102] The apparent parameters obtained by inversion from the measured data in Table 1 are shown in Table 2.

[0103] Table 2 Apparent dynamic parameters obtained from parameter inversion

[0104] The parameters mentioned above are calibration parameters obtained by inversion from measured concentration data. Once soil concentration, air concentration, organ biomass, and organ surface area data are obtained, they can be further converted or calibrated into physical parameters such as interfacial permeability, transpiration transport flow, and particle settling velocity.

[0105] 9. Model Results and Validation.

[0106] The results of simulating the concentration of microplastics in various organs of wheat within 0–100 days using the inverted parameters are shown in Table 3.

[0107] Table 3 Simulation results of the model after parameter inversion

[0108] The simulation results are basically consistent with the measured data, indicating that the model after parameter inversion can characterize the dynamic accumulation process of microplastics in various organs of wheat.

[0109] At 100 days, the proportion of epigenetic metastasis between organs is given by the following formula.

[0110] .

[0111] .

[0112] .

[0113] The results showed that the wheat root was the main accumulation site of microplastics, and the migration of microplastics from the root to the stem, leaves and grains was significantly restricted. The concentration of microplastics in the grains was significantly lower than that in the roots.

[0114] 10. Model Evaluation and Output.

[0115] Using the coefficient of determination Root mean square error and mean absolute error The model fit was evaluated. The calculated parameters show that the model after parameter inversion meets the indicators listed in Table 4.

[0116] Table 4 shows the indices satisfied by the model after parameter inversion.

[0117] This embodiment demonstrates that the present invention can utilize measured data of microplastics in various organs at different growth stages of wheat to invert and calibrate the parameters of a multi-compartment kinetic model, and achieve quantitative simulation of the migration, distribution, and accumulation processes of microplastics in the soil-air-wheat system.

[0118] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database is used for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it is used to predict the concentration of microplastics migrating and accumulating in plants within an agricultural system.

[0119] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0120] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0124] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0125] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0126] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the concentration of microplastics in the plant body in the migration and accumulation of microplastics in an agricultural field system, characterized by, include: Based on the absorption, migration, distribution, and loss pathways of microplastics in the farmland system, and in conjunction with plant organs, a multi-compartment structure for the migration and accumulation of microplastics in farmland is constructed; the multi-compartment structure includes an environmental compartment and a plant compartment. Based on the principle of mass conservation, a set of ordinary differential equations for the change of the total mass of microplastics in the plant compartment with time and a dynamic equation for the change of the total mass of microplastics in the environmental compartment with time are established. Based on the aforementioned set of ordinary differential equations and the aforementioned environmental compartment dynamic equations, a multi-compartment coupled dynamic model is established. The total mass of microplastics in the plant compartment and the environment compartment is converted into microplastic concentration, and the microplastic concentration in the environment compartment is used as the external exposure input, while the microplastic concentration in the plant compartment is used as the internal exposure response. Based on the external exposure input and the internal exposure response, the initial parameters in the multi-compartment coupled dynamics model are inverted and calibrated using measured data of various organs at different growth stages of the plant. Based on the calibrated multi-compartment coupled dynamics model, the cumulative microplastic concentrations in the environment and various organs of crops under different mulching years and different growth stages were obtained.

2. The method according to claim 1, wherein The environmental compartments include a soil compartment and an air compartment. The soil compartment is used to characterize the microplastic storage and exposure levels in the rhizosphere environment. The air compartment is used to characterize the microplastic exposure levels in the air medium of the plant growth environment. The plant compartments include root compartments, stem compartments, leaf compartments, and grain compartments; the root compartments, stem compartments, leaf compartments, and grain compartments are used to characterize the absorption pathways, migration pathways, distribution pathways, and loss pathways of microplastics in different organs of crops, respectively.

3. The method for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system according to claim 2, characterized in that, The absorption pathways include: root absorption pathways from soil compartments to root compartments, and atmospheric absorption pathways from air compartments to stem compartments, leaf compartments, and grain compartments. The migration pathways include: transpiration-driven transport pathways from root compartments to stem compartments and from stem compartments to leaf compartments and grain compartments; The allocation path includes: the redistribution path between the compartments within the plant compartment; The loss pathways include: back-diffusion, growth dilution, degradation, or removal between the compartments within the plant compartment.

4. The method for predicting the concentration of microplastic migration and accumulation in farmland systems according to claim 1, characterized in that, The total mass of microplastics in the plant compartment and the environmental compartment is converted into microplastic concentration. The microplastic concentration in the environmental compartment is used as the external exposure input, and the microplastic concentration in the plant compartment is used as the internal exposure response. Specifically, this includes: The total mass of microplastics in the soil compartments is converted into soil microplastic concentration using soil mass, soil volume, or soil bulk density parameters. The total mass of microplastics in the air compartment is converted into air microplastic concentration using air volume or near-surface mixing layer parameters. The soil microplastic concentration and air microplastic concentration were used as external exposure input concentrations; The total mass of microplastics in the root compartment, stem compartment, leaf compartment, and grain compartment is converted into the microplastic concentration in each compartment. The microplastic concentration in each compartment of the plant compartment is used as the internal exposure response concentration.

5. The method for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system according to claim 4, characterized in that, The process of inverting and calibrating the initial parameters in the multi-compartment coupled kinetic model based on the external exposure input and the internal exposure response, using measured data from various organs at different growth stages of the plant, specifically includes: The initial parameters of the multi-compartment coupled kinetic model are set according to the microplastic particle size, morphology, polymer type, aging degree, crop growth stage and environmental conditions. Input the external exposure concentration under different mulching years, and the microplastic concentration in various organs of plants at different growth stages; By calculating the error between the measured concentration of microplastics in each organ at different growth stages of the crop and the microplastic concentration predicted by the multi-compartment coupled kinetic model, the initial parameters are inverted and calibrated to determine the calibrated multi-compartment coupled kinetic model.

6. The method for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system according to claim 5, characterized in that, The initial parameters include: transpiration rate, organ surface area, organ interface permeability, water-soil partition coefficient, air-water partition coefficient, organ-water partition coefficient, proportion of particulate microplastics in the air, settling velocity of particulate microplastics, and primary degradation rate constant of microplastics in organs.

7. The method for predicting the concentration of microplastics migrating and accumulating in plants within a farmland system according to claim 5, characterized in that, The initial parameters after inversion are calibrated using the least squares method, maximum likelihood estimation, Bayesian inversion, Markov chain Monte Carlo method, particle swarm optimization algorithm, or genetic algorithm.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a method for predicting the concentration of microplastics migrating and accumulating in plants in an agricultural system according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a method for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements a method for predicting the concentration of microplastics migrating and accumulating in plants within an agricultural system as described in any one of claims 1-7.

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