An agricultural soil pollution prediction method

Abstract

This application relates to the technical field of soil pollution component analysis, and discloses an agricultural soil pollution prediction method. The method includes: detecting the physical and chemical properties of soil through hierarchical grid sampling, measuring the heavy metal content, organic matter content and enzyme activity, and obtaining a pollution characteristic data set; monitoring the pollutant diffusion characteristics by using an electrochemical sensing detection array to generate a migration law model; using a multi-parameter monitoring system to detect the pH value, redox potential and ion activity to form a dynamic change sequence; establishing a colloidal interface action response matrix to construct a pollutant accumulation model; measuring the occurrence form and ion concentration of pollutants to generate an interaction map; and finally constructing an early warning system and outputting a prevention and control plan. Through the collaborative application of various analysis and testing means and combined with the research on the interface action mechanism of pollutants in soil, this application realizes the whole-process intelligent monitoring of soil pollution prediction.

Description

Technical Field

[0001] The present application relates to the field of soil pollution component analysis, and in particular to a method for predicting agricultural soil pollution. Background Art

[0002] Agricultural soil pollution monitoring and prediction is an important research topic in the field of environmental protection. At present, the commonly used soil pollution monitoring methods mainly include physical and chemical analysis methods such as atomic absorption spectroscopy, inductively coupled plasma mass spectrometry, and online monitoring systems based on electrochemical sensors. These methods have made certain progress in the determination of pollutant content. At the same time, a variety of mathematical models have been developed in the field of soil pollution prediction, such as regression models based on statistical analysis and prediction models based on machine learning. In the study of pollutant migration and transformation, researchers have conducted in-depth research on the occurrence forms and environmental behaviors of pollutants in soil through chromatography-mass spectrometry, synchrotron radiation technology and other means.

[0003] However, the relevant technologies have the following shortcomings: existing monitoring methods are often limited to the detection of a single pollutant or a certain type of pollutant, and it is difficult to systematically evaluate the interactions between multiple pollutants and their comprehensive impact on the soil environment; conventional prediction models mainly rely on historical data for statistical analysis, and lack an in-depth understanding of the migration and transformation mechanisms of pollutants at the microscopic scale; traditional sampling and analysis methods mostly use laboratory offline detection, which makes it difficult to achieve real-time monitoring of the dynamic changes of pollutants; in addition, existing technologies often lack sufficient consideration of regional differences in the formulation of soil pollution prevention and control plans. Summary of the invention

[0004] The present application provides a method for predicting agricultural soil pollution, which is used to establish a technical system for predicting agricultural soil pollution that can systematically characterize the interactions between multiple pollutants, dynamically monitor the migration and transformation of pollutants, and output targeted prevention and control plans. The present invention realizes intelligent monitoring of the entire process of soil pollution prediction through the coordinated application of multiple analytical and testing methods, combined with the study of the interface mechanism of pollutants in the soil.

[0005] In a first aspect, the present application provides a method for predicting agricultural soil pollution, which comprises: performing multi-point physical and chemical property detection on soil samples in agricultural areas through layered grid sampling, performing heavy metal content determination, organic matter content analysis and enzyme activity test on soil samples, and combining ion chromatography analysis and atomic absorption spectroscopy detection to obtain a soil pollution characteristic data set;

[0006] Based on the soil pollution characteristic data set, an electrochemical sensor detection array is used to dynamically monitor the diffusion characteristics of pollutants in soil samples, and a pollutant migration law characteristic model is generated by combining dielectric property analysis and fluorescence spectrum detection;

[0007] According to the pollutant migration law characteristic model, the multi-parameter online monitoring system is used to detect the soil pH value, redox potential and ion activity in real time. Through multi-channel signal acquisition and data correction, a dynamic change characteristic sequence of soil pollutants is formed.

[0008] According to the dynamic change characteristic sequence of soil pollutants, a response relationship matrix between pollutant concentration and soil colloid interface interaction is established. Combining capillary electrophoresis analysis and mass spectrometry detection, a pollutant accumulation model considering interface adsorption effect is constructed.

[0009] Using the pollutant accumulation model, the occurrence form of pollutants in soil is determined by synchronous thermal analysis and X-ray diffraction methods. The selective ion electrode array is used to detect the change of key ion concentration, and a multi-pollutant interaction characteristic map is generated.

[0010] According to the multi-pollutant interaction characteristic map, a pollutant content early warning system is constructed based on thermogravimetric analysis and colorimetry. Combining with spectral analysis methods, a soil pollution prevention and control plan for different regions is output.

[0011] In the technical solution provided by this application, through hierarchical grid sampling and multi-point physical and chemical property detection, a comprehensive assessment of the farmland soil pollution status is realized, ensuring the representativeness of sampling and the accuracy of data. The electrochemical sensing detection array is used to dynamically monitor the diffusion characteristics of pollutants in soil samples. Combining dielectric property analysis and fluorescence spectrum detection, the migration law of pollutants in soil is effectively captured. The multi-parameter online monitoring system is used to detect the soil pH value, redox potential and ion activity in real time. Through multi-channel signal acquisition and data correction, the dynamic change characteristics of soil pollutants are accurately reflected. A response relationship matrix between pollutant concentration and soil colloid interface interaction is established. Combining capillary electrophoresis analysis and mass spectrometry detection, the influence mechanism of interface adsorption effect on pollutant accumulation is deeply revealed. The occurrence form of pollutants in soil is determined by synchronous thermal analysis and X-ray diffraction methods. The selective ion electrode array is used to detect the change of key ion concentration, and the interaction characteristics between multi-pollutants are obtained. A pollutant content early warning system is constructed based on thermogravimetric analysis and colorimetry. Combining with spectral analysis methods, the accurate output of the soil pollution prevention and control plan for different regions is realized, improving the accuracy and efficiency of pollutant detection. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 It is a schematic diagram of an embodiment of the agricultural soil pollution prediction method in the embodiment of the present application;

[0014] Figure 2 It is a schematic diagram of the spectrogram of the pollutant group in the embodiment of the present application. Specific implementation manners

[0015] The embodiment of the present application provides an agricultural soil pollution prediction method. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0016] For ease of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 An embodiment of the agricultural soil pollution prediction method in the embodiment of the present application includes:

[0017] Step S101: Detect the physical and chemical properties of soil samples at multiple points in the agricultural area through hierarchical grid sampling, perform heavy metal content determination, organic matter content analysis and enzyme activity testing on the soil samples, and combine ion chromatography analysis and atomic absorption spectrometry detection to obtain a soil pollution characteristic data set;

[0018] Step S102: Based on the soil pollution characteristic data set, use an electrochemical sensing detection array to dynamically monitor the diffusion characteristics of pollutants in the soil samples, and combine dielectric property analysis and fluorescence spectrometry detection to generate a pollutant migration law characteristic model;

[0019] Step S103: According to the pollutant migration law characteristic model, use a multi-parameter on-line monitoring system to detect the soil pH value, redox potential and ion activity in real time, and form a dynamic change characteristic sequence of soil pollutants through multi-channel signal acquisition and data correction;

[0020] Step S104: According to the dynamic change characteristic sequence of soil pollutants, establish a response relationship matrix between pollutant concentration and soil colloid interface action, and combine capillary electrophoresis analysis and mass spectrometry detection to construct a pollutant accumulation model considering interface adsorption effect;

[0021] Step S105: Using the pollutant accumulation model, determine the occurrence forms of pollutants in soil by synchronous thermal analysis and X-ray diffraction methods, detect the changes in the concentrations of key ions using a selective ion electrode array, and generate a multi-pollutant interaction characteristic map;

[0022] Step S106: Based on the multi-pollutant interaction characteristic map, construct a pollutant content early warning system based on thermogravimetric analysis and colorimetry, and combine with spectral analysis methods to output a soil pollution prevention and control plan for different regions.

