Ecological pollution migration path analysis and early warning method and system based on soil heavy metals

Abstract

The invention discloses an ecological pollution migration path analysis and early warning method and system based on soil heavy metals, and particularly relates to the technical field of ecological pollution. Monitoring points are arranged around a pollution source, micro CT scanning is combined to analyze permeability changes of soil under different humidity, a synchrotron radiation X-ray spectrometer is utilized to analyze dynamic changes of chemical forms of heavy metal, and the influence of humidity changes on migration paths and form transformation is comprehensively evaluated. The deviation degree of a model prediction result and an actual migration behavior is quantified, early warning signals of different levels are generated for the situation with the high deviation degree, corresponding measures are taken, the complex influence of abnormal soil characteristics on heavy metal migration is dynamically responded, and therefore the harm of soil pollution diffusion to the environment and ecology is effectively prevented and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological pollution, and in particular to an ecological pollution migration path analysis and early warning method and system based on soil heavy metals. Background Art

[0002] The ecological pollution migration path analysis of soil heavy metals refers to the study of the migration and distribution of heavy metal pollutants in the soil to reveal the path of their impact on the ecological environment. This process usually involves multidisciplinary intersections, including geochemistry, environmental science and ecology. The focus of the analysis is on how pollutants migrate through various links of the soil-plant-water ecosystem, affecting the food chain and regional environmental health. For example, heavy metals may enter other environmental media through rainwater leaching, groundwater infiltration or wind erosion, further affecting crop growth, water quality and soil microbial communities.

[0003] Migration pathway analysis and early warning is based on the identification of these migration pathways, using prediction models or monitoring data to predict the possible spread of pollution and propose early warning measures. By real-time monitoring of key pollutant indicators such as heavy metal concentrations, soil pH and precipitation, a dynamic prediction system is established to take prevention and control measures before pollution spreads. For example, if a rapid increase in heavy metal concentrations in the soil of a specific area is detected, it may indicate that the pollution will spread to surrounding areas through groundwater or rainwater runoff. The early warning system can prompt relevant departments to intervene in time to avoid further aggravation of pollution. This kind of analysis and early warning is crucial in pollution prevention and control, helping to reduce ecological damage and protect the environment and public health.

[0004] The prior art has the following deficiencies:

[0005] The soil in some areas may significantly affect the adsorption, desorption and migration behavior of heavy metals due to abnormal physical and chemical properties (such as strong swelling or high organic matter content). These special soil characteristics often do not conform to the standard assumptions of the prediction model, resulting in biased or distorted prediction results. For example, high-organic soil may temporarily "lock" heavy metals and delay their diffusion, but suddenly release them when environmental conditions change (such as increased humidity), causing pollutants to migrate rapidly to key areas such as drinking water sources, and the early warning system fails to identify this high-risk scenario in advance. At the same time, because the prediction model underestimates the accelerating effect of abnormal soil properties on the diffusion of heavy metals, potential high-risk areas may not be identified. For example, high-organic soil in a certain place releases a large amount of locked heavy metals during the alternating process of dryness and wetness, and the pollution quickly penetrates into the groundwater or flows into the irrigation system, but the early warning system fails to capture this dynamic change, resulting in uncontrolled spread of pollution, seriously threatening downstream water resources and farmland ecology. Summary of the invention

[0006] The purpose of the present invention is to provide an ecological pollution migration path analysis and early warning method and system based on soil heavy metals to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an early warning method for ecological pollution migration path analysis based on soil heavy metals, comprising the following steps:

[0008] S1: Set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point;

[0009] S2: For each depth monitoring area, the soil column was scanned regularly under in situ conditions using micro-CT to determine the permeability of the soil column at different humidity levels and to evaluate the impact of humidity changes on water flow and heavy metal migration pathways;

[0010] S3: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0011] S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the degree of deviation between the prediction model prediction results and the actual migration behavior;

[0012] S5: If the degree of deviation is high, further analysis is conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed.

