Rhizosphere microzone nutrient and ion form prediction method and system

Abstract

This invention belongs to the field of rhizosphere ion speciation analysis and prediction, and provides a method and system for predicting nutrients and ion speciation in the rhizosphere microdomain. The method includes: calculating the ion intensity of each compartment at each time point based on ion concentration data and ion charge number; determining the ion activity coefficient based on the ion intensity; and converting the ion concentration into ion activity. For the target heavy metal, based on the complexation equilibrium relationship between metal ions and inorganic ligands and the mass conservation equation, the free ion activity of the target heavy metal and the concentration of each inorganic complex at each time point in each compartment are solved using the ion concentration. Based on the free ion activity of multiple compartments, a spatial gradient distribution of the target heavy metal ion speciation is generated. Based on the inorganic complex concentration at multiple consecutive time points, a dynamic prediction of ion speciation changes over time is generated, realizing the analysis and prediction of the free ion activity of the target heavy metal and the speciation of various inorganic complexes in the rhizosphere microdomain in both spatial and temporal dimensions.

Description

Technical Field

[0001] This application belongs to the field of in-situ monitoring and quantitative prediction of ion speciation in the plant rhizosphere microenvironment, and particularly relates to a method and system for predicting nutrients and ion speciation in the rhizosphere microenvironment. Background Technology

[0002] The rhizosphere is the interface region where plant roots interact most intensely with the soil. Processes such as root exudate release, changes in rhizosphere redox conditions, microbial community remodeling, and the migration and transformation of nutrients and pollutants all occur within the rhizosphere. These processes exhibit significant spatial gradient characteristics and temporal dynamic changes; for example, Cd levels vary at different distances within the rhizosphere. 2+ Fe 2+ Mn 2+ SO4 2- The differences in plasma concentration and its form are significant, and directly affect plant absorption, pollutant fixation / activation, and element cycling processes.

[0003] Ions in the rhizosphere do not exist in a single free state, but rather coexist in multiple forms (free ions, inorganic complexes, organic complexes, adsorbed forms, etc.). The main driving force for plant uptake and soil fixation is the activity of free ions, rather than the total concentration in the solution. Therefore, monitoring only the total ion concentration in the solution is insufficient to reveal the bioavailability of ions in the rhizosphere, and there is an urgent need to establish predictive models that can quantitatively predict the concentration of various ion forms based on monitoring data.

[0004] Current rhizosphere research methods mostly employ root shaking or whole-system mixed sampling, which, while simple to operate, suffer from drawbacks such as unclear rhizosphere boundaries, severe sample mixing, significant root disturbance, and poor sampling repeatability, making it difficult to reflect the true gradient characteristics of the rhizosphere microdomain. Furthermore, existing root box devices primarily focus on stratified sampling of rhizosphere soil but lack in-situ continuous monitoring structures for soil solution ion concentrations. This results in researchers obtaining only static results at a single time point, failing to reveal the dynamic processes of ion migration and transformation. Moreover, current rhizosphere research rarely integrates nutrient and ion speciation prediction models with in-situ dynamic monitoring devices, rendering monitoring data unusable for ion speciation prediction and resulting in predictions lacking rhizosphere spatial gradient resolution. Additionally, some root box devices require disassembly or disruption of the root-soil interface during sampling, easily leading to cross-contamination between compartments and affecting data reliability. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting nutrients and ion speciation in the rhizosphere microdomain, aiming to solve the problems of inaccurate zoning, large sampling disturbances, lack of in-situ dynamic monitoring structures, and lack of model-based ion speciation prediction capabilities in existing rhizosphere research devices.

[0006] To achieve the above objectives, the present invention provides the following technical solution.

[0007] According to an embodiment of the present invention, a method for predicting nutrients and ion speciation in the rhizosphere microdomain is provided, comprising the following steps:

[0008] Data on soil solution ion concentration, environmental parameters, and soil composition were collected by a multi-compartment rhizosphere in-situ monitoring device at multiple rhizosphere distance gradient compartments and at multiple time points.

[0009] Based on ion concentration data and ion charge, the ion intensity of each compartment at each time point is calculated, the ion activity coefficient is determined according to the ion intensity, and the ion concentration is converted into ion activity.

[0010] For the target heavy metal, based on the complexation equilibrium relationship between metal ions and inorganic ligands and the mass conservation equation, the activity of free ions of the target heavy metal and the concentration of each inorganic complex at each time point in each compartment are solved using ion concentration.

[0011] Based on the free ion activity of multiple compartments, a spatial gradient distribution of the target heavy metal ion speciation is generated; and based on the inorganic complex concentration at multiple consecutive time points, a prediction of ion speciation changes over time is generated, realizing the spatiotemporal dynamic prediction of nutrients and ion speciation in the rhizosphere microdomain.

[0012] Furthermore, the step of generating the spatial gradient distribution of the target heavy metal ion speciation includes:

[0013] Calculate the activity of the target heavy metal free ions in the i-th rhizosphere gradient compartment. , represented as:

[0014]

[0015] In the formula, Represents the activity coefficient. Indicates the measured dissolved concentration;

[0016] The activity difference between adjacent compartments is calculated; this activity difference characterizes the spatial gradient variation of ion speciation in the rhizosphere microdomain. Represented as:

[0017] In the formula, This represents the real-time activity of the target heavy metal free ions in the i-th compartment; Indicates the first Real-time activity of target heavy metal free ions in each compartment.

[0018] Furthermore, the step of predicting the change in the generated ion form over time specifically includes:

[0019] Calculate the complex concentration at time t for the complex formed by the target heavy metal and the j-th inorganic ligand. and the concentration after a certain time interval. The dynamic change rate of the complex was obtained. , represented as:

[0020]

[0021] In the formula, Δt represents the time interval;

[0022] Based on the dynamic change rate, the temporal dynamic evolution trend of the complex morphology is determined.