[0023] Specifically, conduct stratified grid sampling for agricultural areas. Sampling is carried out by dividing the agricultural area into 10 m × 10 m detection units, and sampling points are arranged at the center points, vertices, and midpoints of the side lines of each detection unit to form a sampling point array. Soil sampling is carried out at three depths (0 - 20 cm, 20 - 40 cm, 40 - 60 cm) at each sampling point, and the soil samples are mixed and homogenized by the quartering method. Determine the heavy metal content of the processed soil samples, use flame atomic absorption spectrophotometry to determine the contents of copper, zinc, lead, and cadmium, and at the same time combine inductively coupled plasma mass spectrometry to determine the contents of arsenic and mercury. The analysis of organic matter content is carried out by soil digestion using the potassium dichromate oxidation-external heating method, and then determined by an ultraviolet spectrophotometer. The soil urease activity test is carried out by the alkali hydrolysis method. Determine the contents of sulfate, nitrate, and chloride ions by ion chromatography analysis, and detect the contents of sodium, potassium, calcium, and magnesium ions by atomic absorption spectroscopy.

[0024] On the basis of obtaining the soil pollution characteristic data set, arrange an electrochemical sensing detection array for dynamic monitoring. The electrochemical sensing detection array consists of ion-selective electrodes, conductivity electrodes, and redox electrodes, and continuously collects the concentrations of heavy metal ions by potentiometry. Use a four-electrode alternating current impedance spectrometer to measure the dielectric constant and dielectric loss of soil samples, and analyze the polarization characteristics under power frequency electric fields. Fluorescence spectroscopy detection is carried out by excitation-emission matrix scanning using a fluorescence spectrophotometer, and the fluorescence characteristics of organic pollutants are studied by analyzing the peak positions and peak intensity characteristics of three-dimensional fluorescence spectra. Conduct multi-parameter online monitoring of the obtained data, install a composite glass electrode to measure the pH value, a platinum electrode to measure the redox potential, and an ion-selective electrode to measure the ion activity. Amplify and filter the electrode output signals through a signal conditioning circuit, and calibrate the measurement electrodes using the three-point calibration method and temperature compensation. The electrochemical workstation collects potential signals, establishes a potential reference with a reference electrode, and uses digital filtering to eliminate external interference. Measure the potential difference of the target ion using an ion-selective electrode, and calculate the ion activity through the Nernst equation. The multi-channel data acquisition system uses wavelet denoising to perform noise reduction processing on the signals.

[0025] After obtaining the dynamic change characteristic sequence of soil pollutants, ultrasonic dispersion and centrifugal separation are carried out on soil colloids, and a laser particle size analyzer is used to measure the particle size distribution of colloids. Capillary zone electrophoresis is used to separate the surface charges of soil colloids, and a conductivity detector measures the migration time of each component. An inductively coupled plasma mass spectrometer is used to measure the content of pollutant bound to colloids, and tandem mass spectrometry is used for qualitative and quantitative analysis of pollutants. The isothermal adsorption equation is used to calculate the interfacial equilibrium constant and the maximum adsorption capacity, a scanning tunneling microscope is used to observe the microscopic morphology of the colloid surface, and an atomic force microscope is used to measure the surface adsorption force. In order to determine the occurrence form of pollutants in soil, a differential scanning calorimeter is used to heat the soil sample, and the heat flow during the temperature change process is monitored. An X-ray diffractometer is used to measure the crystal phase structure of the soil sample, and the diffraction peaks are analyzed through the Bragg equation. A selective ion electrode is used to measure the potential of potassium ion, sodium ion, and ammonium ion concentrations, and the ion concentration is calculated through the potential titration curve. A thermogravimetric analyzer is used to measure the change law of the sample mass with temperature, and the thermal decomposition products of pollutants are analyzed by combining thermogravimetry-mass spectrometry. An X-ray photoelectron spectrometer is used to measure the valence state of surface elements of the sample, and Auger electron spectroscopy is used to analyze the surface chemical composition.

[0026] Finally, a thermogravimetry-differential thermal synchronous analyzer is used to carry out programmed temperature treatment on the soil sample, and the thermogravimetric curve and differential thermal curve are collected. A visible spectrophotometer is used to measure the absorbance of the color reaction of heavy metals, and the pollutant concentration is calculated by combining with the standard curve. An infrared spectrometer is used to scan the sample in the full wavelength range, and the characteristic absorption peaks are identified and quantitatively analyzed. A UV-visible spectrometer is used to measure the absorption spectrum of the sample, and the second derivative spectrum is used to eliminate background interference. A fluorescence analyzer is used to measure the excitation-emission spectrum of the sample, and the spectral data are analyzed by combining parallel factor analysis. An optical fiber spectrometer is used for in-situ detection, and combined with chemometric analysis to establish the pollutant distribution characteristics.

[0027] For example: After the sampling area is divided into 10 m × 10 m grids, in the soil samples of the 0 - 20 cm soil layer, the copper content measured by flame atomic absorption spectrophotometry is 45.6 mg / kg, the zinc content is 132.8 mg / kg, the lead content is 68.3 mg / kg, and the cadmium content is 0.45 mg / kg. The arsenic content measured by inductively coupled plasma mass spectrometry is 12.4 mg / kg, and the mercury content is 0.28 mg / kg. Ion chromatography analysis shows that the sulfate content is 245 mg / kg, the nitrate content is 168 mg / kg, and the chloride content is 92 mg / kg. Atomic absorption spectrometry detection shows that the sodium ion content is 156 mg / kg, the potassium ion content is 1850 mg / kg, the calcium ion content is 3260 mg / kg, and the magnesium ion content is 425 mg / kg. The pH value is 6.8, the redox potential is 245 mV, and the organic matter content is 2.8%. Through the analysis of the established early warning system, the lead and cadmium contents in the soil of this area are close to the critical values, and targeted prevention and control measures need to be taken, including adjusting the planting structure, improving the physical and chemical properties of the soil, etc.

[0028] In the embodiment of the present application, through hierarchical grid sampling and multi-point physical and chemical property detection, a comprehensive assessment of the farmland soil pollution status is realized, ensuring the representativeness of sampling and the accuracy of data. The electrochemical sensing detection array is used to dynamically monitor the diffusion characteristics of pollutants in soil samples. Combining dielectric property analysis and fluorescence spectrum detection, the migration law of pollutants in soil is effectively captured. The multi-parameter on-line monitoring system is used to detect the soil pH value, redox potential and ion activity in real time. Through multi-channel signal acquisition and data correction, the dynamic change characteristics of soil pollutants are accurately reflected. A response relationship matrix of the interaction between pollutant concentration and soil colloid interface is established. Combining capillary electrophoresis analysis and mass spectrometry detection, the influence mechanism of interface adsorption effect on pollutant accumulation is deeply revealed. The occurrence forms of pollutants in soil are determined by synchronous thermal analysis and X-ray diffraction methods. The selective ion electrode array is used to detect the change of key ion concentration, and the interaction characteristics between multiple pollutants are obtained. Based on thermogravimetric analysis and colorimetry, a pollutant content early warning system is constructed. Combining spectral analysis methods, the accurate output of the soil pollution prevention and control plan for different regions is realized, improving the accuracy and efficiency of pollutant detection.