[0013] Preferably, in S2, after analyzing the influence of pore connectivity on the migration of heavy metals from the surface to the deep layer, a pore connectivity migration interference index is generated. The method for obtaining the pore connectivity migration interference index is:

[0014] The soil pore network is modeled as a weighted graph G(V,E), where V is a node set representing pores, E is an edge set representing the connecting channels between pores, and each edge e ij The weight w ij Represents the migration resistance of heavy metals through channel i→j; Use micro-CT scanning to obtain the three-dimensional pore structure of the soil, extract the geometric parameters of the pore network nodes and edges, and construct a weighted graph G(V,E), where the starting node s is the initial position of heavy metal migration and the end node t is the target position of migration; Use the shortest path algorithm to calculate the shortest path from the starting point s to the end point t, and the shortest path weight W min :W min =min∑( i,j)∈P wij ; P is the set of all paths, initialize the distance from the starting point s to each node d(s,v) = ∞, the starting point d(s,s) = 0; traverse the nodes, select the node u with the shortest distance among the unvisited nodes, and update the distance of u's neighbor node v: d(s,v) = min(d(s,v),d(s,u)+w uv );until d(s,t)=W is calculated min , the pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the migration path of heavy metals: Where W ideal is the shortest path weight under ideal conditions.

[0015] Preferably, in S3, the heavy metal proportion abnormality index is generated after analyzing the proportion of heavy metal chemical forms before and after the humidity change. The method for obtaining the heavy metal proportion abnormality index is:

[0016] Obtain time series data during humidity changes, including the proportion of each heavy metal chemical form, where t is the time point, k is the chemical form category, is the proportion of chemical form k at time t, select the sliding window size w, the window slides on the time series, each time moving a unit time Δt, the proportion of each chemical form k Calculate the rate of change within the sliding window [t, t+w-1] The relative change rate of chemical form k in the window [t, t+w-1], is the proportion of chemical form k at the end point of the window t+w-1; define the abnormal threshold τ: when When , it is marked as abnormal, and the cumulative abnormal weight W of chemical form k is calculated. k : is the indicator function, when The value is 1 when the chemical form is positive, otherwise it is 0; the accumulated abnormal weights of all chemical forms are normalized to calculate the heavy metal proportion abnormal index AS, which is expressed as: Where K is the total number of chemical form categories, max(W k ) is the maximum cumulative anomaly weight among all chemical forms k, which is used for normalization.

[0017] Preferably, in S4, the degree of deviation between the prediction results of the prediction model and the actual migration behavior is evaluated based on the influence of humidity changes on water flow and heavy metal migration paths and the abnormal change trend of different heavy metal chemical forms, specifically:

[0018] The pore connectivity migration interference index and the heavy metal proportion anomaly index were normalized, and the deviation degree between the prediction model prediction results and the actual migration behavior was calculated by the normalized pore connectivity migration interference index and the heavy metal proportion anomaly index.

[0019] Preferably, the obtained deviation value between the prediction result of the prediction model and the actual migration behavior is compared with a reference threshold of the deviation value under normal conditions preset according to historical data. If the deviation value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; if the deviation value between the prediction result of the prediction model and the actual migration behavior is less than the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is low, and a normal signal is generated at this time.

[0020] Preferably, in S5, if the degree of deviation is high, further analysis is performed on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed, specifically:

[0021] If the degree of deviation is high, that is, the deviation value between the prediction results of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation value reference threshold, the deviation values ​​between the prediction results of the prediction model generated within a subsequent fixed time period that are greater than or equal to the deviation value reference threshold and the actual migration behavior are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, different levels of warning signals are generated according to the analysis results, and corresponding processing is made.

[0022] Preferably, if the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, a first-level warning signal is generated, indicating that the high deviation is persistent and stable, and emergency response measures need to be taken quickly;

[0023] If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a secondary warning signal is generated, indicating that the degree of high deviation fluctuates greatly and requires key monitoring and model adjustment;

[0024] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a third-level warning signal is generated, indicating that the overall deviation is low but the fluctuation is large, and the model is optimized;

[0025] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, no warning signal is generated, indicating that the degree of deviation is low and stable, and routine monitoring continues.

