Soil pesticide residue detection method, system, equipment and medium

Abstract

The invention relates to a soil pesticide residue detection method, system, equipment and medium, and relates to the technical field of intelligent detection.The method comprises the following steps that a to-be-detected soil sample is subjected to ultrasonic-assisted extraction, and a pesticide extracting solution is obtained; separating and detecting the extracting solution by using a liquid chromatograph to obtain a pesticide chromatographic peak, and performing integral calculation on concentration data; calculating the adsorption capacity based on the concentration data, generating a pesticide distribution diagram, and obtaining a pollution range diagram through spatial interpolation; determining a pesticide degradation rate according to the pollution range map, obtaining half-life period data, and further calculating an exposure dose; residual toxicity effect analysis is carried out based on the exposure dose, a toxicity effect score is obtained, a corresponding soil remediation scheme is formulated according to the score, and the technical problem that pollution risk assessment is lagged due to the lack of pesticide adsorption behavior in soil in the traditional technology is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, and in particular to a method, system, equipment and medium for detecting pesticide residues in soil. Background Technology

[0002] With the rapid development of modern agriculture, pesticides are widely used as an important means to ensure crop yields. However, long-term excessive or unreasonable application of pesticides leads to the accumulation of large amounts of residues in the soil, which not only disrupts the balance of the soil ecosystem but also threatens human health through bioaccumulation in the food chain. Accurate detection and risk assessment of pesticide residues in soil have become important issues in environmental science and sustainable agricultural development. Traditional detection methods mostly focus on single chemical analyses, which are insufficient to comprehensively reflect the spatial distribution, migration and transformation patterns of pesticides in soil and their long-term ecological risks, thus limiting the systematic understanding and control of pollution.

[0003] Although modern analytical techniques such as liquid chromatography have been applied in pesticide residue detection, the existing technical system still has significant shortcomings. Most detection procedures only stop at the concentration measurement level, lacking in-depth integrated analysis of pesticide adsorption behavior, spatial distribution characteristics, and degradation dynamics in soil. This leads to rough assessment of the pollution range and delayed risk assessment. In addition, there is a lack of effective correlation between detection results and ecotoxicological effects, making it difficult to quantify the actual harm of pesticide residues to organisms, and failing to provide accurate scientific basis for subsequent soil remediation. Consequently, remediation measures are often blind and delayed. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for detecting pesticide residues in soil, comprising the following steps: The soil sample to be tested was subjected to ultrasonic-assisted extraction to obtain a pesticide extract. The pesticide extract was separated and detected by liquid chromatography to obtain pesticide chromatographic peaks, and the pesticide concentration data was obtained by calculating the peak area integral based on the pesticide chromatographic peaks. Based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map. Based on the pesticide distribution map, spatial interpolation is performed to obtain a pollution range map. The degradation rate of pesticides in the soil was determined by measuring the pollution range map to obtain half-life data, and the exposure dose of pesticides was calculated based on the half-life data. The pesticide residue toxicity effect was analyzed based on the exposure dose to obtain a toxicity effect score, and a corresponding soil remediation plan was formulated based on the toxicity effect score.

[0006] Furthermore, the soil sample to be tested was subjected to ultrasonic-assisted extraction to obtain a pesticide extract, including: The soil sample to be tested is classified by particle size using a vibrating screen to obtain a graded soil sample, and a multi-solvent gradient elution is performed on the graded soil sample to obtain a gradient eluent. The gradient eluent is subjected to ultrasonic cavitation treatment to obtain an extraction mixture, and the extraction mixture is then subjected to phase separation by centrifugation to obtain the pesticide extract.

[0007] Furthermore, the pesticide extract is separated and detected using liquid chromatography to obtain pesticide chromatographic peaks. Based on the peak area integration of these peaks, pesticide concentration data is obtained, including: The pesticide extract is filtered through a filter membrane to obtain a clear extract, which is then injected into the column of a liquid chromatograph through a pre-set injection needle to obtain an in-column separated liquid. The in-column separation solution was subjected to ultraviolet detection scanning to obtain the component response signal, and the component response signal was plotted to obtain the chromatographic peak of the pesticide; The pesticide chromatographic peaks are baseline corrected to obtain corrected peak data, and the peak boundaries of the corrected peak data are located to obtain peak boundary parameters. Based on the peak boundary parameters, the calibration peak data is integrally calculated to obtain the peak area value. Then, based on the peak area value and the standard curve, the concentration is converted to obtain the pesticide concentration data. The standard curve is a peak area-concentration correspondence curve pre-determined using pesticide solutions of known concentrations.

[0008] Furthermore, based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map, including: The ratio between the pesticide concentration data and the dry weight data of the soil sample to be tested is calculated to obtain the adsorption capacity per unit mass. The adsorption capacity per unit mass is then correlated and labeled with the sampling depth data corresponding to the adsorption capacity per unit mass to obtain stratified adsorption data. Based on the stratified adsorption data, coordinate mapping is performed to obtain an adsorption amount scatter plot, and contour lines are drawn on the adsorption amount scatter plot to obtain soil profile adsorption contour lines. By extending the adsorption contour lines of the soil profile into a plane, a regional adsorption surface map is obtained. Then, the regional adsorption surface map is colored with a concentration gradient to obtain a pesticide distribution map.

[0009] Furthermore, the degradation rate of pesticides in the soil was determined using the aforementioned pollution extent map to obtain half-life data, including: The pollution range map is analyzed at the pixel level to obtain the pesticide concentration value and spatial coordinates corresponding to each pixel. The pesticide concentration values ​​corresponding to each pixel are then sorted by time series to obtain a time-concentration sequence set. Based on the time-concentration sequence set, the concentration decay trend of each pixel is analyzed to obtain the concentration decay rate of each pixel, and the concentration decay rate is spatially interpolated to obtain the concentration decay rate field. The degradation process of pesticides in soil is kinetically modeled based on the concentration decay rate field to obtain the half-life data of each pixel.

[0010] Furthermore, calculating the pesticide exposure dose based on the half-life data includes: The half-life data were subjected to time cumulative effect analysis to obtain the cumulative curve of pesticide residue change over time, and the cumulative curve was differentiated to obtain the residue increment rate for each time period. Based on the residual increment rate, combined with the initial concentration of pesticides in the soil and the soil-environment interaction coefficient, the weighted exposure dose for each time period is calculated, and the pesticide exposure dose is obtained by integrating the weighted exposure dose over the entire time period.

[0011] Furthermore, based on the stated exposure dose, a residual toxicity effect analysis of the pesticide is performed to obtain a toxicity effect score, including: Soil bioaccumulation coefficients are calculated based on the exposure doses, and toxicity response analysis of soil microbial communities is performed based on the soil bioaccumulation coefficients to obtain microbial population variation data. Ecotoxicological analysis was performed on the microbial population variation data to obtain an ecotoxicity index, and soil enzyme activity was measured based on the ecotoxicity index to obtain the enzyme activity inhibition rate. A dose-response relationship analysis was performed on the enzyme activity inhibition rate to obtain a toxicity effect curve. Based on the toxicity effect curve, the degree of pesticide residue hazard was assessed to obtain a toxicity effect score.

