A soil environmental pollution condition monitoring and analyzing system and method

Abstract

The present application relates to the technical field of soil pollution monitoring and analysis, and particularly relates to a soil environmental pollution monitoring and analysis system and method, which comprises soil environment sampling, sample pretreatment, pollution risk multidimensional prediction and control scheme analysis module, through collecting three types of parameters of fiber length, inorganic particle and organic particle size and crop root distance in the matrix, evaluating the optimal sampling scheme, realizing the matching of the sampler and the soil structure characteristics, adopting the two-step method of alkaline depolymerization and targeted enzymolysis to realize the selective removal of organic matter, taking the conductivity change rate and the residual organic carbon content as the control indexes to improve the pretreatment accuracy, combining the sample conductivity, porosity and dissolved organic carbon content to calculate the rhizosphere influence index, automatically distinguishing the rhizosphere layer and the non-rhizosphere layer, constructing the pollution risk index by comprehensively considering the diffusion flux and the absorption flux, and matching the control scheme based on the risk grade.

Description

Technical Field

[0001] This invention relates to the field of soil pollution monitoring and analysis technology, specifically to a soil environmental pollution status monitoring and analysis system and method. Background Technology

[0002] Substrate cultivation is widely used in vegetable planting, but traditional soil monitoring has obvious shortcomings. It does not take into account the characteristics of substrate fibers, inorganic particles and crop roots when designing sampling schemes, which can easily lead to sampling distortion. It also lacks targeted organic matter removal methods, which interfere with pollutant detection. Furthermore, it cannot accurately assess the risk of non-rhizosphere pollution diffusion and fruit absorption, making it difficult to ensure vegetable safety. This has created a demand for soil pollution monitoring and analysis that is adapted to substrate cultivation scenarios.

[0003] Existing technologies, such as the invention patent application with publication number CN117538503A, disclose a real-time intelligent soil pollution monitoring system and method, which relates to the field of soil pollution monitoring. This system can collect soil pollutant index data in real time through soil pollutant sensors, and through modules such as data fusion, pollution modeling, pollution source tracing, pollution assessment, and pollution early warning, it can comprehensively monitor, analyze, and evaluate the soil environmental quality, as well as predict and warn of future pollution situations, thereby achieving effective protection and management of the soil environment.

[0004] The above solution has at least the following technical problems:

[0005] 1. The above scheme lacks scientific design and parameterized control of the soil sampling process. It only describes the real-time collection of soil pollutant index data by sensors, without addressing the soil sample collection method, the basis for sampler selection, and the control of data representativeness under different soil layer structures. This may lead to significant deviations in monitoring data due to differences in soil matrix, rhizosphere distribution, and moisture content. It is difficult to guarantee the uniformity and comparability of the collected data at the spatial and structural levels, ultimately causing the modeling and prediction results to deviate from the actual soil environmental pollution situation.

[0006] 2. The above scheme lacks technical descriptions of sample pretreatment and interlayer structure differentiation. It only relies on the fusion and modeling of raw signals collected by sensors, without proposing strategies to remove organic matter interference in soil samples, nor does it perform stratified identification and physical differentiation between the rhizosphere and non-rhizosphere layers. This deficiency will lead to the mixing of signals from different sources in the monitoring data, and the pollutant concentration measurement results will have systematic problems of being too high or too low. At the same time, since the rhizosphere and non-rhizosphere layers are not identified, the diffusion law of pollutants between the crop absorption area and the non-absorption area cannot be accurately reflected. Therefore, the pollution source tracking, diffusion simulation and pollution risk assessment lack depth resolution, reducing the scientific credibility and prediction accuracy of the overall system.

[0007] 3. The above-mentioned scheme lacks a description of quantitative assessment and control feedback mechanisms for pollution risks, and only focuses on data fitting, pollution source tracing, and early warning of exceeding standards. It does not establish a dynamic coupling model of pollutant diffusion and plant absorption processes, nor does it propose control strategies based on risk levels. As a result, the system cannot distinguish the spatial gradient and temporal trend of pollution risks, and cannot accurately assess the impact of future pollution changes on crop growth and environmental safety. At the same time, the lack of risk classification and response mechanisms means that the system can only perform static monitoring and alarms, and cannot implement targeted control measures for high-risk areas, such as matrix passivation or rhizosphere improvement. Therefore, it is difficult to achieve the closed-loop function of "from monitoring to treatment" in practical applications, which limits the engineering applicability and sustainable operation capability of the system in actual soil environmental pollution prevention and control scenarios. Summary of the Invention

[0008] To address the aforementioned shortcomings of existing technologies, this invention provides a soil environmental pollution monitoring and analysis system and method that can effectively solve the problems of low sampling accuracy, ambiguous interlayer differentiation, and inaccurate risk assessment in the prior art.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a soil environmental pollution status monitoring and analysis system, including: a soil environmental sampling module, used to collect sampler parameters corresponding to the soil for substrate cultivation and evaluate the optimal sampling scheme for soil sampling.

[0010] The sample pretreatment module is used to collect soil samples according to the optimal sampling plan, selectively remove organic matter through a two-step method of alkaline and enzymatic hydrolysis, and distinguish the morphology of the rhizosphere and non-rhizosphere layers.

[0011] The multidimensional pollution risk prediction module is used to obtain the concentration of pollutants in the rhizosphere and non-rhizosphere layers, analyze the diffusion amount of non-rhizosphere pollutants to the rhizosphere layer, and the amount of pollutants absorbed by soil-grown crops. Combining diffusion risk and absorption risk, it predicts whether the future comprehensive risk level meets the requirements.

