Method for evaluating flux of heavy metals removed from soil of paddy field and dry land crops based on model estimation

Abstract

The invention discloses a model-estimation-based evaluation method for removing soil heavy metal flux of paddy field and dry land crops, relates to the technical field of agricultural environmental science, and determines planting mode characteristics of a target area under each planting mode and an estimation range of the crop removing soil heavy metal flux through standardized field trials. The planting modes comprise single cropping rice, double cropping rice, rice-oil crop rotation, rice-tobacco crop rotation and leaf type dry land crops, and the planting modes are characterized by comprising planting periods and crop types. According to the method, a standardized crop heavy metal absorption parameter database is constructed, planting mode characteristics and rhizosphere soil heavy metal form dynamic change data are combined, the soil heavy metal removal capacity of crops can be more accurately quantified, meanwhile, biomass, heavy metal concentration and flux calibration data are integrated in the crop heavy metal absorption parameter database, and the crop heavy metal absorption parameter database is more accurate. Environmental variation interference is eliminated, and a credible heavy metal removal flux estimation range is formed.

Description

Technical Field

[0001] This invention relates to the field of agricultural environmental science and technology, specifically to a method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation. Background Technology

[0002] With the acceleration of industrialization, soil heavy metal pollution has become increasingly prominent. As major agricultural production systems, paddy fields and dryland crops are directly affected by heavy metals, which directly impacts crop growth and safety. Heavy metals can enter plants through the soil, thus affecting food safety. When humans or animals ingest crops contaminated with heavy metals, it poses a threat to their health. Therefore, assessing the ability of crops to remove heavy metals from the soil is crucial for ensuring food chain safety. Through model estimation, the absorption and removal efficiency of heavy metals by crops under specific conditions can be effectively assessed, providing a scientific basis for soil remediation and pollution control, and helping to formulate effective remediation strategies.

[0003] In existing technologies, heavy metal removal is mostly estimated based on crop biomass, without considering the regulatory effect of crop species on soil heavy metal speciation. Therefore, the problem to be solved by this invention is how to establish a database of crop heavy metal absorption, clarify the absorption capacity of common paddy field and dryland crops for various heavy metals, and analyze and obtain a more realistic estimate of heavy metal removal based on biomass estimation. To this end, a model-based method for assessing the flux of soil heavy metals removed by paddy field and dryland crops is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a model-based method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops, in order to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A model-based method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops includes the following steps: S1. Through standardized field trials, determine the characteristics of planting patterns under various planting patterns in the target area and the estimated range of soil heavy metal flux removed by crops, and construct a standardized database of crop heavy metal absorption parameters. Among them, the planting patterns include single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation and leafy dryland crops, and the characteristics of planting patterns include planting cycle and crop type. S2. Determine key morphological indicators, including bioavailable forms, to quantify the content of different forms of heavy metals in soil. S3. Establish a rhizosphere morphology regulation model to characterize the feedback mechanism of crop species to heavy metal activity. S4. Couple the crop heavy metal absorption parameter database with the rhizosphere morphology regulation model to construct a heavy metal absorption prediction model based on bioavailability; S5. Integrate crop growth models and simulate biomass output to predict crop yields under different planting patterns; S6. Based on the combined heavy metal absorption prediction model and crop yield estimation results, the heavy metal removal flux value is calculated. S7. Output the calculation results of heavy metal removal flux values ​​to obtain an accurate assessment report of soil heavy metal removal flux under various planting patterns in the target area.

[0006] A further improvement to the technical solution of the present invention is that: S1 specifically includes: Experimental fields covering five planting patterns—single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation, and leafy dryland crops—were systematically laid out in the target area. The planting cycle and crop combination were clarified, field management measures were recorded simultaneously, soil samples were collected at different levels to determine the total amount of heavy metals, the dynamic changes of heavy metal speciation in rhizosphere soil were analyzed, crop samples were collected by organ to determine biomass and heavy metal concentration, and the range of heavy metal removal flux was preliminarily estimated. Based on experimental data, the rotation sequence, coexistence period and root distribution depth of different planting patterns were sorted out, the influence mechanism of planting cycle on heavy metal absorption was quantified, a crop-heavy metal affinity classification standard was established, biomass, heavy metal concentration and flux data were calibrated to standard temperature, humidity and soil conditions, environmental variation interference was eliminated, and a structured heavy metal absorption parameter database framework was formed. A multi-level database architecture was designed, with planting mode, crop type, and soil type as indexes. Core fields such as planting cycle, biomass, and heavy metal concentration were integrated. Cluster analysis was used to divide the high, medium, and low estimation ranges of heavy metal removal flux, and a database of crop heavy metal absorption parameters covering five planting modes was output to clarify the reliable estimation range.

[0007] A further improvement of the technical solution of the present invention is that: the experimental field is divided into blocks according to soil type, each planting mode is repeated 3 times, the area of ​​a single experimental field is ≥50m², and field management measures are recorded simultaneously, covering fertilizer application, irrigation system, pesticide type and frequency; The soil sampling included basic soil samples and rhizosphere soil samples. For basic soil samples, they were collected in layers of 0-20cm and 20-40cm before the test to determine the total heavy metal content. For rhizosphere soil, rhizosphere soil was collected throughout the crop's growth period, i.e., within 5mm of the roots, and separated using the root-shaking method.