[0012] The present invention also provides a soil pesticide residue detection system, comprising: The extraction module is used to perform ultrasonic-assisted extraction on the soil sample to obtain a pesticide extract. The detection module is used to separate and detect the pesticide extract using a liquid chromatograph to obtain pesticide chromatographic peaks, and to calculate pesticide concentration data based on the peak area integration of the pesticide chromatographic peaks. The calculation module is used to calculate the adsorption capacity of the soil sample to be tested based on the pesticide concentration data, obtain a pesticide distribution map, and perform spatial interpolation processing based on the pesticide distribution map to obtain a pollution range map. The measurement module is used to measure the degradation rate of pesticides in the soil through the pollution range map, obtain half-life data, and calculate the exposure dose of pesticides based on the half-life data. The analysis module is used to perform residual toxicity analysis on pesticides based on the exposure dose, obtain a toxicity score, and formulate a corresponding soil remediation plan based on the toxicity score.

[0013] This invention provides a method for detecting pesticide residues in soil, comprising the following steps: ultrasonic-assisted extraction of a soil sample to obtain a pesticide extract; separation and detection of the pesticide extract using liquid chromatography to obtain pesticide chromatographic peaks, and calculation of pesticide concentration data based on peak area integration; calculation of adsorption capacity of the soil sample based on the pesticide concentration data to obtain a pesticide distribution map, and spatial interpolation of the pesticide distribution map to obtain a contamination range map; determination of the degradation rate of pesticides in the soil using the contamination range map to obtain half-life data, and further determination of the degradation rate of pesticides in the soil based on the pesticide concentration data. Half-life data is used to calculate the exposure dose of pesticides; based on the exposure dose, residual toxicity effect analysis of pesticides is performed to obtain a toxicity effect score, and a corresponding soil remediation plan is formulated based on the toxicity effect score. This solves the technical problem that traditional technologies lack data on the adsorption behavior of pesticides in soil, which leads to a lag in pollution risk assessment. It realizes the ability to estimate the long-term exposure dose of pesticides through half-life data and use it as an input parameter to conduct residual toxicity effect analysis, quantify the potential risks of different pollutants to ecosystems or human health, and generate comparable toxicity effect scores. This achieves the technical effect of a technological leap from chemical detection to ecotoxicological assessment. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the steps of a method for detecting pesticide residues in soil according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the steps of a soil pesticide residue detection system in one embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0018] The following describes in detail, with reference to the accompanying drawings, a method for detecting pesticide residues in soil according to an embodiment of the present invention. First, the method for detecting pesticide residues in soil according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

[0019] Figure 1 This invention provides a method for detecting pesticide residues in soil, comprising the following steps: Step S1: Perform ultrasonic-assisted extraction on the soil sample to be tested to obtain pesticide extract.

[0020] Specifically, ultrasonic-assisted extraction is performed on the soil samples to obtain pesticide extract. This step involves field operations in the farmland area where pesticide residues need to be assessed. Representative locations are selected to collect soil samples, ensuring that the collected samples reflect the overall pollution characteristics of the area. The collected soil samples are then mixed and homogenized to remove impurities such as gravel and root residues. An appropriate amount of the soil sample is then accurately weighed and transferred to a clean glass container. A suitable organic solvent of appropriate polarity is added to wet the soil matrix, allowing pesticide residues to transfer from the solid phase to the liquid phase. Finally, the container is filled with the extract... The soil sample and solvent container are placed in an ultrasonic cleaning device. The device is started for vibration treatment, and ultrasonic energy is used to make the solvent penetrate into the soil microporous structure and release the adsorbed pesticide components. After vibration, solid-liquid separation is achieved by centrifugation. The supernatant is retained as the desired pesticide extract. This process is suitable for the extraction and analysis of various chemical pesticides in soils in agricultural production areas. For example, when processing soil samples after long-term application of insecticides in an orchard, this method can effectively obtain an extract containing the target pesticide, providing a basic sample for subsequent separation and detection of the pesticide extract by liquid chromatography.

[0021] Step S2: The pesticide extract is separated and detected by liquid chromatography to obtain pesticide chromatographic peaks, and the pesticide concentration data is obtained by calculating the peak area integral based on the pesticide chromatographic peaks.

[0022] Specifically, the pesticide extract is separated and detected using liquid chromatography (LC) to obtain pesticide chromatographic peaks. Based on the peak area integration of these peaks, pesticide concentration data is obtained. This step involves filtering the pesticide extract obtained in the previous step through a microporous membrane and then injecting it into the LC injection system. Under the separation action of the chromatographic column, gradient elution of the mobile phase separates the target compounds of different polarities in the pesticide extract one by one. Each component flows through the detector in sequence according to its retention time. The detector records signal changes in real time and outputs the corresponding pesticide chromatographic peaks. Each pesticide chromatographic peak corresponds to a specific pesticide residue. The pesticide chromatographic peaks are identified and analyzed using workstation software. Retention time comparison confirms the presence or absence of the target pesticide. Subsequently, peak area integration is performed on the pesticide chromatographic peak, and the peak area is converted into quantitative information based on a pre-established standard curve to obtain pesticide concentration data. This process is applicable to the analysis of multiple pesticide residues in orchard soil. For example, when testing pesticide extracts obtained from soil samples in an apple-growing area through ultrasonic-assisted extraction, liquid chromatography can be used to separate and detect the pesticide extracts, accurately identifying and quantifying imidacloprid and cyhalothrin residues. The obtained pesticide concentration data provides necessary input parameters for subsequent adsorption capacity calculation of the soil samples based on the pesticide concentration data.

[0023] Step S3: Calculate the adsorption capacity of the soil sample to be tested based on the pesticide concentration data to obtain a pesticide distribution map, and perform spatial interpolation processing based on the pesticide distribution map to obtain a pollution range map.

[0024] Specifically, based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map. Then, spatial interpolation is performed on the pesticide distribution map to obtain a pollution range map. This step is implemented by first matching the pesticide concentration data obtained by liquid chromatography with the geographical coordinates of the corresponding sampling points. Using soil physicochemical properties combined with an empirical adsorption model, the pesticide adsorption capacity at each point in the soil sample to be tested is estimated, reflecting the pesticide's retention capacity on the soil particle surface. The results are expressed in spatial grid form, forming a preliminary pesticide distribution map. Subsequently, Kriging interpolation is used to process the discrete point data in the pesticide distribution map into a continuous data set. By estimating pesticide concentration levels in unsampled areas based on spatial autocorrelation, the pollution status of the entire study area can be fully presented. After smoothing and rendering, a pollution range map with geographical reference significance is generated. This process is applicable to environmental assessment scenarios for agricultural land such as orchards. For example, in a citrus planting area, pesticide concentration data obtained from multiple soil samples are analyzed. The adsorption capacity is calculated using the above method to generate a corresponding pesticide distribution map. Then, spatial interpolation is performed on the map, and the resulting pollution range map clearly shows the high-risk areas where imidacloprid pesticides spread along the fertilization path within the orchard. This provides a spatial basis for subsequent determination of pesticide degradation rates in the soil using the pollution range map.

[0025] Step S4: Degradation rate of pesticides in soil is determined by the pollution range map to obtain half-life data, and the exposure dose of pesticides is calculated based on the half-life data.