[0029] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0030] (1) Divide the agricultural area into multiple 10 m × 10 m detection units through a preset sampling point spacing. The sampling point array is composed of the center point, vertex and midpoint of the side line of the detection unit, and the initial sampling layout data is obtained;

[0031] (2)According to the initial sampling point data, soil sampling is carried out at three depths of 0 - 20 cm, 20 - 40 cm, and 40 - 60 cm for each sampling point, and the soil samples are mixed and homogenized by the quartering method to obtain a stratified soil sample set;

[0032] (3)The contents of copper, zinc, lead, and cadmium in the stratified soil sample set are determined by flame atomic absorption spectrophotometry, and the contents of arsenic and mercury are determined by inductively coupled plasma mass spectrometry to obtain heavy metal content data;

[0033] (4)The stratified soil sample set is digested by the potassium dichromate oxidation - external heating method, the organic matter content is determined by an ultraviolet spectrophotometer, and the soil urease activity is determined by the alkali hydrolysis method to obtain soil basic property data;

[0034] (5)Based on the heavy metal content data and soil basic property data, the contents of sulfate, nitrate, and chloride ions in the soil samples are quantitatively analyzed by ion chromatography, and sodium, potassium, calcium, and magnesium ions are detected by atomic absorption spectrometry to obtain ion composition characteristic data;

[0035] (6)The heavy metal content data, soil basic property data, and ion composition characteristic data are subjected to data standardization processing and outlier rejection, and the characteristic weights are determined by principal component analysis to obtain a soil pollution characteristic data set.

[0036] Specifically, an electrochemical sensing detection array is arranged at the pre - determined sampling points. The array includes three core electrodes: an ion - selective electrode for the selective detection of specific heavy metal ions, a conductivity electrode for measuring the conductivity of the soil solution, and a redox electrode for monitoring the redox state of the soil. Potentiometric determination is to determine the target ion concentration by measuring the potential difference between the working electrode and the reference electrode. According to the Nernst equation principle, the magnitude of the potential difference has a logarithmic relationship with the concentration of the target ion. By collecting data once every 30 minutes and continuously monitoring for 24 hours, the dynamic change process of the heavy metal ion concentration is recorded.

[0037] After obtaining the time-series data of pollutant concentration, a four-electrode alternating current impedance spectrometer is used to measure the dielectric properties of soil samples. The four-electrode method applies an alternating voltage through the two outer electrodes, measures the voltage drop through the two inner electrodes, and simultaneously measures the current passing through the sample to calculate the impedance value. The dielectric constant represents the ability of a material to store electrical energy, while the dielectric loss reflects the degree to which a material consumes electrical energy. The polarization characteristic analysis under power frequency electric field includes measuring the dielectric parameters of the sample at different frequencies (usually 50 Hz - 1 MHz) and recording the response characteristics of the polarization intensity varying with the electric field. The fluorescence spectral analysis of soil samples is carried out using a fluorescence spectrophotometer. By continuously changing the excitation wavelength and emission wavelength, a three-dimensional fluorescence spectrogram is obtained. The process of excitation-emission matrix scanning is to record a complete emission spectrum at each excitation wavelength, ultimately forming a three-dimensional data matrix. The peak position information reflects the molecular structure characteristics of organic pollutants, and the peak intensity is related to the pollutant concentration. The fluorescence characteristic spectrum analysis focuses on the characteristic peaks of organic pollutants such as humus and aromatic compounds. During the data fusion process, the soil dielectric parameter set and the fluorescence characteristic spectrum of organic pollutants are subjected to feature extraction through covariance matrix decomposition. Covariance matrix decomposition first calculates the covariance matrix between the two sets of data, then performs eigenvalue decomposition on the matrix to extract the main variation characteristics. By analyzing the eigenvectors and eigenvalues, the main influencing factors during the pollutant migration process are identified.

[0038] When observing the surface morphology of soil particles using a scanning electron microscope, the sample needs to be pretreated such as drying and gold spraying. The scanning electron microscope can provide microscopic morphology information of the sample surface, with a resolution reaching the nanometer level. The energy dispersive spectrometer determines the elemental composition and its relative content on the sample surface by detecting the X-ray energy distribution, quantitatively characterizing the distribution characteristics of pollutants on the soil particle surface. Finally, based on the obtained interfacial distribution characteristics, the anisotropic diffusion coefficient is calculated. Anisotropic diffusion refers to the different diffusion rates of pollutants in different directions in the soil, and the diffusion coefficients in the horizontal and vertical directions need to be calculated separately. The interfacial mass transfer resistance analysis considers the resistance of pollutant transfer from the soil particle surface to the solution bulk phase, and combines with the migration rate of pollutants to construct a complete migration law characteristic model.

[0039] For example: First, a dynamic monitoring of cadmium ions in the soil solution is carried out by an ion-selective electrode for 24 hours, and data is collected every 30 minutes. The measured cadmium ion concentration fluctuates between 0.08 - 0.15 mg / L. The four-electrode alternating current impedance spectroscopy measurement shows that the dielectric constant of the soil sample at a frequency of 1 kHz is 12.5, and the dielectric loss is 0.85. Fluorescence spectroscopy analysis reveals that characteristic peaks appear at an excitation wavelength of 280 nm and an emission wavelength of 340 nm, and the peak intensity is 2850 (relative unit), indicating the presence of aromatic organic matter pollution. Through covariance matrix decomposition, the eigenvalue of the first principal component is 3.24, and the corresponding eigenvector shows that the pollutant migration is mainly affected by the soil organic matter content. Scanning electron microscope observation shows that obvious needle-like crystals exist on the surface of soil particles, and energy spectrum analysis confirms that it is the morphological form of cadmium compounds. After calculation, the horizontal diffusion coefficient is 3.2×10^-6 cm^2 / s, and the vertical diffusion coefficient is 1.8×10^-6 cm^2 / s, indicating that the pollutant has a stronger migration ability in the horizontal direction.

[0040] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0041] (1) According to the soil pollution characteristic dataset, an electrochemical sensing detection array composed of an ion-selective electrode, a conductivity electrode, and a redox electrode is arranged at the sampling point, and the heavy metal ion concentration is continuously collected by potentiometry to obtain the time series data of pollutant concentration;

[0042] (2) Based on the time series data of pollutant concentration, a four-electrode alternating current impedance spectrometer is used to measure the dielectric constant and dielectric loss of the soil sample, and combined with the polarization characteristic analysis under the power frequency electric field, a soil dielectric parameter set is obtained;

[0043] (3) The soil sample is scanned by an excitation-emission matrix using a fluorescence spectrophotometer, and combined with the analysis of the peak position and peak intensity of the three-dimensional fluorescence spectrum, an organic pollutant fluorescence characteristic spectrum is formed;

[0044] (4) The soil dielectric parameter set and the organic pollutant fluorescence characteristic spectrum are fused in data, and the pollutant migration characteristics are extracted by covariance matrix decomposition to obtain the pollutant diffusion characteristic data;

[0045] (5) According to the pollutant diffusion characteristic data, the surface morphology of soil particles is observed by a scanning electron microscope, and the surface element distribution is measured in cooperation with an energy spectrum analyzer to obtain the pollutant interface distribution characteristics;

[0046] (6) Based on the pollutant interface distribution characteristics, through the calculation of the anisotropic diffusion coefficient and the analysis of the interfacial mass transfer resistance, combined with the migration rate of the pollutant in the soil, a pollutant migration law characteristic model is generated.

