Evaluation method of soil heavy metal removal fluxes by rice and upland crops based on model estimation

Abstract

The application discloses a method for evaluating rice field and dry land crop removal of soil heavy metal flux based on model estimation, relates to the field of agricultural environmental science and technology, and determines the planting mode characteristics and the estimation range of the crop removal of soil heavy metal flux under various planting modes in a target region through a standardized field test, and constructs a standardized crop heavy metal absorption parameter database, wherein the planting modes include single-cropping rice, double-cropping rice, rice-oil rotation, rice-tobacco rotation and leafy dry land crops, and the planting mode characteristics include planting cycles and crop types. Through the construction of the standardized crop heavy metal absorption parameter database, the combination of the planting mode characteristics and the dynamic change data of the heavy metal form of rhizosphere soil, the removal ability of crops to soil heavy metals can be more accurately quantified, meanwhile, the crop heavy metal absorption parameter database integrates the biomass, heavy metal concentration and flux calibration data, eliminates the interference of environmental variation, and forms a reliable heavy metal removal flux estimation range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural environmental science, in particular to a method for evaluating the flux of soil heavy metals removed by paddy and upland crops based on model estimation. BACKGROUND

[0002] With the acceleration of industrialization, soil heavy metal pollution problems are increasingly prominent, paddy and upland crops as the main agricultural production system, are affected by heavy metals, which is directly related to the growth and safety of crops, heavy metals can enter the plant body through the soil, thereby affecting food safety, when humans or animals ingest crops contaminated by heavy metals, it will pose a threat to health, therefore, evaluating the removal capacity of crops for soil heavy metals is crucial to ensure food chain safety, and through model estimation, the absorption and removal efficiency of crops for heavy metals under specific conditions can be effectively evaluated, which provides a scientific basis for soil remediation and pollution control and helps to develop effective management strategies.

[0003] In the prior art, the removal amount of heavy metals is estimated based on crop biomass, without considering the regulation of crop types on soil heavy metal forms, therefore, how to establish a crop heavy metal absorption database, and clarify the absorption capacity of common paddy and upland crops for various heavy metals, on the basis of estimating the removal amount of heavy metals based on biomass, analyzing the estimated value of the removal amount of heavy metals that is more in line with the actual situation, is the problem to be solved by the present application, therefore, the present application proposes a method for evaluating the flux of soil heavy metals removed by paddy and upland crops based on model estimation. SUMMARY

[0004] The present application aims to provide a method for evaluating the flux of soil heavy metals removed by paddy and upland crops based on model estimation, to solve the problems raised in the background art.

[0005] To solve the above technical problems, the technical solution adopted by the present application is:

[0006] The method for evaluating the flux of soil heavy metals removed by paddy and upland crops based on model estimation comprises the following steps:

[0007] S1, through standardized field tests, determine the planting pattern characteristics and the estimated range of the flux of soil heavy metals removed by crops under various planting patterns in the target area, and construct a standardized crop heavy metal absorption parameter database, wherein the planting patterns include single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and leafy upland crops, and the planting pattern characteristics include planting cycle and crop type;

[0008] S2, measure the key form indicators including available state, and quantify the content of different form components of soil heavy metals;

[0009] S3, establish rhizosphere morphological regulation model, represent the feedback mechanism of crop types on heavy metal activity;

[0010] S4, coupling crop heavy metal absorption parameter database and rhizosphere morphological regulation model, build heavy metal absorption prediction model based on biological availability;

[0011] S5, integrate crop growth model and simulate biomass output, estimate crop yield under different planting modes;

[0012] S6, the heavy metal absorption prediction model and the crop yield estimation result are combined, and the heavy metal removal flux value is calculated;

[0013] S7, output the calculation result of the heavy metal removal flux value, obtain the accurate target area under different planting modes The removal of soil heavy metal flux evaluation report.

[0014] The further improvement of the technical scheme of the application is that the S1 specifically comprises:

[0015] In the test field of the target area system layout covering single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and leafy dryland crops five kinds of planting modes, the planting cycle and crop type combination are clear, the field management measures are recorded synchronously, the soil samples are collected layer by layer to determine the total amount of heavy metals, the dynamic change of heavy metal forms in rhizosphere soil is analyzed, the crop samples are collected by organs to determine the biomass and heavy metal concentration, and the heavy metal removal flux range is preliminarily estimated;

[0016] Based on the test data, the rotation sequence, symbiotic period and root distribution depth of different planting modes are sorted out, the influence mechanism of planting cycle on heavy metal absorption is quantified, the crop-heavy metal affinity grading standard is established, the biomass, heavy metal concentration and flux data are calibrated to standard temperature and humidity and soil conditions, the environmental variation interference is eliminated, and the structured heavy metal absorption parameter database framework is formed;

[0017] Design a multi-level database architecture, with planting mode-crop type-soil type as index, integrate the core fields of planting cycle, biomass and heavy metal concentration, divide the high, medium and low estimation intervals of heavy metal removal flux through cluster analysis, output the crop heavy metal absorption parameter database covering five kinds of planting modes, and clear the reliable estimation range.