[0026] Specifically, the degradation rate of pesticides in the soil is determined using the pollution extent map to obtain half-life data. Based on this half-life data, the pesticide exposure dose is calculated. First, several representative monitoring points need to be selected on the pollution extent map. These points must cover areas with different concentration gradients to ensure the comprehensiveness and accuracy of the data. Then, soil samples are collected from these monitoring points and brought back to the laboratory. Gas chromatography-mass spectrometry (GC-MS) is used to analyze the changes in pesticide residue levels. After a period of time, soil samples are collected again at the same location and depth, and the above detection steps are repeated. By comparing the changes in pesticide concentration between the two sampling periods, the degradation rate constant is determined, thereby deriving the... The study analyzes the half-life data of pesticides under specific environmental conditions. This data, combined with previous adsorption capacity calculations and spatial distribution information from pollution extent maps, estimates the average pesticide residue in soil over a given period. Based on this, the potential pesticide exposure dose to organisms or humans within a specific area is calculated. For example, in an orchard setting, the degradation process of imidacloprid pesticides can be studied. Typical plots are selected based on pollution extent maps for long-term monitoring. The obtained half-life data is used to assess the differences in pesticide degradation rates under different seasons and management practices, allowing for adjustments to application strategies to reduce environmental pollution risks while ensuring crop yield and quality. Throughout this process, continuous iteration and optimization of experimental protocols allow for a more precise understanding of the dynamic changes of pesticides in soil, providing theoretical support for developing scientifically sound agricultural chemical management policies.

[0027] Step S5: Based on the exposure dose, perform residual toxicity analysis on the pesticide to obtain a toxicity score, and formulate a corresponding soil remediation plan based on the toxicity score.

[0028] Specifically, based on the exposure dose, a residual toxicity effect analysis of the pesticide is performed to obtain a toxicity effect score. Based on this score, a corresponding soil remediation plan is formulated. This step involves using the exposure dose calculated in the previous step as an input parameter, combining it with known pesticide toxicology database information, and employing the risk quotient method or hazard index model to assess its potential impact on non-target organisms and the ecosystem. By comparing the exposure dose with the baseline toxicity thresholds of various receptors (such as earthworms, microorganisms, and plant roots), the ecological risk level at different concentration levels is quantified, thereby generating a quantifiable toxicity effect score. This score comprehensively reflects the overall threat level of pesticide residues to soil health. Subsequently, risk areas are divided according to the toxicity effect score, and intensive remediation is prioritized for high-score areas. The approach involves monitoring and slow-release regulation in areas with medium scores, while maintaining natural recovery in areas with low scores. Ultimately, based on the scoring results and considering factors such as soil type, climate conditions, and vegetation cover, appropriate technical pathways are selected to formulate corresponding soil remediation plans. For example, in an orchard application scenario, after calculating a high exposure dose for detected organophosphorus pesticides using the aforementioned process, residual toxicity effect analysis yielded a high toxicity effect score, indicating a significant inhibitory effect on soil organisms. Accordingly, when formulating corresponding soil remediation plans, biochar adsorption combined with plant-microbe synergistic remediation technology is prioritized to reduce bioavailability and promote degradation and transformation. For plots with low toxicity effect scores, crop rotation, fallow, and organic fertilizer improvement strategies are adopted to achieve scientific allocation and precise treatment of remediation resources.

[0029] In a specific embodiment, the soil sample to be tested is subjected to ultrasonic-assisted extraction to obtain a pesticide extract, comprising: The soil sample to be tested is classified by particle size using a vibrating screen to obtain a graded soil sample, and a multi-solvent gradient elution is performed on the graded soil sample to obtain a gradient eluent. The gradient eluent is subjected to ultrasonic cavitation treatment to obtain an extraction mixture, and the extraction mixture is then subjected to phase separation by centrifugation to obtain the pesticide extract.

[0030] Specifically, ultrasonic-assisted extraction is performed on the soil sample to be tested to obtain a pesticide extract. This includes classifying the soil sample by particle size using a vibrating sieve to obtain a graded soil sample, and performing multi-solvent gradient elution based on the graded soil sample to obtain a gradient eluent. The gradient eluent is then subjected to ultrasonic cavitation treatment to obtain an extraction mixture, and the extraction mixture is subjected to phase separation by centrifugation to obtain the pesticide extract. The specific implementation of this step involves collecting representative soil samples from the orchard soil area. After collection, the soil samples are transferred to a laboratory environment for preliminary treatment. After removing non-soil components such as plant debris and gravel, the soil samples are evenly spread on a drying tray and air-dried under ventilated conditions to avoid the decomposition of pesticide components due to high temperatures. Once the samples reach a constant dryness, they are transferred to a vibrating screen for sieving. The vibrating screen separates soil particles step by step according to the difference in pore size, thereby achieving particle size classification of the soil samples and obtaining graded soil samples with different particle size ranges, such as fine sand group, silt group, and clay group. Since the adsorption capacity of graded soil samples with different particle sizes is different, it is necessary to process the graded soil samples of each layer in the soil samples to be tested independently. Subsequently, a multi-solvent gradient elution method was used for elution of each graded soil sample. First, a weakly polar solvent was used for the initial elution to extract non-polar pesticide components adsorbed on the organic matter surface. Then, a moderately polar solvent was used for the second elution to release moderately polar residues bound to humic acid or phytohexidine. Finally, a strongly polar solvent was used for the third elution to fully extract ionic or highly water-soluble pesticides and their metabolites. The eluent was collected after each elution and combined to form a gradient eluent, ensuring coverage of the solubility characteristics of different types of pesticides. The gradient eluent was then placed in a dedicated ultrasonic reactor for ultrasonic cavitation treatment. Under the action of high-frequency sound waves, a large number of microbubbles were generated inside the liquid and violently collapsed, forming a local high-temperature and high-pressure environment. This enhanced the molecular diffusion rate and disrupted the binding force between pesticides and soil particles, allowing the target compounds to more fully transfer into the liquid phase and forming a homogeneous extraction mixture. After ultrasonic cavitation treatment, the extraction mixture is loaded into a centrifuge tube, and the centrifuge is started to rotate at high speed. The density difference is used to achieve effective separation of the solid and liquid phases. After centrifugation, the clear liquid at the top is carefully extracted, which is the pesticide extract. This liquid does not contain suspended particles and is rich in the target pesticide residues. It can be used for subsequent separation and detection of the pesticide extract by liquid chromatography.This method is applicable to soil testing scenarios in orchards after long-term application of multiple types of pesticides. For example, in a citrus orchard, the continuous use of pyrethroid and triazole fungicides has resulted in complex and unevenly distributed residual components in the soil. By performing particle size classification and multi-solvent gradient elution on the collected soil samples, different binding forms of pesticides can be extracted in a targeted manner. Then, the mass transfer process is enhanced by ultrasonic cavitation, ultimately obtaining a pesticide extract with high purity and high recovery rate, which significantly improves the accuracy and representativeness of subsequent test results.

[0031] In a specific embodiment, the pesticide extract is separated and detected using a liquid chromatograph to obtain pesticide chromatographic peaks. Based on the peak area integration of these peaks, pesticide concentration data is obtained, including: The pesticide extract is filtered through a filter membrane to obtain a clear extract, which is then injected into the column of a liquid chromatograph through a pre-set injection needle to obtain an in-column separated liquid. The in-column separation solution was subjected to ultraviolet detection scanning to obtain the component response signal, and the component response signal was plotted to obtain the chromatographic peak of the pesticide; The pesticide chromatographic peaks are baseline corrected to obtain corrected peak data, and the peak boundaries of the corrected peak data are located to obtain peak boundary parameters. Based on the peak boundary parameters, the calibration peak data is integrally calculated to obtain the peak area value. Then, based on the peak area value and the standard curve, the concentration is converted to obtain the pesticide concentration data. The standard curve is a peak area-concentration correspondence curve pre-determined using pesticide solutions of known concentrations.