[0008] A further improvement to the technical solution of the present invention is that: S2 specifically includes: The collected soil samples were air-dried, ground, sieved, and then uniformly reduced in size. The operation definitions for each form were clarified, and continuous extraction was carried out according to the operation definitions for heavy metal forms. Temperature, oscillation frequency, and time were controlled to maintain the stability of the forms. Blank samples, standard substances, and parallel samples were processed simultaneously to ensure that the recovery rate and precision met the quality control requirements. After centrifugation and filtration of each extract, each form is extracted according to the operation definition of each form. The concentration of each form of heavy metal is determined by inductively coupled plasma mass spectrometry. The contents of each form are summarized, and the relative deviation between the sum and the total amount of soil digestion is calculated. If the deviation exceeds the limit, the analysis needs to be repeated to ensure the reliability of the data. The proportion of each form was calculated, the dominant form was identified through cluster analysis, and a regression model was established by combining soil pH, organic matter, and cation exchange capacity to quantify the influence weight of environmental factors. The sum of exchangeable and carbonate-bound forms was used as the available form index, and the predictive ability was verified by regression analysis of crop root heavy metal concentration and available form content.

[0009] A further improvement to the technical solution of this invention lies in that: the definitions of each morphological operation are specifically as follows: Exchangeable states were extracted with ammonium acetate, carbonate-bound states were treated with sodium acetate buffer, iron-manganese oxide-bound states were reduced with hydroxylamine hydrochloride, organic matter-bound states were treated with hydrogen peroxide, and residue states were digested with aqua regia-perchloric acid. The specific steps for extracting the morphology from each form are as follows: The exchangeable residue was extracted with 0.11 mol / L ammonium acetate (pH 7.0) for 2 h with shaking, followed by centrifugation at 4000 rpm for 15 min. The carbonate-bound residue was extracted with 0.5 mol / L sodium acetate (pH 5.0) for 5 h. The iron-manganese oxide-bound residue was extracted with 0.25 mol / L hydroxylamine hydrochloride (pH 2.0) for 6 h. The organic-bound residue was oxidized with 30% hydrogen peroxide (pH 2.0) and then extracted with 0.02 mol / L ammonium nitrate for 1 h. The final residue was digested with aqua regia and perchloric acid until clear.

[0010] A further improvement to the technical solution of the present invention is that: S3 specifically includes: We collected crop rhizosphere soil, root exudates and plant biomass, used synchrotron X-ray fluorescence spectroscopy to locate the micro-regional distribution of heavy metals, combined with laser ablation mass spectrometry to analyze the concentration gradient at the root-soil interface, and simultaneously measured soil physicochemical properties, microbial community structure and enzyme activity to construct a soil-plant-microbe dataset. Five forms of heavy metals in rhizosphere soil were extracted stepwise. Their coordination environment with rhizosphere components was characterized by in-situ spectroscopy. Combined with the analysis of root exudate components, the regulatory effects of crops on rhizosphere redox conditions, pH and organic ligands were quantified, revealing the feedback mechanism of form transformation. Preliminary rhizosphere morphology regulation models were constructed for different crops. Using initial soil properties as input and crop type as selector, rhizosphere process parameters were used to predict the concentration of available heavy metals. The preliminary rhizosphere morphology regulation models were verified and calibrated through field measurement data to form a generalizable rhizosphere morphology regulation model.

[0011] A further improvement to the technical solution of the present invention is that: S4 specifically includes: By integrating a database of crop heavy metal absorption parameters with a rhizosphere morphology regulation model, a unified data interface was established to extract soil physicochemical properties, total heavy metal content, morphological distribution, and crop-specific absorption parameters. After data cleaning and normalization, a standardized parameter set covering multiple soil types and scenarios was constructed. The dynamic coupling of rhizosphere biomorphic regulation mechanisms and heavy metal biomorphic transformation processes was studied. Based on synchrotron radiation spectroscopy analysis of adsorption-desorption pathways in the rhizosphere, combined with a chemical biomorphic equilibrium model to quantify biomorphic transformation rates, the regulatory mechanism of the rhizosphere microenvironment on the bioavailability of heavy metals was analyzed, and an interpretable process-driven model was formed. Using the bioavailability of heavy metals as the target variable, a preliminary heavy metal absorption prediction model was constructed by integrating a standard parameter set and a process-driven model and employing a hybrid strategy of random forest-dynamic equations. The parameters were then optimized using Bayesian optimization to output the bioavailable concentration and crop uptake. Finally, a bioavailability-based heavy metal absorption prediction model was formed through cross-validation on independent datasets.

[0012] A further improvement to the technical solution of the present invention is that: S5 specifically includes: Using historical meteorological data, soil property data, and field observation data on crop growth period and yield in the target area, the crop types and field management parameters of the selected crop growth model are calibrated and verified to ensure that it can accurately simulate the growth dynamics of a specific crop in the local environment. Design simulation scenarios for different planting patterns, clarify management measures for crop rotation sequences and sowing / harvest dates, and integrate long-term historical meteorological data or future climate scenario data of the target area, as well as physicochemical property data of corresponding soil types, as driving inputs for crop growth models; Run crop growth models in batches to simulate crop growth processes under various planting patterns over a multi-year timescale, and output simulated crop yield values ​​for each season and aboveground biomass.