[0018] The further improvement of the technical scheme of the application is that the test field is divided into blocks according to soil type, each planting mode is set with 3 times of repetition, the area of single test field is greater than or equal to 50 square meters, the field management measures are recorded synchronously, covering fertilizer amount, irrigation system, pesticide use type and frequency;

[0019] The soil sampling comprises a basic soil sample and a rhizosphere soil sample, wherein, for the basic soil sample, the total heavy metal content is determined by collecting the basic soil sample in layers of 0-20 cm and 20-40 cm before the test; and for the rhizosphere soil, the rhizosphere soil is collected during the whole growth period of the crop, i.e., within 5 mm from the root system, and the rhizosphere soil is separated by the root shaking method.

[0020] The further improvement of the technical scheme of the present application is that the S2 specifically comprises:

[0021] The collected soil samples are uniformly divided after air drying, grinding and sieving, the operation definitions of various forms are defined, and continuous extraction is performed according to the operation definitions of the heavy metal forms, the temperature, the oscillation frequency and the time are controlled to maintain the stability of the forms, the blank samples, the standard substances and the parallel samples are synchronously processed, and the recovery rate and the precision meet the quality control requirements;

[0022] After each extraction solution is centrifuged and filtered, the forms are extracted according to the operation definitions of the forms, the concentrations of the heavy metal forms are determined by inductively coupled plasma mass spectrometry, the contents of the forms are summarized, the relative deviation of the sum of the forms and the total digestion amount of the soil is calculated, and if the deviation is out of tolerance, the data is reanalyzed to ensure the reliability of the data;

[0023] The proportion of each form is calculated, the dominant form is identified through cluster analysis, a regression model is established by combining the soil pH, the organic matter and the cation exchange capacity, the influence weight of the environmental factors is quantified, the sum of the exchangeable state and the carbonate-bound state is taken as the effective state index, and the prediction ability is verified through the regression analysis of the heavy metal concentration of the crop root system and the effective state content.

[0024] The further improvement of the technical scheme of the present application is that the operation definitions of the forms are specifically:

[0025] The exchangeable state is extracted by using ammonium acetate, the carbonate-bound state is treated by using sodium acetate buffer, the iron-manganese oxide-bound state is reduced by using hydroxylamine hydrochloride, the organic matter-bound state is oxidized by using hydrogen peroxide, and the residual state is digested by using aqua regia-hydrochloric acid;

[0026] The form extraction of the forms specifically comprises:

[0027] The exchangeable state is extracted by oscillation using 0.11 mol / L ammonium acetate (pH 7.0) for 2 h, and the centrifugal separation parameters are 4000 rpm+15 min; the residue of the carbonate-bound state is extracted by using 0.5 mol / L sodium acetate (pH 5.0) for 5 h; the residue of the iron-manganese oxide-bound state is reduced by using 0.25 mol / L hydroxylamine hydrochloride (pH 2.0) for 6 h; the residue of the organic matter-bound state is oxidized by using 30% hydrogen peroxide (pH 2.0), and then 0.02 mol / L ammonium nitrate is added for extraction for 1 h; and the final residue of the residual state is digested by using aqua regia-hydrochloric acid until it is clear.

[0028] The further improvement of the technical scheme of the present application is that the S3 specifically comprises:

[0029] Collecting crop rhizosphere soil, root exudates and plant biomass, using synchrotron X-ray fluorescence spectroscopy to locate heavy metal micro-area distribution, combining laser ablation mass spectrometry to analyze root-soil interface concentration gradient, synchronously measuring soil physical and chemical properties, microbial community structure and enzyme activity, and constructing soil-plant-microorganism dataset;

[0030] Extracting five forms of heavy metals in rhizosphere soil step by step, characterizing the coordination environment of the forms with rhizosphere components through in-situ spectroscopy, combining with root exudate composition analysis, quantifying the regulation of crops on rhizosphere redox conditions, pH and organic ligands, and revealing the feedback mechanism of form transformation;

[0031] A preliminary rhizosphere form regulation model is constructed for different crops, taking soil initial properties as input and crop species as selector, calling rhizosphere process parameters to predict effective heavy metal concentration, and verifying and calibrating the preliminary rhizosphere form regulation model through field measured data to form a generalizable rhizosphere form regulation model.

[0032] The further improvement of the technical scheme of the present application is that the S4 specifically comprises:

[0033] Integrating crop heavy metal absorption parameter database and rhizosphere form regulation model, establishing a unified data interface, extracting soil physical and chemical properties, total heavy metal content, form distribution and crop species-specific absorption parameters, and constructing a standardized parameter set covering multiple soil types and multiple scenarios after data cleaning and normalization processing;

[0034] Dynamically coupling rhizosphere form regulation mechanism and heavy metal form transformation process, analyzing adsorption-desorption path in rhizosphere based on synchrotron radiation spectroscopy, quantifying form transformation rate combined with chemical form balance model, analyzing the regulation mechanism of rhizosphere microenvironment on heavy metal bioavailability, and forming an interpretable process-driven model;

[0035] Taking heavy metal bioavailability as target variable, integrating standard parameter set and process-driven model, using a hybrid strategy of random forest-dynamic equation to construct a preliminary heavy metal absorption prediction model, outputting bioavailable concentration and crop absorption amount through Bayesian optimization parameter tuning, and finally forming a heavy metal absorption prediction model based on bioavailability through cross-validation of independent dataset.