[0023] Furthermore, the activity of target heavy metal free ions and the concentration of each inorganic complex in each compartment at each time point are determined, including:

[0024] Establish multiple sets of complexation equilibrium equations and total dissolved mass conservation equations for elements;

[0025] The activity of free ions, the concentration of each complex form, and their proportion are obtained by iterative solution.

[0026] Furthermore, it also includes:

[0027] The predicted target heavy metal free ion activity is compared with the actual measured cumulative heavy metal absorption in the plant to calculate the prediction error. When the error exceeds the preset threshold, the complexation equilibrium constant and / or ligand complexation ability characteristic parameters are automatically adjusted and iterated until the accuracy requirements are met or the maximum number of iterations is reached.

[0028] Furthermore, the environmental parameters include pH, temperature, and CO2 partial pressure;

[0029] The soil solution ion concentration data includes at least Ca. 2+ Mg 2+ K + Na + Fe 2+ Mn 2+ Cd 2+ Zn 2+ NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - And the concentration of dissolved organic carbon;

[0030] The soil composition information includes at least the Fe2O3 content, Al2O3 content, organic carbon content, and cation exchange capacity.

[0031] According to another embodiment of the present invention, a rhizosphere microdomain nutrient and ion speciation prediction system is provided, including a prediction model calculation unit, which is connected to the multi-compartment rhizosphere in-situ monitoring device for data transmission, and is used to implement the prediction method provided in the foregoing embodiment and output the spatiotemporal dynamic prediction results of rhizosphere microdomain nutrients and ion speciation.

[0032] in:

[0033] The multi-compartment rhizosphere in-situ monitoring device is used to construct multiple rhizosphere distance gradient compartments and collect environmental parameters, soil solution ion concentration data and soil composition information in each compartment.

[0034] Furthermore, the multi-zone rhizosphere in-situ monitoring device includes:

[0035] The box is divided into multiple independent soil compartments, forming a spatial gradient that includes the root growth zone, near-rhizosphere zone, far-rhizosphere zone and non-rhizosphere zone.

[0036] Nylon mesh isolation components, installed between adjacent compartments, are used to prevent plant roots from penetrating while allowing solutes and microorganisms to migrate across compartments;

[0037] Soil solution sampling kits are installed in indoor areas of each zone for in-situ sampling of soil solution.

[0038] Soil sampling kit, used for in-situ non-destructive sampling of soil in each compartment.

[0039] Furthermore, the soil solution sampling assembly includes:

[0040] Sampling interface;

[0041] Microporous sampling tubes, including Rhizon samplers, hollow fiber samplers, or microporous ceramic head samplers, are driven by external negative pressure for sampling.

[0042] Fixing components are used to fix the soil solution sampling components.

[0043] Furthermore, it also includes a microelectrode monitoring component, which includes a pH microelectrode and / or a redox potential microelectrode, for real-time dynamic monitoring of each compartment and transmitting the data to the prediction model calculation unit.

[0044] Compared with existing technologies, the beneficial effects of the rhizosphere microdomain nutrient and ion speciation prediction method and system of the present invention are:

[0045] This invention utilizes multiple independent soil compartments to construct a spatial gradient including a root growth zone, a near-rhizosphere zone, a far-rhizosphere zone, and a non-rhizosphere zone. It combines soil solution sampling components and soil sampling components in each compartment to achieve in-situ continuous sampling at multiple time points with very low root-soil interface disturbance, providing high-quality input data for prediction models.

[0046] In the prediction model, the independent ion concentration, environmental parameters, and soil composition information of each compartment are solved through a simultaneous iterative solution of ion activity correction, complexation balance calculation, and mass conservation constraints. The model also introduces a spatial gradient formula based on the activity difference between adjacent compartments and a dynamic prediction method based on the temporal change rate of complex concentration. Ultimately, the model achieves quantitative analysis of the gradient distribution pattern of target heavy metal free ion activity and various inorganic complex forms in the rhizosphere microdomain in the spatial dimension and the dynamic evolution trend in the temporal dimension, revealing the intrinsic relationship between ion form and plant bioavailability.

[0047] In summary, this invention can be applied to research fields such as the migration and solidification of rhizosphere heavy metal pollutants, nutrient ion cycling, rhizosphere redox processes, and elemental coupling cycles. Attached Figure Description

[0048] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0049] In the attached diagram:

[0050] Figure 1 This is a flowchart illustrating the implementation of a method for predicting nutrients and ion speciation in the rhizosphere microdomain according to the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of the multi-zone rhizosphere in-situ monitoring device provided by the present invention;

[0052] Figure 3 This is a schematic diagram of the structure of the soil solution sampling component provided by the present invention.

[0053] Figure 4 A schematic diagram of the structure of the soil sampling component provided by the present invention.

[0054] Figure 5 This is a verification graph showing the correlation analysis between the predicted free cadmium ion activity and the cadmium accumulation in plant roots in this invention.

[0055] Figure 6 This is a verification graph showing the correlation analysis between the predicted free cadmium ion activity and the cadmium accumulation in plant roots in this invention.

[0056] Figure 7This is a verification graph showing the correlation analysis between the predicted free cadmium ion activity and the cadmium accumulation in plant stems in this invention.

[0057] Figure 8 This is a verification graph showing the correlation analysis between the predicted free cadmium ion activity and the cadmium accumulation in plant leaves in this invention.

[0058] Figure 9 The correlation diagram between the activity of free heavy metal ions calculated by the model and the accumulation of heavy metals in various organs of the plant at days 15, 30, and 45 is used to verify the relationship.

[0059] Figure 10 This is a verification graph showing the correlation between the activity of free heavy metal ions calculated by the model and the accumulation of heavy metals in various organs of the plant at days 30 and 45.

[0060] Figure 11 The activity of free heavy metal ions in the soil of different treatment groups on days 15, 30, and 45 (-log) 10 The comparison and verification graph of the values.