[0032] Specifically, the pesticide extract is separated and detected using a liquid chromatograph to obtain pesticide chromatographic peaks. Peak area integration is performed on these peaks to obtain pesticide concentration data. This includes filtering the pesticide extract through a filter membrane to obtain a clarified extract, injecting the clarified extract into the chromatographic column of the liquid chromatograph using a pre-set injection needle to obtain an in-column separated solution; performing ultraviolet (UV) scanning on the in-column separated solution to obtain component response signals, and plotting the component response signals to obtain the pesticide chromatographic peaks; performing baseline correction on the pesticide chromatographic peaks to obtain corrected peak data, and locating the peak boundaries of the corrected peak data to obtain peak boundary parameters; integrating the corrected peak data based on the peak boundary parameters to obtain peak area values, and performing concentration conversion based on the peak area values ​​and a standard curve to obtain pesticide concentration data. The standard curve is a peak area-concentration correlation curve pre-determined using pesticide solutions of known concentrations. The implementation process of this step is as follows: First, the pesticide extract obtained in the previous step is transferred to a clean sample vial. The extract is then pressure-filtered using an organic filter membrane with a suitable pore size to remove any small particles or insoluble impurities, preventing blockage of the chromatographic system flow path. After filtration, the liquid portion that has passed through the filter membrane is collected; this is the clarified extract. This clarified extract maintains the chemical stability of the target pesticide and has good flowability, making it suitable for use in precision analytical instruments. Subsequently, the clarified extract is placed in the sample tray of an autosampler. A predetermined volume of the clarified extract is drawn up through a pre-set injection needle and accurately injected into the injection valve of the liquid chromatograph according to the set program. Driven by the mobile phase, the clarified extract enters the chromatographic column along the elution gradient. The column is filled with a stationary phase material containing specific functional groups, which can selectively retain and elute different pesticide molecules based on their polarity, hydrophobicity, and spatial structure differences. This allows the components to separate sequentially within the column, forming an in-column separation liquid that flows out one by one over time. This in-column separation liquid continuously flows through the flow cell of the ultraviolet detector. The ultraviolet detector continuously scans the separated liquid in the column within a preset wavelength range. When a pesticide component containing a conjugated structure passes through the detection cell, it absorbs ultraviolet light of a specific wavelength and generates a corresponding electrical signal, which is the component response signal. This signal is recorded in real time by the data acquisition system and converted into a digital signal. Subsequently, the component response signal is plotted into a continuous chromatographic curve based on the time series. Each significant peak appearing in the spectrum is the chromatographic peak of the pesticide. The position of each peak reflects its retention time and can be used for qualitative identification.To further improve quantitative accuracy, baseline correction is performed on the pesticide chromatographic peaks. An automatic algorithm identifies background drift or noise interference in the chromatogram, adjusts the original signal to stabilize the baseline, and eliminates deviations caused by solvent effects or system fluctuations, resulting in corrected peak data. Subsequently, peak boundary localization is performed on the corrected peak data. The system automatically determines the start and end points of the chromatographic peak based on slope changes and curvature characteristics, defining the complete peak profile and forming peak boundary parameters. Based on these parameters, the corrected peak data is integrated. The integration algorithm accumulates the signal intensity within the peak boundary to obtain the peak area value, which is positively correlated with the pesticide content in the sample. Finally, the obtained peak area value is substituted into a pre-established standard curve for concentration conversion. This standard curve is plotted by measuring a series of pesticide standard solutions of known concentrations using the same detection procedure, reflecting the linear or non-linear relationship between peak area and concentration. Interpolation is used to determine the actual pesticide content in the current sample, ultimately outputting the pesticide concentration data. This method is applicable to the detection of multiple pesticide residues in orchard soil. For example, after pretreatment to obtain pesticide extract from a soil sample collected in an apple orchard, the sample is filtered through a membrane to obtain a clear extract. After injection into a liquid chromatograph, acetamiprid and tebuconazole are successfully separated in the column. The corresponding pesticide chromatographic peaks are generated by ultraviolet detection scanning. After baseline correction and peak boundary positioning, the peaks are accurately integrated and converted using a standard curve to obtain the specific residue concentration in the soil. This provides a reliable input for subsequent adsorption capacity calculation of the soil sample based on the pesticide concentration data.

[0033] In a specific embodiment, the adsorption capacity of the soil sample to be tested is calculated based on the pesticide concentration data to obtain a pesticide distribution map, including: The ratio between the pesticide concentration data and the dry weight data of the soil sample to be tested is calculated to obtain the adsorption capacity per unit mass. The adsorption capacity per unit mass is then correlated and labeled with the sampling depth data corresponding to the adsorption capacity per unit mass to obtain stratified adsorption data. Based on the stratified adsorption data, coordinate mapping is performed to obtain an adsorption amount scatter plot, and contour lines are drawn on the adsorption amount scatter plot to obtain soil profile adsorption contour lines. By extending the adsorption contour lines of the soil profile into a plane, a regional adsorption surface map is obtained. Then, the regional adsorption surface map is colored with a concentration gradient to obtain a pesticide distribution map.

[0034] Specifically, based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map. This includes calculating the ratio between the pesticide concentration data and the dry weight data of the soil sample to obtain the adsorption capacity per unit mass, and associating and labeling the adsorption capacity per unit mass with the sampling depth data corresponding to the adsorption capacity per unit mass to obtain stratified adsorption data. Based on the stratified adsorption data, coordinate mapping is performed to obtain an adsorption scatter plot, and contour lines are drawn on the adsorption scatter plot to obtain soil profile adsorption contour lines. Planar extension is drawn using the soil profile adsorption contour lines to obtain a regional adsorption surface map, and the regional adsorption surface map is colored with a concentration gradient to obtain a pesticide distribution map. The implementation process involves first matching the pesticide concentration data obtained through liquid chromatography with the dry weight data of the soil samples collected at the corresponding sampling points in the laboratory. The pesticide concentration data represents the content of residual pesticide in a unit volume of soil, while the dry weight data of the soil samples reflects the solid mass of a unit volume of soil after water removal. The ratio of these two values ​​yields the adsorption capacity per unit mass, which characterizes the amount of pesticide that can be adsorbed per unit mass of soil particles, more accurately reflecting the enrichment degree of pesticides in the solid phase and avoiding concentration deviations caused by differences in soil moisture content. Subsequently, the calculated adsorption capacity per unit mass is correlated with its corresponding sampling depth data. For example, the adsorption capacity per unit mass of surface soil, subsurface soil, and deep soil is correlated with their respective vertical positions, forming stratified adsorption data containing vertical distribution information. This data not only reflects the differences in pesticide distribution in horizontal space but also fully preserves its vertical migration characteristics within the soil profile. Next, the stratified adsorption data is imported into a geographic information system or professional mapping software for coordinate mapping. Using geographic plane coordinates as the horizontal axis and sampling depth as the vertical axis, the adsorption capacity per unit mass of each sampling point is marked as a point in a two-dimensional coordinate system, forming an adsorption scatter plot. This plot visually displays the distribution of adsorption intensity at different depths for each point. Based on this, contour lines are drawn from the adsorption scatter plot. Inverse distance weighted interpolation or Kriging interpolation algorithms are used to estimate the adsorption values ​​of unsampled points, and points with the same adsorption capacity per unit mass are connected to form a continuous closed curve, which is the soil profile adsorption contour line. These contour lines clearly reveal the enrichment zone, migration path, and retention depth of pesticides in the vertical direction. For example, in some areas, a clear high-adsorption closed loop may be observed below the cultivated layer, indicating that its downward migration is hindered and accumulation occurs. Subsequently, based on the geographical location of each sampling point, the soil profile adsorption contour lines of multiple soil profiles were horizontally connected and extended on the horizontal plane. The discrete profile information was extended into a continuous three-dimensional adsorption structure through spatial interpolation, generating a regional adsorption surface map covering the entire study area. This map reflects the overall pattern of pesticide adsorption capacity on the land surface.Finally, the adsorption surface map of the region is subjected to concentration gradient coloring, with different colors assigned according to the adsorption capacity per unit mass. For example, light green is used for low-value areas, yellow for medium-value areas, and dark red for high-value areas, making the spatial distribution trend of pesticides clear at a glance. The resulting pesticide distribution map not only contains absolute concentration information of pesticide residues but also integrates soil adsorption capacity and spatial structure characteristics, comprehensively presenting the spatial heterogeneity of pollution. This method is applicable to the assessment of orchard soil environment. For example, in a grape-growing area, for soil samples after long-term application of the herbicide atrazine, the adsorption capacity per unit mass is calculated through the above process, and stratified adsorption data is generated. Adsorption scatter plots and soil profile adsorption isolines are drawn, revealing a high adsorption band near a depth of 30 cm. Further planar extension and gradient coloring are used to obtain a pesticide distribution map, clearly showing that the northeastern part of the orchard is a high-risk accumulation area. This provides an accurate spatial input basis for subsequent spatial interpolation based on the pesticide distribution map to obtain a pollution range map.