[0047] Specifically, based on the obtained soil pollution characteristic dataset, an electrochemical sensing detection array is deployed at the sampling points. This array includes three types of electrodes: ion-selective electrodes, conductivity electrodes, and redox electrodes. Ion-selective electrodes are a type of selective membrane electrode whose potential change responds only to specific ions. Conductivity electrodes are used to measure the total concentration of ions in the soil, and redox electrodes detect the potential difference of redox couples in the soil. In practical applications, the concentration of heavy metal ions is determined by potentiometry based on the Nernst equation principle, measuring the potential difference between the working electrode and the reference electrode. The potential difference has a linear relationship with the logarithm of the ion concentration. When continuously collecting data, data is collected every 5 minutes for 48 hours continuously to record the dynamic change trend of the target heavy metal ion concentration. After obtaining the time-series data of the pollutant concentration, a four-electrode alternating current impedance spectrometer is used to measure the soil samples. The principle of the four-electrode method is to apply an alternating excitation signal through two external electrodes and measure the response signal with two internal electrodes. Dielectric constant characterizes the ability of a material to store charge, and dielectric loss reflects the degree of power dissipation of a material. Under the condition of a power frequency electric field, an alternating electric field in the range of 50 Hz - 10 kHz is applied to the soil samples, and the impedance values and phase angles at different frequencies are recorded to obtain polarization characteristic information. The measurement results form a parameter set containing frequency, impedance modulus value, and phase angle.

[0048] The fluorescence spectral analysis of soil organic pollutants uses three-dimensional fluorescence spectral technology. The fluorescence spectrophotometer adopts a cross-scanning mode, with an excitation wavelength range of 200 - 400 nm, an emission wavelength range of 250 - 500 nm, and a wavelength interval set at 5 nm. A three-dimensional data matrix is obtained for each sample, which contains information on three dimensions: excitation wavelength, emission wavelength, and fluorescence intensity. By analyzing the position and intensity of the characteristic peaks, the presence and relative content of different types of organic pollutants are determined.

[0049] The data fusion of the soil dielectric parameter set and the fluorescence characteristic spectrum of organic pollutants uses the covariance matrix decomposition method. First, the two groups of data are standardized to eliminate the influence of dimensions. Then, the covariance matrix between the two groups of data is calculated, and the matrix is decomposed into eigenvalues and eigenvectors. The magnitude of the eigenvalue represents the importance of this feature in the overall change, and the eigenvector reflects the correlation relationship between variables. By analyzing the eigenvectors corresponding to the main eigenvalues, the key features of pollutant migration are extracted. Before the scanning electron microscope observation, the soil samples need to be dried and sputter-coated with gold. The scanning electron microscope uses the secondary electron signal generated by the interaction between the electron beam and the sample surface to obtain the topographic image of the sample surface, with a resolution of up to 1 - 10 nm. Energy-dispersive spectroscopy is to obtain information on the types and contents of elements by detecting the characteristic X-rays generated by the excitation of elements on the sample surface by the electron beam. By observing the topography and analyzing the element distribution, a distribution characteristic database of pollutants on the soil particle surface is established.

[0050] Finally, calculate the anisotropic diffusion coefficient based on the interface distribution characteristics. The diffusion rate of pollutants in soil varies in the horizontal and vertical directions, and this anisotropy stems from the layered structural characteristics of the soil. The interfacial mass transfer resistance analysis considers the resistance encountered by pollutants during the migration process at the solid-liquid interface, including the diffusion layer resistance and the surface adsorption resistance. By analyzing the diffusion coefficient and the interfacial mass transfer coefficient of pollutants, and combining with the measured migration rate data, a complete characteristic model of the migration law is constructed.

[0051] For example, the actual operation process is as follows: First, deploy an electrochemical sensing detection array. The copper ion-selective electrode collects data every 5 minutes within 48 hours, and it is recorded that the copper ion concentration fluctuates between 0.5 - 2.3 mg / L, with a fluctuation period of approximately 6 hours. The four-electrode alternating current impedance spectroscopy measurement shows that at a frequency of 100 Hz, the dielectric constant of the soil sample is 18.3, and the dielectric loss is 0.92, showing obvious polarization characteristics as the frequency increases. Three-dimensional fluorescence spectroscopy analysis detects a characteristic peak with an intensity of 3200 at an excitation wavelength of 260 nm and an emission wavelength of 350 nm, indicating the presence of polycyclic aromatic hydrocarbon pollutants. After covariance matrix decomposition, the maximum eigenvalue of 4.56 is obtained, and the corresponding eigenvector shows that the migration of copper ions is mainly affected by the soil pH value and the organic matter content. Scanning electron microscopy observation finds that there are blocky deposits on the surface of soil particles, and energy spectrum analysis confirms that it is in the form of copper oxide, with a content accounting for about 8.2% of the total surface elements. Calculate the diffusion coefficient in the horizontal direction , the diffusion coefficient in the vertical direction , and the interfacial mass transfer resistance coefficient .

[0052] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] (1) Based on the characteristic model of pollutant migration law, install a composite glass electrode, a platinum electrode, and an ion-selective electrode at the sampling point, and amplify and filter the electrode output signal through a signal conditioning circuit to obtain the initial data of soil chemical parameters;

[0054] (2) Use the initial data of soil chemical parameters to perform three-point calibration and temperature compensation on the measurement electrode, and combine with a standard buffer solution to measure the potential difference to obtain a corrected pH value data set;

[0055] (3) Collect the potential signal measured by the platinum electrode through an electrochemical workstation, establish a potential reference with a reference electrode, and use digital filtering to eliminate external interference to form a redox potential data sequence;

[0056] (4) According to the pH value data set and the redox potential data sequence, use the ion-selective electrode to measure the potential difference of the target ion, and calculate the ion activity through the Nernst equation to obtain the ion activity distribution data;

[0057] (5) Input the pH value data group, redox potential data sequence, and ion activity distribution data into a multi-channel data acquisition system, and perform noise reduction processing on the signals using wavelet denoising to obtain purified monitoring data;

[0058] (6) Conduct time series analysis and spatial interpolation processing on the purified monitoring data, and combine adaptive threshold filtering to eliminate outliers, forming a dynamic change characteristic sequence of soil pollutants.

[0059] Specifically, based on the pollutant migration law characteristic model, install three key electrodes at the sampling points: a composite glass electrode for pH value measurement, a platinum electrode for redox potential measurement, and an ion-selective electrode for specific ion detection. The signal conditioning circuit processes the weak signals output by the electrodes, including a preamplifier that amplifies the microvolt-level signal to the millivolt level, and a band-pass filter that eliminates power frequency interference and high-frequency noise. The amplification factor is usually set to 1000 times, and the filtering bandwidth is 0.1 - 100 Hz to ensure signal quality.

[0060] The initial data of soil chemical parameters undergoes three-point calibration and temperature compensation processing. For three-point calibration, standard buffer solutions with pH values of 4.00, 6.86, and 9.18 are selected. Each calibration point is measured three times repeatedly, and the average value is taken to establish a calibration curve. Temperature compensation uses a platinum resistance thermometer for temperature measurement. For every 1°C increase, the pH reading is corrected by -0.015 pH units. The calibrated pH value data group is obtained by measuring the potential difference with the standard buffer solution. When the electrochemical workstation collects the potential signal of the platinum electrode, an Ag / AgCl electrode is used as the reference electrode to establish a potential reference. Digital filtering uses a Butterworth low-pass filter with a cut-off frequency set to 10 Hz and an attenuation slope of -20 dB / decade to effectively eliminate external electromagnetic interference. The sampling frequency is 100 Hz, and the recording time for each data point is 1 second, forming a redox potential data sequence.

[0061] For the determination of ion activity, based on the pH value data group and redox potential data sequence, use an ion-selective electrode to measure the potential difference of the target ion. The ion activity calculation uses the extended Nernst equation:

[0062] ;

[0063] where: E is the measured electrode potential (V), is the standard electrode potential (V), is the activity coefficient correction factor (dimensionless), is the gas constant (8.314 J·mol⁻¹·K⁻¹), is the absolute temperature (K), is the ion valence number (dimensionless), is the Faraday constant (96485 C·mol⁻¹), is the activity of the target ion (mol·L⁻¹), is the influence factor of soil solution ionic strength (dimensionless), is the ion interference correction coefficient (dimensionless).