[0036] The further improvement of the technical scheme of the present application is that the S5 specifically comprises:

[0037] The crop growth model is calibrated and verified by using historical meteorological data, soil property data and field observation data of crop growth period and yield data of the selected crops, so that the crop growth model can accurately simulate the growth dynamic process of the specific crops in the local environment.

[0038] The simulation scenarios of different planting modes are designed, the management measures of crop rotation sequence and sowing / harvesting date are determined, the long-term historical meteorological data or future climate scenario data of the target region and the physical and chemical property data of the corresponding soil types are integrated as the driving input of the crop growth model.

[0039] The crop growth model is batch-run to simulate the crop growth process of various planting modes in a multi-year time scale, and the crop yield simulation values of crop yield and aboveground biomass in each season are output.

[0040] The further improvement of the technical scheme of the present application is that the S6 specifically comprises:

[0041] The constructed heavy metal absorption prediction model is called, the soil properties, total amount of heavy metals and rhizosphere parameters of the target field plot are input, and the predicted value of the heavy metal concentration in the harvestable organs of crops based on the biological availability is output after operation, and the crop yield simulation value simulated by the crop growth model under the same scenario is obtained synchronously;

[0042] According to the basic principle that the heavy metal removal flux value is equal to the product of the heavy metal concentration in the harvestable organs of crops and the crop yield simulation value, the heavy metal removal flux value is directly calculated for a single crop season.

[0043] For the rotation mode, the heavy metal removal flux value of each crop in the period is calculated respectively, and the sum is accumulated in sequence to obtain the total heavy metal removal flux value of the rotation mode in a complete cycle, and then the calculation result is formatted as structured data output.

[0044] The further improvement of the technical scheme of the present application is that the S7 specifically comprises:

[0045] The calculation results of the heavy metal removal flux value are classified and summarized according to the planting mode to generate a structured data table, which includes the fields of crop name, yield, heavy metal concentration, single-season flux and total flux of rotation;

[0046] The data in the structured data table is converted into a visual chart by an automatic script, the differences of the heavy metal removal flux of different planting modes are displayed, an evaluation report template is prepared, the calculation process description, result analysis and optimization suggestions are embedded, and a PDF format removal soil heavy metal flux evaluation report is output.

[0047] Due to the adoption of the above technical scheme, the present application has the following technical progress compared with the prior art:

[0048] The application provides an evaluation method for estimating the removal flux of soil heavy metals by paddy field and dry land crops based on model estimation, which can more accurately quantify the removal capacity of crops for soil heavy metals by constructing a standardized crop heavy metal absorption parameter database, combining the planting mode characteristics and the dynamic change data of the heavy metal forms in the rhizosphere soil, and simultaneously, the crop heavy metal absorption parameter database integrates the biomass, heavy metal concentration and flux calibration data, eliminates the interference of environmental variation, forms a reliable heavy metal removal flux estimation range, and significantly improves the scientificity and reliability of the evaluation results, thereby providing accurate basis for soil remediation.

[0049] The application provides an evaluation method for estimating the removal flux of soil heavy metals by paddy field and dry land crops based on model estimation, which can more accurately quantify the removal capacity of crops for soil heavy metals by constructing a standardized crop heavy metal absorption parameter database, combining the planting mode characteristics and the dynamic change data of the heavy metal forms in the rhizosphere soil, and simultaneously, the crop heavy metal absorption parameter database integrates the biomass, heavy metal concentration and flux calibration data, eliminates the interference of environmental variation, forms a reliable heavy metal removal flux estimation range, and significantly improves the scientificity and reliability of the evaluation results, thereby providing accurate basis for soil remediation. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0051] Figure 1 The working flowchart of the evaluation method for estimating the removal flux of soil heavy metals by paddy field and dry land crops based on model estimation of the present application is shown in the figure.

[0052] Figure 2 The method flowchart of the evaluation method for estimating the removal flux of soil heavy metals by paddy field and dry land crops based on model estimation of the present application is shown in the figure. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0054] Embodiment 1, as Figure 1 , Figure 2As shown, the present application provides an evaluation method for estimating the removal of soil heavy metals by rice and upland crops based on model estimation, comprising the following steps:

[0055] S1, through standardized field test, determine the planting mode characteristics and the estimation range of crop removal of soil heavy metals under various planting modes in the target area, and construct a standardized crop heavy metal absorption parameter database, wherein the planting modes include single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and leafy upland crops, the planting mode characteristics include planting cycle and crop type, the test field is systematically arranged according to soil type, 3 repetitions are set for each planting mode, the area of a single test field is ≥50m², the field management measures are recorded synchronously, including fertilizer application amount, irrigation system, pesticide type and frequency;

[0056] In addition, single-crop rice: only one season of mid-season rice is planted, and the growth period (120-150 days) is recorded; double-crop rice: early rice (90-110 days) + late rice (110-130 days), and the connection time of the two crops is clear; rice-oil rotation: rice→oilseed rape (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 upland crops: spinach, swamp cabbage, etc. (30-60 days / season), using continuous cropping or intercropping mode;

[0057] The test field is arranged according to soil type, 3 repetitions are set for each planting mode, the area of a single test field is ≥50m², and the field management measures are recorded synchronously, including fertilizer application amount, irrigation system, pesticide type and frequency;

