A method for evaluating remediation efficiency of heavy metal pollution in farmland soil

Abstract

The present application relates to the technical field of soil remediation, and discloses a method for evaluating the remediation efficiency of heavy metal pollution in agricultural soil, comprising the following steps: step one: obtaining crop planting management data, heavy metal pollution detection data of agricultural soil samples and remediation management data; step two: evaluating the ability of each crop to absorb and accumulate heavy metals from the soil to generate an enrichment index; step three: according to the remediation timestamp record, evaluating the sampling rationality of each crop in the corresponding agricultural area to generate a sampling score, realizing the quantitative evaluation of the adaptability of the sampling strategy, and being high in multi-dimensional evaluation accuracy; step four: evaluating the remediation effect of heavy metal pollution in each crop in the corresponding agricultural area to generate an efficiency index; step five: setting a threshold interval to judge the pollution level of the crop, the adaptability of the sampling strategy and the remediation level of the heavy metal pollution, outputting the corresponding judgment result and response measures, and forming a full-process closed-loop management and control of agricultural land, which is good in intelligent management and control remediation effect.

Description

Technical Field

[0001] This invention relates to the field of soil remediation technology, specifically to a method for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil. Background Technology

[0002] Heavy metal pollution in agricultural land soil refers to the accumulation of toxic heavy metals such as cadmium, lead, mercury, and arsenic in arable land soil, exceeding environmental background or safety thresholds, due to factors such as industrial emissions, mining, wastewater irrigation, long-term application of pesticides and fertilizers, and atmospheric deposition. This type of pollution is characterized by its high degree of concealment, complex migration and transformation, and difficulty in natural degradation. It easily enters the food chain through crop absorption, thereby threatening human health and ecological security. Therefore, the remediation of heavy metal pollution in agricultural land soil is of great significance, not only affecting food security and agricultural product quality but also directly impacting sustainable agricultural development and regional ecological stability. Conventional remediation methods mainly include physical remediation (such as soil replacement and isolation), chemical remediation (such as solidification / stabilization and leaching), and bioremediation (such as phytoremediation and microbial remediation), each differing in cost, timeframe, and applicability. In practical applications, physical remediation is fast-acting but involves large-scale engineering and is costly, and can easily damage soil structure. It is suitable for emergency remediation of locally highly polluted sites. Chemical remediation reduces the bioavailability of heavy metals by altering their forms, and has the advantages of relatively simple operation and rapid results, but may introduce the risk of secondary pollution, requiring strict control of the type and dosage of chemicals. Bioremediation relies on the absorption, enrichment, or transformation of heavy metals by plants or microorganisms, and is environmentally friendly and low-cost, but has a longer remediation cycle and is greatly affected by environmental conditions. To improve the overall remediation effect, a comprehensive remediation model that couples multiple technologies is often adopted, combined with long-term monitoring and dynamic assessment, to achieve continuous control of pollution risks and gradual restoration of soil function.

[0003] Currently, traditional methods for assessing the remediation effectiveness of heavy metal pollution in agricultural land soil tend to overlook spatiotemporal heterogeneity. Due to differences in the scale of sampling points, local high-risk pollution patches are easily obscured, leading to a bias towards averaging in the assessment results and reducing the ability to identify risks. In addition, traditional assessment methods lack dynamic feedback response mechanisms, resulting in a disconnect between crop response and soil indicators. Some assessments do not include the actual absorption by crops in the core indicators, making it difficult to identify the risk of soil meeting standards but agricultural products exceeding standards in a timely and effective manner. Consequently, it is difficult to achieve accurate quantification of remediation effects, risk warnings, and optimization adjustments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil. This method has the advantages of high accuracy in multidimensional assessment and excellent intelligent control and remediation effects. It solves the problems of traditional methods for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil, which tend to ignore spatiotemporal heterogeneity and lack dynamic feedback response mechanisms.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil, comprising the following steps: Step 1: Using a database and inductively coupled plasma mass spectrometry, acquire planting management data for all crops, heavy metal pollution detection data for farmland soil samples, and heavy metal pollution remediation management data, and classify them into crop datasets, soil datasets, and remediation datasets. Step 2: Based on the crop and soil datasets, assess the ability of each crop to absorb and accumulate heavy metals from the soil, and generate the corresponding enrichment index. ; Step 3: Based on the crop dataset, soil dataset, and remediation dataset, and according to the remediation timestamp records, after each remediation operation is completed, evaluate the sampling rationality of the farmland area corresponding to each crop and generate the corresponding sampling score. ; Step 4: Based on the soil dataset, remediation dataset, and enrichment index and sampling score The study assesses the remediation effectiveness of each crop in agricultural land areas contaminated with heavy metals, and generates corresponding efficiency indices. ; Step 5: Set a fixed range for the enrichment threshold. and performance threshold range Combined with enrichment index Sampling and scoring and efficiency index It determines the pollution level of crops, the suitability of sampling strategies, and the remediation level of heavy metal pollution, and outputs the corresponding judgment results and response measures.

[0006] Preferably, in step one, the crop dataset includes the edible part type of the crop, the heavy metal content of the roots, the heavy metal content of the above-ground stems, the heavy metal content of the leaves, the total root length, the root dry matter, the total number of plants measured, and the measured planting area. The edible part type includes root vegetables, leafy vegetables, and fruits.

[0007] Preferably, in step one, the soil dataset includes the content of individual heavy metal elements in the soil sample, the average total content of deep heavy metals, the average total content of surface heavy metals, and the measured volume, wherein the heavy metal elements include cadmium. Mercury Arsenic lead element Chromium copper element Zinc and nickel element .

[0008] Preferably, in step one, the repair dataset includes the sampling depth of farmland, the number of sampling profile layers, the number of sampling points, the sampling point density, the sampling frequency, the sampling timestamp, the total number of samplings, and the repair timestamp.