[0026] The present invention also provides an ecological pollution migration path analysis and early warning system based on soil heavy metals, including a monitoring point layout module, a penetration assessment module, a chemical form analysis module, a deviation assessment module and an early warning response module:

[0027] Monitoring point layout module: set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point;

[0028] Infiltration assessment module: For each depth monitoring area, micro-CT is used to regularly scan the soil column under in situ conditions to determine the permeability coefficient of the soil column at different humidity levels and evaluate the impact of humidity changes on water flow and heavy metal migration paths;

[0029] Chemical form analysis module: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0030] Deviation assessment module: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the deviation between the prediction model prediction results and the actual migration behavior is evaluated;

[0031] Early warning response module: If the degree of deviation is high, further analysis will be conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and early warning signals of different levels will be generated based on the analysis results, and corresponding processing will be carried out.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] 1. The present invention solves the problem of prediction deviation or distortion caused by abnormal soil characteristics in the prior art by constructing a dynamic analysis and early warning system based on the migration path of heavy metal ecological pollution in soil. This method comprehensively captures the dynamic impact of humidity changes on migration paths and morphological transformation by setting monitoring points, using micro-CT scanning to analyze permeability changes, and using synchrotron radiation X-ray spectrometers to analyze the chemical forms of heavy metals. It also combines the pore connectivity migration interference index and the heavy metal proportion anomaly index to accurately evaluate the degree of deviation between the model prediction and the actual migration behavior, thereby greatly improving the ability to identify potential high-risk areas and the adaptability of the prediction model.

[0034] 2. The present invention can generate multi-level early warning signals according to the degree of deviation, and take measures such as emergency response, key monitoring or model optimization to ensure the accuracy and classification of response measures. At the same time, regular monitoring is continued in areas with low deviation to reduce the burden on the system. The overall method is highly flexible and practical, which not only improves the prediction accuracy of soil heavy metal migration, but also provides scientific support for drinking water source protection, farmland ecological security and regional environmental governance, effectively reducing ecological and environmental risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The figure is a flow chart of the method of the present invention.

[0037] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1, please refer to Figure 1 and Figure 2 As shown, the ecological pollution migration path analysis and early warning method based on soil heavy metals described in this embodiment includes the following steps:

[0040] S1: Set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point;

[0041] S2: For each depth monitoring area, the soil column was scanned regularly under in situ conditions using micro-CT to determine the permeability of the soil column at different humidity levels and to evaluate the impact of humidity changes on water flow and heavy metal migration pathways;

[0042] S3: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0043] S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the degree of deviation between the prediction model prediction results and the actual migration behavior;

[0044] S5: If the degree of deviation is high, further analysis is conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed.

[0045] In S1, several monitoring points are set around the pollution source in different directions and distances, and multiple depth monitoring areas are set along the soil profile at each monitoring point, specifically:

[0046] With the pollution source as the center, radial monitoring points are set up in different directions (such as east, south, west, north, or according to terrain features). According to the estimation of diffusion range and migration rate, multiple groups of different radius distances are selected for monitoring point layout (such as 50 meters, 100 meters, 500 meters). Focus on strengthening the density of monitoring points in high-risk areas such as downwind of pollution sources, groundwater flow direction, and low-lying areas. If the direction of pollution diffusion is unknown, the points can be initially distributed in a uniform manner, and the distribution can be optimized based on the results. In combination with the type of pollution source and the surrounding environmental zoning (such as industrial areas, agricultural areas, and residential areas), different densities of monitoring points are deployed in different areas.

[0047] Profile depth options include: Shallow layer (0-20cm): monitor the concentration of heavy metals in the surface soil, pay attention to surface runoff and biological contact risks. Middle layer (20-50cm): monitor the penetration dynamics and adsorption behavior of heavy metals. Deep layer (>50cm): monitor the diffusion of heavy metals in the groundwater recharge layer.

[0048] At each monitoring point, multiple monitoring units are set up along the profile (such as a monitoring unit every 10 cm) to obtain complete soil profile information. Humidity and temperature sensors: Soil units buried at different depths to record soil environmental changes in real time. Heavy metal monitoring sensors: such as ion-selective electrodes or micro-CT sensors, used to dynamically monitor heavy metal migration behavior. Micro-pore water samplers are installed in deep monitoring areas to regularly extract soil pore water samples and analyze the dynamic changes of dissolved heavy metals. Sampling channels are set up in each monitoring unit to facilitate the acquisition of soil samples or the use of instruments for on-site analysis (such as portable synchrotron radiation X-ray instruments).

[0049] According to the preliminary monitoring results, the distribution of heavy metal concentrations and migration directions are analyzed, and the number of monitoring points in high-concentration areas is appropriately increased, while the number of points in low-risk areas is reduced. In long-term dynamic monitoring, the monitoring points and profile settings are optimized in combination with environmental changes (such as seasonal precipitation) to enhance the representativeness and timeliness of the data.