[0012] The regulation scheme analysis module is used to evaluate regulation schemes based on the comprehensive risk level when the future comprehensive risk level does not meet the requirements.

[0013] Preferably, the process of collecting fiber length, maximum particle size of inorganic and organic particles in the substrate, and average distance between crop roots in the substrate corresponding to the soil is as follows: Select each sub-sampling point in the substrate-grown soil, and based on the set number of samplings, collect the fiber length, particle size of inorganic and organic particles in the substrate, and distance between crop roots in the substrate at each sub-sampling point.

[0014] Calculate the mean, median, coefficient of variation, and 95th percentile for each subsample point.

[0015] The 95th percentile values ​​of fiber length, inorganic particle size, and organic particle size were used as the longest fiber length, the largest inorganic particle size, and the largest organic particle size, respectively, and the mean value of the distance between crop roots was used as the average distance between roots.

[0016] Preferably, the optimal sampling scheme for evaluating soil sampling is specifically implemented as follows: the coefficient of variation corresponding to the sampler parameters of each sub-sample point is compared with a first threshold and a second threshold, respectively.

[0017] If the coefficients of variation corresponding to the sampler parameters are all less than or equal to the set first threshold, then the uniform sampler is used;

[0018] If the coefficient of variation corresponding to the sampler parameters is greater than the set second threshold, then the sampler is selected based on the subsample points.

[0019] If the first threshold is less than the second threshold, and the coefficient of variation corresponding to the sampler parameters is between the first threshold and the second threshold, then compare the 95th percentile value of the fiber length and the maximum particle size of each subsample point with the median value.

[0020] If the ratio of the 95th percentile value of the maximum particle size corresponding to a certain subsample point to the median is greater than or equal to the set third threshold, then a local individual sampler is used.

[0021] By combining the longest fiber length, the maximum particle size of inorganic and organic particles, and the average root spacing of each sub-sampling point, the optimal diameter of the sampler corresponding to each sub-sampling point is calculated.

[0022] When using a uniform sampler, sampling points are reselected, and the mean of the optimal diameter of each subsample point is calculated. The result is the diameter corresponding to the uniform sampler.

[0023] When a sampler is selected based on sub-sampling points, soil samples are collected from each sub-sampling point, and the sampling depth and location are recorded to obtain the optimal soil sampling scheme.

[0024] Preferably, the selective removal of organic matter through a two-step alkaline and enzymatic hydrolysis method is carried out as follows: In the alkaline depolymerization stage, each soil sample is dynamically extracted using a pH alkaline buffer solution, and the organic matter dissolution rate is monitored by controlling the change rate of solution conductivity.

[0025] Extraction is stopped when the rate of change is less than or equal to the set rate of change threshold.

[0026] In the targeted enzymatic hydrolysis stage, after the alkali treatment is completed, a compound enzyme is introduced into each soil sample, and the amount of compound enzyme added to each soil sample is calculated based on the residual organic carbon content of the alkali treatment in each soil sample, thereby completing the enzymatic hydrolysis.

[0027] Preferably, the morphological differentiation between the rhizosphere and non-rhizosphere layers is carried out as follows: based on the sampling depth of each soil sample, the layers at each depth are divided.

[0028] By combining the electrical conductivity, porosity, and dissolved organic carbon content of each soil sample at each depth, the rhizosphere influence index of each soil sample at each depth was calculated.

[0029] If the maximum rhizosphere influence index in a soil sample is selected, and the maximum rhizosphere influence index in the soil sample exceeds a set stratification threshold, then the depth layer corresponding to the maximum rhizosphere influence index is determined to be the rhizosphere interface of the soil sample.

[0030] Preferably, the process for analyzing the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants is as follows: by combining the porosity, soil moisture content, soil temperature and pollutant baseline diffusion coefficient of each soil sample, the time-varying effective diffusion coefficient corresponding to each soil sample is calculated.

[0031] The diffusion flux of each soil sample was calculated by combining the time-varying effective diffusion coefficient, pollutant concentration, and rhizosphere depth.

[0032] By combining the diffusion flux, dissolved organic carbon concentration, and total organic carbon concentration of each soil sample, the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants within a set time period was calculated.

[0033] Preferably, the specific process for determining the pollutant absorption of the soil-grown crops is as follows: obtaining the rhizosphere pollutant concentration, measured pollutant concentration, root surface area, and root permeability coefficient of each soil sample, and calculating the absorption flux of the pollutants at the interface between the rhizosphere and the crop roots for each soil sample.

[0034] By combining the absorption flux, rhizosphere porosity, dissolved oxygen concentration and soil temperature of each soil sample, the total amount of pollutants absorbed within a set time period was calculated.

[0035] In a second aspect, this invention provides a method for monitoring and analyzing soil environmental pollution, comprising:

[0036] S1. Soil environmental sampling: For soils grown in substrate cultivation, collect the corresponding sampler parameters and evaluate the optimal soil sampling scheme.

[0037] S2. Sample pretreatment: Soil samples were collected according to the optimal sampling plan. Organic matter was selectively removed by a two-step method of alkaline treatment and enzymatic hydrolysis, and the morphology of the rhizosphere and non-rhizosphere layers was distinguished.

[0038] S3. Multidimensional prediction of pollution risk: Obtain the concentration of pollutants in the rhizosphere and non-rhizosphere layers, analyze the diffusion amount of non-rhizosphere pollutants to the rhizosphere layer, and the amount of pollutants absorbed by soil-grown crops. Combine diffusion risk and absorption risk to predict whether the future comprehensive risk level meets the requirements.