[0009] Preferably, in step two, the enrichment index The calculation process is as follows: S11. Based on the crop dataset and soil dataset, extract the first... Planting management data for the crops and heavy metal pollution detection data for soil samples from the corresponding agricultural land areas; S12, Calculate the first Crop growth and heavy metal elements Root absorption rate ; S13, Calculate the... Crop growth and heavy metal elements Transshipment efficiency ; S14, Calculate the... Crop growth and heavy metal elements migration efficiency ; S15. Based on S11-S14, calculate the first... Crop growth and heavy metal elements transfer coefficient ; S16. Based on S11-S15, calculate the first... Enrichment index of all heavy metal elements in crops .

[0010] Preferably, in step three, sampling and scoring... The evaluation process is as follows: S21. Based on the crop dataset, soil dataset, and remediation dataset, extract the first... Planting management data for crops, heavy metal pollution detection data for soil samples from corresponding agricultural land areas, and heavy metal pollution remediation management data; S22. Calculate the vertical migration coefficient of heavy metals in soil samples. ; S23, Calculate the first Crop root activity intensity coefficient ; S24, Calculate the first Planting density coefficient of crops ; S25, Sampling Scoring The assignment rules are as follows: Sampling and scoring all crops The initial value is set to 0 points. One point is added if any of the following conditions are met, with a maximum score of 7 points. The determination conditions are as follows: (1) The sampling depth coverage of the corresponding area of ​​agricultural land is the first Maximum burial depth of the root distribution layer of a crop; (2) Vertical migration coefficient of heavy metals in soil samples When the value is <0.3, the number of sampling profile layers in the corresponding area of ​​agricultural land is ≤3, and 0.3 < the vertical migration coefficient of heavy metals in the soil sample. When the vertical migration coefficient of heavy metals in the soil sample is less than 0.6, and the number of sampling profile layers in the corresponding area of ​​farmland is less than 6, the vertical migration coefficient of heavy metals in the soil sample is... When the value is greater than 0.5, the number of sampling profile layers in the corresponding area of ​​agricultural land is ≥6. (3) Crop root activity intensity coefficient When the value is ≥0.7, the number of sampling points within 5cm of the crop root system is ≥3, and the crop root activity intensity coefficient is... When <0.7, the number of sampling points within 5cm of the crop root system is ≥1; (4) Crop planting density coefficient In areas with a coefficient ≥0.8, the sampling point density is ≥0.5 per 1000 square meters, and the crop planting density coefficient is [missing information]. In areas with a strength <0.8, the sampling point density should be ≥0.2 points / 1000㎡; (5) The sampling frequency during the tillering stage of crops is ≥ once every 3 days, the sampling frequency during the grain filling stage of crops is ≥ once every 7 days, and the sampling frequency during the harvest stage of crops is ≥ once every 2 days, and the sampling is uninterrupted throughout the entire growth period from the tillering stage to the harvest stage; (6) From the tillering stage to the harvest stage, if any of the following events occur: a single rainfall of ≥25mm, a single field irrigation of ≥10mm, or a single day-night temperature difference of ≥8℃, and two additional sampling records have been added to the sampling timestamp record: one 12 hours before the event and one 24 hours after the event. (7) From the tillering stage to the harvest stage, two additional sampling records have been added to the sampling timestamp record: 12 hours before heavy metal pollution remediation and 24 hours after heavy metal pollution remediation.

[0011] Preferably, in step four, the efficiency index The calculation process is as follows: S31. Based on the soil dataset and remediation dataset, extract the [number] timestamp records according to the remediation timestamps. Heavy metal pollution detection data of soil samples from agricultural land areas corresponding to the crops planted; S32, Regarding the first Calculate the heavy metal pollution reduction efficiency of soil samples in agricultural land areas corresponding to crop planting. ; S33, Regarding the first Plant crops and calculate the rate of decline of enrichment index. ; S34, Regarding the first Calculate the sampling stability coefficient for the agricultural land area corresponding to the crop. ; S35. Based on S31-S34, calculate the effectiveness index of heavy metal pollution remediation in agricultural land soil using a weighted method. .

[0012] Preferably, in step five, the pollution level assessment process is as follows: Let the upper limit of the enrichment threshold interval be denoted as The lower limit critical value of the enrichment threshold interval is denoted as ; If enrichment index < This indicates that crops have a weak ability to accumulate heavy metals, with a pollution level of 1. Response measures include prioritizing the planting of the corresponding crops. ≤ Enrichment Index ≤ This indicates that crops have a moderate capacity to accumulate heavy metals, with a pollution level of 2. Response measures include promoting the planting of corresponding crops and simultaneously applying passivating agents to reduce the probability of heavy metal migration into crops. If the enrichment index... > This indicates that crops have a strong ability to accumulate heavy metals, and the pollution level is 3. Response measures include strictly prohibiting the planting of the corresponding crops and prioritizing the remediation of soil heavy metal pollution in agricultural areas.

[0013] Preferably, in step five, sampling and scoring... A score of <2 indicates low suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, restarting the field survey, and optimizing sampling parameters. A score of 2 or higher indicates a low suitability. A score of ≤5 indicates a moderate suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, optimizing the substandard sampling indicators, adding additional sampling points, and increasing the sampling frequency. (Sampling score) A score greater than 5 indicates a high suitability of the sampling strategy for the corresponding crop farmland. Response measures include continuing to advance the sampling process and maintaining existing sampling parameters.