[0058] Soil sampling includes basic soil samples and rhizosphere soil samples, wherein, for the basic soil samples, the total heavy metal content is measured by collecting the samples in layers of 0-20cm and 20-40cm before the test; for the rhizosphere soil, the rhizosphere soil within 5mm from the root system is collected during the whole growth period of the crops, and the root shaking method is used for separation;

[0059] Specific work content: In the target area, layout multi-type planting mode test field in the system, covering single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and typical planting mode of leafy dry land crops, and determine the planting cycle and crop type combination of various planting modes, record field management measures synchronously, collect basic soil samples according to soil profile layering, measure total heavy metal content, collect rhizosphere soil samples during the whole growth period of crops, analyze dynamic changes of heavy metal forms, collect crop samples by organs at harvest time, measure biomass and heavy metal concentration, and preliminarily estimate the range of heavy metal removal flux under various planting modes. For biomass determination, collect samples by organs (roots, stems, leaves, grains / economic parts) at harvest time, kill green at 105°C, and dry at 70°C to constant weight, and record dry matter weight. For heavy metal concentration analysis, after digesting the crop samples, measure the heavy metal concentration (mg / kg) of each organ by ICP-MS. Single-crop rice planting mode covers single-crop rice, double-crop rice planting mode covers early rice and late rice, rice-oil rotation planting mode covers rice and rape, rice-tobacco rotation planting mode covers rice and tobacco, and leafy dry land crop planting mode covers leafy dry land crops. Based on field test data, sort out the ecological characteristics of different planting modes, including crop rotation sequence, symbiotic period length and root distribution depth parameters, quantify the influence mechanism of planting cycle on heavy metal absorption, establish a grading standard for crop type and heavy metal affinity, calibrate the original data of biomass, heavy metal concentration and heavy metal removal flux to standard temperature and humidity and soil conditions to eliminate environmental variation interference, form a structured and expandable heavy metal absorption parameter database framework. In which, rice roots are concentrated in the 0-20cm soil layer, accounting for more than 80%; tobacco roots can reach 40cm; rape roots are distributed shallowly (0-30cm); design a multi-level database architecture, taking planting mode-crop type-soil type as the index, integrate planting cycle, biomass and heavy metal concentration core fields, divide high, medium and low estimated intervals of heavy metal removal flux through cluster analysis, output crop heavy metal absorption parameter database covering five planting modes in the target area, and determine the reliable estimated range of crop removal of soil heavy metal flux under various planting modes. High estimated interval: flux>75th percentile, medium estimated interval: 25th-75th percentile, low estimated interval: flux<25th percentile.

[0060] S2, determining key morphological indexes including effective state, quantifying different morphological component contents of soil heavy metals, collecting soil samples after air drying, grinding and sieving, uniformly dividing, defining each morphological operation definition, and continuously extracting according to the heavy metal morphological operation definition, controlling temperature, oscillation frequency and time to maintain morphological stability, synchronously processing blank samples, standard substances and parallel samples, ensuring that the recovery rate and precision meet the quality control requirements, after each extraction liquid is centrifuged and filtered, each morphological operation is morphologically extracted according to the morphological operation definition, the concentration of each morphological heavy metal is determined by inductively coupled plasma mass spectrometry, the content of each morphological state is summarized, the relative deviation of the sum and the total digestion amount of the soil is calculated, and the deviation is reanalyzed if the deviation is out of tolerance, to ensure the reliability of the data, the proportion of each morphological state is calculated, the dominant morphological state is identified through cluster analysis, a regression model is established combining soil pH, organic matter and cation exchange capacity, the influence weight of environmental factors is quantified, the sum of exchangeable state and carbonate-bound state is taken as the effective state index, and the prediction ability is verified through regression analysis of the concentration of heavy metals in crop roots and the content of effective state;

[0061] In addition, the specific morphological operation definition is as follows: the exchangeable state is extracted by ammonium acetate, the carbonate-bound state is treated by sodium acetate buffer, the iron-manganese oxide-bound state is reduced by hydroxylamine hydrochloride, the organic matter-bound state is oxidized by hydrogen peroxide, and the residual state is digested by aqua regia-hydrochloric acid;

[0062] The specific morphological extraction of each morphological state is as follows: the exchangeable state is extracted by 0.11 mol / L ammonium acetate (pH 7.0) for 2 h, and the centrifugation separation parameters are 4000 rpm+15 min; the residual of the carbonate-bound state is extracted by 0.5 mol / L sodium acetate (pH 5.0) for 5 h; the residual of the iron-manganese oxide-bound state is reduced by 0.25 mol / L hydroxylamine hydrochloride (pH 2.0) for 6 h; the residual of the organic matter-bound state is oxidized by 30% hydrogen peroxide (pH 2.0) and then extracted by 0.02 mol / L ammonium nitrate for 1 h; the residual state: the final residual is digested by aqua regia-hydrochloric acid until it is clear;