[0039] S4. Analysis of Control Plans: When the overall risk level does not meet the requirements in the future, the control plan is evaluated based on the overall risk level.

[0040] The technical solution provided by this invention has the following advantages compared with the known prior art:

[0041] 1. In the soil sampling and assessment process, this invention collects three key structural parameters in the matrix: the longest fiber length, the maximum particle size of inorganic and organic particles, and the average distance between crop roots. Based on the dual criteria of coefficient of variation and skewness ratio, the optimal sampling scheme is determined. This facilitates the dynamic matching of sampler specifications with soil structural characteristics, avoids the problem of insufficient representativeness caused by traditional uniform sampling methods, and thus improves the structural consistency and data reliability in the sample acquisition stage.

[0042] 2. In the sample pretreatment process of this invention, the selective removal of organic matter is achieved through a two-step synergistic approach of alkaline depolymerization and targeted enzymatic hydrolysis. The reaction endpoint is determined by monitoring the rate of change in solution conductivity and the residual organic carbon content. This facilitates precise control of the degree of organic matter removal and avoids the problems of over-removal or incomplete removal in traditional acid-base single treatment methods, thereby ensuring the accuracy and repeatability of subsequent pollutant concentration analysis.

[0043] 3. In the process of distinguishing between the rhizosphere and non-rhizosphere morphology, the embodiments of the present invention construct a rhizosphere influence index by comprehensively calculating the electrical conductivity, porosity and dissolved organic carbon content of the sample profile, and use the depth layer corresponding to the maximum rhizosphere influence index as the interface determination basis. This is conducive to achieving adaptive identification of the boundaries between soil profile layers, overcoming the defects of strong subjectivity and blurred layer boundaries in the existing technology of artificial stratification, thereby providing a more accurate spatial stratification basis for the analysis of pollutant migration and absorption.

[0044] 4. In the process of pollution diffusion and absorption analysis, the embodiments of the present invention introduce a time-varying effective diffusion coefficient and a rhizosphere interface absorption flux model, and comprehensively consider multi-dimensional environmental parameters such as porosity, water content, temperature, and dissolved oxygen. This is conducive to dynamically depicting the real process of non-rhizosphere pollutants migrating to the rhizosphere and the absorption behavior of crop roots. It avoids the errors caused by the static assumptions of diffusion and absorption processes in traditional models, thereby improving the realism and prediction accuracy of pollution migration simulation.

[0045] 5. In the process of comprehensive risk prediction and control decision-making, the embodiments of the present invention construct a comprehensive pollution risk index by weighted calculation of diffusion and absorption, and automatically match the corresponding control schemes, such as matrix passivation and rhizosphere replacement, based on the risk level results. This is conducive to achieving forward-looking early warning and dynamic response to pollution risks, and improving the system's intelligence level and practical application value in soil pollution control. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0047] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0048] Figure 2 This is a schematic diagram of the implementation steps of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0050] The present invention will be further described below with reference to embodiments.

[0051] Please see Figure 1 As shown, a soil environmental pollution status monitoring and analysis system includes at least:

[0052] The soil environment sampling module is used to collect the corresponding sampler parameters for soil in substrate cultivation and to evaluate the optimal soil sampling scheme.

[0053] In a specific embodiment, the process of collecting fiber length, maximum particle size of inorganic and organic particles in the substrate, and average distance between crop roots in the substrate corresponding to the soil is as follows: Select each sub-sampling point in the substrate-grown soil, and based on the set number of samplings, collect the fiber length, particle size of inorganic and organic particles in the substrate, and distance between crop roots in each sub-sampling point.

[0054] Calculate the mean, median, coefficient of variation, and 95th percentile for each subsample point.

[0055] The 95th percentile values ​​of fiber length, inorganic particle size, and organic particle size were used as the longest fiber length, the largest inorganic particle size, and the largest organic particle size, respectively, and the mean value of the distance between crop roots was used as the average distance between roots.

[0056] It should be noted that the substrate cultivation area or the length of the planting trough should be evenly divided into several sub-areas. Within each sub-area, avoid abnormal areas such as substrate clumping or water accumulation. Randomly select 3 to 5 points as sub-sampling points. For each sub-sampling point, collect samples at different depths at the main distribution depth of the crop roots according to the set number of times.

[0057] It should also be noted that the standard deviation of each item in the sampler parameters is calculated separately, and then the standard deviation is divided by the corresponding mean to obtain the coefficient of variation. The calculation process of the 95th percentile value and the standard deviation is the existing calculation process, and will not be elaborated on here.

[0058] In a specific embodiment, the process of evaluating the optimal sampling scheme for soil sampling is as follows: the coefficient of variation corresponding to the sampler parameters of each sub-sample point is compared with a first threshold and a second threshold.

[0059] If the coefficients of variation corresponding to the sampler parameters are all less than or equal to the set first threshold, then the uniform sampler is used.

[0060] If the coefficient of variation corresponding to the sampler parameters is greater than the set second threshold, then the sampler is selected based on the subsample points.

[0061] If the first threshold is less than the second threshold, and the coefficient of variation corresponding to the sampler parameters is between the first threshold and the second threshold, then compare the 95th percentile value of the fiber length and the maximum particle size of each subsample point with the median value.

[0062] If the ratio of the 95th percentile value of the maximum particle size corresponding to a certain subsample point to the median is greater than or equal to the set third threshold, then a local individual sampler is used.

[0063] By combining the longest fiber length, the maximum particle size of inorganic and organic particles, and the average root spacing of each sub-sampling point, the optimal diameter of the sampler corresponding to each sub-sampling point is calculated.