[0014] Preferably, in step five, the repair level assessment process is as follows: The upper limit critical value of the performance threshold range is denoted as... The middle limit critical value of the effectiveness threshold range is denoted as The lower limit critical value of the effectiveness threshold range is denoted as ; If efficiency index < This indicates that the remediation has failed and the safe use of agricultural land cannot be guaranteed. The remediation level is Level 1, and the response measures include failing the agricultural land acceptance inspection, requiring a redesign of the remediation plan, and changing the remediation technology approach. ≤Efficiency Index ≤ This indicates that the remediation effect has not met expectations, and there is a risk of localized pollution rebound. The remediation level is Level 2, and the response measures include supplementing water and fertilizer regulation and passivation remediation measures for farmland that has failed acceptance inspection. ≤Efficiency Index < This indicates that the restoration effect has met the standards and the requirements for safe use of agricultural land. The restoration level is 3, and the response measures include acceptance of the agricultural land, local supplementary water and fertilizer regulation, and passivation restoration measures. If the efficiency index... > This indicates that the restoration effect is excellent and fully meets the requirements for safe use of agricultural land. The restoration level is 4, and the response measures include ensuring that the agricultural land passes inspection and maintaining the regular sampling and testing frequency.

[0015] Compared with existing technologies, this invention provides a method for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil, which has the following beneficial effects: 1. This invention comprehensively collects crop planting and management data, heavy metal pollution detection data from farmland soil samples, and heavy metal pollution remediation management data. It then constructs three standardized datasets for crops, soil, and remediation, laying a systematic, complete, and standardized data foundation for the entire process assessment. For crops with different edible parts, it constructs an enrichment index covering the entire heavy metal transport chain from soil to edible parts of the crop. The assessment system accurately quantifies the heavy metal accumulation capacity of crops and the associated food safety risks, and establishes a multi-dimensional sampling and scoring system by combining soil heavy metal migration characteristics and crop growth and root activity features. The mechanism enables a quantitative assessment of the adaptability of sampling strategies, and the multi-dimensional assessment has high accuracy.