[0013] A further improvement to the technical solution of the present invention is that: S6 specifically includes: The system calls up the existing heavy metal absorption prediction model, inputs the soil properties, total heavy metal content and rhizosphere parameters of the target field, and outputs the predicted heavy metal concentration of harvestable crop organs based on bioavailability. Simultaneously, it obtains the simulated crop yield value simulated by the crop growth model under the same scenario. Based on the fundamental principle that the heavy metal removal flux is equal to the product of the heavy metal concentration in the harvestable organs of the crop and the simulated crop yield, the heavy metal removal flux is directly calculated for a single crop season. For the crop rotation pattern, the heavy metal removal flux value of each crop in the cycle is calculated separately and summed in time sequence to obtain the total heavy metal removal flux value of the crop rotation pattern in a complete cycle. The calculation results are then formatted into structured data output.

[0014] A further improvement to the technical solution of the present invention is that S7 specifically includes: The calculation results of heavy metal removal flux values ​​are categorized and summarized according to planting patterns to generate a structured data table containing fields such as crop name, yield, heavy metal concentration, single-season flux, and total flux of crop rotation. The data in the structured data table is converted into visual charts by an automated script to show the differences in heavy metal removal flux under different planting patterns. An assessment report template is also prepared, which includes a description of the calculation process, results analysis and optimization suggestions, and outputs a PDF-format assessment report on the removal flux of heavy metals in the soil.

[0015] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: This invention provides a model-based method for assessing the removal flux of heavy metals from soil by paddy and dryland crops. By constructing a standardized database of crop heavy metal uptake parameters and combining planting pattern characteristics with dynamic changes in rhizosphere soil heavy metal speciation data, the method can more accurately quantify the crop's ability to remove heavy metals from the soil. At the same time, the crop heavy metal uptake parameter database integrates biomass, heavy metal concentration, and flux calibration data to eliminate environmental variability interference and form a reliable range for estimating heavy metal removal flux, significantly improving the scientific validity and reliability of the assessment results and providing a precise basis for soil remediation.

[0016] This invention provides a model-based method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops. By coupling a database of crop heavy metal absorption parameters with a rhizosphere morphology regulation model, a bioavailability-based heavy metal absorption prediction model is constructed. A hybrid strategy of random forest and dynamic equations is adopted, with the upper layer capturing nonlinear relationships and the lower layer describing the morphological transformation process. The model outputs the bioavailable concentration of heavy metals and the amount absorbed by crops. It is applicable to different soil types and pollution scenarios, enhancing the adaptability and predictive ability of the assessment method. Attached Figure Description