[0050] S2: For each depth monitoring area, micro-CT was used to regularly scan the soil column under in situ conditions, the permeability coefficient of the soil column at different humidity levels was determined, and the impact of humidity changes on water flow and heavy metal migration pathways was evaluated.

[0051] Select typical in-situ soil columns at each depth unit in the monitoring area. The diameter and height of the soil column are based on the technical requirements of the monitoring equipment (usually 5-10 cm in diameter and no more than 20 cm in height). The complete soil column is obtained through non-disturbance sampling technology to keep its pore structure and physical properties intact. The soil column is fixed in the scanning cabin of the micro CT device to ensure that there are no cracks and external damage on the surface of the soil column, and the direction of the soil column is marked (such as up and down, east, west, and north).

[0052] Set high resolution (e.g. 10 μm or higher) according to the target pore size to clearly capture changes in pore structure. Scan the entire soil column, covering from the surface to the deep layer. Determine the scanning cycle based on the rate of moisture change (e.g. scan once every 4 hours in the initial stage, and then extend it to once a day).

[0053] Perform baseline scans on the dry soil column to obtain the initial pore structure image. Gradually increase the ambient humidity (e.g., from 10% to 90%), repeat the scans under different humidity conditions, and record the dynamic changes of soil pores.

[0054] The three-dimensional pore structure of the soil column was reconstructed through micro-CT scanning data. The porosity (pore volume / total volume) was calculated, the pore connectivity (the interconnection between pores) was analyzed, and the changes in crack width and depth were evaluated.

[0055] The permeability coefficient (k) is calculated using Darcy's law combined with pore structure data. The expression is: Q is the amount of water flowing per unit time, L is the height of the soil column, A is the cross-sectional area, and ΔP is the water head difference. Compare the permeability coefficients under different humidity conditions and analyze the dynamic impact of humidity changes on soil permeability

[0056] After analyzing the influence of pore connectivity on the migration of heavy metals from the surface to the deep layer, the pore connectivity migration interference index is generated. The method for obtaining the pore connectivity migration interference index is as follows:

[0057] The soil pore network is modeled as a weighted graph G(V,E), where V is a node set representing pores, E is an edge set representing the connecting channels between pores, and each edge e ij The weight w ij represents the migration resistance of heavy metals through channel i→j;

[0058] The weights can be determined by pore geometry and soil properties: Channel length between pores iii and jjj, k ij is the permeability coefficient of the channel (related to pore size, connectivity, etc.), A ij is the cross-sectional area of ​​the channel.

[0059] Micro-CT scanning is used to obtain the three-dimensional pore structure of the soil, and the geometric parameters of the pore network nodes and edges are extracted to construct a weighted graph G(V,E). The starting node s is the initial location of heavy metal migration (surface pores), and the end node t is the target location of migration (deep pores). The shortest path algorithm (such as Dijkstra algorithm) is used to calculate the shortest path from the starting point s to the end point t. The shortest path weight W min :W min =min∑ i,j)∈P w ij ; P is the set of all possible paths, initialize the distance from the starting point s to each node d(s,v) = ∞, and the starting point d(s,s) = 0; traverse the nodes, select the node u with the shortest distance among the unvisited nodes, and update the distance of u's neighbor node v: d(s,v) = min(d(s,v),d(s,u)+w uv );until d(s,t)=W is calculated min , the pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the migration path of heavy metals: Where W ideal It is the shortest path weight under ideal conditions (i.e. the theoretical minimum weight when there is no obstruction in connectivity).

[0060] When the pore connectivity migration interference index is larger, it means that the connectivity of soil pores is poor and the degree of obstruction of migration paths is higher. In this case, the impact of humidity changes on water flow and heavy metal migration paths may be more significant. For example, when humidity increases, due to poor pore connectivity, water flow may flow along cracks or deviate, resulting in more irregular heavy metal migration paths or even sudden migration. This uneven migration may aggravate the spread of pollution in high-risk areas and is difficult to accurately predict through conventional models.

[0061] On the contrary, when the pore connectivity migration interference index is smaller, it means that the connectivity of soil pores is better, the migration path is smoother and more regular. In this case, the effect of humidity change on water flow and heavy metal migration path is relatively gentle, and the migration behavior tends to be linear or diffusion-type. This indicates that water flow and heavy metal migration are more likely to gradually penetrate into the deep layer through the evenly distributed pore network, and the pollution diffusion range and rate are easier to predict and control, reducing the risk of sudden pollution events.