[0061] The above figures include the following reference numerals:

[0062] 1. Box body; 2. Nylon mesh isolation assembly; 3. External negative pressure sampling device; 4. Drain outlet; 41. Water inlet; 5. Positioning slot; 6. Sampling pipeline; 7. Nylon mesh partition;

[0063] 8. Soil solution sampling assembly; 81. Fixing assembly; 82. Top of the box; 83. Nylon mesh; 84. Microporous sampling tube; 85. Porous filter section; 86. Sampling interface; 87. Soil compartment;

[0064] 9. Soil sampling assembly; 91. Handle; 92. Bulldozer rod; 93. Scale markings; 94. Soil sampling tube; 95. Cutting end; 96. Soil column sample; 97. Bulldozer rod head; 98. Soil to be sampled. Detailed Implementation

[0065] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0066] This invention enables simulation of rhizosphere spatial gradient zoning, in-situ dynamic monitoring of soil solution ions, and prediction of the spatiotemporal evolution of ion speciation, thereby addressing the problems of inaccurate zoning, large sampling disturbances, lack of in-situ dynamic monitoring structures, and lack of model-based ion speciation prediction capabilities in existing rhizosphere research devices.

[0067] The prediction model of this invention is constructed based on the principle of chemical-thermodynamic equilibrium of soil solution and includes four modules: an ion activity correction calculation module, a multi-metal ion complexation equilibrium calculation module, an element mass conservation constraint solution module, and a spatiotemporal dynamic prediction structured output module. Among these, the ion activity correction, complexation equilibrium calculation, and mass conservation constraints all employ standard chemical calculation theories and methods commonly used in the field, ensuring the accuracy and universality of the calculation results.

[0068] The main innovation of this invention is the structured output of spatiotemporal dynamic prediction. This invention breaks through the technical limitations of traditional single static calculation by combining traditional static equilibrium calculation with the multi-compartment gradient spatial structure of the rhizosphere and multi-temporal dynamic data.

[0069] in:

[0070] The calculation of corrected ion activity is a standard method in this field. Specifically, the driving force of ion reactions in soil solutions is usually determined by ion activity, rather than simply ion concentration. Ion activity is a dimensionless quantity, calculated by multiplying the ion concentration by the activity coefficient. The activity coefficient is related to the ionic strength of the solution, which is determined by the concentration and charge of each ion in the solution. This part belongs to the standard chemical thermodynamic calculation method known in this field.

[0071] The prediction model of this invention calculates ion intensity using measured ion concentration data from each compartment and determines the activity coefficient based on the ion intensity, thereby realizing the conversion of ion concentration to ion activity.

[0072] In this embodiment, the input parameters for the ion activity correction calculation module include: compartment k and the measured molar concentration of each ion component in the soil solution at time point t. Units: mol / L, and the charge number z of each ion. i The output parameters of the ion activity correction calculation module are the activity of the target ion at the corresponding compartment k and time point t. and activity coefficient .

[0073] The specific calculation process includes the following steps:

[0074] 1. Steps for calculating ionic strength:

[0075] Based on the concentration and charge of each ion in the solution at time point t, the ionic strength I(k,t) of the solution is calculated. Ionic strength can comprehensively reflect the strength of electrostatic interaction between ions in the solution and is the core basic parameter for solving the activity coefficient.

[0076] Its expression is:

[0077]

[0078] in, denoted as k, and t as the ionic strength (mol / L); n represents the total number of ion species involved in the ionic strength calculation. Let z be the molar concentration (mol / L) of the j-th ion at compartment k and time point t; j Let be the charge number of the j-th ion; Represents the square of the charge number, used to characterize the weight of the influence of ion charge on the electrostatic environment of the solution;

[0079] 2. Steps for solving the activity coefficient:

[0080] After obtaining the ionic strength I(k,t), the activity coefficient γi(k,t) of ion i is calculated using the Davies activity coefficient empirical equation. This equation is applicable to most rhizosphere soil solution systems in farmland and wetlands, effectively improving the repeatability and engineering application value of the overall model calculation. The expression is as follows:

[0081]

[0082]

[0083] in, It is a logarithm to the base 10; Z is the activity coefficient of ion i; i I is the charge number of ion i; I(k,t) is the ionic strength (mol / L); A is the Debye-Hückel constant, whose value is related to the dielectric constant of the solvent and temperature. For example, under the condition of 25℃ aqueous solution, A is usually taken as 0.509, which is fully suitable for the routine testing environment of soil solution.

[0084] 3. Ion activity conversion steps:

[0085] Based on the obtained activity coefficient , ion concentration Converted to ion activity This completes the standardized correction of basic parameters, providing core input for subsequent complexation equilibrium simulations;

[0086] Its expression is: In the formula, Let i be the ion activity of ion i in compartment k and at time point t. denoted as mol / L;

[0087] Furthermore, for the calculation of metal ion complexation equilibrium, Cd in the rhizosphere soil solution... 2+ Fe 2+ Mn 2+ Zn 2+ Metal ions can react with Cl - SO42- HCO3 - CO3 2- When inorganic ligands form complexes, the complexation process alters the activity of free ions, thereby affecting plant uptake and soil interface fixation / activation processes. The model establishes the complexation equilibrium between metal ions and ligands based on the law of mass action, and calculates the formation of each complex species using known equilibrium constants. The general formula for the complexation reaction between metal ions and ligands can be expressed as: In the formula, M is used to uniformly refer to various positively charged free heavy metal ions, and z is the charge number of the corresponding heavy metal ion; L is used to uniformly refer to various negatively charged inorganic ligand ions, and n is the charge number of the corresponding inorganic ligand ion; (ML) is the heavy metal inorganic complex product generated after the reversible complexation reaction of heavy metal ions and inorganic ligands, which follows the reversible reaction equilibrium law of solution as a whole.

[0088] In this embodiment of the invention, the equilibrium constant for the complexation reaction is expressed as:

[0089]

[0090] Among them, K ML Let a be the complexation equilibrium constant. M a L a ML The values ​​represent the activities of free metal ions, ligands, and complexes, respectively.

[0091] Based on this standard equilibrium relationship and combined with measured dynamic ion parameters, this invention accurately predicts the concentration and proportion distribution of various inorganic complexes under different environmental conditions.