[0064] When using a uniform sampler, sampling points are reselected, and the mean of the optimal diameter of each subsample point is calculated. The result is the diameter corresponding to the uniform sampler.

[0065] When a sampler is selected based on sub-sampling points, soil samples are collected from each sub-sampling point, and the sampling depth and location are recorded to obtain the optimal soil sampling scheme.

[0066] It should be noted that the first threshold and the second threshold are determined based on the statistical distribution of matrix structure homogeneity, respectively, to characterize the case where the structural differences between sample points are negligible. The first threshold is based on the historical statistical fluctuation range of the cultivation matrix under homogeneous conditions, to characterize the case where the structural differences between sample points are significant and local independent sampling is required, such as 0.15 to 0.25. The second threshold is based on the upper limit of heterogeneity of multiple types of matrix, such as 0.45 to 0.55.

[0067] The third threshold is used to determine whether there is significant heterogeneity or local abnormal coarse particle distribution in the matrix structure of a certain sub-sampling point. It is obtained by statistical analysis of the measured data of sampling resistance of different matrix types, such as soil, humus, vermiculite and coconut coir. When the ratio of the 95th percentile of the largest particle size in inorganic particles to the median is greater than or equal to this threshold, it indicates that there is significant coarse particle enrichment in the area, and a local individual sampler is used for sampling.

[0068] It should be noted that the maximum particle size corresponding to the subsample point refers to the maximum value between the particle size of inorganic particles and the particle size of organic particles.

[0069] It should be noted that the optimal diameter dynamic fitting formula is expressed as follows: ,in This is represented as the optimal diameter. It is a function with maximum value. , and These represent the coefficients for ensuring the diameter of the annular sampler can cover the longest fiber in the matrix to avoid fiber breakage, the coefficient for ensuring the structural stability of the collected matrix sample block through the interlocking of inorganic and organic particles, and the coefficient for controlling the sampler diameter to be less than a reasonable proportion of the average spacing between crop roots to reduce root damage. , , , The longest fiber length, the maximum particle size of inorganic particles in the matrix, the maximum particle size of organic particles in the matrix, and the average distance between crop roots.

[0070] It should also be noted that, , and All results were determined through experimental verification and feedback from practical applications. First, different types of substrates, such as coconut coir and peat, were selected, and the fiber length range was measured. Then, comparative experiments were conducted using sampler diameters of 1.0, 1.1, 1.2, and 1.3 times the fiber length. The results showed that diameters of 1.0 to 1.1 times the fiber length resulted in breakage due to fiber bending or uneven distribution; diameters of 1.3 times the fiber length resulted in complete sampling but with redundant sampling; only diameters of 1.2 times the fiber length met the requirement of 100% coverage of the longest fiber and avoidance of breakage. Therefore, 1.2 was determined as the optimal diameter. The value of , and The process of obtaining the value and The process of obtaining the value is the same, so I will not go into details here.

[0071] It should be noted that selecting a sampler based on subsample points means that each subsample point is sampled using a different aperture according to its own structural characteristics. Using a local individual sampler means that a large-aperture sampler is used only at that subsample point, while other subsample points still use a uniform aperture.

[0072] The sample pretreatment module is used to collect soil samples according to the optimal sampling plan, selectively remove organic matter through a two-step method of alkaline and enzymatic hydrolysis, and distinguish the morphology of the rhizosphere and non-rhizosphere layers.

[0073] In one specific embodiment, the selective removal of organic matter through a two-step alkaline and enzymatic hydrolysis method is carried out as follows: In the alkaline depolymerization stage, each soil sample is dynamically extracted using a pH alkaline buffer solution, and the organic matter dissolution rate is monitored by controlling the change rate of solution conductivity.

[0074] Extraction is stopped when the rate of change is less than or equal to the set rate of change threshold.

[0075] In the targeted enzymatic hydrolysis stage, after the alkali treatment is completed, a compound enzyme is introduced into each soil sample, and the amount of compound enzyme added to each soil sample is calculated based on the residual organic carbon content of the alkali treatment in each soil sample, thereby completing the enzymatic hydrolysis.

[0076] It should be noted that the formula for calculating the dosage of the compound enzyme for each soil sample is as follows: ,in For the first The dosage of compound enzyme in each soil sample Each soil sample is numbered. , The value of is a positive integer. For the first The difference between the residual organic carbon content after alkali treatment and the preset target residual organic carbon content in each soil sample is a positive number. This is the enzyme efficiency coefficient. This refers to the duration of enzyme activity.

[0077] It should also be noted that after alkaline depolymerization, each soil sample was tested. The total organic carbon or dissolved organic carbon of the sample after alkali treatment was measured, and then converted to the amount of organic carbon per unit mass of soil, i.e., the residual organic carbon content, according to the solid-liquid ratio. Based on the statistical average of the same type of matrix after complete organic matter removal treatment, a preset target residual organic carbon content was obtained. The difference between the two values ​​was calculated, and the result is the residual organic carbon content. .

[0078] The values ​​were determined through small-scale calibration experiments. Soil samples were selected, and different dosages of the compound enzyme were set, such as 50 to 400 enzyme activity units per kilogram of soil. The amount of organic carbon removed during the reaction time was measured. and the amount of enzyme and The relationship is fitted, and the organic carbon removal capacity per unit enzyme per unit time is calculated from the initial slope of the fitted curve, which is the enzyme efficiency coefficient.