[0062] S3: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms.

[0063] Based on the dynamic monitoring results of humidity changes on permeability, pore connectivity and migration paths, the impact is considered serious when the following conditions occur: the pore connectivity migration interference index (PCMII) increases significantly, indicating that humidity causes significant changes in migration paths. The migration depth or diffusion range of heavy metals increases or shifts abnormally.

[0064] For high-risk areas where migration paths change significantly in monitoring points, representative soil columns or profiles are preferred for morphological analysis. Samples are collected from soil profiles that are significantly affected by humidity changes, covering the surface layer (0-20cm), middle layer (20-50cm), and deep layer (>50cm). When collecting samples, keep the soil moist and intact to avoid the transformation of heavy metal forms due to environmental changes. The collected soil samples are placed in the dedicated sample chamber of the portable synchrotron radiation XAS instrument to ensure uniform distribution to improve the accuracy of the spectral data.

[0065] Light source wavelength range: Select the absorption edge of heavy metal elements (such as L edge of Pb, K edge of As). Set to high-resolution mode (such as 0.1-0.2eV) to accurately separate the characteristic peaks of different chemical states. Perform multi-point scans on the soil sample to obtain high-resolution spectra of heavy metal chemical forms at different locations. Record the X-ray absorption spectrum of the sample, focusing on the edge absorption region (XANES) and fine structure (EXAFS).

[0066] Using XANES data, the main chemical forms of different heavy metals are identified: Soluble state: free ions or complexes (easy to migrate). Oxidized state: oxides or hydroxides bound to the surface of minerals (low mobility). Organically bound state: bound to organic matter or humus (mobility is related to environmental conditions). Residual state: strongly bound to the mineral lattice (extremely low mobility). Based on the absorption spectrum database of standard samples, a spectral fitting algorithm (such as linear combination fitting method, LCF) is used to calculate the proportion of different chemical forms. The analytical results are associated with the depth of the soil profile to generate a distribution map of heavy metal chemical forms at different depths and locations.

[0067] The heavy metal proportion anomaly index is generated by analyzing the proportion of heavy metal chemical forms before and after humidity changes. The method for obtaining the heavy metal proportion anomaly index is as follows:

[0068] Obtain time series data during humidity changes, including the proportion of each heavy metal chemical form Where: t is the time point (or the number of humidity conditions, such as different humidity gradients), k is the chemical form category (such as soluble, oxidized, organically bound, residual), The proportion of chemical form k at time t, select the sliding window size w (such as w = 3 means that every three time points are a window). The window slides on the time series, each time it moves a unit time Δt, and the proportion of each chemical form k Calculate the rate of change within the sliding window [t, t+w-1] The relative change rate (percentage) of chemical form k in the window [t, t+w-1], is the proportion of chemical form k at the end of window t+w-1. Define the abnormal threshold τ: Calculate the cumulative abnormal weight W of chemical form k k : is the indicator function, when The value is 1 when the chemical form is zero, otherwise it is 0. The accumulated abnormal weights of all chemical forms are normalized to calculate the heavy metal proportion abnormal index AS, which is expressed as: Where K is the total number of chemical form categories, max(W k ) is the maximum cumulative anomaly weight among all chemical forms k, which is used for normalization.

[0069] When the abnormal index of heavy metal proportion is larger, it means that the proportion of different heavy metal chemical forms has undergone significant abnormal changes. This trend shows that humidity changes have a stronger impact on certain forms, such as a significant increase in the proportion of soluble state, a rapid decrease in oxidation state, or a sudden release of organic binding state, which may make heavy metals more easily migrate or released into the environment. Such drastic changes usually indicate the presence of potentially unstable chemical conditions in the soil, such as changes in redox potential or pH fluctuations caused by increased humidity, which may greatly increase the ecological risk of heavy metals.

[0070] On the contrary, when the abnormal index of heavy metal proportion is smaller, it means that the proportion of different heavy metal chemical forms tends to be stable. This trend shows that the influence of humidity change on chemical forms is relatively limited, the conversion rate between forms is low, and the form distribution of heavy metals in the soil is maintained in a relatively balanced state. At this time, the migration risk is low, the environmental release capacity of heavy metals is weak, and the soil has a good self-stabilization ability.