[0064] Ion activity The specific calculation formula is as follows:

[0065] ;

[0066] where is the temperature correction factor (J·mol⁻¹).

[0067] After the obtained pH value data set, redox potential data sequence, and ion activity distribution data are input into the multi-channel data acquisition system, wavelet denoising technology is used for signal processing. The db4 wavelet basis function is selected, the decomposition level is 4 layers, the soft threshold method is used to process the wavelet coefficients, and the purified monitoring data is reconstructed. Time series analysis and spatial interpolation processing are performed on the purified monitoring data. The time series analysis uses the autocorrelation analysis method to identify the periodic characteristics of the data, and the spatial interpolation uses the Kriging method for interpolation calculation. The adaptive threshold filter sets the dynamic threshold window to ±3 times the standard deviation, and the data points outside this range are marked as outliers and removed.

[0068] For example: In the pH value measurement, the slope of the standard curve obtained by three-point calibration is -58.2 mV / pH, and the theoretical Nernst slope at a temperature of 25°C is -59.2 mV / pH, with an error within 2%. The redox potential measurement shows that the original signal has a 50 Hz power frequency interference with an amplitude of about 2 mV, and the interference is suppressed to below 0.1 mV after digital filtering. In the determination of the activity of copper ions, the electrode potential E is 125 mV, the standard electrode potential is 260 mV, the ion valence number z is 2, and the temperature T is 298 K. The calculated activity of copper ions is . After wavelet denoising processing, the signal-to-noise ratio is increased from the original 8.5 dB to 15.3 dB. The time series analysis shows that the data has an obvious 24-hour periodicity, and a complete pollutant concentration distribution map is formed after spatial interpolation.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] (1) From the dynamic change characteristic sequence of soil pollutants, ultrasonic dispersion and centrifugal separation are performed on soil colloids, and a laser particle size analyzer is used to measure the particle size distribution of colloids to obtain soil colloid characteristic parameters;

[0071] (2) Separate the surface charge of soil colloids by capillary zone electrophoresis according to the soil colloid characteristic parameters, combine with a conductivity detector to measure the migration time of each component, and obtain the colloid electrophoresis migration spectrum;

[0072] (3) Based on the colloid electrophoresis migration spectrum, use an inductively coupled plasma mass spectrometer to measure the content of colloid-bound pollutants, and qualitatively and quantitatively analyze the pollutants by tandem mass spectrometry to form colloid-adsorbed pollutant data;

[0073] (4) Input the colloid-adsorbed pollutant data into the isothermal adsorption equation, calculate the interfacial equilibrium constant and the maximum adsorption capacity through the Langmuir adsorption theory, and obtain the interfacial adsorption equilibrium parameters;

[0074] (5) Use the interfacial adsorption equilibrium parameters, observe the microscopic morphology of the colloid surface by scanning tunneling microscope, and combine with an atomic force microscope to measure the surface adsorption force to obtain the response relationship matrix;

[0075] (6) Based on the response relationship matrix, analyze the changes in the surface functional groups of the colloid by Fourier transform infrared spectroscopy, and combine with the surface coordination theory to construct a pollutant accumulation model for the interfacial adsorption effect.

[0076] Specifically, ultrasonically disperse and centrifuge the soil colloids extracted from the soil pollutant dynamic change characteristic sequence. During the ultrasonic dispersion process, select an ultrasonic processor with a power of 500 W, a frequency of 20 kHz, and a processing time of 30 minutes to ensure that the colloid particles are fully dispersed. For centrifugation separation, use a high-speed centrifuge with a rotation speed set at 10,000 rpm and a centrifugation time of 15 minutes to sediment and separate the particles with a particle size greater than 2 μm. Use a laser particle size analyzer to measure the particle size distribution of the obtained colloid suspension. The laser wavelength is 633 nm, the measurement range is 0.1 - 1000 μm, and repeat the measurement 3 times and take the average value to obtain the particle size distribution curve and characteristic parameters of the soil colloids. According to the obtained soil colloid characteristic parameters, use capillary zone electrophoresis technology to separate the surface charge of the colloids. The capillary length is 60 cm, the inner diameter is 75 μm, the buffer solution is a phosphate buffer solution with pH = 7.0, and the electric field strength is set at 300 V / cm. During the electrophoresis process, use a conductivity detector to monitor the migration of each component in real time, and record the migration time and peak area of different components. The response time of the conductivity detector is set at 0.1 s, and the sensitivity is , and the detection data is processed by baseline correction and peak area integration to form a complete colloid electrophoresis migration spectrum.

[0077] Subsequently, according to the separation effect of each component in the capillary electrophoresis migration spectrum, an inductively coupled plasma mass spectrometer was used to determine the content of colloidal-bound pollutants. The plasma temperature was controlled at 6000 - 8000 K, the radio frequency power was 1300 W, and the sampling depth was 10 mm. Tandem mass spectrometry analysis was carried out in the multiple reaction monitoring mode, the collision gas pressure was 2.5 mTorr, and the dwell time was set at 100 ms. Quantitative analysis was performed by measuring the peak intensity ratio of characteristic ion pairs, and the matrix effect was corrected by combining the standard addition method. Finally, accurate content data of colloidal adsorbed pollutants were obtained. The data of colloidal adsorbed pollutants were substituted into the Langmuir isothermal adsorption equation to calculate the interfacial equilibrium constant and the maximum adsorption capacity. The adsorption experiment was carried out in a constant temperature oscillator, the temperature was controlled at 25 ± 0.5 °C, the oscillation speed was 150 rpm, and the equilibrium time was 24 hours. By changing the initial pollutant concentration (0.1 - 10 mg / L), the distribution of pollutants in the solution and the solid phase at equilibrium was measured, and the isothermal adsorption curve was fitted to calculate the relevant parameters.

[0078] When observing the microscopic morphology of the colloid surface using a scanning tunneling microscope, the sample preparation adopted the ultra-thin film coating technology. The scanning range was 500 nm × 500 nm, the tunneling current was set at 2 nA, and the bias voltage was 50 mV. The atomic force microscope measured the surface adsorption force in the contact mode. The elastic constant of the probe was 0.1 N / m, and the scanning rate was 1 Hz. The surface adsorption characteristics were analyzed through the force-distance curve, and the response relationship matrix was constructed by combining the morphology and mechanical data. Finally, based on the response relationship matrix, a Fourier transform infrared spectrometer was used to characterize the functional groups on the colloid surface. The scanning wave number range was , and the resolution was . The scanning was performed 32 times. By analyzing the position and intensity changes of the characteristic absorption peaks, the binding mechanism of pollutants to the surface active sites of the colloid was analyzed in combination with the surface coordination theory.