[0092] Furthermore, for solving the mass conservation constraint, in order to ensure that the prediction results satisfy element conservation, the prediction model of this invention further introduces the mass conservation constraint, that is, the total dissolved concentration of a certain element in the solution should be equal to the sum of its free state concentration and the concentration of all complex forms.

[0093] This invention solves the free ion concentration, free ion activity, concentration of various inorganic complex forms, and proportion of various complex forms in the total dissolved state of the corresponding heavy metals at different rhizosphere compartments and time points by simultaneously solving multiple sets of complex equilibrium equations and element mass conservation equations, and adopting an iterative solution method. This achieves full-coverage quantitative analysis of multiple ion forms.

[0094] This invention takes the general conservation law of heavy metal elements as an example. The conservation relationship of total dissolved concentration is the standard mass conservation formula in this field, which is existing and well-known technology without structural modifications. The specific expression is as follows:

[0095]

[0096] in, The target was to detect the total dissolved concentration of heavy metal elements in soil solution. This represents the concentration of free ions dissolved in the heavy metal. This represents the total number of inorganic complexes that can be stably formed by this heavy metal in the rhizosphere soil solution system.

[0097] The following describes the specific implementation of the rhizosphere microdomain nutrient and ion speciation prediction method and system of the present invention.

[0098] Please refer to Figure 1 In one embodiment of the present invention, a method for predicting rhizosphere microdomain nutrients and ion speciation is provided. The prediction method of this embodiment includes the following steps:

[0099] Step S101: Acquire soil solution ion concentration data, environmental parameters, and soil composition information collected by the multi-compartment rhizosphere in-situ monitoring device at multiple rhizosphere distance gradient compartments and multiple time points;

[0100] Furthermore, the input parameters of the prediction model of this invention include environmental parameters, soil solution ion concentration data, and soil composition information; wherein:

[0101] Environmental parameters include pH, temperature, and CO2 partial pressure;

[0102] The soil solution ion concentration data includes at least Ca. 2+ Mg 2+ K + Na + Fe 2+ Mn 2+ Cd 2+ Zn 2+ NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - and the concentration of dissolved organic carbon (DOC);

[0103] The soil composition information includes at least the Fe2O3 content, Al2O3 content, organic carbon (SOC) content, and cation exchange capacity (CEC).

[0104] Also includes:

[0105] Step S102: Based on ion concentration data and ion charge number, calculate the ion intensity of each compartment at each time point, determine the ion activity coefficient based on the ion intensity, and convert the ion concentration into ion activity;

[0106] Step S103: For the target heavy metal, based on the complexation equilibrium relationship between metal ions and inorganic ligands and the mass conservation equation, use the ion concentration to solve for the free ion activity of the target heavy metal and the concentration of each inorganic complex at each time point in each compartment.

[0107] Step S104: Based on the free ion activity of multiple compartments, generate the spatial gradient distribution of the target heavy metal ion speciation; and based on the inorganic complex concentration at multiple consecutive time points, generate a prediction of the ion speciation over time, thereby realizing the spatiotemporal dynamic prediction of rhizosphere microdomain nutrients and ion speciation.

[0108] The prediction model in this embodiment performs the following calculations: Based on the measured ion concentrations of soil solutions in each zone, the ionic strength of the solution is calculated, and the corresponding ion activity coefficients are further solved; a complexation equilibrium system of metal ions and inorganic ligands is constructed according to the law of mass action, and the free ion activity and the concentrations of various complexation products are obtained by combining the mass conservation equation; the calculation results from different zones are integrated, and the distribution characteristics and variation patterns of ion speciation along the spatial gradient are analyzed and output; multiple time-series monitoring data are introduced as model input parameters to deduce and output dynamic prediction results of ion speciation evolution over time. The output results of the prediction model include the free ion activities of various heavy metals at each time point in each zone, and the heavy metals include at least Cd. 2+ Fe 2+ Mn 2+ Zn 2+ It can output the concentrations of various inorganic complexes corresponding to each heavy metal and their proportions in the total dissolved content, as well as the spatial variation law of ion speciation distribution in different distance gradient zones and the dynamic prediction results of ion speciation changes over time.

[0109] Specifically, in this embodiment, for the spatiotemporal dynamic prediction output structure, the present invention combines the chemical equilibrium calculation framework with the rhizosphere microdomain multi-compartment distance gradient structure, so that the prediction model can take the independent monitoring data of each compartment as input and synchronously output the multi-compartment ion speciation results within the same calculation framework, thereby obtaining the spatial gradient distribution of ion speciation in the rhizosphere microdomain.

[0110] Meanwhile, the prediction model of this invention supports continuous input calculation at multiple time points, enabling dynamic prediction of ion speciation over time and obtaining the spatiotemporal variation law of ion speciation.

[0111] This invention is based on the standard activity calculation principle combined with a partitioned spatial structure to construct an expression for calculating spatial distribution. The step of generating the spatial gradient distribution of the target heavy metal ion speciation includes:

[0112] Calculate the activity of the target heavy metal free ions in the i-th rhizosphere gradient compartment. , represented as:

[0113]

[0114] In the formula, Represents the activity coefficient. Indicates the measured dissolved concentration;

[0115] The activity difference between adjacent compartments is calculated to characterize the spatial gradient variation of ion speciation in the rhizosphere microdomain.

[0116] Poor activity Represented as:

[0117] In the formula, This represents the real-time activity of the target heavy metal free ions in the i-th compartment; Indicates the first Real-time activity of target heavy metal free ions in each compartment; The difference in the activity of heavy metal free ions between two adjacent gradient compartments can be used to accurately quantify the magnitude of the spatial gradient change in ion speciation from the root surface to the soil in the rhizosphere microenvironment, and intuitively reflect the spatial heterogeneity of the rhizosphere microenvironment.