[0016] 2. This invention integrates three core dimensions—heavy metal pollution reduction efficiency, crop enrichment risk reduction, and sampling stability—to construct a standardized remediation efficacy index. This enables a comprehensive and accurate quantitative assessment of pollution remediation effectiveness. Through a tiered threshold system, it simultaneously determines the crop pollution level, sampling strategy suitability, and remediation effectiveness level, and outputs targeted response measures. This forms a closed-loop management system for heavy metal pollution in agricultural land soil, from risk identification and process quality control to remediation effectiveness assessment, resulting in excellent intelligent management and remediation effects. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1 Based on the enrichment index experimental data (Table 1), sampling scoring experimental data (Table 2), and efficacy index experimental data (Table 3), this invention provides a method for evaluating the remediation efficacy of heavy metal pollution in agricultural land soil, comprising the following steps: Step 1: Using a database and inductively coupled plasma mass spectrometry, acquire planting management data for all crops, heavy metal pollution detection data for farmland soil samples, and heavy metal pollution remediation management data, and classify them into crop datasets, soil datasets, and remediation datasets. The crop dataset includes the types of edible parts of crops, the heavy metal content in roots, the heavy metal content in aboveground stems, the heavy metal content in leaves, the total root length, the dry matter content of roots, the total number of plants measured, and the measured planting area. The types of edible parts include root vegetables, leafy vegetables, and fruits. The soil dataset includes the content of individual heavy metal elements in soil samples, the average total content of heavy metals in deep layers, the average total content of heavy metals in the surface layer, and the measured volume. The heavy metal elements include cadmium. Mercury Arsenic lead element Chromium copper element Zinc and nickel element ; The restored dataset includes the sampling depth of farmland, the number of sampling profile layers, the number of sampling points, the sampling point density, the sampling frequency, the sampling timestamp, the total number of samplings, and the restoration timestamp. Step 2: Based on the crop and soil datasets, assess the ability of each crop to absorb and accumulate heavy metals from the soil, and generate the corresponding enrichment index. ; Enrichment Index The calculation process is as follows: S11. Based on the crop dataset and soil dataset, extract the first... Planting management data for the crops and heavy metal pollution detection data for soil samples from the corresponding agricultural land areas; Specifically, the heavy metal content of crops is tested in the roots, stems and edible parts. All heavy metal content data are based on dry weight and the unit is uniformly mg / kg. The average value is obtained after three or more repeated tests. S12, Calculate the first Crop growth and heavy metal elements Root absorption rate This is used to characterize the core ability of crop roots to absorb heavy metals from the soil, and its expression is as follows: In the formula, This indicates the heavy metal elements in soil samples from the corresponding agricultural area. Content, heavy metal elements Corresponding to fixed serial numbers 1-8, , , , , , , , , Indicating heavy metal elements in crop roots The content; S13, Calculate the... Crop growth and heavy metal elements Transshipment efficiency This is used to characterize the transport capacity of heavy metals from the root system to the above-ground stem, and its expression is as follows: In the formula, This indicates the amount of heavy metals in the above-ground stems of crops (stems only, excluding leaves and fruits). The content; S14, Calculate the... Crop growth and heavy metal elements migration efficiency The expression for is as follows: This is used to characterize the cross-organ migration ability of heavy metals from roots to edible parts. In the formula, This indicates the heavy metal elements in the fruit parts of a crop (including leaves and fruits, but excluding roots and above-ground stems). The content; S15. Based on S11-S14, calculate the first... Crop growth and heavy metal elements transfer coefficient Its expression is as follows: Regarding the first Crop growth and heavy metal elements Root absorption rate Transshipment efficiency and migration efficiency The standardized procedure for dimensionless processing is as follows: First, collect multiple [items / items]. Using crop sample data, determine the minimum value of the corresponding indicator. and maximum value Then, the original data is converted using the following formula. Convert to dimensionless value ; After processing, all parameters are mapped to Within the interval, there are no negative values, preserving the relative magnitude and physical meaning of the indicators, laying the foundation for subsequent weighted calculations; If the first The edible parts of the crops are root vegetables, namely underground roots or tubers; In the formula, This represents the correction coefficient for differences in the varietal characteristics of root vegetables, used to correct for inherent differences in the ability of different root vegetable varieties to accumulate heavy metals. Indicates the first Crop growth and heavy metal elements The dimensionless value of root absorption rate. Indicates the first Root vegetables are less susceptible to heavy metals The transfer coefficient; If the first The edible parts of the crop are leafy vegetables, that is, the above-ground stems and leaves; In the formula, , and All are weights, and satisfy the following conditions: , Indicates the first Crop growth and heavy metal elements The dimensionless value of transport efficiency. Indicates the first Crop growth and heavy metal elements The dimensionless value of migration efficiency. Indicates the first Leafy vegetables are susceptible to heavy metal elements The transfer coefficient; If the first The edible parts of the crop are fruits, specifically the reproductive organs. In the formula, , and All are weights, and satisfy the following conditions: , , Indicates the first Fruit crops are susceptible to heavy metal elements The transfer coefficient; S16. Based on S11-S15, calculate the first... Enrichment index of all heavy metal elements in crops Its expression is as follows: If the first The edible parts of the crops are root vegetables. In the formula, Indicates heavy metal elements The weight, And satisfy , , , , Indicates the first Enrichment index of all heavy metal elements in root vegetables; Specifically, the weighting priority can be expressed as: cadmium element Mercury Arsenic is classified as a first-tier element, belonging to the category of highly carcinogenic and toxic elements. The first-tier elements account for ≥50% of the total weight. lead element Chromium is classified as a highly toxic controlled element, belonging to the second tier. The total weight of elements in the second tier is ≥20%. copper element Nickel Zinc belongs to the third tier and is classified as a low-toxicity element or an essential trace element. It belongs to the fourth tier and is a low-toxicity essential trace element; If the first The edible parts of the crop are leafy vegetables. In the formula, Indicates heavy metal elements The weight, And satisfy , , , , Indicates the first Enrichment index of all heavy metal elements in leafy vegetables; If the first The edible parts of this crop are fruits. In the formula, Indicates heavy metal elements The weight, And satisfy , , , , Indicates the first Enrichment index of all heavy metal elements in fruit crops; The weighting constraints across root vegetables, leafy vegetables, and fruit crops are as follows: Fruits are susceptible to cadmium. Vegetables have the strongest translocation and enrichment capacity, followed by leafy vegetables, and root vegetables have the weakest. Leafy vegetables are less susceptible to mercury. The enrichment capacity of phytoestrogens is the strongest, followed by fruits, and the weakest of root vegetables. Specifically, a complete transmission chain is constructed from "soil → root absorption → stem translocation → edible part enrichment," comprehensively covering all risk nodes in the management of agricultural land soil pollution. Roots are the heavy metal absorption end, stems are the translocation channel, and edible parts are the nutrient enrichment end. Independent weighting systems are established for root vegetables, leafy vegetables, and fruit crops, fully considering the differences in edible parts and heavy metal translocation patterns among different crops. This aligns with the basic biological laws of heavy metal enrichment in crops, and is achieved through enrichment indices. It can intuitively characterize the crop's ability to absorb heavy metals from the soil, the degree of accumulation, and the migration efficiency of edible parts, accurately reflecting the level of food safety risks; The following is the experimental data of the enrichment index, as shown in Table 1: Table 1: Experimental Data of the Enrichment Index In Table 1, the enrichment index experimental data show that leafy vegetable crop, Chinese cabbage, was selected as the experimental target. In the standardized dimensionless processing procedure, the minimum root absorption rate of pak choi is 0.02, the maximum root absorption rate is 3, the minimum translocation efficiency of pak choi is 0.1, the maximum translocation efficiency is 0.8, the minimum migration efficiency of pak choi is 0.1, and the maximum migration efficiency is 0.9. The weights are set as follows: , , , ; Ultimately, the enrichment index of leafy vegetable bok choy for all heavy metal elements was determined. It is 0.4277; Set a fixed range of enrichment threshold intervals. Used to quickly determine the pollution level of crops, enrichment threshold range The calibration method is as follows: Using a soil heavy metal ecological environment monitoring database, farmland samples with different pollution levels were screened, covering various situations such as no pollution (Level 1), slightly polluted (Level 2), and heavily polluted (Level 3). Core monitoring data of soil heavy metals in the sample areas (such as heavy metal types, contents, occurrence forms, migration coefficients, etc.), farmland planting records, and subsequent crop growth monitoring results (such as crop enrichment index) were extracted. (Changes in biomass, quality indicators, etc.) Different candidate threshold ranges were set. In each calibration experiment, the soil heavy metal pollution level of the sample area was classified according to the candidate threshold range. The matching degree between the classification results and the actual soil pollution investigation conclusions was recorded. Then, combined with regional soil ecological dynamic monitoring data, the enrichment index of the sample area under different pollution control intervention intensities was simulated. The changing trends were analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on soil heavy metal pollution level assessment and subsequent farmland planting configuration. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times low-level pollution areas were misclassified as high-level pollution areas due to improper interval settings (counted as over-assessment), the number of times high-level pollution areas were misclassified as low-level pollution areas (counted as under-assessment), and the degree of fit between the soil heavy metal pollution level classification results and the subsequent farmland planting configuration effectiveness (such as crop suitability, planting income, pollution control efficiency, etc.) were statistically analyzed. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with the subsequent farmland planting configuration effectiveness was selected as the enrichment threshold interval. The preferred range; In the enrichment index experimental data in Table 1, the enrichment threshold range The preferred range is 0.3-0.6. It was determined that 0.3 < the enrichment index of leafy vegetable bok choy < 0.6, indicating that the crop has a moderate ability to accumulate heavy metals, and the pollution level is level 2. The response measures include promoting the planting of the corresponding crop and simultaneously applying passivating agents to reduce the probability of heavy metal migration to the crop. Step 3: Based on the crop dataset, soil dataset, and remediation dataset, and according to the remediation timestamp records, after each remediation operation is completed, evaluate the sampling rationality of the farmland area corresponding to each crop and generate the corresponding sampling score. ; Sampling score The evaluation process is as follows: S21. Based on the crop dataset, soil dataset, and remediation dataset, extract the first... Planting management data for crops, heavy metal pollution detection data for soil samples from corresponding agricultural land areas, and heavy metal pollution remediation management data; S22. Calculate the vertical migration coefficient of heavy metals in soil samples. Its expression is as follows: In the formula, This indicates the average total content of deep heavy metals in soil samples from areas corresponding to agricultural land. Deep soil refers to soil layers below 20 cm. This indicates the average total content of surface heavy metals in soil samples from the corresponding agricultural land area, with the surface soil layer being 0-20cm. Specifically, the vertical migration coefficient of heavy metals The higher the value, the stronger the vertical migration ability of heavy metals in the soil profile, and the higher the risk of deep pollution. S23, Calculate the first Crop root activity intensity coefficient Its expression is as follows: In the formula, Indicates the first unit volume of soil sample. The total length of the root system of the crop. Indicates the first soil sample in the corresponding soil sample The root dry matter content of crops This indicates the measured volume of the corresponding soil sample; Specifically, root activity intensity index The higher the value, the stronger the root activity of the crop in that soil layer, and the higher the potential for absorption and accumulation of heavy metals in the rhizosphere soil. S24, Calculate the first Planting density coefficient of crops Its expression is as follows: In the formula, Indicates the area corresponding to agricultural land, the first The total number of plants actually planted with the crop. Indicates the area corresponding to agricultural land, the first The measured planting area of ​​the crops. Indicates the first Standard values ​​for crop planting density; Specifically, planting density coefficient The higher the value, the denser the crop growth; S25, Sampling Scoring The assignment rules are as follows: Sampling and scoring all crops The initial value is set to 0 points. One point is added if any of the following conditions are met, with a maximum score of 7 points. The determination