[0063] The specific work content is: the collected soil samples are dried, ground and passed through a 2mm nylon screen, and the animal and plant residues and stones are removed to ensure the uniformity of the samples. For samples that need to analyze the micro-area morphology, the original structure is preserved by using freeze-drying technology, and then the samples are divided into the required amount by quartering method. According to the characteristics of the target heavy metal and the type of soil, the operation definition of each form is determined: exchangeable state, carbonate bound state, iron and manganese oxide bound state, organic matter bound state and residual state. The temperature (25±1℃), oscillation frequency (200rpm) and extraction time are controlled during the extraction process to avoid the transformation of the form. The blank sample, standard material and parallel sample (repeated 3 times) are processed synchronously, and the recovery rate (target value 85-115%) and relative standard deviation (≤10%) are calculated to ensure the reliability of the extraction process. According to the preselected scheme, the form extraction is carried out in turn: the exchangeable state is extracted by oscillation with 0.11mol / L ammonium acetate (pH7.0) for 2h, and the supernatant is taken after centrifugal separation (4000rpm, 15min); the carbonate bound state: the residue is extracted with 0.5mol / L sodium acetate (pH5.0) for 5h; the iron and manganese oxide bound state: the residue is reduced and extracted with 0.25mol / L hydroxylamine hydrochloride (pH2.0) for 6h; the organic matter bound state: after the residue is oxidized with 30%H 2 O 2 (pH2.0), 0.02mol / L ammonium nitrate is added and extracted for 1h; the residual state: the final residue is digested with aqua regia-perchloric acid until clear; the extraction solution is filtered through a 0.45μm filter membrane, and the concentration of heavy metal is determined by inductively coupled plasma mass spectrometry (ICP-MS), the sum of the contents of each form is calculated, and the relative deviation (≤15%) of the sum and the total digestion amount is verified. When the deviation is too large, the extraction and analysis need to be re-extracted. The proportion of each form is calculated (such as the exchangeable state proportion = exchangeable state content / total content × 100%), the dominant form is identified by cluster analysis, and the regression model of form-pH / organic matter / cation exchange capacity is established combined with the physical and chemical properties of soil to quantify the influence weight of environmental factors on the transformation of the form. The sum of the exchangeable state and the carbonate bound state is used as the effective state index to evaluate the bioavailability of heavy metals. By comparing the heavy metal concentration in the crop root system with the effective state content, the prediction ability of the form index is verified (R²≥0.75 is considered to be significantly correlated);

[0064] S3, Establish rhizosphere morphological regulation model, characterize the feedback mechanism of crop species to heavy metal activity, collect crop rhizosphere soil, root exudates and plant biomass, use synchrotron X-ray fluorescence spectrum to locate heavy metal micro-area distribution, combine laser ablation inductively coupled plasma mass spectrometry to analyze root-soil interface concentration gradient, simultaneously measure soil physical and chemical properties, microbial community structure and enzyme activity, construct soil-plant-microorganism dataset, step by step extract five forms of heavy metals in rhizosphere soil, characterize the coordination environment of heavy metals and rhizosphere components by in-situ spectroscopy, combine root exudate composition analysis, quantify the regulation of crops on rhizosphere redox conditions, pH and organic ligand, reveal the feedback mechanism of morphological transformation, and construct a preliminary rhizosphere morphological regulation model for different crops, taking the initial properties of soil as input and crop species as selector, calling rhizosphere process parameters to predict the concentration of available heavy metals, verifying and calibrating the preliminary rhizosphere morphological regulation model through field measured data, and forming a generalizable rhizosphere morphological regulation model;

[0065] Specific work content is: collect crop rhizosphere soil (0-5mm from root surface), simultaneously collect root exudates and plant aboveground / underground biomass, use high-resolution imaging technology (synchrotron X-ray fluorescence spectrum) to locate the micro-area distribution of heavy metals in the rhizosphere, combine laser ablation inductively coupled plasma mass spectrometry to analyze the concentration gradient of heavy metals at the root-soil interface, simultaneously measure soil physical and chemical properties (pH, organic matter, cation exchange capacity, oxidation-reduction potential), microbial community structure (16S rRNA / ITS sequencing) and enzyme activity (urease, dehydrogenase, etc.), and construct a soil-plant-microorganism dataset; step by step extract exchangeable state, carbonate bound state, iron and manganese oxide bound state, organic matter bound state and residual state, characterize the coordination environment change of heavy metals and rhizosphere components by in-situ diffuse reflectance infrared spectroscopy and X-ray absorption near-edge structure spectroscopy, and combine root exudate composition analysis, quantify the regulation of crop species on rhizosphere redox conditions, pH and organic ligand supply, and reveal the feedback mechanism of heavy metal morphological transformation; construct a preliminary rhizosphere morphological regulation model for each crop, take the initial input of rhizosphere undisturbed soil properties, take the crop species as selector, automatically call the corresponding rhizosphere process parameters and morphological response equation, output the predicted rhizosphere available heavy metal concentration, verify and calibrate the preliminary rhizosphere morphological regulation model by comparing the model predicted value with the field measured rhizosphere available content, form a generalizable rhizosphere morphological regulation model, and provide a theoretical basis for heavy metal pollution prevention and control under different crop systems;

[0066] S4, Coupling crop heavy metal absorption parameter database and rhizosphere morphological regulation model, constructing heavy metal absorption prediction model based on biological availability;

[0067] S5, Integrate crop growth model and simulate biomass output, estimate crop yield under different planting modes;

[0068] S6, the heavy metal absorption prediction model and the crop yield estimation result are integrated, and a heavy metal removal flux value is calculated;

[0069] S7, the calculation result of the heavy metal removal flux value is output, and a precise target area various planting mode under the removal soil heavy metal flux evaluation report is obtained.