[0118] In the time dimension, the prediction model of this invention relies on continuous time-series monitoring data to calculate the concentration of various heavy metal inorganic complexes at any time node, and then deduce the rate of change of ionic speciation at adjacent time nodes to achieve dynamic trend prediction.

[0119] Specifically, the step of predicting the change of ion speciation over time in this invention includes:

[0120] Calculate the complex concentration at time t for the complex formed by the target heavy metal and the j-th inorganic ligand. and the concentration after time intervals The dynamic change rate of the complex was obtained. , represented as:

[0121]

[0122] In the formula, Δt is the fixed time interval between two consecutive sets of monitoring data; The rate of change of the inorganic complex at time t;

[0123] This invention determines the time-dynamic evolution trend of complex morphology based on the dynamic change rate.

[0124] The time-series calculation formula of this invention is constructed based on the standard complexation equilibrium principle combined with time-series dynamic data. The calculation of complex concentration relies entirely on well-known equilibrium principles, expressed as:

[0125]

[0126] In the formula, This represents the real-time concentration of the inorganic complex formed by the target heavy metal and the j-th inorganic ligand at any monitoring time t. , These represent the concentrations of heavy metal free ions and matched inorganic ligand ions at time t, respectively. , These are the activity coefficients of heavy metal ions and inorganic ligand ions at time t, respectively. is the activity coefficient of the complex; m is the charge number of the metal ion; is the standard equilibrium constant corresponding to the complexation reaction, and is a fixed, well-known parameter;

[0127] Through the aforementioned time-series calculation logic, this invention can accurately determine the dynamic increase or decrease trend and the rate of evolution of ion forms, thereby achieving quantitative dynamic prediction of rhizosphere ion forms.

[0128] Furthermore, in a specific implementation of the present invention, the step of solving the target heavy metal free ion activity and the concentration of each inorganic complex at each time point in each compartment specifically includes: establishing multiple sets of complexation equilibrium equations and total dissolved mass conservation equations; and obtaining the free ion concentration, free ion activity, concentration of each complex form and its proportion through iterative solution.

[0129] The quantitative output of the prediction model of this invention covers the full-dimensional speciation parameters of various soil heavy metals under different gradient compartments in the rhizosphere and at different monitoring time points, specifically including:

[0130] Real-time activity of various target heavy metal free ions in each zone at each time point, wherein the heavy metals include at least Cd. 2+ Fe 2+ Mn 2+ Zn 2+ ;

[0131] Real-time concentrations of various inorganic complexes corresponding to various target heavy metals can be output, including the content of various inorganic complex forms formed by each heavy metal with inorganic ligands such as chloride ions, sulfate ions, bicarbonate ions, and carbonate ions.

[0132] The percentage of each heavy metal free ion form and each inorganic complex form in the total dissolved concentration of the corresponding heavy metal;

[0133] Spatial distribution differences and gradient variation patterns of various heavy metal ion speciations among different rhizosphere distance gradient compartments;

[0134] The dynamic changes and prediction results of the full ionic forms of various heavy metals over time.

[0135] Furthermore, the prediction model of this invention introduces a model verification and optimization module, which is used to complete the accuracy verification of prediction results of various heavy metal ion speciations and adaptive iterative optimization of model parameters.

[0136] Specifically, this includes: comparing the predicted target heavy metal free ion activity with the measured cumulative heavy metal absorption of the corresponding plant, and calculating the correlation or prediction error; if the error exceeds the preset threshold, automatically adjusting the complexation equilibrium constant and / or ligand complexation ability characteristic parameters, and iteratively optimizing until the accuracy requirements are met or the maximum number of iterations is reached.

[0137] In the above iterative optimization mechanism: the free ion activity results of various target heavy metals calculated by the model are matched and compared with the actual data of the corresponding heavy metal absorption accumulation in plant roots and aboveground parts obtained by actual measurement. By calculating quantitative evaluation indicators such as correlation coefficient and prediction error, the system evaluates the overall prediction accuracy and reliability of the model for the bioavailability of various heavy metals.

[0138] When the predicted heavy metal values ​​are significantly correlated with the measured cumulative amount in plants, the overall prediction results of the model are deemed to be true and reliable and consistent with the migration and absorption patterns of heavy metals in the rhizosphere.

[0139] When the error between the model prediction result and the measured independent verification data exceeds the system's preset error threshold, the model closed-loop optimization mechanism is automatically triggered. Based on the data residual back feedback mechanism, the core parameters such as the complexation equilibrium constant correction parameter and the inorganic ligand complexation ability characteristic parameter are iteratively corrected and dynamically updated to continuously reduce the prediction deviation until the overall error meets the preset accuracy threshold or reaches the system's maximum number of iterations. Finally, the model with the optimal parameters and higher accuracy for predicting the speciation of heavy metal ions and the corresponding spatiotemporal dynamic prediction results are output.

[0140] The model verification and optimization module of this invention improves the model prediction accuracy by analyzing the correlation between measured and predicted root ion absorption data.

[0141] like Figures 2 to 4 As shown, according to another embodiment of the present invention, a rhizosphere microdomain nutrient and ion speciation prediction system is provided. The prediction system includes a prediction model calculation unit, which is connected to a multi-compartment rhizosphere in-situ monitoring device for data transmission. The system is used to output the spatiotemporal dynamic prediction results of rhizosphere microdomain nutrients and ion speciation based on the prediction method provided above.

[0142] Specifically, in one implementation of the present invention, a multi-compartment rhizosphere in-situ monitoring device is used to construct multiple rhizosphere distance gradient compartments and collect environmental parameters, soil solution ion concentration data and soil composition information within each compartment.

[0143] Furthermore, the multi-compartment rhizosphere in-situ monitoring device includes a housing 1. The internal space of the housing 1 is divided by a nylon mesh isolation component 2 to form multiple independent soil compartments, creating a spatial gradient including a root growth zone, a near-rhizosphere zone, a far-rhizosphere zone, and a non-rhizosphere zone, thus forming multiple independent soil compartments. In this embodiment, the nylon mesh isolation component 2 is disposed between adjacent compartments to prevent plant roots from penetrating while allowing solutes and microorganisms to migrate across zones. Specifically, the nylon mesh isolation component 2 is a nylon mesh partition 7 with a pore size of 400 mesh. It does not isolate nutrients from root exudates but allows the migration of root exudates and microorganisms while preventing plant roots from penetrating, allowing for the non-destructive removal of the entire root system.