[0079] In the enzymatic hydrolysis experiment, the change curve of organic carbon removal over time is monitored simultaneously. When the removal rate decreases for two consecutive time periods and reaches its minimum value, it is determined to be the end point of enzyme action, and the corresponding reaction time is the enzyme action duration.

[0080] In a specific embodiment, the morphological distinction between the rhizosphere and non-rhizosphere layers is carried out as follows: based on the sampling depth of each soil sample, the layers at different depths are divided.

[0081] By combining the electrical conductivity, porosity, and dissolved organic carbon content of each soil sample at each depth, the rhizosphere influence index of each soil sample at each depth was calculated.

[0082] If the maximum rhizosphere influence index in a soil sample is selected, and the maximum rhizosphere influence index in the soil sample exceeds a set stratification threshold, then the depth layer corresponding to the maximum rhizosphere influence index is determined to be the rhizosphere interface of the soil sample.

[0083] It should be noted that after sample collection, the conductivity change was measured layer by layer along the sampling depth using a profile conductivity probe; porosity was calculated by measuring the bulk density and particle density of samples at each depth layer; and the dissolved organic carbon content was obtained by extracting dissolved organic matter from the sample using a water-soluble extraction method, and the carbon content was measured by a total organic carbon analyzer, thus obtaining the dissolved organic carbon content data for each depth layer.

[0084] It should be noted that the formula for calculating the rhizosphere influence index for each soil sample at each depth is as follows: ,in , , and Represented as the first The soil sample corresponds to the first Rhizosphere influence index, sample profile electrical conductivity, porosity, and dissolved organic carbon content at each depth layer. , and These are respectively represented as the sample profile conductivity weighting factor, porosity weighting factor, and dissolved organic carbon content weighting factor.

[0085] It should be noted that, Indicates the conductivity of the sample profile as a function of depth. The gradient change is used to reflect the degree to which the ionic activity and solution conductivity in the rhizosphere change with depth; Porosity as a function of depth The gradient of change reflects the compactness of the soil structure in the rhizosphere zone; Indicates the concentration of dissolved organic carbon as a function of depth. The gradient reflects the distribution characteristics of rhizosphere organic matter.

[0086] It should be noted that, , and The setting is based on the contribution of each parameter to rhizosphere identification. The specific method is as follows: by conducting experimental measurements on samples of different soil types, such as sandy soil, loamy soil, and clay soil, principal component analysis or grey relational analysis is used to calculate the contribution rate of each gradient index to the rhizosphere interface determination, and the weight coefficient is obtained by normalization. The specific calculation method of the contribution rate is the conventional calculation method in the existing technology, which will not be elaborated in detail here.

[0087] In the sample pretreatment process, this invention achieves selective removal of organic matter through a two-step synergistic approach of alkaline depolymerization and targeted enzymatic hydrolysis. The reaction endpoint is determined by monitoring the rate of change in solution conductivity and the residual organic carbon content. This facilitates precise control of the degree of organic matter removal and avoids the problems of over-removal or incomplete removal in traditional acid-base single-treatment methods, thereby ensuring the accuracy and repeatability of subsequent pollutant concentration analysis.

[0088] The multidimensional pollution risk prediction module is used to obtain the concentration of pollutants in the rhizosphere and non-rhizosphere layers, analyze the diffusion amount of non-rhizosphere pollutants to the rhizosphere layer, and the amount of pollutants absorbed by soil-grown crops. Combining diffusion risk and absorption risk, it predicts whether the future comprehensive risk level meets the requirements.

[0089] In a specific embodiment, the process of analyzing the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants is as follows: the time-varying effective diffusion coefficient of each soil sample is calculated by combining the porosity, soil moisture content, soil temperature and pollutant baseline diffusion coefficient of each soil sample.

[0090] The diffusion flux of each soil sample was calculated by combining the time-varying effective diffusion coefficient, pollutant concentration, and rhizosphere depth.

[0091] By combining the diffusion flux, dissolved organic carbon concentration, and total organic carbon concentration of each soil sample, the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants within a set time period was calculated.

[0092] It should be noted that time-domain reflectometry or frequency-domain reflectometry moisture sensors were deployed at the sampling site to monitor the volumetric water content at each sampling depth in real time. Simultaneously, soil temperature probes were buried at the same location to continuously record temperature changes at different depths. A portion of the samples were then taken back to the laboratory, where the water content was determined using a drying method, and this determination was used to correct the sensor data, thus obtaining the true water content and soil temperature of each soil sample. Based on the types of pollutants in the soil, the diffusion coefficients of similar pollutants in water were obtained from literature. .

[0093] It should be noted that the formula for calculating the time-varying effective diffusion coefficient for each soil sample is as follows: ,in , , and Represented as the first The time-varying effective diffusion coefficient, porosity, soil moisture content, and soil temperature corresponding to each soil sample Expressed as the baseline diffusion coefficient of pollutants, the function It is used to characterize the combined effects of soil porosity, moisture content and temperature on the diffusion behavior of pollutants in the actual environment. It is used to correct the baseline diffusion coefficient under standard conditions to the time-varying effective diffusion coefficient of each soil sample under actual conditions. The setting method is the same as the setting method of the Millington–Quirk soil diffusion correction model in the prior art, so it will not be described in detail here.

[0094] The formula for calculating the diffusion flux of each soil sample is as follows: ,in , and Represented as the first The diffusion flux, pollutant concentration, and depth corresponding to each soil sample Indicates the first The partial derivative of pollutant concentration with respect to depth in a soil sample, i.e., the rate of change of pollutant concentration in the depth direction.

[0095] A value greater than 0 indicates that the pollutant diffuses from the deeper matrix to the shallower matrix. A value less than 0 indicates that the pollutant diffuses from the shallow matrix to the deep matrix.