[0017] 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 recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a schematic diagram illustrating the workflow of the model-based estimation method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops according to the present invention. Figure 2 This is a schematic diagram of the method flow for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation, as proposed in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, as Figure 1 , Figure 2 As shown, this invention provides a method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation, comprising the following steps: S1. Through standardized field trials, determine the characteristics of each planting pattern under different planting models in the target area and the estimated range of soil heavy metal flux removal by crops. Construct a standardized database of crop heavy metal absorption parameters. The planting models include single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation, and leafy dryland crops. The characteristics of each planting model include planting cycle and crop type. In the target area, systematically deploy experimental fields covering five planting models: single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation, and leafy dryland crops. Clarify the combination of planting cycle and crop type, simultaneously record field management measures, collect soil samples at different levels to determine the total amount of heavy metals, analyze the dynamic changes of heavy metal speciation in rhizosphere soil, and collect crop samples by organ to determine biomass and heavy metal concentration. This study preliminarily estimates the range of heavy metal removal fluxes. Based on experimental data, it analyzes the rotation sequence, coexistence period, and root distribution depth of different planting patterns, quantifies the impact mechanism of planting cycle on heavy metal absorption, establishes a crop-heavy metal affinity classification standard, calibrates biomass, heavy metal concentration, and flux data to standard temperature, humidity, and soil conditions to eliminate environmental variation interference, forms a structured heavy metal absorption parameter database framework, designs a multi-level database architecture, uses planting pattern-crop type-soil type as index, integrates the core fields of planting cycle, biomass, and heavy metal concentration, and divides the high, medium, and low estimation ranges of heavy metal removal fluxes through cluster analysis. It outputs a crop heavy metal absorption parameter database covering five planting patterns, and clarifies the reliable estimation range. In addition, single-season rice: only one season of medium-season rice is planted, and the growth period is recorded (120-150 days); double-season rice: early rice (90-110 days) + late rice (110-130 days), and the connection time between the two crops is clearly defined; rice-rapeseed rotation: rice → rapeseed (220-240 days), and the rotation interval is recorded; rice-tobacco rotation: rice → tobacco (120-140 days), and the tobacco transplanting period is recorded; leafy dryland crops: spinach, water spinach, etc. (30-60 days / season), using continuous cropping or intercropping patterns; The experimental fields were divided into blocks according to soil type. Each planting pattern was replicated three times. The area of ​​each experimental field was ≥50m². Field management measures were recorded simultaneously, including fertilizer application, irrigation system, and types and frequency of pesticides used. Soil sampling includes basic soil samples and rhizosphere soil samples. For basic soil samples, they are collected in layers of 0-20cm and 20-40cm before the test to determine the total heavy metal content. For rhizosphere soil, rhizosphere soil is collected throughout the crop's growth period, i.e., within 5mm of the root system, and is separated using the root shaking method. The specific work content includes: systematically setting up experimental fields for various planting patterns in the target area, covering typical planting patterns of single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation, and leafy dryland crops; clarifying the planting cycle and crop combination of each planting pattern; simultaneously recording field management measures; collecting basic soil samples by soil profile layering to determine the total heavy metal content; collecting rhizosphere soil samples throughout the crop's growth period to analyze the dynamic changes in heavy metal speciation; collecting crop samples by organ at harvest to determine biomass and heavy metal concentrations; and preliminarily estimating the range of heavy metal removal fluxes under each planting pattern. For biomass determination, samples were collected from different organs (roots, stems, leaves, grains / economic parts) at harvest, blanched at 105℃, and dried at 70℃ to constant weight. The dry matter weight was recorded. For heavy metal concentration analysis, after digesting crop samples, the heavy metal concentration (mg / kg) of each organ was determined by ICP-MS. The data included single-season rice planting patterns (covering single-season rice), double-season rice planting patterns (covering early and late rice), rice-oilseed rape rotation patterns (covering rice and rapeseed), rice-tobacco rotation patterns (covering rice and tobacco), and leafy dryland crop planting patterns (covering leafy dryland crops). Based on field trial data, the following was compiled... The ecological characteristics of different planting patterns, including parameters such as crop rotation sequence, coexistence period duration, and root distribution depth, were investigated to quantify the impact mechanism of planting cycle on heavy metal absorption. A grading standard for the affinity between crop species and heavy metals was established. Raw data on biomass, heavy metal concentration, and heavy metal removal flux were uniformly calibrated to standard temperature, humidity, and soil conditions to eliminate environmental variability interference and form a structured and scalable database framework for heavy metal absorption parameters. Specifically, rice roots were concentrated in the 0-20cm soil layer, accounting for over 80%; tobacco roots could reach a depth of 40cm; and rapeseed roots were more distributed... Shallow (0-30cm); A multi-level database architecture was designed, with planting mode, crop type, and soil type as the index. Core fields such as planting cycle, biomass, and heavy metal concentration were integrated. Cluster analysis was used to divide the high, medium, and low estimation intervals of heavy metal removal flux. The database of crop heavy metal absorption parameters covering five planting modes in the target area was output. The reliable estimation range of crop heavy metal removal flux in soil under each planting mode was clarified. High estimation interval: flux > 75th percentile, medium estimation interval: 25th-75th percentile, low estimation interval: flux < 25th percentile; S2. Determine key morphological indicators, including the available form, to quantify the content of different forms of heavy metals in the soil. Collected soil samples are air-dried, ground, sieved, and then uniformly reduced in size. Define the operation for each form and perform continuous extraction according to the definition of heavy metal form operation. Control temperature, oscillation frequency, and time to maintain form stability. Simultaneously process blank samples, standard substances, and parallel samples to ensure that the recovery rate and precision meet the quality control requirements. After centrifugation and filtration of each step of the extract, extract each form according to the definition of each form operation. Use inductively coupled plasma mass spectrometry to determine the concentration of each form of heavy metal. Summarize the content of each form and calculate the relative deviation between the sum and the total amount of soil digestion. If the deviation exceeds the limit, re-analysis is required to ensure data reliability. Calculate the proportion of each form and identify the dominant form through cluster analysis. Establish a regression model based on soil