[0144] In a preferred embodiment, the box body 1 is a transparent acrylic box or a transparent plastic box, with dimensions of 21cm × 10cm × 10cm. Additionally, a positioning slot 5 is provided on the inner wall of the box body 1, through which the nylon mesh isolation component 2 is detachably installed and fixed. If the positioning slot 5 is located on the inner side wall surface of the box body 1, and the positioning slot 5 matches the edge dimensions of the nylon mesh isolation component 2, it is used to snap the nylon mesh isolation component 2 into the positioning slot 5, thereby achieving fixed installation of the nylon mesh isolation component 2 within the box body 1 and forming multiple independent soil compartments.

[0145] Preferably, the box body 1 is provided with a water inlet 41 and a drain outlet 4, which can be used to regulate the moisture content of each compartment.

[0146] Furthermore, the multi-zone rhizosphere in-situ monitoring device of this embodiment also includes a soil solution sampling component 8. The soil solution sampling component 8 is installed in each zone chamber and is used to sample the soil solution in situ. The soil solution data collected by sampling is directly used as the input parameters of the prediction model, realizing direct data connection between device monitoring and model prediction.

[0147] like Figure 3 As shown, the soil solution sampling assembly 8 of this embodiment includes a sampling interface 86 and a microporous sampling tube 84. The sampling interface 86 is externally connected to a vacuum interface, that is, connected to an external negative pressure sampling device 3.

[0148] The microporous sampling tube 84 can be a Rhizon sampler, a hollow fiber sampler, or a microporous ceramic head sampler, and sampling is driven by external negative pressure. Specifically, the microporous sampling tube 84 is connected to the external negative pressure sampling device 3 through a sampling pipeline 6, wherein the end of the sampling pipeline 6 is connected to the sampling interface 86.

[0149] Furthermore, the soil solution sampling assembly 8 also includes a fixing assembly 81 and a box top 82. The fixing assembly 81 is used to fix the sampling assembly; the fixing assembly 81 is set on top of the nylon mesh isolation assembly 2.

[0150] In this embodiment, the nylon mesh partition 7 is made of nylon mesh 83, and the porous filter section 85 at the bottom of the microporous sampling tube 84 extends into the interior of the soil chamber 87, that is, it is inserted into the soil.

[0151] In an optional implementation, the prediction system may further include a microelectrode monitoring component, which is installed in each compartment to monitor the changes in pH value and redox potential Eh in each compartment in real time, and transmit the monitoring data to the spatiotemporal dynamic prediction model module in real time; specifically, the microelectrode monitoring component includes a pH microelectrode and / or a redox potential microelectrode, which is used to perform real-time dynamic monitoring of each compartment and transmit the data to the prediction model calculation unit.

[0152] The multi-compartment rhizosphere in-situ monitoring device also includes a soil sampling component 9, which is an independent external sampling tool. The soil sampling component 9 is used to perform in-situ non-destructive sampling of soil in each compartment. It is convenient and quick to sample soil in each compartment, avoiding cross-contamination. The soil samples are used to determine soil composition information and provide solid phase input parameters for the prediction model.

[0153] Please continue to refer to Figure 4 In one specific implementation, the soil sampling component 9 of the present invention adopts a hollow column soil sampling component, including a soil sampling tube 94 with an outer diameter of 1.5 cm and millimeter scales on the outer wall, that is, the outer surface of the soil sampling tube 94 has scale markings 93, which are used to control the sampling depth and ensure the consistency of sampling in different compartments.

[0154] Furthermore, the bottom opening of the soil sampling tube 94 has a cutting end 95, which facilitates insertion into the soil to be sampled 98; the soil sampling assembly 9 also includes a bulldozer 92, the top of which has a handle 91, and the bottom of which has a bulldozer head 97. During soil sampling, a soil column sample 96 can be collected in the inner cavity of the soil sampling tube 94 below the bulldozer head 97.

[0155] The multi-compartment rhizosphere in-situ monitoring device of the present invention, by constructing a rhizosphere gradient compartment structure with different distances, realizes in-situ collection of environmental parameters such as pH, Eh, EC, temperature, and moisture of soil solution in each compartment and obtains soil solution samples;

[0156] The prediction model is based on the principle of ion activity correction, complexation equilibrium equation and mass conservation constraint. It is adapted to the common occurrence rules of various nutrient ions and heavy metal ions in the rhizosphere microdomain. It can quantitatively predict the activity of various free ions and the concentration of inorganic complex forms at different spatial locations and time points in the rhizosphere microdomain, and output the spatial gradient distribution characteristics and temporal dynamic change rules of various ion forms.

[0157] The model validation and optimization module uses correlation analysis between the actual absorption data of elements in plant roots, stems, leaves and other tissues and the model prediction results to achieve iterative correction of model parameters and effectively improve prediction accuracy.

[0158] In summary, this invention breaks through the technical bottleneck of traditional fixed-point and static detection, and realizes the integrated in-situ monitoring, accurate calculation, spatiotemporal dynamic prediction and accuracy optimization of rhizosphere microdomain nutrients and heavy metal ion speciation. It can provide high-precision and quantitative technical support and analytical tools for the study of plant rhizosphere nutrient cycling mechanism, analysis of heavy metal pollutant migration and transformation law, farmland soil environment management and crop growth regulation research.

[0159] Example 1:

[0160] like Figures 5 to 11 As shown, in Figure 11 middle:

[0161] CK: Blank control;

[0162] OS: Rapeseed straw charcoal;

[0163] RS: Rice straw charcoal;

[0164] RH: Rice husk charcoal;

[0165] This invention verifies the prediction results based on measured data of ion absorption in plant roots, stems, and leaves. It comprehensively analyzes the dynamic changes of soil solution ions in different distance gradient compartments and the ion speciation distribution patterns predicted by the model, and evaluates the ion migration and transformation in the rhizosphere microdomain and plant effectiveness.