[0079] For example: After ultrasonic dispersion and centrifugal separation of soil colloids, laser particle size analysis showed that the median particle size of the colloid was 0.8 μm, and the distribution range was 0.2 - 2 μm. Three characteristic peaks were obtained by capillary electrophoresis separation. The migration time of the main peak was 8.5 min, corresponding to negatively charged silicon-aluminum oxide colloids. Inductively coupled plasma mass spectrometry analysis showed that cadmium mainly existed in the bound state, with a content of 15.6 mg / kg. The maximum adsorption capacity calculated by the Langmuir isothermal adsorption experiment was 42.5 mg / g. The scanning tunneling microscope observed that the colloid surface presented a flaky structure, and the atomic force microscope measured the surface adsorption force to be 2.8 nN. Fourier transform infrared spectroscopy showed characteristic hydroxyl peaks at and , indicating that cadmium ions were mainly adsorbed through surface hydroxyl coordination. These data together constituted a complete interfacial adsorption effect pollutant accumulation model.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] (1) Based on the pollutant accumulation model, use a differential scanning calorimeter to heat the soil sample, continuously monitor the heat flow during the temperature change process, and obtain the pollutant pyrolysis characteristic curve;

[0082] (2) For the pollutant pyrolysis characteristic curve, use an X-ray diffractometer to measure the crystal phase structure of the soil sample, analyze the diffraction peaks in combination with Bragg's equation, and obtain the lattice parameter data;

[0083] (3) According to the lattice parameter data, use a selective ion electrode to measure the potentials of potassium ion, sodium ion, and ammonium ion concentrations, calculate the ion concentrations through the potentiometric titration curve, and form the key ion concentration data;

[0084] (4) According to the key ion concentration data, use a thermogravimetric analyzer to measure the variation law of the sample mass with temperature, and combine thermogravimetry-mass spectrometry to analyze the thermal decomposition products of the pollutants to obtain the thermal decomposition product composition data;

[0085] (5) For the thermal decomposition product composition data, use an X-ray photoelectron spectrometer to measure the surface element valence states of the sample, and combine Auger electron spectroscopy to analyze the surface chemical composition to obtain the surface chemical state distribution map;

[0086] (6) Based on the surface chemical state distribution map, use a Raman spectrometer to characterize the molecular structure of the pollutants, and cooperate with nuclear magnetic resonance spectroscopy to analyze the organic component structure to generate the multi-pollutant interaction characteristic map.

[0087] Specifically, based on the pollutant accumulation model, first use a differential scanning calorimeter to perform thermal analysis on the soil sample. Differential scanning calorimetry is a method for studying the thermal behavior of substances by measuring the endothermic or exothermic changes of the sample during programmed temperature rise. During the analysis process, place the soil sample in a platinum crucible, heat it from room temperature to 800 °C at a heating rate of 10 °C / min under nitrogen atmosphere protection, with a gas flow rate of 50 mL / min, and record the temperature and heat flow data at the same time. The heat flow data is collected in real time by a high-precision temperature difference sensor built in the calorimeter, with a sampling interval of 0.1 s, to obtain the DSC curve reflecting the pollutant pyrolysis characteristics. After analyzing the obtained pyrolysis characteristic curve, use an X-ray diffractometer to measure the crystal phase structure of the soil sample. The X-ray diffraction analysis uses a Cu target X-ray tube, with a working voltage of 40 kV, a current of 30 mA, a scanning step of 0.02 °, a scanning speed of 4 ° / min, and a scanning range of 5 - 80 °. Analyze the diffraction peaks through Bragg's equation to determine the interplanar spacing and unit cell parameters of the sample. The diffraction pattern is processed by background subtraction and peak shape fitting, and the Jade software is used for qualitative phase analysis to obtain the complete lattice parameter data.

[0088] According to the lattice parameter data, the concentrations of key ions in the soil sample were determined. The selective ion electrode determination included three target ions, potassium ions, sodium ions, and ammonium ions. For each ion, a dedicated ion-selective electrode was used, and a silver / silver chloride reference electrode was used to establish a potential measurement system. During the potentiometric titration, an automatic potentiometric titrator was used to control the titration rate. A potential value was recorded every 0.1 mL, and a potential-volume curve was plotted. The ion concentration was calculated through the inflection point of the curve. Using the obtained key ion concentration data, the thermal decomposition behavior of the sample was further studied using a thermogravimetric analyzer. The thermogravimetric analysis was carried out under a nitrogen atmosphere, with a gas flow rate of 60 mL / min, a heating rate of 20 °C / min, and a temperature range from room temperature to 1000 °C. The thermogravimetry-mass spectrometry technique connected the thermogravimetric analyzer to the mass spectrometer through a transfer line, and the transfer line temperature was maintained at 200 °C to prevent sample condensation. The mass spectrometry used an electron impact source, with an ionization voltage of 70 eV and a scanning mass range of m / z from 1 to 300, to obtain the real-time mass spectrum of the thermal decomposition products.

[0089] After importing the composition data of the thermal decomposition products, the elemental valence states on the surface of the sample were analyzed using an X-ray photoelectron spectrometer. The X-ray photoelectron spectroscopy analysis used a monochromatic Al Kα ray source with a power of 150 W, and the vacuum was better than . The binding energy information of the surface elements was obtained through full-spectrum scanning and narrow scanning, and the surface chemical composition was analyzed in combination with Auger electron spectroscopy. The Auger electron spectroscopy analysis used a 5 keV electron beam, and the detector worked in a constant analysis energy mode.

[0090] Finally, a Raman spectrometer and a nuclear magnetic resonance spectrometer were used to characterize the molecular structure of the pollutant. The Raman spectroscopy used a 532 nm laser as the excitation light source, with a laser power of 50 mW and a spectral resolution of , and the scanning range was . The nuclear magnetic resonance spectroscopy analysis used a 600 MHz superconducting nuclear magnetic resonance spectrometer. The sample was dissolved in a deuterated solvent, and the ¹H and ¹³C spectra were collected.

[0091] For example, two exothermic peaks were found at 268 °C and 425 °C by differential scanning calorimetry, corresponding to the pyrolysis processes of different types of polycyclic aromatic hydrocarbons. X-ray diffraction analysis showed the presence of mineral phases such as quartz and kaolinite in the soil sample, and it was determined through lattice parameter analysis that heavy metals mainly existed in the form of oxides. Selective ion electrode measurement showed that the potassium ion concentration was 156 mg / L, the sodium ion concentration was 89 mg / L, and the ammonium ion concentration was 34 mg / L. Thermogravimetry-mass spectrometry analysis indicated that a series of characteristic fragment ions were detected in the temperature range of 250 - 450 °C, confirming the existence form of polycyclic aromatic hydrocarbons. X-ray photoelectron spectroscopy and Auger electron spectroscopy analysis determined the valence state information of heavy metals. Combining with Raman spectroscopy and nuclear magnetic resonance spectroscopy data, the interaction mechanism between pollutants was elucidated, providing a scientific basis for pollution prevention and control.

[0092] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0093] (1) Based on the multi-pollutant interaction characteristic spectrum, the soil sample was subjected to programmed temperature treatment by a thermogravimetry-differential thermal synchronous analyzer, and the thermogravimetric curve and differential thermal curve were collected to obtain pollutant content change data;

[0094] (2) According to the pollutant content change data, the absorbance of the heavy metal color reaction was measured by a visible spectrophotometer, and the pollutant concentration was calculated by combining with the standard curve to obtain a quantitative analysis data set;

[0095] (3) The quantitative analysis data set was used to perform a full-band scan of the sample by an infrared spectrometer, and the characteristic absorption peaks were identified and quantitatively analyzed to form spectral characteristic data;

[0096] (4) Using the spectral characteristic data, the absorption spectrum of the sample was measured by a UV-visible spectrometer, and the background interference was eliminated by combining with the second derivative spectrum to obtain the pollutant component spectrogram;

[0097] (5) For the pollutant component spectrogram, the excitation-emission spectrum of the sample was measured by a fluorescence analyzer, and the spectral data was analyzed by combining with parallel factor analysis to obtain the pollutant concentration warning threshold;

[0098] (6) Based on the pollutant concentration warning threshold, the soil sample was in-situ detected by an optical fiber spectrometer, and the pollutant distribution characteristics were established by cooperating with chemometric analysis, and a sub-regional soil pollution prevention and control plan was output.