pH, organic matter, and cation exchange capacity to quantify the influence weight of environmental factors. Use the sum of exchangeable and carbonate-bound forms as the available form indicator. Verify the predictive ability through regression analysis of heavy metal concentration in crop roots and available form content. In addition, the specific operation definitions for each form are as follows: exchangeable form is extracted with ammonium acetate, carbonate-bound form is treated with sodium acetate buffer, iron-manganese oxide-bound form is reduced with hydroxylamine hydrochloride, organic matter-bound form is treated with hydrogen peroxide, and residue form is digested with aqua regia-perchloric acid. The specific extraction methods for each form were as follows: Exchangeable residues were extracted with 0.11 mol / L ammonium acetate (pH 7.0) using shaking for 2 h, followed by centrifugation at 4000 rpm for 15 min; carbonate-bound residues were extracted with 0.5 mol / L sodium acetate (pH 5.0) for 5 h; iron-manganese oxide-bound residues were extracted with 0.25 mol / L hydroxylamine hydrochloride (pH 2.0) for 6 h; organic-bound residues were oxidized with 30% hydrogen peroxide (pH 2.0) and then extracted with 0.02 mol / L ammonium nitrate for 1 h; the final residues were digested with aqua regia and perchloric acid until clear. The specific work involved: Collected soil samples were air-dried, ground, and passed through a 2mm nylon sieve to remove plant and animal debris and stones, ensuring sample homogeneity. For samples requiring micro-region morphology analysis, freeze-drying was used to preserve the original structure. Subsequently, the samples were reduced to the required analytical volume using a quartering method. Based on the target heavy metal characteristics and soil type, the operational definitions for each morphology were clarified: exchangeable state, carbonate-bound state, iron-manganese oxide-bound state, organic matter-bound state, and residue state. During extraction, the temperature (25±1℃), oscillation frequency (200rpm), and extraction time were controlled to avoid morphological transformation. Blank samples, standard substances, and other materials were processed simultaneously. The extraction process was repeated 3 times to calculate the recovery rate (target value 85-115%) and relative standard deviation (≤10%) to ensure the reliability of the extraction process. The pre-selected extraction scheme was followed by the following extraction methods: exchangeable form: extraction with 0.11 mol / L ammonium acetate (pH 7.0) for 2 h with shaking, followed by centrifugation (4000 rpm, 15 min) and collection of the supernatant; carbonate-bound form: extraction of the residue with 0.5 mol / L sodium acetate (pH 5.0) for 5 h; iron-manganese oxide-bound form: extraction of the residue with 0.25 mol / L hydroxylamine hydrochloride (pH 2.0) for 6 h; organic matter-bound form: extraction of the residue with 30% H2O. 2 O 2 After oxidation at pH 2.0, extract with 0.02 mol / L ammonium nitrate for 1 h; Residue: The final residue was digested with aqua regia-perchloric acid until clear; After filtration through a 0.45 μm filter membrane, the heavy metal concentration was determined by inductively coupled plasma mass spectrometry (ICP-MS), and the contents of each form were summarized to verify the relative deviation between the sum and the total digestion amount (≤15%). If the deviation exceeded the limit, re-extraction and analysis were required; The proportion of each form was calculated (e.g., exchangeable form proportion = exchangeable form content / total content × 100%), and the dominant form was identified through cluster analysis. Combined with soil physicochemical properties, a regression model of form-pH / organic matter / cation exchange capacity was established to quantify the influence weight of environmental factors on form transformation. The sum of exchangeable and carbonate-bound forms was used as the available form index to assess the bioavailability of heavy metals. The predictive ability of the form index was verified by comparing the heavy metal concentration in crop roots with the available form content (R²≥0.75 was considered significant). S3. Establish a rhizosphere morphology regulation model to characterize the feedback mechanism of crop species to heavy metal activity. Collect rhizosphere soil, root exudates, and plant biomass. Use synchrotron X-ray fluorescence spectroscopy to locate the micro-regional distribution of heavy metals. Combine laser ablation mass spectrometry to analyze the concentration gradient at the root-soil interface. Simultaneously measure soil physicochemical properties, microbial community structure, and enzyme activity. Construct a soil-plant-microbe dataset. Extract five forms of heavy metals from rhizosphere soil stepwise. Characterize their coordination environment with rhizosphere components using in-situ spectroscopy. Combine root exudate component analysis to quantify the regulatory effects of crops on rhizosphere redox conditions, pH, and organic ligands. Reveal the feedback mechanism of morphology transformation. Construct preliminary rhizosphere morphology regulation models for different crops. Use initial soil properties as input and crop species as selectors. Call rhizosphere process parameters to predict the concentration of available heavy metals. Validate and calibrate the preliminary rhizosphere morphology regulation model through field measurement data to form a generalizable rhizosphere morphology regulation model. The specific work involved: collecting rhizosphere soil samples (0-5 mm from the root surface), simultaneously collecting root exudates and aboveground / belowground biomass of the plant; using high-resolution imaging technology (synchrotron radiation X-ray fluorescence spectroscopy) to locate the micro-regional distribution of heavy metals in the rhizosphere; combining laser ablation inductively coupled plasma mass spectrometry to analyze the concentration gradient of heavy metals at the root-soil interface; and simultaneously measuring soil physicochemical properties (pH, organic matter, cation exchange capacity, redox potential), microbial community structure (16S rRNA / ITS sequencing), and enzyme activities (urease, dehydrogenase, etc.) to construct a soil-plant-microbe dataset; and extracting exchangeable, carbonate-bound, iron-manganese oxide-bound, organic matter-bound, and residual states stepwise using in-situ diffuse reflectance infrared light. Near-edge structure spectroscopy and X-ray absorption spectroscopy characterize the changes in the coordination environment of heavy metals and rhizosphere components. Combined with the analysis of root exudate components, the regulatory effects of crop species on rhizosphere redox conditions, pH, and organic ligand supply are quantified, revealing the feedback mechanism of heavy metal speciation. A preliminary rhizosphere speciation regulation model is constructed for each crop type. Using the original rhizosphere soil properties as the initial input and the crop type as the selector, the model automatically calls the corresponding rhizosphere process parameters and speciation response equations, outputting the predicted rhizosphere available heavy metal concentration. By comparing the model predictions with the field measured rhizosphere available content, the preliminary rhizosphere speciation regulation model is validated and calibrated, forming a generalizable rhizosphere speciation regulation model, providing a theoretical basis for the prevention and control of heavy metal pollution under different crop systems. S4. Couple the crop heavy metal absorption parameter database with the rhizosphere morphology regulation model to construct a heavy metal absorption prediction model based on bioavailability; S5. Integrate crop growth models and simulate biomass output to predict crop yields under different planting patterns; S6. Based on the combined heavy metal absorption prediction model and crop yield estimation results, the heavy metal removal flux value is calculated. S7. Output the calculation results of heavy metal removal flux values ​​to obtain an accurate assessment report of soil heavy metal removal flux under various planting patterns in the target area.