[0166] The prediction method provided in this embodiment utilizes a multi-compartment device combined with a model to conduct dynamic monitoring and speciation prediction of Cd ions in the rhizosphere microdomain, and includes the following steps:

[0167] (1) Assembly of the device: Install the nylon mesh partition 7 into the positioning slot 5 of the box 1 to form 7 soil compartments inside the box 1; arrange microporous sampling tubes 84 in each compartment, and connect the microporous sampling tubes 84 to the outside of the box 1 and connect them to the vacuum negative pressure interface through the sampling pipeline 6; arrange pH microelectrodes in each compartment to monitor the pH value changes of each compartment in real time.

[0168] (2) Soil filling and planting: Soil samples are filled into each compartment according to experimental needs, and plant seeds or seedlings are planted in the root growth chamber.

[0169] (3) Cultivation and management: Water and nutrient management of the device is carried out under greenhouse or artificial climate chamber conditions to maintain stable soil moisture content and enable the plants to grow to the target growth period;

[0170] (4) Dynamic sampling of soil solution: Soil solution in the near rhizosphere, far rhizosphere and non-rhizosphere zones is sampled in situ at predetermined time intervals using a soil solution sampling device; pH and temperature data of each zone are recorded simultaneously.

[0171] (5) Ion concentration determination: The ion concentration of the collected soil solution samples was determined, and the ions included Cd. 2+ Fe 2+ Mn 2+ Zn 2+ Ca 2+ Mg 2+ K + Na + NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - and the concentration of DOC;

[0172] (6) Integration of model input parameters: Integrate environmental condition parameters, soil solution ion concentration parameters, and soil composition information parameters into the model input; where:

[0173] Environmental parameters: measured pH values ​​in each compartment, CO2 partial pressure calculated from alkalinity and pH, and measured temperature;

[0174] Soil solution ion concentration parameters: Cd 2+ Fe 2+ Mn 2+ Zn 2+ Ca 2+ Mg 2+ K + Na + NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - and the measured concentration of DOC;

[0175] Soil composition information parameters: Fe2O3, Al2O3, organic carbon content, and cation exchange capacity determined from the soil sample obtained in step (9);

[0176] (7) Model calculation: Input the integrated input parameters into the model, run the calculation, and output Cd for each zone at each time point. 2+ Free ion activity and speciation of various Cd complexes (CdCl) + CdSO4, CdHCO3 +Concentrations and proportions of (e.g., CdCO3, etc.); and plotted spatial distribution curves and time-varying curves.

[0177] (8) Model validation: The predicted free Cd 2+ Correlation analysis was performed between activity and Cd uptake by plant roots to verify the accuracy of free ion activity in predicting plant effectiveness; if the deviation exceeds the acceptable range, the process was returned to step (6) to adjust the input parameters for iterative optimization.

[0178] (9) In-situ soil sampling: After the experiment, soil samples were taken from different compartments using a soil sampling assembly. The soil samples were analyzed for pH, Eh, Fe2O3, Al2O3, organic carbon, available metal content and cation exchange capacity. The data obtained were used to verify and optimize the solid phase input parameters of the model.

[0179] (10) Data analysis: Comprehensively analyze the dynamic changes of soil solution ions in different distance gradient compartments and the distribution patterns of ion forms predicted by the model, and evaluate the ion migration and transformation in the rhizosphere microdomain and plant effectiveness.

[0180] Example 2:

[0181] In the prediction method provided in this embodiment, the changes in ion speciation over time are dynamically and quantitatively predicted. The results are then compared and analyzed with the actual data on the cumulative absorption of heavy metals in plant roots and aboveground parts obtained from actual measurements. By calculating quantitative evaluation indicators such as correlation coefficient and prediction error, the overall prediction accuracy and reliability of this model for the bioavailability of various heavy metals are systematically evaluated.

[0182] The prediction method in this embodiment includes the following steps:

[0183] (1) Assembly of the device: Install the nylon mesh partition in the positioning slot of the box to form 7 soil compartments inside the box; arrange microporous sampling tubes in each compartment, lead the sampling tubes out to the outside of the box and connect them to the vacuum negative pressure interface; arrange pH microelectrodes in each compartment to monitor the pH value changes of each compartment in real time.

[0184] (2) Cultivation and management: Water and nutrient management of rice grown in this device under artificial climate chamber conditions;

[0185] (3) Dynamic sampling of soil solution: On the 15th, 30th and 45th day after rice seedling establishment, soil solution samplers were used to sample the soil solution of each treatment in situ at different times, and data such as pH and temperature of each compartment were recorded simultaneously.

[0186] (4) Ion concentration determination: The ion concentration of the collected soil solution samples was determined, and the ions included Cd. 2+ Fe 2+ Mn 2+ Zn2+ Ca 2+ Mg 2+ K + Na + NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - and DOC;

[0187] (5) Model Input Parameter Integration: Environmental condition parameters, soil solution ion concentration parameters, and soil composition information parameters are integrated into the model input. Soil solution ion concentration is based on measured Cd. 2+ Fe 2+ Mn 2+ Concentration is the core input;

[0188] (6) Model calculation: Input the integrated input parameters into the model, run the calculation, and output the Cd values ​​for each treatment at each time point (days 15, 30, and 45 after seedling establishment). 2+ Fe 2+ Mn 2+ The activity of free ions and the concentration and proportion of each complex form were determined; and a comparison diagram of ion forms between different biochar treatments and dynamic change curves of each treatment with culture time were plotted.

[0189] (7) Model validation: The predicted free Cd 2+ Fe 2+ Mn 2+ Activity and Cd in various tissues of rice 2+ Fe 2+ Mn 2+ The cumulative amount is used to perform correlation analysis to verify the prediction accuracy of free ion activity on plant effectiveness; if the deviation exceeds the acceptable range, return to step (5) to adjust the input parameters for iterative optimization.