conditions are as follows: (1) The sampling depth coverage of the corresponding area of ​​agricultural land is the first Maximum burial depth of the root distribution layer of a crop; (2) Vertical migration coefficient of heavy metals in soil samples When the value is <0.3, heavy metals remain in the topsoil. For agricultural land areas, the number of sampling profile layers is ≤3, and the vertical migration coefficient of heavy metals in soil samples is <0.3. When the vertical migration coefficient of heavy metals in the soil sample is less than 0.6, and the number of sampling profile layers in the corresponding area of ​​farmland is less than 6, the vertical migration coefficient of heavy metals in the soil sample is... When the concentration is greater than 0.5, heavy metals tend to migrate vertically to deeper soil layers, and the number of sampling profile layers in the corresponding agricultural land area should be ≥6. (3) Crop root activity intensity coefficient When the value is ≥0.7, the number of sampling points within 5cm of the crop root system is ≥3, ensuring that these independent sampling points can accurately capture the distribution characteristics of heavy metals in the rhizosphere microdomain and the crop root activity intensity coefficient. When <0.7, the number of sampling points within 5cm of the crop root system is ≥1; (4) Crop planting density coefficient In areas with a coefficient ≥0.8, the sampling point density is ≥0.5 per 1000 square meters, and the crop planting density coefficient is [missing information]. In areas with a strength <0.8, the sampling point density should be ≥0.2 points / 1000㎡; (5) The sampling frequency during the tillering stage of crops is ≥ once every 3 days, the sampling frequency during the grain filling stage of crops is ≥ once every 7 days, and the sampling frequency during the harvest stage of crops is ≥ once every 2 days, and the sampling is uninterrupted throughout the entire growth period from the tillering stage to the harvest stage; (6) From the tillering stage to the harvest stage, if any of the following events occur: a single rainfall of ≥25mm, a single field irrigation of ≥10mm, or a single day-night temperature difference of ≥8℃, and two additional sampling records have been added to the sampling timestamp record: one 12 hours before the event and one 24 hours after the event. (7) From the tillering stage to the harvest stage, two additional sampling records have been added to the sampling timestamp records: 12 hours before heavy metal pollution remediation and 24 hours after heavy metal pollution remediation. The following is the sampling and scoring experiment data, as shown in Table 2: Table 2: Sampling and Scoring Experiment Data In Table 2, the leafy vegetable crop, Chinese cabbage, was selected as the experimental target in the sampling and scoring experimental data. The entire growth period of leafy vegetable crop Chinese cabbage includes: 30 days for tillering, 25 days for grain filling, and 10 days for harvest; Remediation Record: Only one remediation operation was carried out during the entire growth period, with the remediation time stamp being the 15th day of the tillering stage. Sampling was conducted every 2 days during the tillering stage, every 5 days during the grain-filling stage, and every day during the harvest stage. There was no interruption in sampling throughout the entire growth period. During the entire growth period, there were single rainfall events ≥25mm, single field irrigation events ≥10mm, and single day-night temperature differences ≥8℃. For all events, two additional sampling records were added, one 12 hours before the event and one 24 hours after the event. The remediation method was in-situ application of soil heavy metal passivating agent, and supplementary sampling was completed 12 hours before the passivation remediation operation and 24 hours after the remediation operation. The relevant records are complete. Based on the assessment, the sampling score of the leafy vegetable crop, bok choy, was... A score of >5 indicates a high suitability of the corresponding crop farmland sampling strategy. Response measures include continuing to advance the sampling process and maintaining existing sampling parameters. Step 4: Based on the soil dataset, remediation dataset, and enrichment index and sampling score The study assesses the remediation effectiveness of each crop in agricultural land areas contaminated with heavy metals, and generates corresponding efficiency indices. ; Performance Index The calculation process is as follows: S31. Based on the soil dataset and remediation dataset, extract the [number] timestamp records according to the remediation timestamps. Heavy metal pollution detection data of soil samples from agricultural land areas corresponding to the crops planted; S32, Regarding the first Calculate the heavy metal pollution reduction efficiency of soil samples in agricultural land areas corresponding to crop planting. Its expression is as follows: In the formula, Indicates the heavy metal elements in the soil sample before remediation. content, Indicating heavy metal elements in the soil sample after remediation content, Indicates heavy metal elements Pollution reduction efficiency; In the formula, Indicates heavy metal elements The weight, And satisfy Weighting priority and enrichment index The weighting of heavy metal toxicity in the calculation remains consistent. S33, Regarding the first Plant crops and calculate the rate of decline of enrichment index. Its expression is as follows: In the formula, Indicates the number before repair Enrichment index of crops. Indicates the repaired number Enrichment index of crops; S34, Regarding the first Calculate the sampling stability coefficient for the agricultural land area corresponding to the crop. Its expression is as follows: In the formula, Indicates the first Secondary sampling score , Indicates the total number of samples. This represents the average of the sampled scores. Indicates the first The coefficient of variation of the sampling scores within the agricultural land area corresponding to the crop; Specifically, the coefficient of variation of the sampled scores The smaller the value, the stronger the sampling stability; the sampling stability coefficient. The closer it gets to 1, the more fixed its value range becomes. This can directly meet the needs of subsequent dimensionless calculations; S35. Based on S31-S34, calculate the effectiveness index of heavy metal pollution remediation in agricultural land soil using a weighted method. Its expression is as follows: Efficiency of reducing heavy metal pollution in soil samples enrichment index decline rate and sampling stability coefficient Unified mapping to Within the interval, there are no negative values. The relative magnitude and physical meaning of the indicators are preserved, laying the foundation for subsequent weighted calculations. The standardization rules using the truncation extremum method are as follows: In the formula, Represents the original data. This represents the standardized dimensionless value; the larger the value, the better the repair efficacy. In the formula, , and All are weights, and satisfy the following conditions: , ; The following are the experimental data for the efficiency index, as shown in Table 3: Table 3: Experimental Data for the Efficiency Index In Table 3, the experimental data of the efficacy index were selected as leafy vegetable Chinese cabbage as the experimental target. According to the remediation time stamp (15th day of the tillering stage), the detection data of 8 heavy metals in the top 0-20cm soil of the corresponding farmland were extracted before and after remediation. The total number of samplings was 5 (initial sampling at the tillering stage, sampling after the remediation operation, sampling after the rainfall event, sampling in the middle of the grain filling stage, and sampling before the harvest). There was no interruption in sampling throughout the entire growth period. All data are the average dry weight of 3 repeated measurements, and the unit is uniformly mg / kg. It is completely consistent with the enrichment index experimental data in Table 1 and the sampling score experimental data in Table 2. Heavy metal pollution reduction efficiency of soil samples enrichment index decline rate and sampling stability coefficient All Within the interval, therefore, after standardization using the truncation extremum method, all original values ​​are retained; The weights are set as follows: , , , , , , , , , , ; Set a fixed range of performance thresholds. Used to quickly determine the remediation level of heavy metal pollution in agricultural land soil, with an effectiveness threshold range. The calibration method is as follows: Using a monitoring database of heavy metal pollution remediation in agricultural land soil, regional samples of different remediation levels were screened, covering various situations such as remediation failure (Level 1), remediation effect not meeting expectations (Level 2), remediation effect meeting standards (Level 3), and remediation effect being excellent (Level 4). Core monitoring data of heavy metals in the soil of the sample areas (such as heavy metal content, occurrence forms, migration coefficients, etc.), pollution remediation project implementation records, and subsequent crop growth monitoring results (such as enrichment index) were extracted. (Including changes in crop survival rate, quality indicators, etc.), different candidate threshold ranges were set. In each calibration experiment, the soil heavy metal pollution remediation level of the sample area was classified according to the candidate threshold range. The matching degree between the classification results and the actual soil pollution remediation investigation conclusions was recorded. Then, combined with regional soil ecological dynamic monitoring data, the effectiveness index of the sample area under different pollution remediation intervention intensities was simulated. The changing trends were analyzed to adjust the upper and lower limits of the candidate threshold intervals. Multiple verification experiments were conducted to record the impact of threshold interval settings on the assessment of soil heavy metal pollution remediation effectiveness and subsequent farmland planting configuration. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times low-level remediation areas were misclassified as high-level remediation areas due to improper interval settings (counted as over-assessment), the number of times high-level remediation areas were misclassified as low-level remediation areas (counted as under-assessment), and the degree of fit between the soil heavy metal pollution remediation level classification results and the subsequent farmland planting configuration effectiveness (such as crop suitability, planting income, pollution control efficiency, etc.) were statistically analyzed. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with the subsequent farmland planting configuration effectiveness was selected as the effectiveness threshold interval. The preferred range; In Table 3, the efficiency index experimental data shows the efficiency threshold range. The preferred range is 0.3-0.8. It was determined that 0.3 < leafy vegetable such as Chinese cabbage < 0.6, indicating that the remediation effect did not meet expectations and there is a risk of local pollution rebound. The remediation level is 2. The response measures include the failure of farmland acceptance, supplementing water and fertilizer regulation and passivation remediation measures. Step 5: Set a fixed range for the enrichment threshold. and performance threshold range Combined with enrichment index Sampling and scoring and efficiency index It determines the pollution level of crops, the suitability of sampling strategies, and the remediation level of heavy metal pollution, and outputs the corresponding judgment results and response measures. The pollution level assessment process is as follows: Let the upper limit of the enrichment threshold interval be denoted as The lower limit critical value of the enrichment threshold interval is denoted as ; If enrichment index < This indicates that crops have a weak ability to accumulate heavy metals, with a pollution level of 1. Response measures include prioritizing the planting of the corresponding crops. ≤ Enrichment Index ≤ This indicates that crops have a moderate capacity to accumulate heavy metals, with a pollution level of 2. Response measures include promoting the planting of corresponding crops and simultaneously applying passivating agents to reduce the probability of heavy metal migration into crops. If the enrichment index... > This indicates that crops have a strong ability to accumulate heavy metals, and the pollution level is 3. Response measures include strictly prohibiting the planting of the corresponding crops and prioritizing the remediation of soil heavy metal pollution in agricultural areas. Sampling score A score of <2 indicates low suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, restarting the field survey, and optimizing sampling parameters. A score of 2 or higher indicates a low suitability. A score of ≤5 indicates a moderate suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, optimizing the substandard sampling indicators, adding additional sampling points, and increasing the sampling frequency. (Sampling score) A score greater than 5 indicates a high suitability of the sampling strategy for the corresponding crop farmland. Response measures include continuing to advance the sampling process and maintaining the existing sampling parameters. The repair level assessment process is as follows: The upper limit critical value of the performance threshold range is denoted as... The middle limit critical value of the effectiveness threshold range is denoted as The lower limit critical value of the effectiveness threshold range is denoted as ; If efficiency index < This indicates that the remediation has failed and the safe use of agricultural land cannot be guaranteed. The remediation level is Level 1, and the response measures include failing the agricultural land acceptance inspection, requiring a redesign of the remediation plan, and changing the remediation technology approach. ≤Efficiency Index ≤ This indicates that the remediation effect has not met expectations, and there is a risk of localized pollution rebound. The remediation level is Level 2, and the response measures include supplementing water and fertilizer regulation and passivation remediation measures for farmland that has failed acceptance inspection. ≤Efficiency Index < This indicates that the restoration effect has met the standards and the requirements for safe use of agricultural land. The restoration level is 3, and the response measures include acceptance of the agricultural land, local supplementary water and fertilizer regulation, and passivation restoration measures. If the efficiency index... > This indicates that the restoration effect is excellent and fully meets the requirements for safe use of agricultural land. The restoration level is 4, and the response measures include ensuring that the agricultural land passes inspection and maintaining the regular sampling and testing frequency.