[0035] In a specific embodiment, a regional adsorption surface map is obtained by extending the adsorption contour lines of the soil profile into a plane, including: Feature points are extracted from the adsorption contour lines of the soil profile to obtain a set of contour line node coordinates. Based on the set of contour line node coordinates, a triangular mesh is formed to obtain spatial mesh data. Spatial interpolation is performed on the spatial grid data using radial basis functions to obtain a continuous distribution function, and an equipotential surface is constructed based on the continuous distribution function to obtain a three-dimensional adsorption distribution body. The three-dimensional adsorption distribution is horizontally sliced ​​to obtain a multi-layer adsorption profile, and a depth overlay projection is performed based on the multi-layer adsorption profile to obtain a regional adsorption surface map.

[0036] Specifically, the adsorption isolines of the soil profile are extended in a plane to obtain a regional adsorption surface map. This includes extracting feature points from the adsorption isolines of the soil profile to obtain a set of isoline node coordinates, and performing triangular meshing based on the set of isoline node coordinates to obtain spatial grid data. Spatial interpolation is then performed on the spatial grid data using radial basis functions to obtain a continuous distribution function, and equipotential surfaces are constructed based on the continuous distribution function to obtain a three-dimensional adsorption distribution volume. The three-dimensional adsorption distribution volume is then horizontally sliced ​​to obtain a multi-layer adsorption profile map, and a depth-stacked projection is performed based on the multi-layer adsorption profile map to obtain a regional adsorption surface map. The specific implementation of this step involves first importing the adsorption isolines of the soil profile generated in the previous step into the geographic information system software. The graphics processing module is used to vectorize the adsorption isolines of the soil profile, identifying key turning points and locations of significant curvature changes on each isoline, and extracting the discrete point set that constitutes the contour of the isolines, which is the set of isoline node coordinates. These coordinate points accurately record the spatial distribution characteristics of the adsorption per unit mass in each sampling profile. Subsequently, based on the coordinate set of the contour line nodes, the Delaunay triangulation algorithm was used to triangulate all nodes into a mesh, connecting the originally scattered points into a continuous network of triangular facets, forming spatial grid data covering the entire study area. This data structure not only preserves the vertical and horizontal position information of the original sampling points, but also expresses spatial proximity through geometric topological relationships, providing support for subsequent high-precision interpolation. Next, radial basis functions were applied to perform spatial interpolation on the spatial grid data. This method constructs a basis function with distance decay characteristics centered on each node, and fits a continuous distribution function covering the entire region through linear combination. It can accurately reflect the gradual change law of pesticide adsorption in three-dimensional space, and is especially suitable for complex orchard environments with undulating terrain or uneven sampling density. On this basis, equipotential surfaces were constructed based on the continuous distribution function, connecting points with the same adsorption amount into smooth surfaces in three-dimensional space, forming a three-dimensional adsorption distribution volume with a hierarchical structure. This volume data fully presents the enrichment morphology of pesticides at different depths and horizontal ranges underground. For example, high adsorption areas may appear as upward-convex "dome"-shaped structures, indicating the core location of pollution. The three-dimensional adsorption distribution body is then horizontally sliced, and multiple cross sections are cut at fixed intervals along the depth direction. Each cross section reflects the adsorption distribution at a specific depth layer, generating a series of multi-layer adsorption profile maps with geographic coordinates. These layers not only independently display the pollution status at each depth, but are also interconnected to form a vertical sequence.Finally, based on the multi-layer adsorption profile, depth overlay projection is performed, and the images of each layer are fused according to depth weight. The three-dimensional information is compressed into a two-dimensional plane using maximum value projection or weighted average projection, highlighting the spatial pattern of the overall adsorption intensity. The resulting regional adsorption surface map not only inherits the scientific basis of the adsorption isolines of the original soil profile, but also realizes the transformation from profile to surface region through three-dimensional reconstruction and projection technology. It is suitable for pollution assessment scenarios of agricultural land such as orchards. For example, in a peach orchard, for the residual distribution formed after long-term application of abamectin, the adsorption isolines of multiple soil profiles are extended into a regional adsorption surface map through the above process, clearly revealing that there is a large area of ​​deep high adsorption zone in the middle of the orchard, providing a spatial basis for the subsequent generation of pollution range map and the formulation of remediation strategy.

[0037] In a specific embodiment, the degradation rate of pesticides in the soil is determined using the pollution range map to obtain half-life data, including: The pollution range map is analyzed at the pixel level to obtain the pesticide concentration value and spatial coordinates corresponding to each pixel. The pesticide concentration values ​​corresponding to each pixel are then sorted by time series to obtain a time-concentration sequence set. Based on the time-concentration sequence set, the concentration decay trend of each pixel is analyzed to obtain the concentration decay rate of each pixel, and the concentration decay rate is spatially interpolated to obtain the concentration decay rate field. The degradation process of pesticides in soil is kinetically modeled based on the concentration decay rate field to obtain the half-life data of each pixel.