[0021] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: S4 specifically includes: This study integrates a database of crop heavy metal absorption parameters with a rhizosphere speciation regulation model, establishes a unified data interface, and extracts soil physicochemical properties, total heavy metal content, speciation distribution, and crop-specific absorption parameters. After data cleaning and normalization, a standardized parameter set covering multiple soil types and scenarios is constructed. The rhizosphere speciation regulation mechanism and heavy metal speciation transformation process are dynamically coupled. Based on synchrotron radiation spectroscopy, the adsorption-desorption pathway in the rhizosphere is analyzed. Combined with a chemical speciation equilibrium model, the speciation transformation rate is quantified, and the regulation mechanism of the rhizosphere microenvironment on the bioavailability of heavy metals is analyzed, forming an interpretable process-driven model. With heavy metal bioavailability as the target variable, a preliminary heavy metal absorption prediction model is constructed by integrating the standard parameter set and the process-driven model using a hybrid strategy of random forest and dynamic equations. Through Bayesian optimization and parameter tuning, the bioavailable concentration and crop uptake are output. After cross-validation with independent datasets, a bioavailability-based heavy metal absorption prediction model is finally formed. The specific work involves: integrating a database of crop heavy metal absorption parameters with a rhizosphere speciation regulation model; establishing a unified data interface; extracting soil physicochemical properties, total heavy metal content, and speciation distribution data; combining crop-specific absorption parameters; eliminating heterogeneity through data cleaning; normalizing multi-source data; constructing a standardized parameter set covering different soil types and pollution scenarios; and forming a dynamic knowledge base containing rules relating heavy metal speciation, soil properties, and crop absorption. The work also includes dynamically coupling the rhizosphere speciation regulation mechanism with the heavy metal speciation transformation process, analyzing the adsorption-desorption reaction pathways of heavy metals in the rhizosphere using synchrotron radiation spectroscopy, and quantifying the speciation transformation rate using a chemical speciation equilibrium model. Furthermore, the regulatory mechanism of the rhizosphere microenvironment on the bioavailability of heavy metals was analyzed, forming an interpretable process-driven model. With the bioavailability of heavy metals as the core target variable, the standard parameter set and the process-driven model were integrated, and a preliminary heavy metal absorption prediction model was constructed using a hybrid modeling strategy. The upper layer used a random forest algorithm to capture nonlinear relationships, and the lower layer embedded kinetic equations to describe the morphological transformation process. Bayesian optimization was used to achieve adaptive parameter tuning. The output of the preliminary heavy metal absorption prediction model included the bioavailable concentration of heavy metals and the amount absorbed by crops. Cross-validation was performed using independent datasets to evaluate the generalization ability of the model under complex environmental conditions, thus forming a heavy metal absorption prediction model based on bioavailability. S5 specifically includes: Using historical meteorological data, soil property data, and field observation data on crop growth period and yield in the target area, the crop types and field management parameters of the selected crop growth model are calibrated and validated to ensure that it can accurately simulate the growth dynamics of specific crops in the local environment. Simulation scenarios for different planting patterns are designed, and management measures for crop rotation sequences and sowing / harvest dates are clarified. Long-term historical meteorological data or future climate scenario data of the target area, as well as the physicochemical property data of the corresponding soil types, are integrated as driving inputs for the crop growth model. The crop growth model is run in batches to simulate the crop growth process of each planting pattern on a multi-year timescale and output simulated crop yield values ​​for each season and aboveground biomass. The specific work involves: selecting a validated crop growth model to simulate the growth dynamics of the target crop, achieving localized calibration of the crop growth model parameters, specifically by using historical meteorological data, soil property data, and field observations of crop growth period and yield data from the target area to calibrate and validate crop types and field management parameters, ensuring that the crop growth model can accurately simulate the growth dynamics of different types of crops under specific environments; based on the validated crop growth model, setting up simulation scenarios representing different planting patterns, with each planting pattern requiring a clearly defined crop rotation sequence, sowing and harvesting dates, and management measures; the driving data includes long-term historical meteorological data or future climate scenario data of the target area, as well as the physicochemical properties data of the corresponding soil type; and simulating the crop growth process of each planting pattern over a multi-year timescale by running the crop growth model in batches, outputting simulated crop yield values ​​including the yield of each season's crops and aboveground biomass. S6 specifically includes: The system calls upon the established heavy metal absorption prediction model, inputs the soil properties, total heavy metal content, and rhizosphere parameters of the target field, and outputs the predicted heavy metal concentration in harvestable crop organs based on bioavailability. Simultaneously, it obtains the simulated crop yield value under the same scenario from the crop growth model. Based on the fundamental principle that the heavy metal removal flux value is equal to the product of the heavy metal concentration value in harvestable crop organs and the simulated crop yield value, the system directly calculates the heavy metal removal flux value for a single crop season. For a crop rotation pattern, the system calculates the heavy metal removal flux value for each crop in the cycle and sums them up in time sequence to obtain the total heavy metal removal flux value of the crop rotation pattern in a complete cycle. The calculation results are then formatted into structured data output. The specific work involves: calling the constructed heavy metal absorption prediction model, inputting the soil physicochemical properties, total heavy metal content, and rhizosphere process parameters under a specific planting pattern, and outputting the predicted heavy metal concentration value of harvestable crop organs after calculation. This value is an accurate prediction value based on bioavailability. The simulation results of the crop growth model are also obtained, and the simulated crop yield values ​​of each season's crop yield and aboveground biomass under the corresponding planting pattern and scenario are extracted. Based on the basic principle that heavy metal removal flux value = heavy metal concentration value of harvestable crop organs × simulated crop yield value, for a single crop season, the predicted heavy metal concentration is multiplied by the simulated crop yield value to obtain the heavy metal removal flux value of the crop from a unit area of ​​land through harvest. For planting patterns that include crop rotation, the heavy metal removal flux value of each crop in the rotation cycle is calculated separately, and the values ​​are summed according to their planting sequence to finally calculate the total heavy metal removal flux value of the planting pattern in a complete cycle. The calculation results of the heavy metal removal flux value are then output in a formatted manner. S7 specifically includes: The calculation results of heavy metal removal flux values ​​are categorized and summarized according to planting patterns to generate a structured data table containing fields such as crop name, yield, heavy metal concentration, single-season flux, and total flux of crop rotation. An automated script converts the data in the structured data table into visual charts to show the differences in heavy metal removal flux under different planting patterns. An assessment report template is also prepared, embedding calculation process descriptions, result analysis, and optimization suggestions, and outputting a PDF format assessment report on heavy metal removal flux in the soil.