[0099] Specifically, based on the multi-pollutant interaction characteristic spectrum, a thermogravimetric-differential thermal synchronous analyzer is used to analyze the soil samples. This analyzer measures the mass change (thermogravimetric curve) and temperature change (differential thermal curve) of the sample simultaneously through programmed temperature treatment. During the heating process, precise temperature control is adopted, starting from room temperature and rising to 1000 °C at a rate of 10 °C / min under nitrogen atmosphere protection with a flow rate of 60 mL / min. The mass loss and heat change of the sample are recorded in real time. The thermogravimetric curve reflects the mass change during the decomposition process of the sample, while the differential thermal curve shows the energy change during the endothermic or exothermic process. According to the obtained pollutant content change data, a visible spectrophotometer is used for the colorimetric determination of heavy metals. For the color reaction, a heavy metal-specific color reagent is selected. For example, copper ions form a yellow complex with sodium diethyldithiocarbamate, and the absorbance is measured at a wavelength of 460 nm. The standard curve is plotted using a series of standard solutions with a concentration range of 0.1 - 5.0 mg / L. The absorbance values at each concentration point are measured, and the standard curve equation is obtained by least squares fitting to calculate the actual concentration of pollutants in the sample.

[0100] The quantitative analysis data set is input into a Fourier transform infrared spectrometer for full-band scanning analysis. The scanning wavenumber range is , and the resolution is . The scan is accumulated 32 times to improve the signal-to-noise ratio. The obtained infrared spectrum is subjected to baseline correction and smoothing processing. The characteristic absorption peaks are identified through a peak position database to determine the attribution of each functional group. The area integration method is used for the quantitative analysis of the characteristic peaks to calculate the relative content of different functional groups. The obtained spectral characteristic data are further used for ultraviolet-visible spectral analysis. The ultraviolet-visible spectrometer measurement uses a quartz cuvette, with a scanning range of 190 - 800 nm, a scanning speed of 300 nm / min, and a slit width of 2 nm. To eliminate matrix interference, the original spectrum is subjected to second derivative processing. The second derivative spectrum can effectively eliminate the influence of baseline drift and background absorption, obtaining a pollutant component spectrum diagram, as shown in Figure 2 . The subtle differences in the spectrum are highlighted to improve the identification accuracy of substances.

[0101] For the obtained pollutant component spectrum diagram, an excitation-emission spectrum measurement is carried out using a fluorescence analyzer. The excitation wavelength range is 200 - 400 nm, the emission wavelength range is 250 - 600 nm, and the scanning interval is 5 nm. Parallel factor analysis is used to analyze the three-dimensional fluorescence data. Parallel factor analysis is a multi-dimensional data analysis method that can decompose complex spectral signals into independent components and give the relative contributions of each component. According to the spectral characteristics and concentration distribution of each component, a pollutant concentration warning threshold is set.

[0102] Finally, an in-situ detection of the soil sample is carried out using an optical fiber spectrometer. The optical fiber spectrometer uses a reflection probe to directly contact the soil and collect the reflection spectrum data in real time. By chemometric methods such as partial least squares regression and principal component analysis, a quantitative relationship between the spectral data and the pollutant content is established, and a spatial distribution map of the pollutant is drawn.

[0103] For example, through thermogravimetry-differential thermal synchronous analysis, it is found that obvious weight loss and exothermic peaks appear near 350 °C, corresponding to the decomposition of chromium oxide. After chromium reacts with the chromogenic reagent diphenylcarbazide, the absorbance measured at 540 nm is 0.625, and the chromium content is obtained as 2.8 mg / kg by referring to the standard curve. Infrared spectroscopy analysis shows a characteristic peak of chromate at a certain position, and the relative content of hexavalent chromium is determined by peak area integration. After the ultraviolet-visible spectrum is processed by second derivative, a characteristic peak appears at 372 nm, confirming the existence of hexavalent chromium. Three-dimensional fluorescence spectroscopy combined with parallel factor analysis identifies three main forms of chromium compounds, and it is determined that an early warning is triggered when the total chromium content exceeds 3.0 mg / kg. Finally, through the in-situ detection of the optical fiber spectrometer, a pollution distribution map is drawn, showing that the chromium content is relatively high in the eastern area of the farmland and needs to be treated preferentially.

[0104] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0105] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An agricultural soil pollution prediction method, characterized in that The agricultural soil pollution prediction method includes: Performing multi-point physical and chemical property detections on soil samples in the agricultural area through hierarchical grid sampling, conducting heavy metal content determination, organic matter content analysis, and enzyme activity tests on the soil samples, and combining ion chromatography analysis and atomic absorption spectrometry detection to obtain a soil pollution characteristic data set; among them, the hierarchical grid sampling includes dividing the agricultural area into 10 m × 10 m detection units, arranging sampling points at the center points, vertices, and midpoints of the sides of each detection unit to form a sampling point array, and taking soil samples at three depths of 0 - 20 cm, 20 - 40 cm, and 40 - 60 cm at each sampling point, and using the quartering method to mix and homogenize the soil samples to obtain a hierarchical soil sample set; Based on the soil pollution characteristic data set, using an electrochemical sensing detection array to dynamically monitor the diffusion characteristics of pollutants in the soil samples, and combining dielectric property analysis and fluorescence spectrometry detection to generate a pollutant migration law characteristic model; According to the pollutant migration law characteristic model, using a multi-parameter on-line monitoring system to perform real-time detection of the soil pH value, redox potential, and ion activity, and forming a dynamic change characteristic sequence of soil pollutants through multi-channel signal acquisition and data correction; According to the dynamic change characteristic sequence of soil pollutants, establishing a response relationship matrix between pollutant concentration and soil colloid interface interaction, and combining capillary electrophoresis analysis and mass spectrometry detection to construct a pollutant accumulation model considering interface adsorption effect, including: ultrasonically dispersing and centrifugally separating the soil colloid from the dynamic change characteristic sequence of soil pollutants, using a laser particle size analyzer to measure the colloid particle size distribution to obtain soil colloid characteristic parameters; according to the soil colloid characteristic parameters, separating the surface charge of the soil colloid by capillary zone electrophoresis, combining a conductivity detector to measure the migration time of each component to obtain a colloid electrophoresis migration spectrum; based on the colloid electrophoresis migration spectrum, using an inductively coupled plasma mass spectrometer to measure the content of colloid-bound pollutants, and performing qualitative and quantitative analysis of the pollutants by tandem mass spectrometry to form colloid-adsorbed pollutant data; inputting the colloid-adsorbed pollutant data into the isothermal adsorption equation, calculating the interface equilibrium constant and maximum adsorption capacity through the Langmuir adsorption theory to obtain interface adsorption equilibrium parameters; using the interface adsorption equilibrium parameters, observing the microscopic morphology of the colloid surface by scanning tunneling microscopy, and combining atomic force microscopy to measure the surface adsorption force to obtain a response relationship matrix; based on the response relationship matrix, analyzing the changes in surface functional groups of the colloid by Fourier transform infrared spectroscopy, and combining the surface coordination theory to construct a pollutant accumulation model considering interface adsorption effect; Using the pollutant accumulation model, determining the occurrence forms of pollutants in the soil by synchronous thermal analysis and X-ray diffraction methods, and using a selective ion electrode array to detect the changes in the concentrations of key ions to generate a multi-pollutant interaction characteristic map; According to the multi-pollutant interaction characteristic map, constructing a pollutant content early warning system based on thermogravimetric analysis and colorimetry, and combining spectral analysis methods to output a soil pollution prevention and control plan for each region.