[0038] Specifically, the degradation rate of pesticides in the soil is measured using the pollution range map to obtain half-life data. This includes pixel-level analysis of the pollution range map to obtain the pesticide concentration value and spatial coordinates corresponding to each pixel, and time-series sorting of the pesticide concentration values ​​corresponding to each pixel to obtain a time-concentration sequence set. Based on the time-concentration sequence set, concentration decay trend analysis is performed on each pixel to obtain the concentration decay rate of each pixel, and spatial interpolation is performed on the concentration decay rate to obtain a concentration decay rate field. Based on the concentration decay rate field, the degradation process of pesticides in the soil is kinetically modeled to obtain the half-life data of each pixel. Specifically, the pollution range map is first imported into image processing software. Using the pixel analysis function of the software, the pesticide concentration value represented by each pixel and its spatial coordinates on the map are accurately extracted. This is done to ensure the accuracy and reliability of subsequent analysis, because each pixel actually represents the average pesticide concentration level in a small, specific area. Then, based on the extracted pesticide concentration values ​​and their corresponding spatial coordinates, the concentration values ​​at different time points of the same location are organized and sorted according to the sampling time sequence to form a time-concentration sequence set. This step is crucial because it provides fundamental data support for understanding the changes in pesticide concentration over time at each location. For example, in an orchard, pesticides may gradually decrease over time due to factors such as natural degradation and biological action. Next, based on the time-concentration sequence set, statistical analysis methods or mathematical models are used to analyze the concentration decay trend of each pixel to determine the decay rate of pesticide concentration over time at each pixel. This process involves complex calculations and model fitting, but ultimately it can reveal whether the rate of pesticide concentration decline is consistent or whether there are acceleration or deceleration phenomena. For example, in some areas, the pesticide degradation rate may be faster due to higher microbial activity. Subsequently, the concentration decay rate calculated for all pixels was extended to the entire study area using spatial interpolation techniques from a geographic information system, generating a continuous concentration decay rate field. This step helps identify the spatial distribution characteristics of pesticide degradation rates, such as which areas have faster degradation rates and which have slower rates, thus providing necessary input parameters for further kinetic modeling. Based on the constructed concentration decay rate field, a more in-depth kinetic model of the pesticide degradation process can be performed. By fitting known chemical reaction kinetic equations, the degradation behavior of pesticides under different environmental conditions can be predicted, and the half-life data of the pesticide at each pixel—that is, the time required for the pesticide concentration to decrease to half of its initial concentration—can be estimated. This is of great significance for assessing pesticide residue risk.Finally, based on the half-life data calculated for each pixel, the original pollution extent map can be recolored using color coding. This allows regions with different half-lives to display different colors, visually illustrating the spatial distribution of pesticide degradation rates across the entire study area. For example, in a vineyard, the half-life distribution map generated using this process clearly shows the rapid degradation characteristics in the northeastern region due to higher microbial activity, while the southwest, with its relatively lower degradation rate, becomes a key potential pollution area. Such half-life distribution maps not only help understand the degradation dynamics of pesticides in soil but also provide a scientific basis for developing targeted environmental management strategies.

[0039] In a specific embodiment, the degradation process of pesticides in soil is kinetically modeled based on the concentration decay rate field to obtain the half-life data of each pixel, including: The concentration decay rate field is smoothed by Gaussian kernel to obtain the degradation rate density matrix, and the degradation rate density matrix is ​​decomposed by nonlinear diffusion to obtain a multi-scale degradation characteristic spectrum. Based on the multi-scale degradation characteristic spectrum, differential equations were constructed for the degradation process of pesticides in soil to obtain degradation kinetic characterization equations. The degradation kinetic characterization equations were then calculated and processed numerically to obtain the half-life data of each pixel.

[0040] Specifically, the degradation process of pesticides in soil is kinetically modeled based on the concentration decay rate field to obtain the half-life data of each pixel. This includes applying Gaussian kernel smoothing to the concentration decay rate field to obtain a degradation rate density matrix, and then performing nonlinear diffusion decomposition on the degradation rate density matrix to obtain a multi-scale degradation feature spectrum. Based on the multi-scale degradation feature spectrum, differential equations are constructed for the degradation process of pesticides in soil to obtain a degradation kinetic characterization equation. The degradation kinetic characterization equation is then calculated numerically to obtain the half-life data of each pixel. To achieve this process, the acquired concentration decay rate field data must first be imported into analysis software. In this software, a Gaussian kernel smoothing algorithm is applied to process the original data to remove noise and ensure the consistency and continuity of information between adjacent pixels, thereby forming a more accurate and smooth degradation rate density matrix. This step is particularly important in the processing because it directly affects the accuracy of all subsequent analysis results. Next, to further extract degradation features at different scales, nonlinear diffusion decomposition technology was used to process the obtained degradation rate density matrix. This step can identify various degradation modes, ranging from locally rapid changes to globally slow changes, providing a basis for a deeper understanding of the complex degradation behavior of pesticides in soil. Subsequently, based on the obtained multi-scale degradation feature spectrum and combined with the principles of chemical reaction kinetics, differential equations were constructed for the degradation process of pesticides in soil. These equations not only describe the change of pesticide concentration over time but also reflect the interaction between pesticides and environmental factors, such as the influence of temperature and humidity on the degradation rate. After completing the construction of the degradation kinetic characterization equation, numerical methods were used to solve the equation. This step involves complex mathematical calculations and simulations, aiming to calculate the corresponding half-life value for each pixel, i.e., the time required for the pesticide concentration to drop to half of its initial value. In a specific agricultural application scenario, such as after a new type of herbicide is used in a certain farmland, in order to assess the degradation of this herbicide in different plots, the concentration decay rate data at multiple sampling points in the area can be collected first. Then, the data can be processed and analyzed according to the steps mentioned above, and finally a map reflecting the degradation rate of pesticides at each location can be generated, which includes the half-life information corresponding to each pixel. Such a map is of great significance for understanding the persistence of herbicides in the field and formulating corresponding management strategies.

[0041] In a specific embodiment, calculating the pesticide exposure dose based on the half-life data includes: The half-life data were subjected to time cumulative effect analysis to obtain the cumulative curve of pesticide residue change over time, and the cumulative curve was differentiated to obtain the residue increment rate for each time period. Based on the residual increment rate, combined with the initial concentration of pesticides in the soil and the soil-environment interaction coefficient, the weighted exposure dose for each time period is calculated, and the pesticide exposure dose is obtained by integrating the weighted exposure dose over the entire time period.