[0190] The above solutions are merely illustrative examples of preferred embodiments and are not intended to limit the scope of the invention. Appropriate substitutions and / or modifications can be made according to user needs when implementing this invention.

[0191] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0192] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A method for predicting nutrients and ion speciation in the rhizosphere microdomain, characterized in that, Includes the following steps: Data on soil solution ion concentration, environmental parameters, and soil composition were collected by a multi-compartment rhizosphere in-situ monitoring device at multiple rhizosphere distance gradient compartments and at multiple time points. Based on ion concentration data and ion charge, the ion intensity of each compartment at each time point is calculated, the ion activity coefficient is determined according to the ion intensity, and the ion concentration is converted into ion activity. For the target heavy metal, based on the complexation equilibrium relationship between metal ions and inorganic ligands and the mass conservation equation, the activity of free ions of the target heavy metal and the concentration of each inorganic complex at each time point in each compartment are solved using ion concentration. Based on the free ion activity of multiple compartments, a spatial gradient distribution of the target heavy metal ion speciation is generated; and based on the inorganic complex concentration at multiple consecutive time points, a prediction of ion speciation changes over time is generated, realizing the spatiotemporal dynamic prediction of nutrients and ion speciation in the rhizosphere microdomain.

2. The method for predicting rhizosphere microdomain nutrients and ion speciation according to claim 1, characterized in that, The step of generating the spatial gradient distribution of the target heavy metal ion speciation includes: Calculate the activity of the target heavy metal free ions in the i-th rhizosphere gradient compartment. , is represented as: In the formula, Represents the activity coefficient. Indicates the measured dissolved concentration; The activity difference between adjacent compartments is calculated. This activity difference characterizes the spatial gradient variation of ion speciation in the rhizosphere microdomain. Represented as: In the formula, This represents the real-time activity of the target heavy metal free ions in the i-th compartment; Indicates the first Real-time activity of target heavy metal free ions in each compartment.

3. The method for predicting rhizosphere microdomain nutrients and ion speciation according to claim 2, characterized in that, The step of predicting the change in the form of generated ions over time includes: Calculate the complex concentration at time t for the complex formed by the target heavy metal and the j-th inorganic ligand. and the concentration after a certain time interval. The dynamic change rate of the complex was obtained. And based on the dynamic rate of change, the time dynamic evolution trend of the complex morphology is determined; Among them, the dynamic rate of change Represented as: In the formula, Δt is the fixed time interval between two consecutive sets of monitoring data; Let be the rate of dynamic change of the inorganic complex at time t.

4. The method for predicting rhizosphere microdomain nutrients and ion speciation according to claim 3, characterized in that, The calculation involves determining the target heavy metal free ion activity and the concentration of each inorganic complex in each compartment at each time point, specifically including: Establish multiple sets of complexation equilibrium equations and total dissolved mass conservation equations for elements; The activity of free ions, the concentration of each complex form, and their proportion are obtained by iterative solution.

5. The method for predicting rhizosphere microdomain nutrients and ion speciation according to claim 4, characterized in that, Also includes: The prediction error is calculated by comparing the predicted target heavy metal free ion activity with the actual measured cumulative heavy metal uptake in the plant. When the error exceeds the preset threshold, the complexation equilibrium constant and / or ligand complexation ability characteristic parameters are automatically adjusted, and iterative optimization is performed until the accuracy requirements are met or the maximum number of iterations is reached.

6. The method for predicting rhizosphere microdomain nutrients and ion speciation according to claim 5, characterized in that, The environmental parameters include pH, temperature, and CO2 partial pressure; The soil solution ion concentration data includes at least Ca. 2+ Mg 2+ K + Na + Fe 2+ Mn 2+ Cd 2+ Zn 2+ NO3 - NH4 + SO4 2- PO4 3- Cl - HCO3 - And the concentration of dissolved organic carbon; The soil composition information includes at least the Fe2O3 content, Al2O3 content, organic carbon content, and cation exchange capacity.

7. A rhizosphere microdomain nutrient and ion speciation prediction system, characterized in that, Prediction systems include: The prediction model calculation unit is connected to the multi-chamber rhizosphere in-situ monitoring device for implementing the prediction method as described in any one of claims 1 to 6, and outputting the spatiotemporal dynamic prediction results of rhizosphere microdomain nutrients and ion speciation; wherein, the multi-chamber rhizosphere in-situ monitoring device is used to construct multiple rhizosphere distance gradient chambers and collect environmental parameters, soil solution ion concentration data and soil composition information in each chamber.

8. The rhizosphere microdomain nutrient and ion speciation prediction system according to claim 7, characterized in that, The multi-zone rhizosphere in-situ monitoring device includes: The box is divided into multiple independent soil compartments, forming a spatial gradient that includes the root growth zone, near-rhizosphere zone, far-rhizosphere zone and non-rhizosphere zone. Nylon mesh isolation components, installed between adjacent compartments, are used to prevent plant roots from penetrating while allowing solutes and microorganisms to migrate across compartments; Soil solution sampling kits are installed in indoor areas of each zone for in-situ sampling of soil solution. Soil sampling kit, used for in-situ non-destructive sampling of soil in each compartment.

9. The rhizosphere microdomain nutrient and ion speciation prediction system according to claim 8, characterized in that, The soil solution sampling assembly includes: Sampling interface; Microporous sampling tubes, including Rhizon samplers, hollow fiber samplers, or microporous ceramic head samplers, are driven by external negative pressure for sampling. Fixing components are used to fix the soil solution sampling components.

10. The rhizosphere microdomain nutrient and ion speciation prediction system according to claim 9, characterized in that, It also includes a microelectrode monitoring component, which includes a pH microelectrode and / or a redox potential microelectrode, for real-time dynamic monitoring of each compartment and transmitting the data to the prediction model calculation unit.

Images