2. The agricultural soil pollution prediction method according to claim 1, wherein Multi-point physical and chemical property detection of soil samples in agricultural areas is carried out through hierarchical grid sampling. Heavy metal content determination, organic matter content analysis and enzyme activity test are performed on soil samples. Combining ion chromatography analysis and atomic absorption spectrometry detection, a soil pollution characteristic data set is obtained, including: The contents of copper, zinc, lead and cadmium in the stratified soil sample set are determined by flame atomic absorption spectrophotometry, and the contents of arsenic and mercury are determined by inductively coupled plasma mass spectrometry to obtain heavy metal content data; The stratified soil sample set is digested by the potassium dichromate oxidation-external heating method. The organic matter content is determined by an ultraviolet spectrophotometer, and the soil urease activity is determined by the alkali hydrolysis method to obtain soil basic property data; Based on the heavy metal content data and soil basic property data, the contents of sulfate, nitrate and chloride ions in soil samples are quantitatively analyzed by ion chromatography, and sodium, potassium, calcium and magnesium ions are detected by atomic absorption spectrometry to obtain ion composition characteristic data; The heavy metal content data, soil basic property data and ion composition characteristic data are subjected to data standardization processing and outlier removal, and the characteristic weights are determined by principal component analysis to obtain a soil pollution characteristic data set; According to the pollutant migration law characteristic model, the soil pH value, redox potential and ion activity are detected in real time by a multi-parameter on-line monitoring system. Through multi-channel signal acquisition and data correction, a dynamic change characteristic sequence of soil pollutants is formed, including: Based on the pollutant migration law characteristic model, a composite glass electrode, a platinum electrode and an ion-selective electrode are installed at the sampling point, and the output signal of the electrode is amplified and filtered by a signal conditioning circuit to obtain initial soil chemical parameter data; Using the initial soil chemical parameter data, three-point calibration and temperature compensation are carried out on the measuring electrode, and the potential difference is measured in combination with a standard buffer solution to obtain a calibrated pH value data set; The potential signal measured by the platinum electrode is collected by an electrochemical workstation, a potential reference is established in combination with a reference electrode, and digital filtering is used to eliminate external interference to form a redox potential data sequence; According to the pH value data set and the redox potential data sequence, the potential difference of the target ion is measured by an ion-selective electrode, and the ion activity is calculated by the Nernst equation to obtain ion activity distribution data; The pH value data set, the redox potential data sequence and the ion activity distribution data are input into a multi-channel data acquisition system, and wavelet denoising is used to perform noise reduction processing on the signal to obtain purified monitoring data; Time series analysis and spatial interpolation processing are carried out on the purified monitoring data, and outliers are eliminated by combining adaptive threshold filtering to form a dynamic change characteristic sequence of soil pollutants.

3. The agricultural soil pollution prediction method according to claim 1, characterized in that Based on the soil pollution characteristic data set, an electrochemical sensing detection array is used to dynamically monitor the diffusion characteristics of pollutants in soil samples. Combining dielectric property analysis and fluorescence spectrum detection, a pollutant migration law characteristic model is generated, including: According to the soil pollution characteristic dataset, an electrochemical sensing detection array composed of ion-selective electrodes, conductivity electrodes, and redox electrodes is arranged at the sampling points. The concentration of heavy metal ions is continuously collected by potentiometry to obtain time-series data of pollutant concentrations. By collecting data every 30 minutes and continuously monitoring for 24 hours, the dynamic change process of heavy metal ion concentrations is recorded. Based on the time-series data of pollutant concentrations, a four-electrode alternating current impedance spectrometer is used to measure the dielectric constant and dielectric loss of soil samples. Combining with the polarization characteristic analysis under power frequency electric fields, a set of soil dielectric parameters is obtained. The soil samples are scanned by an excitation-emission matrix using a fluorescence spectrophotometer. By analyzing the peak positions and peak intensities of the three-dimensional fluorescence spectra, a fluorescence characteristic spectrum of organic pollutants is formed. The set of soil dielectric parameters and the fluorescence characteristic spectrum of organic pollutants are data-fused. The migration characteristics of pollutants are extracted by covariance matrix decomposition to obtain data on pollutant diffusion characteristics. According to the data on pollutant diffusion characteristics, a scanning electron microscope is used to observe the surface morphology of soil particles, and an energy-dispersive spectrometer is used to determine the surface element distribution, obtaining the pollutant interface distribution characteristics. Based on the pollutant interface distribution characteristics, through the calculation of the anisotropic diffusion coefficient and the analysis of interfacial mass transfer resistance, combined with the migration rate of pollutants in the soil, a characteristic model of pollutant migration law is generated.

4. The agricultural soil pollution prediction method according to claim 1, characterized in that Using the pollutant accumulation model, the occurrence forms of pollutants in the soil are determined by synchronous thermal analysis and X-ray diffraction methods. A selective ion electrode array is used to detect the changes in the concentrations of key ions, generating a characteristic map of multi-pollutant interactions, including: Based on the pollutant accumulation model, the soil samples are heated by a differential scanning calorimeter, and the heat flow during the temperature change process is continuously monitored to obtain the pollutant pyrolysis characteristic curve. For the pollutant pyrolysis characteristic curve, an X-ray diffractometer is used to determine the crystal phase structure of the soil samples. Combining with the Bragg equation to analyze the diffraction peaks, lattice parameter data are obtained. Based on the lattice parameter data, a selective ion electrode is used to measure the potentials of potassium ions, sodium ions, and ammonium ions. The ion concentrations are calculated through the potentiometric titration curve to form key ion concentration data. According to the key ion concentration data, a thermogravimetric analyzer is used to determine the change law of the sample mass with temperature. Combining with thermogravimetry-mass spectrometry to analyze the thermal decomposition products of pollutants, data on the composition of thermal decomposition products are obtained. The data on the composition of thermal decomposition products are used to determine the valence states of surface elements of the sample by an X-ray photoelectron spectrometer. Combining with Auger electron spectroscopy to analyze the surface chemical composition, a surface chemical state distribution map is obtained. Based on the surface chemical state distribution map, a Raman spectrometer is used to characterize the molecular structure of pollutants, and nuclear magnetic resonance spectroscopy is used to analyze the structure of organic components, generating a characteristic map of multi-pollutant interactions.

5. The agricultural soil pollution prediction method according to claim 1, characterized in that Based on the characteristic map of multi-pollutant interactions, a pollutant content early warning system is constructed based on thermogravimetric analysis and colorimetry. Combining with spectral analysis methods, a soil pollution prevention and control plan for different regions is output, including: Based on the multi-pollutant interaction characteristic spectrum, the soil sample is subjected to programmed temperature treatment by a thermogravimetric-differential thermal synchronous analyzer, the thermogravimetric curve and the differential thermal curve are collected, and the pollutant content change data are obtained; According to the pollutant content change data, the absorbance of the heavy metal color reaction is measured by a visible spectrophotometer, and the pollutant concentration is calculated in combination with the standard curve to obtain a quantitative analysis data set; The quantitative analysis data set is used to perform a full-band scan of the sample by an infrared spectrometer, and the characteristic absorption peaks are identified and quantitatively analyzed to form spectral characteristic data; Using the spectral characteristic data, the absorption spectrum of the sample is measured by a UV-visible spectrometer, and the background interference is eliminated by combining the second derivative spectrum to obtain the pollutant component spectrogram; For the pollutant component spectrogram, the excitation-emission spectrum of the sample is measured by a fluorescence analyzer, and the spectral data are analyzed by combining parallel factor analysis to obtain the pollutant concentration warning threshold; Based on the pollutant concentration warning threshold, the soil sample is in-situ detected by an optical fiber spectrometer, and the pollutant distribution characteristics are established in cooperation with chemometric analysis, and the sub-regional soil pollution prevention and control plan is output.

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