[0070] Embodiment 2, as shown in Figure 1 , Figure 2 The application provides a technical solution based on embodiment 1: S4 specifically includes:

[0071] The crop heavy metal absorption parameter database and the rhizosphere morphological regulation model are integrated, a unified data interface is established, soil physical and chemical properties, total heavy metal content, morphological distribution and crop specific absorption parameters are extracted, after data cleaning and normalization processing, a standardized parameter set covering multiple soil types and multiple scenes is constructed, the rhizosphere morphological regulation mechanism and the heavy metal morphological transformation process are dynamically coupled, the adsorption-desorption path in the rhizosphere is analyzed based on the synchrotron radiation spectrum, the morphological transformation rate is quantified by combining the chemical morphological balance model, the regulation mechanism of the rhizosphere microenvironment on the heavy metal bioavailability is analyzed, and an interpretable process-driven model is formed, taking the heavy metal bioavailability as a target variable, fusing the standard parameter set and the process-driven model, adopting a mixed strategy of random forest-dynamics equation to construct a preliminary heavy metal absorption prediction model, and outputting the bioavailable concentration and crop absorption amount through Bayesian optimization parameter tuning, and finally forming a heavy metal absorption prediction model based on bioavailability through independent data set cross-validation;

[0072] Specific work content is: the integration of crop heavy metal absorption parameter database and rhizosphere morphological regulation model, establish a unified data interface, extract soil physical and chemical properties, total heavy metal and morphological distribution data, combine crop species-specific absorption parameters, eliminate isomerism through data cleaning, normalize multi-source data, construct a standardized parameter set covering different soil types and pollution scenarios, form a dynamic knowledge base containing heavy metal morphological-soil property-crop absorption correlation rules; dynamically couple the rhizosphere morphological regulation mechanism with the heavy metal morphological transformation process, analyze the adsorption-desorption reaction path of heavy metals in the rhizosphere based on synchrotron radiation spectroscopy, combine the chemical form balance model to quantify the morphological transformation rate, and then analyze the regulation mechanism of the rhizosphere microenvironment on heavy metal bioavailability, and form an interpretable process-driven model; taking heavy metal bioavailability as the core target variable, integrating the standard parameter set and the process-driven model, using a hybrid modeling strategy to build a preliminary heavy metal absorption prediction model, using random forest algorithm in the upper layer to capture nonlinear relationship, embedding kinetic equation in the lower layer to describe morphological transformation process, realizing parameter adaptive optimization through Bayesian optimization, the preliminary heavy metal absorption prediction model output includes heavy metal bioavailable concentration and crop absorption amount, using independent data set for cross-validation, evaluating the generalization ability of the model under complex environmental conditions, and then forming a heavy metal absorption prediction model based on bioavailability;

[0073] S5 specifically includes:

[0074] Using target area historical meteorological data, soil property data and crop growth period and yield data observed in the field, the crop species and field management parameters of the selected crop growth model are calibrated and verified to ensure that they can accurately simulate the growth dynamic process of specific crops in the local environment. Different planting mode simulation scenarios are designed, crop rotation sequences and sowing / harvesting dates management measures are specified, long-term historical meteorological data or future climate scenario data of the target area and corresponding soil type physical and chemical property data are integrated as the driving input of the crop growth model, and the crop growth model is batch run to simulate the crop growth process of various planting modes on a multi-year time scale. The output is the simulated value of crop yield and aboveground biomass of crop yield in each season;

[0075] The specific work content is: selecting a verified crop growth model, simulating the growth dynamic simulation process of the target crop, realizing the localization calibration of the crop growth model parameters, that is, using the historical meteorological data, soil property data and field observed crop growth period and yield data of the target area to calibrate and verify the crop types and field management parameters, so as to ensure that the crop growth model can accurately simulate the growth dynamics of different types of crops in a specific environment; on the basis of the verified crop growth model, simulation scenarios representing different planting modes are set, each planting mode needs to define its crop rotation sequence, sowing and harvesting date management measures, wherein the driving data includes long-term historical meteorological data or future climate scenario data of the target area, and the physicochemical property data of the corresponding soil type, the crop growth model is run in batches to simulate the crop growth process of each planting mode in a multi-year time scale, and the crop yield simulation value including the yield and aboveground biomass of each season crop is output;

[0076] S6 specifically comprises:

[0077] The constructed heavy metal absorption prediction model is called, the soil properties, total heavy metal content and rhizosphere parameters of the target field plot are input, and the predicted value of the heavy metal concentration in the harvestable organ of the crop based on the bioavailability is output after operation. The crop yield simulation value simulated by the crop growth model under the same scenario is obtained synchronously. According to the basic principle that the heavy metal removal flux value is equal to the product of the heavy metal concentration value in the harvestable organ of the crop and the crop yield simulation value, the heavy metal removal flux value is directly calculated for a single crop season, and for a rotation mode, the heavy metal removal flux value of each crop in the period is calculated respectively, and the sum is accumulated in sequence to obtain the total heavy metal removal flux value of the rotation mode in a complete cycle. Then the calculation result is formatted as structured data output.