[0042] Specifically, calculating the pesticide exposure dose based on the half-life data includes performing a time-cumulative effect analysis on the half-life data to obtain a cumulative curve of pesticide residue changes over time, and differentiating the cumulative curve to obtain the residue increment rate for each time period; based on the residue increment rate, combined with the initial concentration of pesticide in the soil and the soil-environment interaction coefficient, calculating the weighted exposure dose for each time period, and integrating the weighted exposure dose over the entire time period to obtain the pesticide exposure dose. The specific implementation of this step is as follows: First, the half-life data corresponding to each pixel in the half-life distribution map generated in the previous step is extracted as the core parameter reflecting the degradation rate of pesticides in different spatial locations. Since the half-life determines the exponential decay law of pesticide concentration over time, the pesticide residue change process from the initial application to multiple subsequent time nodes can be calculated based on this data. By establishing an exponential decay model, the trajectory of pesticide concentration evolution over time in each spatial unit is simulated, and then integrated to form a cumulative curve of pesticide residue change over time covering the entire research period. This cumulative curve not only reflects the decreasing trend of total pesticide amount, but also includes the local fluctuation characteristics caused by environmental heterogeneity. The cumulative curve was then mathematically differentiated to calculate its slope changes over different time periods, thus obtaining the residual increment rate for each time period. This value reflects the dynamic flux of pesticides actually decreasing or increasing in the soil per unit time. Especially in cases of multiple applications or external inputs, it can capture the complex behavior of alternating concentration increases and decreases. For example, during periods of frequent rainfall, leaching may cause pesticides from the surface layer to migrate to deeper layers, resulting in a localized "increase." These details are retained in the increment rate for subsequent evaluation. Based on this, the initial concentration of pesticides in the soil was obtained and introduced as a starting condition. This initial concentration was then corrected using a pre-set soil-environment interaction coefficient. This coefficient comprehensively considers the influence of environmental factors such as soil texture, organic matter content, pH, temperature, humidity, and microbial activity on pesticide migration and transformation, quantifying the regulatory effect of environmental conditions on the exposure process. For example, clay soils, due to their strong adsorption capacity, reduce pesticide bioavailability, while sandy soils, due to their high permeability, make pesticides more easily move and be released. The residual increment rate is used together with the two parameters mentioned above in a weighted calculation to construct a weighted exposure dose model. Within each time slice, the effective exposure level that has potential impact on organisms or ecosystems during that time period is calculated based on the current residual increment rate, the decay margin of the initial concentration, and the adjustment weight of the soil-environment interaction coefficient. This is the weighted exposure dose, which reflects the dynamic exposure risk in the real environment better than traditional static estimation.Finally, the weighted exposure doses over all consecutive time periods were integrated over time. Numerical integration methods such as the trapezoidal method or Simpson's method were used to sum the weighted exposure dose sequences over the entire study period, thereby obtaining the total pesticide exposure dose throughout the entire time period. This result not only includes information on the absolute residue of pesticides, but also integrates the duration of time, environmental regulation effects, and dynamic change characteristics, realizing a leap from single-concentration point measurement to full-process exposure load assessment. This method is applicable to long-term risk assessment scenarios in agricultural ecosystems such as orchards. For example, in a citrus orchard, considering the pollution caused by long-term use of acetamiprid, the degradation pathway of acetamiprid in different plots is derived using the half-life data, generating a cumulative curve of pesticide residue changes over time. The rate of residue increment in each growing season is obtained through differentiation. Combined with the local soil's acidic nature and moderate organic matter content, a soil-environment interaction coefficient is set. Finally, the pesticide exposure dose covering a three-year management cycle is calculated, providing spatiotemporally continuous input parameters for subsequent pesticide residue toxicity effect analysis based on the exposure dose. This ensures that the toxicity effect score can truly reflect the ecological cumulative risk under long-term low-dose exposure.

[0043] In a specific embodiment, the pesticide residual toxicity effect is analyzed based on the exposure dose to obtain a toxicity effect score, including: Soil bioaccumulation coefficients are calculated based on the exposure doses, and toxicity response analysis of soil microbial communities is performed based on the soil bioaccumulation coefficients to obtain microbial population variation data. Ecotoxicological analysis was performed on the microbial population variation data to obtain an ecotoxicity index, and soil enzyme activity was measured based on the ecotoxicity index to obtain the enzyme activity inhibition rate. A dose-response relationship analysis was performed on the enzyme activity inhibition rate to obtain a toxicity effect curve. Based on the toxicity effect curve, the degree of pesticide residue hazard was assessed to obtain a toxicity effect score.

[0044] Specifically, based on the exposure dose, a residual toxicity effect analysis of the pesticide is performed to obtain a toxicity effect score. This includes calculating the soil bioaccumulation coefficient based on the exposure dose, and performing a toxicity response analysis on the soil microbial community based on the soil bioaccumulation coefficient to obtain microbial population variation data; performing ecotoxicological analysis on the microbial population variation data to obtain an ecotoxicity index, and measuring soil enzyme activity based on the ecotoxicity index to obtain an enzyme activity inhibition rate; performing dose-response relationship analysis on the enzyme activity inhibition rate to obtain a toxicity effect curve, and assessing the pesticide residue hazard level based on the toxicity effect curve to obtain a toxicity effect score. The implementation process of this step is as follows: first, the pesticide exposure dose calculated in the previous step is used as the basic input parameter. Combined with the distribution behavior characteristics of the target pesticide in soil organisms and referring to the bioaccumulation kinetic parameters in the existing toxicology database, the soil bioaccumulation coefficient is calculated. This coefficient reflects the strength of the trend of pesticide transfer and accumulation from the soil environment to typical soil organisms such as earthworms, nematodes, and protozoa. The higher the value, the stronger the organism's ability to absorb the pesticide and the greater the potential risk of toxicity transfer. Subsequently, based on the soil bioaccumulation coefficient, representative sampling points were selected to conduct in-situ or simulated culture experiments. Soil samples were collected and cultured in a controlled microenvironment, and microbial community samples were periodically extracted. High-throughput sequencing technology was used to amplify and compare gene fragments of bacteria, fungi, and actinomycetes. By analyzing indicators such as community structure composition, dominant species succession, and changes in diversity indices, microbial population variation data caused by pesticide exposure were identified. For example, the abundance of certain sensitive bacterial genera, such as nitrifying bacteria or nitrogen-fixing bacteria, decreased significantly, while drug-resistant or degrading bacteria, such as Pseudomonas, were relatively enriched. These changes constitute the core evidence for toxicity response analysis. Next, ecotoxicological analysis was performed on the microbial population variation data. Taking into account multiple dimensions such as species richness, evenness, phylogenetic diversity, and functional gene abundance, a weighted evaluation model was constructed to quantify the degree of damage to the stability of the soil microbial ecosystem caused by pesticides, and a standardized ecotoxicity index was output. This index can transform complex community changes into comparable values ​​to characterize the relative toxicity levels under different regions or different pesticide treatments. Based on this, according to the pollution level indicated by the ecotoxicity index, soil enzyme activity was measured in the corresponding soil samples. The focus was on detecting the activity levels of enzymes closely related to the carbon, nitrogen, and phosphorus cycles, such as dehydrogenase, urease, phosphatase, and β-glucosidase. The catalytic reaction rate was determined by colorimetric or fluorescence methods and compared with uncontaminated control soil to calculate the enzyme activity inhibition rate. This value directly reflects the intensity of pesticide interference with key biochemical processes in the soil. The higher the inhibition rate, the more severe the damage to the soil's ecological function.Subsequently, a dose-response analysis was performed on the enzyme activity inhibition rate. The enzyme activity inhibition rate data at different exposure dose levels were normalized, and S-shaped or logarithmic dose-response curves were fitted. Nonlinear regression methods were used to determine characteristic parameters such as the slope, half-maximum effect concentration, and plateau value of the curves, thereby establishing a quantitative relationship model between pesticide exposure dose and biological response. This toxicity effect curve not only reveals the threshold and saturation point of toxicity but can also be used to extrapolate the potential risks under long-term low-dose exposure. Finally, based on the toxicity effect curves, the pesticide residue hazard level was assessed. Low, medium, and high toxicity intervals were divided based on curve characteristics. A comprehensive toxicity effect score was generated by comprehensively considering the weights of the ecotoxicity index and enzyme activity inhibition rate. This score is expressed in a dimensionless form, facilitating horizontal comparisons between different pesticides or different plots, and providing a scientific basis for subsequently developing corresponding soil remediation plans based on the toxicity effect score. This method is applicable to agricultural land scenarios such as orchards where chemical pesticides are applied for a long time. For example, in a pear orchard, for the detection of organochlorine pesticide residues, the above process was used to derive the soil bioaccumulation coefficient based on the calculated exposure dose. It was found that the bioaccumulation trend in earthworms was obvious. Further microbial community analysis revealed that the proportion of actinomycetes was significantly reduced. Ecotoxicological analysis yielded a high ecotoxicity index. Soil enzyme activity measurement showed that the dehydrogenase activity inhibition rate was at a high level. Further, a steep toxicity effect curve was obtained by fitting the dose-response relationship. Finally, the toxicity effect score was assessed as being at a high-risk level. Based on this, it was determined that remediation measures should be prioritized in this area to ensure the gradual restoration of soil ecological function.