[0022] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation, characterized in that, Includes the following steps: S1. Through standardized field trials, determine the characteristics of planting patterns under various planting patterns in the target area and the estimated range of soil heavy metal flux removed by crops, and construct a standardized database of crop heavy metal absorption parameters. Among them, the planting patterns include single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation and leafy dryland crops, and the characteristics of planting patterns include planting cycle and crop type. S2. Determine key morphological indicators, including bioavailable forms, to quantify the content of different forms of heavy metals in soil. S3. Establish a rhizosphere morphology regulation model to characterize the feedback mechanism of crop species to heavy metal activity. S4. Couple the crop heavy metal absorption parameter database with the rhizosphere morphology regulation model to construct a heavy metal absorption prediction model based on bioavailability; S5. Integrate crop growth models and simulate biomass output to predict crop yields under different planting patterns; S6. Based on the combined heavy metal absorption prediction model and crop yield estimation results, the heavy metal removal flux value is calculated. S7. Output the calculation results of heavy metal removal flux values ​​and obtain the soil heavy metal removal flux assessment report for each planting pattern in the target area.

2. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 1, characterized in that: S1 specifically includes: Experimental fields covering five planting patterns—single-season rice, double-season rice, rice-oilseed rotation, rice-tobacco rotation, and leafy dryland crops—were systematically laid out in the target area. The planting cycle and crop combination were clarified, field management measures were recorded simultaneously, soil samples were collected at different levels to determine the total amount of heavy metals, the dynamic changes of heavy metal speciation in rhizosphere soil were analyzed, crop samples were collected by organ to determine biomass and heavy metal concentration, and the range of heavy metal removal flux was preliminarily estimated. Based on experimental data, the rotation sequence, coexistence period and root distribution depth of different planting patterns were sorted out, the influence mechanism of planting cycle on heavy metal absorption was quantified, a crop-heavy metal affinity classification standard was established, and biomass, heavy metal concentration and flux data were calibrated to standard temperature, humidity and soil conditions to form a structured heavy metal absorption parameter database framework. A multi-level database architecture was designed, with planting mode, crop type, and soil type as indexes. Core fields such as planting cycle, biomass, and heavy metal concentration were integrated. Cluster analysis was used to divide the high, medium, and low estimation ranges of heavy metal removal flux, and a database of crop heavy metal absorption parameters covering five planting modes was output to clarify the reliable estimation range.

3. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 2, characterized in that: The experimental fields were divided into blocks according to soil type. Each planting pattern was replicated three times. The area of ​​a single experimental field was ≥50m². Field management measures were recorded simultaneously, including fertilizer application, irrigation system, and types and frequencies of pesticides used. The soil sampling included basic soil samples and rhizosphere soil samples. For basic soil samples, they were collected in layers of 0-20cm and 20-40cm before the test to determine the total heavy metal content. For rhizosphere soil, rhizosphere soil was collected throughout the crop's growth period, i.e., within 5mm of the roots, and separated using the root-shaking method.

4. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 1, characterized in that: S2 specifically includes: The collected soil samples were air-dried, ground, sieved, and then uniformly reduced in size. The operation definitions for each form were clarified, and continuous extraction was carried out according to the operation definitions for heavy metal forms. Temperature, oscillation frequency, and time were controlled to maintain the stability of the forms. Blank samples, standard substances, and parallel samples were processed simultaneously to ensure that the recovery rate and precision met the quality control requirements. After centrifugation and filtration of each extract, each form is extracted according to the operation definition of each form. The concentration of each form of heavy metal is determined by inductively coupled plasma mass spectrometry. The contents of each form are summarized, and the relative deviation between the sum and the total amount of soil digestion is calculated. If the deviation exceeds the limit, the analysis needs to be repeated to ensure the reliability of the data. The proportion of each form was calculated, the dominant form was identified through cluster analysis, and a regression model was established by combining soil pH, organic matter, and cation exchange capacity to quantify the influence weight of environmental factors. The sum of exchangeable and carbonate-bound forms was used as the available form index, and the predictive ability was verified by regression analysis of crop root heavy metal concentration and available form content.

5. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 4, characterized in that: The specific definitions of each morphological operation are as follows: Exchangeable states were extracted with ammonium acetate, carbonate-bound states were treated with sodium acetate buffer, iron-manganese oxide-bound states were reduced with hydroxylamine hydrochloride, organic matter-bound states were treated with hydrogen peroxide, and residue states were digested with aqua regia-perchloric acid. The specific steps for extracting the morphology from each form are as follows: The exchangeable residue was extracted with 0.11 mol / L ammonium acetate by shaking for 2 h, followed by centrifugation at 4000 rpm for 15 min. The carbonate-bound residue was extracted with 0.5 mol / L sodium acetate for 5 h. The iron-manganese oxide-bound residue was extracted with 0.25 mol / L hydroxylamine hydrochloride by reduction for 6 h. The organic-bound residue was oxidized with 30% hydrogen peroxide and then extracted with 0.02 mol / L ammonium nitrate for 1 h. The final residue was digested with aqua regia and perchloric acid until clear.

6. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 1, characterized in that: S3 specifically includes: We collected crop rhizosphere soil, root exudates and plant biomass, used synchrotron X-ray fluorescence spectroscopy to locate the micro-regional distribution of heavy metals, combined with laser ablation mass spectrometry to analyze the concentration gradient at the root-soil interface, and simultaneously measured soil physicochemical properties, microbial community structure and enzyme activity to construct a soil-plant-microbe dataset. Five forms of heavy metals in rhizosphere soil were extracted stepwise. Their coordination environment with rhizosphere components was characterized by in-situ spectroscopy. Combined with the analysis of root exudate components, the regulatory effects of crops on rhizosphere redox conditions, pH and organic ligands were quantified, revealing the feedback mechanism of form transformation. Preliminary rhizosphere morphology regulation models were constructed for different crops. Using initial soil properties as input and crop type as selector, rhizosphere process parameters were used to predict the concentration of available heavy metals. The preliminary rhizosphere morphology regulation models were verified and calibrated through field measurement data to form a generalizable rhizosphere morphology regulation model.

7. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 1, characterized in that: S4 specifically includes: By integrating a database of crop heavy metal absorption parameters with a rhizosphere morphology regulation model, a unified data interface was established to extract soil physicochemical properties, total heavy metal content, morphological distribution, and crop-specific absorption parameters. After data cleaning and normalization, a standardized parameter set covering multiple soil types and scenarios was constructed. The dynamic coupling of rhizosphere biomorphic regulation mechanisms and heavy metal biomorphic transformation processes was studied. Based on synchrotron radiation spectroscopy analysis of adsorption-desorption pathways in the rhizosphere, combined with a chemical biomorphic equilibrium model to quantify biomorphic transformation rates, the regulatory mechanism of the rhizosphere microenvironment on the bioavailability of heavy metals was analyzed, and an interpretable process-driven model was formed. Using the bioavailability of heavy metals as the target variable, a preliminary heavy metal absorption prediction model was constructed by integrating a standard parameter set and a process-driven model and employing a hybrid strategy of random forest-dynamic equations. The parameters were then optimized using Bayesian optimization to output the bioavailable concentration and crop uptake. Finally, a bioavailability-based heavy metal absorption prediction model was formed through cross-validation on independent datasets.

8. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 7, characterized in that: S5 specifically includes: Using historical meteorological data, soil property data, and field observation data on crop growth period and yield in the target area, the crop types and field management parameters of the selected crop growth model were calibrated and validated. Design simulation scenarios for different planting patterns, clarify management measures for crop rotation sequences and sowing / harvest dates, and integrate long-term historical meteorological data or future climate scenario data of the target area, as well as physicochemical property data of corresponding soil types, as driving inputs for crop growth models; Run crop growth models in batches to simulate crop growth processes under various planting patterns over a multi-year timescale, and output simulated crop yield values ​​for each season and aboveground biomass.

9. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 8, characterized in that: S6 specifically includes: The system calls up the existing heavy metal absorption prediction model, inputs the soil properties, total heavy metal content and rhizosphere parameters of the target field, and outputs the predicted heavy metal concentration of harvestable crop organs based on bioavailability. Simultaneously, it obtains the simulated crop yield value simulated by the crop growth model under the same scenario. Based on the fundamental principle that the heavy metal removal flux is equal to the product of the heavy metal concentration in the harvestable organs of the crop and the simulated crop yield, the heavy metal removal flux is directly calculated for a single crop season. For the crop rotation pattern, the heavy metal removal flux value of each crop in the cycle is calculated separately and summed in time sequence to obtain the total heavy metal removal flux value of the crop rotation pattern in a complete cycle. The calculation results are then formatted into structured data output.

10. The method for assessing the removal of soil heavy metal fluxes by paddy and dryland crops based on model estimation according to claim 1, characterized in that: Specifically, S7 includes: The calculation results of heavy metal removal flux values ​​are categorized and summarized according to planting patterns to generate a structured data table containing fields such as crop name, yield, heavy metal concentration, single-season flux, and total flux of crop rotation. The data in the structured data table is converted into visual charts by an automated script to show the differences in heavy metal removal flux under different planting patterns. An assessment report template is also prepared, which includes a description of the calculation process, results analysis and optimization suggestions, and outputs a PDF-format assessment report on the removal flux of heavy metals in the soil.

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