[0078] The specific work content is: 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 mode, and the predicted heavy metal concentration value in the harvestable organ of the crop is output after operation of the heavy metal absorption prediction model. The value is a precise prediction value based on bioavailability, and the simulation result of the crop growth model is obtained, and the crop yield simulation value of each season crop and aboveground biomass under the corresponding planting mode and scenario is extracted; based on the basic principle that the heavy metal removal flux value = the heavy metal concentration value in the harvestable organ of the crop × the crop yield simulation value, wherein for a single crop season, the predicted heavy metal concentration and the crop yield simulation value are multiplied to obtain the heavy metal removal flux value of the crop from the unit area of land through the harvested product in that season, and for a planting mode including a rotation mode, the heavy metal removal flux value of each crop in the rotation period is calculated respectively, and the sum is accumulated according to the planting sequence, and finally the total heavy metal removal flux value of the planting mode in a complete cycle is calculated, and then the calculation result of the heavy metal removal flux value is formatted and output.

[0079] S7 specifically comprises:

[0080] The calculation results of heavy metal removal flux values are classified and summarized according to planting patterns to generate a structured data table containing fields of crop name, yield, heavy metal concentration, single-season flux and total flux of crop rotation. The data in the structured data table are converted into visual charts by an automated script to show the differences in heavy metal removal fluxes of different planting patterns, and an evaluation report template is prepared by embedding calculation process description, result analysis and optimization suggestions to output a PDF format removal soil heavy metal flux evaluation report.

[0081] The above merely describes a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which shall be encompassed within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for estimating the flux of heavy metals removed from the soil by rice and upland crops based on model estimates, characterized by, Comprise the following steps: S1, through standardized field test, determine the characteristics of planting patterns and the estimated range of crop removal of heavy metals in soil flux in various planting patterns in the target area, and construct a standardized crop heavy metal absorption parameter database, wherein the planting patterns include single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and leafy dryland crops, and the characteristics of the planting patterns include planting cycle and crop type, specifically including: In the test field of the target area system layout covering five kinds of planting patterns of single-crop rice, double-crop rice, rice-oil rotation, rice-tobacco rotation and leafy dryland crops, the combination of planting cycle and crop type is determined, the field management measures are recorded synchronously, the soil samples are collected in layers to measure the total amount of heavy metals, the dynamic change of heavy metal forms in rhizosphere soil is analyzed, the crop samples are collected in organs to measure the biomass and heavy metal concentration, and the range of heavy metal removal flux is preliminarily estimated; Based on the test data, the rotation sequence, symbiotic period and root distribution depth of different planting patterns are sorted out, the influence mechanism of planting cycle on heavy metal absorption is quantified, the crop-heavy metal affinity classification standard is established, the biomass, heavy metal concentration and flux data are calibrated to the standard temperature and humidity and soil conditions, and the structured heavy metal absorption parameter database framework is formed; A multi-level database architecture is designed, taking planting pattern-crop type-soil type as index, integrating the core fields of planting cycle, biomass and heavy metal concentration, dividing the high, medium and low estimated intervals of heavy metal removal flux through cluster analysis, outputting the crop heavy metal absorption parameter database covering five kinds of planting patterns, and determining the reliable estimated range; S2, measure the key form indexes including available state, and quantify the content of different form components of soil heavy metals; S3, establish a rhizosphere form regulation model to represent the feedback mechanism of crop type on heavy metal activity, specifically including: Collect crop rhizosphere soil, root exudates and plant biomass, use synchrotron X-ray fluorescence spectrum to locate heavy metal micro-area distribution, combine laser ablation mass spectrometry to analyze root-soil interface concentration gradient, synchronously measure soil physical and chemical properties, microbial community structure and enzyme activity, and construct a soil-plant-microorganism dataset; Extract five forms of heavy metals in rhizosphere soil step by step, characterize their coordination environment with rhizosphere components through in-situ spectrum technology, combine root exudate composition analysis, quantify the regulation of crops on rhizosphere oxidation-reduction condition, pH and organic ligand, and reveal the feedback mechanism of form transformation; Construct a preliminary rhizosphere form regulation model for different crops, take soil initial properties as input and crop type as selector, call rhizosphere process parameters to predict available state heavy metal concentration, verify and calibrate the preliminary rhizosphere form regulation model through field measured data, and form a generalizable rhizosphere form regulation model; S4, couple the crop heavy metal absorption parameter database with the rhizosphere form regulation model, and construct a heavy metal absorption prediction model based on biological availability, specifically including: The integrated phytoremediation parameter database and rhizosphere morphological regulation model are used to establish a unified data interface, extract soil physical and chemical properties, total heavy metal content, morphological distribution and crop-specific absorption parameters, and construct a standardized parameter set covering multiple soil types and multiple scenarios after data cleaning and normalization processing; The rhizosphere morphological regulation mechanism and heavy metal morphological transformation process are dynamically coupled, the adsorption-desorption path in the rhizosphere is analyzed based on synchrotron radiation spectroscopy, the morphological transformation rate is quantified by combining the chemical morphological equilibrium model, the regulation mechanism of the rhizosphere microenvironment on heavy metal bioavailability is analyzed, and an interpretable process-driven model is formed; Taking heavy metal bioavailability as the target variable, the standard parameter set and the process-driven model are integrated, a preliminary heavy metal absorption prediction model is constructed by using the mixed strategy of random forest-dynamic equation, the bioavailable concentration and crop absorption amount are output by optimizing the parameters through Bayesian optimization, and finally the heavy metal absorption prediction model based on bioavailability is formed through cross-validation of independent data sets; S5, the crop growth model is integrated to simulate biomass output, and the crop yield under different planting modes is estimated; S6, the heavy metal absorption prediction model and the crop yield estimation results are integrated to calculate the heavy metal removal flux value; S7, the calculation results of the heavy metal removal flux value are output to obtain the removal soil heavy metal flux evaluation report under the target area planting mode.

2. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 1, characterized in that: The test field is divided into blocks according to soil type, each planting mode is set with 3 repetitions, the single block test field area is greater than or equal to 50m², and the field management measures are recorded simultaneously, including fertilizer amount, irrigation system, pesticide use type and frequency; The soil sampling includes basic soil samples and rhizosphere soil samples, wherein, for the basic soil samples, the total heavy metal content is measured by collecting the 0-20cm and 20-40cm layers before the test; for the rhizosphere soil, the rhizosphere soil within 5mm from the root system is collected during the whole growth period of the crop, and the root shaking method is used for separation.

3. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 1, characterized in that: The S2 specifically includes: The collected soil samples are uniformly divided after air drying, grinding and sieving, the operation definitions of each form are clarified, and continuous extraction is carried out according to the heavy metal form operation definition, the temperature, oscillation frequency and time are controlled to maintain the form stability, the blank samples, standard materials and parallel samples are processed synchronously, and the recovery rate and precision meet the quality control requirements; After each extraction liquid is centrifuged and filtered, each form is extracted according to the operation definition of each form, the concentration of each form heavy metal is measured by inductively coupled plasma mass spectrometry, the content of each form is summarized, the relative deviation of the sum and the total digestion amount of the soil is calculated, and the deviation exceeding the error needs to be reanalyzed to ensure the reliability of the data; The proportion of each form is calculated, the dominant form is identified by cluster analysis, a regression model is established combining soil pH, organic matter and cation exchange capacity to quantify the influence weight of environmental factors, the sum of exchangeable state and carbonate-bound state is taken as the effective state index, and the prediction ability is verified by regression analysis of the heavy metal concentration of crop root system and the effective state content.

4. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 3, characterized in that: The operation definition of each form is specifically: Exchangeable state is extracted by ammonium acetate, carbonate bound state is treated by sodium acetate buffer, iron and manganese oxide bound state is reduced by hydroxylamine hydrochloride, organic matter bound state is oxidized by hydrogen peroxide, and residual state is digested by aqua regia-perchloric acid; The form extraction of each form is specifically: The exchangeable state is extracted by oscillation using 0.11 mol / L ammonium acetate for 2 h, and the centrifugal separation parameters are 4000 rpm+15 min; the residual of the carbonate bound state is extracted by 0.5 mol / L sodium acetate for 5 h; the residual of the iron and manganese oxide bound state is reduced by 0.25 mol / L hydroxylamine hydrochloride for 6 h; the residual of the organic matter bound state is oxidized by 30% hydrogen peroxide, and then 0.02 mol / L ammonium nitrate is added for extraction for 1 h; and the final residual of the residual state is digested by aqua regia-perchloric acid until it is clear.

5. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 1, characterized in that: The S5 specifically comprises: The crop species and field management parameters of the selected crop growth model are calibrated and verified by using historical meteorological data, soil property data and crop growth period and yield data observed in the field; Different planting mode simulation scenarios are designed, the management measures of crop rotation sequence and sowing / harvesting date are determined, the long-term historical meteorological data or future climate scenario data of the target area and the physicochemical property data of the corresponding soil types are integrated as the driving input of the crop growth model; The crop growth model is batch-run to simulate the crop growth process of various planting modes in a multi-year time scale, and the crop yield simulation value of the crop yield and aboveground biomass in each season is output.

6. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 5, characterized in that: The S6 specifically comprises: The constructed heavy metal absorption prediction model is called, the soil properties, total amount of heavy metals and rhizosphere parameters of the target field plot are input, and the predicted value of the heavy metal concentration in the harvestable organ of the crop based on the bioavailability is output after calculation, and the crop yield simulation value simulated by the crop growth model under the same scenario is obtained synchronously; According to the basic principle that the heavy metal removal flux value is equal to the product of the heavy metal concentration value in the harvestable organ of the crop and the crop yield simulation value, the heavy metal removal flux value is directly calculated for a single crop season; For the crop rotation mode, the heavy metal removal flux value of each crop in the period is calculated respectively, and the sum is accumulated in sequence to obtain the total heavy metal removal flux value of the crop rotation mode in a complete cycle, and then the calculation result is formatted as structured data output.

7. The method for estimating the fluxes of heavy metals removed from soil by paddy and upland crops based on model estimation according to claim 1, characterized in that: The S7 specifically comprises: The calculation results of the heavy metal removal flux value are classified and summarized according to the planting mode to generate a structured data table, which includes the fields of 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 a visual chart by an automatic script, the differences in the heavy metal removal flux of different planting modes are displayed, an evaluation report template is prepared, the calculation process description, result analysis and optimization suggestions are embedded, and a PDF format removal soil heavy metal flux evaluation report is output.

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