[0096] The amount of non-rhizosphere pollutants diffuse into the rhizosphere pollutants within a given time period. The calculation formula is: , The value of is a positive integer, where and This represents the start and end times of a specified time period. Represented as the first The concentration of soluble organic matter in a soil sample that can form complexes or adsorb with pollutants. Represented as the first All organic carbon in the soil sample, including dissolved and particulate-bound carbon, Indicates the first The active proportion of organic carbon in a soil sample, i.e., the percentage of organic carbon that participates in adsorption. It is the adsorption coefficient of the target pollutant by dissolved organic matter in the rhizosphere matrix.

[0097] The acquisition process involves, for a specific rhizosphere matrix sample, first collecting the rhizosphere matrix, extracting dissolved organic matter and measuring its concentration; then mixing the dissolved organic matter with different concentrations of target pollutants, isothermally shaking until adsorption equilibrium is reached, and measuring the concentration of free pollutants; finally, calculating the concentration using an adsorption isotherm model. The process of taking the average value by repeating experiments and fitting the adsorption isotherm model is a mature existing technology in the fields of environmental science and soil chemistry, and will not be elaborated on further here.

[0098] In a specific embodiment, the pollutant absorption of the soil-grown crops is determined by the following process: obtaining the rhizosphere pollutant concentration, measured pollutant concentration, root surface area, and root permeability coefficient of each soil sample, and calculating the absorption flux of the pollutants at the interface between the rhizosphere and the crop roots for each soil sample.

[0099] By combining the absorption flux, rhizosphere porosity, dissolved oxygen concentration and soil temperature of each soil sample, the total amount of pollutants absorbed within a set time period was calculated.

[0100] It should be noted that, ,in , , and Represented as the first The data included the absorption flux of pollutants at the rhizosphere-root interface, root surface area, rhizosphere pollutant concentration, and measured pollutant concentration within the crop roots for each soil sample. It is expressed as the root permeability coefficient.

[0101] The concentration of pollutants in the rhizosphere layer was determined by collecting rhizosphere soil from the area adjacent to the crop roots, selectively removing organic matter using a two-step alkaline-enzymatic hydrolysis method, and then measuring the concentration using instruments such as atomic absorption spectrophotometer and high-performance liquid chromatography. The actual pollutant concentration was determined by taking soil samples from the area corresponding to the rhizosphere layer and directly measuring the concentration according to standard methods for soil pollutant detection. The root surface area was calculated automatically by excavating and cleaning the complete root system of the corresponding crop, scanning the root images using root scanning image analysis software such as WinRHIZO, and then automatically calculating the root permeability coefficient. The coefficient was calculated by selecting complete, healthy root systems, measuring the water permeability of the roots under different pressures using a root pressure chamber or osmometer, and combining the permeability area with the pressure difference. The specific calculation process is consistent with existing methods for determining root hydraulic characteristics based on Darcy's law, and will not be elaborated further here.

[0102] Total amount of pollutants absorbed within a set time period The calculation formula is: ,in Represented as the first The dissolved oxygen concentration corresponding to each soil sample was determined in situ using a portable dissolved oxygen meter. The probe was inserted into the rhizosphere soil at the same sampling depth, and the reading was recorded after stabilization. It is used to characterize the combined effects of soil porosity, dissolved oxygen concentration, and temperature on the diffusion behavior of pollutants in the real environment. and Represented as the first Porosity and soil temperature corresponding to each soil sample.

[0103] In the process of distinguishing between the rhizosphere and non-rhizosphere morphology, this invention constructs a rhizosphere influence index by comprehensively calculating the electrical conductivity, porosity, and dissolved organic carbon content of the sample profile. The depth layer corresponding to the maximum rhizosphere influence index is used as the interface determination criterion. This facilitates the adaptive identification of the boundaries between soil profile layers and overcomes the shortcomings of the existing technology, such as strong subjectivity and blurred layer boundaries in manual stratification. This provides a more accurate spatial stratification basis for the analysis of pollutant migration and absorption.

[0104] In the analysis of pollution diffusion and absorption, this invention introduces a time-varying effective diffusion coefficient and a rhizosphere interface absorption flux model, comprehensively considering multi-dimensional environmental parameters such as porosity, water content, temperature, and dissolved oxygen. This is beneficial for dynamically depicting the real process of non-rhizosphere pollutants migrating to the rhizosphere and the absorption behavior of crop roots, avoiding the errors caused by static assumptions about diffusion and absorption processes in traditional models, thereby improving the realism and prediction accuracy of pollution migration simulation.

[0105] The regulation scheme analysis module is used to evaluate regulation schemes based on the comprehensive risk level when the future comprehensive risk level does not meet the requirements.

[0106] In a specific embodiment, the process of predicting whether the future comprehensive risk level meets the requirements is as follows: the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants within a set time period and the total amount of pollutants absorbed by soil-grown crops are weighted and calculated to obtain the comprehensive pollution risk index.

[0107] The comprehensive pollution risk index is compared with a pre-set risk threshold range to obtain the risk level corresponding to the comprehensive pollution risk index. When the risk level is high, it indicates that the future comprehensive risk level does not meet the requirements.

[0108] It should be noted that the weighted calculation process is a standard calculation process in existing technology, and the process of setting the weight factors is the same as... , and The setup process is the same, so I won't go into too much detail here.