[0020] In this embodiment, by comprehensively collecting and classifying standardized datasets for crops, soil, and remediation, a systematic, complete, and standardized data foundation is laid for the full-process assessment. For crops with different edible parts, an enrichment index covering the entire heavy metal transport chain from soil to edible parts of crops is constructed. The assessment system accurately quantifies the heavy metal accumulation capacity of crops and the associated food safety risks, and establishes a multi-dimensional sampling and scoring system by combining soil heavy metal migration characteristics and crop growth and root activity features. This mechanism enables a quantitative assessment of the adaptability of sampling strategies, effectively ensuring the representativeness and reliability of monitoring data. It integrates three core dimensions—heavy metal pollution reduction efficiency, the extent of crop enrichment risk reduction, and sampling stability—to construct a standardized remediation efficacy index. This enables a comprehensive and accurate quantitative assessment of pollution remediation effectiveness. Through a tiered threshold system, it simultaneously determines the crop pollution level, sampling strategy suitability, and remediation effectiveness level, and outputs targeted response measures. This forms a closed-loop management system for heavy metal pollution in agricultural land soil, from risk identification and process quality control to remediation effectiveness assessment, significantly improving the scientific nature, accuracy, and operability of heavy metal pollution control and safe utilization of agricultural land.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating the remediation effectiveness of heavy metal pollution in agricultural land soil, characterized in that, Includes the following steps: Step 1: Using a database and inductively coupled plasma mass spectrometry, acquire planting management data for all crops, heavy metal pollution detection data for farmland soil samples, and heavy metal pollution remediation management data, and classify them into crop datasets, soil datasets, and remediation datasets. Step 2: Based on the crop and soil datasets, assess the ability of each crop to absorb and accumulate heavy metals from the soil, and generate the corresponding enrichment index. ; Step 3: Based on the crop dataset, soil dataset, and remediation dataset, and according to the remediation timestamp records, after each remediation operation is completed, evaluate the sampling rationality of the farmland area corresponding to each crop and generate the corresponding sampling score. ; Step 4: Based on the soil dataset, remediation dataset, and enrichment index and sampling score The study assesses the remediation effectiveness of each crop in agricultural land areas contaminated with heavy metals, and generates corresponding efficiency indices. ; Step 5: Set a fixed range for the enrichment threshold. and performance threshold range Combined with enrichment index Sampling and scoring and efficiency index It determines the pollution level of crops, the suitability of sampling strategies, and the remediation level of heavy metal pollution, and outputs the corresponding judgment results and response measures.

2. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 1, characterized in that: In step one, the crop dataset includes the edible part type of the crop, the heavy metal content of the roots, the heavy metal content of the aboveground stems, the heavy metal content of the leaves, the total root length, the root dry matter, the total number of plants measured, and the measured planting area. The edible part type includes root vegetables, leafy vegetables, and fruits.

3. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 2, characterized in that: In step one, the soil dataset includes the content of individual heavy metal elements in the soil sample, the average total content of heavy metals in the deep layer, the average total content of heavy metals in the surface layer, and the measured volume. The heavy metal elements include cadmium. Mercury Arsenic lead element Chromium copper element Zinc and nickel element .

4. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 3, characterized in that: In step one, the repair dataset includes the sampling depth of farmland, the number of sampling profile layers, the number of sampling points, the sampling point density, the sampling frequency, the sampling timestamp, the total number of samplings, and the repair timestamp.

5. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 4, characterized in that: In step two, the enrichment index The calculation process is as follows: S11. Based on the crop dataset and soil dataset, extract the first... Planting management data for the crops and heavy metal pollution detection data for soil samples from the corresponding agricultural land areas; S12, Calculate the first Crop growth and heavy metal elements Root absorption rate ; S13, Calculate the... Crop growth and heavy metal elements Transshipment efficiency ; S14, Calculate the... Crop growth and heavy metal elements migration efficiency ; S15. Based on S11-S14, calculate the first... Crop growth and heavy metal elements transfer coefficient ; S16. Based on S11-S15, calculate the first... Enrichment index of all heavy metal elements in crops .

6. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 5, characterized in that: In step three, sampling and scoring are performed. The evaluation process is as follows: S21. Based on the crop dataset, soil dataset, and remediation dataset, extract the first... Planting management data for crops, heavy metal pollution detection data for soil samples from corresponding agricultural land areas, and heavy metal pollution remediation management data; S22. Calculate the vertical migration coefficient of heavy metals in soil samples. ; S23, Calculate the first Crop root activity intensity coefficient ; S24, Calculate the first Planting density coefficient of crops ; S25, Sampling Scoring The assignment rules are as follows: Sampling and scoring all crops The initial value is set to 0 points. One point is added if any of the following conditions are met, with a maximum score of 7 points. The determination conditions are as follows: (1) The sampling depth coverage of the corresponding area of ​​agricultural land is the first Maximum burial depth of the root distribution layer of a crop; (2) Vertical migration coefficient of heavy metals in soil samples When the value is <0.3, the number of sampling profile layers in the corresponding area of ​​agricultural land is ≤3, and 0.3 < the vertical migration coefficient of heavy metals in the soil sample. When the vertical migration coefficient of heavy metals in the soil sample is less than 0.6, and the number of sampling profile layers in the corresponding area of ​​farmland is less than 6, the vertical migration coefficient of heavy metals in the soil sample is... When the value is greater than 0.5, the number of sampling profile layers in the corresponding area of ​​agricultural land is ≥6. (3) Crop root activity intensity coefficient When the value is ≥0.7, the number of sampling points within 5cm of the crop root system is ≥3, and the crop root activity intensity coefficient is... When <0.7, the number of sampling points within 5cm of the crop root system is ≥1; (4) Crop planting density coefficient In areas with a coefficient ≥0.8, the sampling point density is ≥0.5 per 1000 square meters, and the crop planting density coefficient is [missing information]. In areas with a strength <0.8, the sampling point density should be ≥0.2 points / 1000㎡; (5) The sampling frequency during the tillering stage of crops is ≥ once every 3 days, the sampling frequency during the grain filling stage of crops is ≥ once every 7 days, and the sampling frequency during the harvest stage of crops is ≥ once every 2 days, and the sampling is uninterrupted throughout the entire growth period from the tillering stage to the harvest stage; (6) From the tillering stage to the harvest stage, if any of the following events occur: a single rainfall of ≥25mm, a single field irrigation of ≥10mm, or a single day-night temperature difference of ≥8℃, and two additional sampling records have been added to the sampling timestamp record: one 12 hours before the event and one 24 hours after the event. (7) From the tillering stage to the harvest stage, two additional sampling records have been added to the sampling timestamp record: 12 hours before heavy metal pollution remediation and 24 hours after heavy metal pollution remediation.

7. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 6, characterized in that: In step four, the efficiency index The calculation process is as follows: S31. Based on the soil dataset and remediation dataset, extract the [number] timestamp records according to the remediation timestamps. Heavy metal pollution detection data of soil samples from agricultural land areas corresponding to the crops planted; S32, Regarding the first Calculate the heavy metal pollution reduction efficiency of soil samples in agricultural land areas corresponding to crop planting. ; S33, Regarding the first Plant crops and calculate the rate of decline of enrichment index. ; S34, Regarding the first Calculate the sampling stability coefficient for the agricultural land area corresponding to the crop. ; S35. Based on S31-S34, calculate the effectiveness index of heavy metal pollution remediation in agricultural land soil using a weighted method. .

8. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 7, characterized in that: In step five, the pollution level assessment process is as follows: Let the upper limit of the enrichment threshold interval be denoted as The lower limit critical value of the enrichment threshold interval is denoted as ; If enrichment index < This indicates that crops have a weak ability to accumulate heavy metals, with a pollution level of 1. Response measures include prioritizing the planting of the corresponding crops. ≤ Enrichment Index ≤ This indicates that crops have a moderate capacity to accumulate heavy metals, with a pollution level of 2. Response measures include promoting the planting of corresponding crops and simultaneously applying passivating agents to reduce the probability of heavy metal migration into crops. If the enrichment index... > This indicates that crops have a strong ability to accumulate heavy metals, and the pollution level is 3. Response measures include strictly prohibiting the planting of the corresponding crops and prioritizing the remediation of soil heavy metal pollution in agricultural areas.

9. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 8, characterized in that: Step five involves sampling and scoring. A score of <2 indicates low suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, restarting the field survey, and optimizing sampling parameters. A score of 2 or higher indicates a low suitability. A score of ≤5 indicates a moderate suitability of the sampling strategy for the corresponding crop farmland. Response measures include immediately suspending the sampling process, optimizing substandard sampling indicators, adding additional sampling points, and increasing the sampling frequency. (Sampling score) A score greater than 5 indicates a high suitability of the sampling strategy for the corresponding crop farmland. Response measures include continuing to advance the sampling process and maintaining existing sampling parameters.

10. The method for evaluating the remediation efficiency of heavy metal pollution in agricultural land soil according to claim 9, characterized in that: In step five, the repair level assessment process is as follows: The upper limit critical value of the performance threshold range is denoted as... The middle limit critical value of the effectiveness threshold range is denoted as The lower limit critical value of the effectiveness threshold range is denoted as ; If efficiency index < This indicates that the remediation has failed and the safe use of agricultural land cannot be guaranteed. The remediation level is Level 1, and the response measures include failing the agricultural land acceptance inspection, requiring a redesign of the remediation plan, and changing the remediation technology approach. ≤Efficiency Index ≤ This indicates that the remediation effect has not met expectations, and there is a risk of localized pollution rebound. The remediation level is Level 2, and the response measures include addressing the failure of farmland acceptance, supplementing water and fertilizer regulation, and passivation remediation measures. ≤Efficiency Index < This indicates that the restoration effect has met the standards and the requirements for safe use of agricultural land. The restoration level is 3, and the response measures include acceptance of the agricultural land, local supplementary water and fertilizer regulation, and passivation restoration measures. If the efficiency index... > This indicates that the restoration effect is excellent and fully meets the requirements for safe use of agricultural land. The restoration level is 4, and the response measures include ensuring that the agricultural land passes inspection and maintaining the regular sampling and testing frequency.

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