[0045] The soil pesticide residue detection method in the embodiments of the present invention has been described above. The soil pesticide residue detection system in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the soil pesticide residue detection system of the present invention includes: Extraction module 21 is used to perform ultrasonic-assisted extraction on the soil sample to obtain pesticide extract; The detection module 22 is used to separate and detect the pesticide extract using a liquid chromatograph to obtain pesticide chromatographic peaks, and to calculate the pesticide concentration data based on the peak area integral of the pesticide chromatographic peaks. The calculation module 23 is used to calculate the adsorption capacity of the soil sample to be tested based on the pesticide concentration data, obtain a pesticide distribution map, and perform spatial interpolation processing based on the pesticide distribution map to obtain a pollution range map. The measurement module 24 is used to measure the degradation rate of pesticides in the soil through the pollution range map, obtain half-life data, and calculate the exposure dose of pesticides based on the half-life data. Analysis module 25 is used to perform residual toxicity analysis on pesticides based on the exposure dose, obtain a toxicity score, and formulate a corresponding soil remediation plan based on the toxicity score.

[0046] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0047] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0048] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0049] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0052] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for detecting pesticide residues in soil, characterized in that, Includes the following steps: The soil sample to be tested was subjected to ultrasonic-assisted extraction to obtain a pesticide extract. The pesticide extract was separated and detected by liquid chromatography to obtain pesticide chromatographic peaks, and the pesticide concentration data was obtained by calculating the peak area integral based on the pesticide chromatographic peaks. Based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map. Based on the pesticide distribution map, spatial interpolation is performed to obtain a pollution range map. The degradation rate of pesticides in the soil was determined by measuring the pollution range map to obtain half-life data, and the exposure dose of pesticides was calculated based on the half-life data. The pesticide residue toxicity effect was analyzed based on the exposure dose to obtain a toxicity effect score, and a corresponding soil remediation plan was formulated based on the toxicity effect score.

2. The method for detecting pesticide residues in soil according to claim 1, characterized in that, Ultrasonic-assisted extraction was performed on the soil sample to obtain a pesticide extract, including: The soil sample to be tested is classified by particle size using a vibrating screen to obtain a graded soil sample, and a multi-solvent gradient elution is performed on the graded soil sample to obtain a gradient eluent. The gradient eluent is subjected to ultrasonic cavitation treatment to obtain an extraction mixture, and the extraction mixture is then subjected to phase separation by centrifugation to obtain the pesticide extract.

3. The method for detecting pesticide residues in soil according to claim 1, characterized in that, The pesticide extract was separated and detected using liquid chromatography to obtain pesticide chromatographic peaks. Based on the peak area integration of these peaks, pesticide concentration data was obtained, including: The pesticide extract is filtered through a filter membrane to obtain a clear extract, which is then injected into the column of a liquid chromatograph through a pre-set injection needle to obtain an in-column separated liquid. The in-column separation solution was subjected to ultraviolet detection scanning to obtain the component response signal, and the component response signal was plotted to obtain the chromatographic peak of the pesticide; The pesticide chromatographic peaks are baseline corrected to obtain corrected peak data, and the peak boundaries of the corrected peak data are located to obtain peak boundary parameters. Based on the peak boundary parameters, the calibration peak data is integrally calculated to obtain the peak area value. Then, based on the peak area value and the standard curve, the concentration is converted to obtain the pesticide concentration data. The standard curve is a peak area-concentration correspondence curve pre-determined using pesticide solutions of known concentrations.

4. The method for detecting pesticide residues in soil according to claim 1, characterized in that, Based on the pesticide concentration data, the adsorption capacity of the soil sample to be tested is calculated to obtain a pesticide distribution map, including: The ratio between the pesticide concentration data and the dry weight data of the soil sample to be tested is calculated to obtain the adsorption capacity per unit mass. The adsorption capacity per unit mass is then correlated and labeled with the sampling depth data corresponding to the adsorption capacity per unit mass to obtain stratified adsorption data. Based on the stratified adsorption data, coordinate mapping is performed to obtain an adsorption amount scatter plot, and contour lines are drawn on the adsorption amount scatter plot to obtain soil profile adsorption contour lines. By extending the adsorption contour lines of the soil profile into a plane, a regional adsorption surface map is obtained. Then, the regional adsorption surface map is colored with a concentration gradient to obtain a pesticide distribution map.

5. The method for detecting pesticide residues in soil according to claim 1, characterized in that, The degradation rate of pesticides in the soil was determined using the pollution extent map to obtain half-life data, including: The pollution range map is analyzed at the pixel level to obtain the pesticide concentration value and spatial coordinates corresponding to each pixel. The pesticide concentration values ​​corresponding to each pixel are then sorted by time series to obtain a time-concentration sequence set. Based on the time-concentration sequence set, the concentration decay trend of each pixel is analyzed to obtain the concentration decay rate of each pixel, and the concentration decay rate is spatially interpolated to obtain the concentration decay rate field. The degradation process of pesticides in soil is kinetically modeled based on the concentration decay rate field to obtain the half-life data of each pixel.

6. The method for detecting pesticide residues in soil according to claim 1, characterized in that, Calculating the pesticide exposure dose based on the half-life data includes: The half-life data were subjected to time cumulative effect analysis to obtain the cumulative curve of pesticide residue change over time, and the cumulative curve was differentiated to obtain the residue increment rate for each time period. Based on the residual increment rate, combined with the initial concentration of pesticides in the soil and the preset soil-environment interaction coefficient, the weighted exposure dose for each time period is calculated, and the pesticide exposure dose is obtained by integrating the weighted exposure dose over the entire time period.

7. The method for detecting pesticide residues in soil according to claim 1, characterized in that, Based on the aforementioned exposure dose, a residual toxicity effect analysis of the pesticide was performed to obtain a toxicity effect score, including: Soil bioaccumulation coefficients are calculated based on the exposure doses, and toxicity response analysis of soil microbial communities is performed based on the soil bioaccumulation coefficients to obtain microbial population variation data. Ecotoxicological analysis was performed on the microbial population variation data to obtain an ecotoxicity index, and soil enzyme activity was measured based on the ecotoxicity index to obtain the enzyme activity inhibition rate. A dose-response relationship analysis was performed on the enzyme activity inhibition rate to obtain a toxicity effect curve. Based on the toxicity effect curve, the degree of pesticide residue hazard was assessed to obtain a toxicity effect score.

8. A soil pesticide residue detection system, characterized in that, The method for detecting pesticide residues in soil according to any one of claims 1 to 7 includes: The extraction module is used to perform ultrasonic-assisted extraction on the soil sample to obtain a pesticide extract. The detection module is used to separate and detect the pesticide extract using a liquid chromatograph to obtain pesticide chromatographic peaks, and to calculate pesticide concentration data based on the peak area integration of the pesticide chromatographic peaks. The calculation module is used to calculate the adsorption capacity of the soil sample to be tested based on the pesticide concentration data, obtain a pesticide distribution map, and perform spatial interpolation processing based on the pesticide distribution map to obtain a pollution range map. The measurement module is used to measure the degradation rate of pesticides in the soil through the pollution range map, obtain half-life data, and calculate the exposure dose of pesticides based on the half-life data. The analysis module is used to perform residual toxicity analysis on pesticides based on the exposure dose, obtain a toxicity score, and formulate a corresponding soil remediation plan based on the toxicity score.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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