[0109] It should be noted that the preset risk threshold range is based on the upper limit of crop safety and the soil environmental benchmark. It is first referenced to the existing standards, such as the limit of pollutants in crop fruits in the "National Food Safety Standard for Limits of Contaminants in Food" and the risk screening value of rhizosphere pollutants in the "Soil Environmental Quality Standard for Agricultural Land Soil Pollution Risk Control", and then calibrated in combination with the pollutant migration efficiency in substrate cultivation.

[0110] It should also be noted that the risk levels include high risk, medium risk, and low risk.

[0111] In a specific embodiment, the evaluation and control scheme is carried out as follows: if the risk is high, a combination of matrix passivation and rhizosphere replacement is used; if the risk is medium or low, matrix improvement and water control are used.

[0112] It should be noted that, for high-risk cases, rhizosphere replacement should be performed first. This involves removing contaminated rhizosphere soil within 1-2 cm of the corresponding crop roots from each soil sample, with the removal volume matching the rhizosphere sampling volume. Then, a sterilized clean substrate, such as an uncontaminated coconut coir-perlite mixture, should be backfilled, with the same proportions as the original cultivation substrate. Next, substrate passivation should be implemented by uniformly adding a passivating agent, such as quicklime or sepiolite, to the remaining non-rhizosphere substrate after replacement. The dosage is determined based on the substrate contamination concentration, typically 2%-5% of the substrate dry weight. After mixing, the substrate should be left to stand for 24-48 hours. The passivating agent will adsorb and complex with the contaminants, fixing the residual contaminants and preventing their diffusion into the new rhizosphere.

[0113] If the risk level is medium or low, first improve the substrate by adding amendments, such as well-rotted organic fertilizer or vermiculite, to the corresponding cultivation substrate of each soil sample at a rate of 10%-15% of the substrate volume. Mix the substrate evenly to improve its pore structure and enhance its adsorption capacity for pollutants. Then, control the water content by using drip irrigation to keep the substrate moisture content stable at 60%-80% of field capacity. By stabilizing the water environment, the rate of pollutant diffusion can be slowed down, and excessive water can be avoided to prevent pollutant leaching and migration.

[0114] In the process of comprehensive risk prediction and control decision-making, the embodiments of the present invention construct a comprehensive pollution risk index by weighted calculation of diffusion and absorption, and automatically match corresponding control schemes, such as matrix passivation and rhizosphere replacement, based on the risk level results. This is conducive to achieving forward-looking early warning and dynamic response to pollution risks, and improving the system's intelligence level and practical application value in soil pollution control.

[0115] Please see Figure 2 As shown, a method for monitoring and analyzing soil environmental pollution includes the following steps:

[0116] S1. Soil environmental sampling: For soils grown in substrate cultivation, collect the corresponding sampler parameters and evaluate the optimal soil sampling scheme.

[0117] S2. Sample pretreatment: Soil samples were collected according to the optimal sampling plan. Organic matter was selectively removed by a two-step method of alkaline treatment and enzymatic hydrolysis, and the morphology of the rhizosphere and non-rhizosphere layers was distinguished.

[0118] S3. Multidimensional prediction of pollution risk: Obtain the concentration of pollutants in the rhizosphere and non-rhizosphere layers, analyze the diffusion amount of non-rhizosphere pollutants to the rhizosphere layer, and the amount of pollutants absorbed by soil-grown crops. Combine diffusion risk and absorption risk to predict whether the future comprehensive risk level meets the requirements.

[0119] S4. Analysis of Control Plans: When the overall risk level does not meet the requirements in the future, the control plan is evaluated based on the overall risk level.

[0120] In the soil sampling and assessment process, this invention collects three key structural parameters in the matrix: the longest fiber length, the maximum particle size of inorganic and organic particles, and the average distance between crop roots. Based on the dual criteria of coefficient of variation and skewness ratio, the optimal sampling scheme is determined. This facilitates the dynamic matching of sampler specifications with soil structural characteristics, avoids the problem of insufficient representativeness caused by traditional uniform sampling methods, and thus improves the structural consistency and data reliability in the sample acquisition stage.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soil environmental pollution status monitoring and analysis system, characterized in that, include: The soil environment sampling module is used to collect the corresponding sampler parameters for soil in substrate cultivation and to evaluate the optimal soil sampling scheme. The sampler parameters include the longest fiber length in the matrix, the maximum particle size of inorganic and organic particles in the matrix, and the average distance between crop roots. The sample pretreatment module is used to collect soil samples according to the optimal sampling plan, selectively remove organic matter through a two-step method of alkaline and enzymatic hydrolysis, and distinguish the morphology of the rhizosphere and non-rhizosphere layers. The multidimensional pollution risk prediction module is used to obtain the concentration of pollutants in the rhizosphere and non-rhizosphere layers, analyze the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants, and the amount of pollutants absorbed by soil-grown crops, and predict whether the future comprehensive risk level meets the requirements. The specific process for determining whether the predicted future comprehensive risk level meets the requirements is as follows: The comprehensive pollution risk index is obtained by weighting the amount of non-rhizosphere pollutants diffused into the rhizosphere and the total amount of pollutants absorbed by soil-grown crops within a set time period. The comprehensive pollution risk index is compared with the preset risk threshold range to obtain the risk level corresponding to the comprehensive pollution risk index. When the risk level is high, it indicates that the future comprehensive risk level does not meet the requirements. The regulation scheme analysis module is used to evaluate the regulation scheme based on the comprehensive risk level when the future comprehensive risk level does not meet the requirements. The specific process for evaluating and regulating the plan is as follows: For high-risk cases, a combination of matrix passivation and rhizosphere replacement is used; for medium- or low-risk cases, matrix improvement and water regulation are used.

2. The soil environmental pollution status monitoring and analysis system according to claim 1, characterized in that, The specific process for collecting data on fiber length, maximum particle size of inorganic and organic particles in the matrix, and average distance between crop roots in the corresponding soil substrate is as follows: Select each sub-sampling point and collect the fiber length, particle size of inorganic and organic particles in the matrix, and distance between crop roots in each sub-sampling point. Calculate the mean, median, coefficient of variation, and 95th percentile value for each subsample point; The 95th percentile values ​​of fiber length, inorganic particle size, and organic particle size were used as the longest fiber length, the largest inorganic particle size, and the largest organic particle size, respectively, and the mean value of the distance between crop roots was used as the average distance between roots.

3. The soil environmental pollution status monitoring and analysis system according to claim 2, characterized in that, The specific process for evaluating the optimal soil sampling scheme is as follows: If the coefficients of variation corresponding to the sampler parameters are all less than or equal to the set first threshold, then the uniform sampler is used; If all values ​​are greater than the set second threshold, then the sampler is selected based on the subsample points; If the value is between the first threshold and the second threshold, then compare the 95th percentile value of the fiber length and the maximum particle size of each subsample point with the median value. If the ratio of the 95th percentile value of the maximum particle size corresponding to a certain subsample point to the median is greater than or equal to the set third threshold, then a local individual sampler is used; By combining the longest fiber length, the maximum particle size of inorganic and organic particles, and the average root spacing of each sub-sampling point, the optimal diameter of the sampler corresponding to each sub-sampling point is calculated. When using a uniform sampler, sampling points are reselected, and the mean of the optimal diameter of each subsample point is calculated. The result is the diameter corresponding to the uniform sampler. When a sampler is selected based on sub-sampling points, soil samples are collected from each sub-sampling point, and the sampling depth and location are recorded to obtain the optimal soil sampling scheme.

4. The soil environmental pollution monitoring and analysis system according to claim 3, characterized in that, The selective removal of organic matter via a two-step method involving alkaline and enzymatic hydrolysis is described in the following steps: During the alkaline depolymerization stage, each soil sample was dynamically extracted using a pH alkaline buffer solution, and the organic matter dissolution rate was monitored by controlling the rate of change in solution conductivity. Extraction is stopped when the rate of change is less than or equal to the set rate of change threshold. In the targeted enzymatic hydrolysis stage, after the alkali treatment is completed, a compound enzyme is introduced into each soil sample, and the amount of compound enzyme added to each soil sample is calculated based on the residual organic carbon content of the alkali treatment in each soil sample, thereby completing the enzymatic hydrolysis.

5. The soil environmental pollution status monitoring and analysis system according to claim 4, characterized in that, The specific process for distinguishing the morphology of the rhizosphere layer and the non-rhizosphere layer is as follows: Based on the sampling depth of each soil sample, layers at different depths were defined; By combining the electrical conductivity, porosity, and dissolved organic carbon content of each soil sample at each depth, the rhizosphere influence index of each soil sample at each depth was calculated. If the maximum rhizosphere influence index in a soil sample is selected, and the maximum rhizosphere influence index in the soil sample exceeds a set stratification threshold, then the depth layer corresponding to the maximum rhizosphere influence index is determined to be the rhizosphere interface of the soil sample.

6. The soil environmental pollution status monitoring and analysis system according to claim 5, characterized in that, The analysis of the diffusion amount of non-rhizosphere pollutants into the rhizosphere is carried out in the following specific process: By combining the porosity, soil moisture content, soil temperature and pollutant baseline diffusion coefficient of each soil sample, the time-varying effective diffusion coefficient of each soil sample was calculated. The diffusion flux of each soil sample was calculated by combining the time-varying effective diffusion coefficient, pollutant concentration, and rhizosphere depth. By combining the diffusion flux, dissolved organic carbon concentration, and total organic carbon concentration of each soil sample, the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants within a set time period was calculated.

7. The soil environmental pollution status monitoring and analysis system according to claim 6, characterized in that, The specific process for determining the pollutant absorption of crops grown in the soil is as follows: The concentration of pollutants in the rhizosphere, the measured concentration of pollutants, the root surface area and the root permeability coefficient of the corresponding crop roots of each soil sample were obtained, and the absorption flux of the corresponding pollutants in the rhizosphere and the crop root interface of each soil sample was calculated. By combining the absorption flux, rhizosphere porosity, dissolved oxygen concentration and soil temperature of each soil sample, the total amount of pollutants absorbed within a set time period was calculated.

8. A method for monitoring and analyzing soil environmental pollution, applied to the soil environmental pollution monitoring and analysis system described in any one of claims 1 to 7, characterized in that, Includes the following steps: S1. Soil environmental sampling: For soil used in substrate cultivation, collect the corresponding sampler parameters and evaluate the optimal soil sampling scheme. S2. Sample pretreatment: Soil samples were collected according to the optimal sampling plan. Organic matter was selectively removed by a two-step method of alkaline treatment and enzymatic hydrolysis, and the morphology of the rhizosphere and non-rhizosphere layers was distinguished. S3. Multidimensional prediction of pollution risk: Obtain the concentration of pollutants in the rhizosphere and non-rhizosphere, analyze the diffusion amount of non-rhizosphere pollutants to rhizosphere pollutants, and the amount of pollutants absorbed by soil-grown crops. Combine diffusion risk and absorption risk to predict whether the future comprehensive risk level meets the requirements. S4. Analysis of Control Plans: When the overall risk level does not meet the requirements in the future, the control plan is evaluated based on the overall risk level.

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