[0071] S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the degree of deviation between the prediction model prediction results and the actual migration behavior.

[0072] The pore connectivity migration interference index and the heavy metal proportion anomaly index were normalized, and the deviation degree between the prediction model prediction results and the actual migration behavior was calculated by using the normalized pore connectivity migration interference index and the heavy metal proportion anomaly index.

[0073] For example, the present invention can use the following formula to calculate the deviation degree between the prediction results of the prediction model and the actual migration behavior, and the calculation expression is: LK=ln(a1QS+a2AS+1); wherein LK is the deviation degree between the prediction results of the prediction model and the actual migration behavior, QS is the pore connectivity migration interference index, AS is the heavy metal proportion anomaly index, a1 and a2 are the proportional coefficients of the pore connectivity migration interference index and the heavy metal proportion anomaly index, and a2>a1>0.

[0074] The obtained deviation value between the prediction result of the prediction model and the actual migration behavior is compared with the reference threshold of the deviation value under normal conditions preset according to historical data. If the deviation value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; if the deviation value between the prediction result of the prediction model and the actual migration behavior is less than the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is low, and a normal signal is generated at this time.

[0075] When a normal signal is generated, it indicates that the deviation between the prediction model and the actual migration behavior is low, and the system is in a low-risk state. At this time, the current monitoring data can be recorded as a model verification sample, and the model parameters can be updated to further improve the prediction accuracy. At the same time, maintain the regular monitoring frequency, continue to track humidity changes and heavy metal migration, and prevent potential mutations. In addition, monitoring resources can be allocated to high-risk areas first, and the system operation efficiency can be improved by dynamically adjusting the warning threshold and optimizing the monitoring strategy, and provide data support for long-term soil governance and early warning policy improvements.

[0076] S5: If the degree of deviation is high, further analysis is conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed.

[0077] If the degree of deviation is high, that is, the deviation value between the prediction results of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation value reference threshold, the deviation values ​​between the prediction results of the prediction model generated within a subsequent fixed time period that are greater than or equal to the deviation value reference threshold and the actual migration behavior are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, different levels of warning signals are generated according to the analysis results, and corresponding processing is made.

[0078] If the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, a first-level warning signal is generated, indicating that the high deviation is persistent and stable, and emergency response measures need to be taken quickly;

[0079] If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a secondary warning signal is generated, indicating that the degree of high deviation fluctuates greatly and requires key monitoring and model adjustment;

[0080] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a third-level warning signal is generated, indicating that the overall deviation is low but the fluctuation is large, and appropriate attention can be paid to and the model can be optimized;

[0081] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, no warning signal is generated, indicating that the degree of deviation is low and stable, and routine monitoring continues.

[0082] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding handling measures according to different warning signal levels.

[0083] In this embodiment, first, monitoring points are set up around the pollution source in different directions and distances, and multiple deep monitoring areas are arranged along the soil profile; then, the soil column in each deep monitoring area is scanned in situ by micro CT, the permeability coefficient under humidity change is measured, and its impact on water flow and heavy metal migration path is evaluated; when the humidity change has a serious impact, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, the form ratio is resolved and the abnormal change trend is analyzed; based on the above data, the degree of deviation between the prediction model and the actual migration behavior is evaluated; finally, the high deviation situation is further analyzed, warning signals of different levels are generated and corresponding treatment measures are taken to ensure the accuracy of the prediction and the ability to prevent and control environmental risks.

[0084] Example 2: The ecological pollution migration path analysis and early warning system based on soil heavy metals described in this example includes a monitoring point layout module, a penetration assessment module, a chemical form analysis module, a deviation assessment module and an early warning response module:

[0085] Monitoring point layout module: set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point;

[0086] Infiltration assessment module: For each depth monitoring area, micro-CT is used to regularly scan the soil column under in situ conditions to determine the permeability coefficient of the soil column at different humidity levels and evaluate the impact of humidity changes on water flow and heavy metal migration paths;

[0087] Chemical form analysis module: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0088] Deviation assessment module: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the deviation between the prediction model prediction results and the actual migration behavior is evaluated;

[0089] Early warning response module: If the degree of deviation is high, further analysis will be conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and early warning signals of different levels will be generated based on the analysis results, and corresponding processing will be carried out.

[0090] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0091] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0092] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An early warning method for ecological pollution migration path analysis based on soil heavy metals, characterized by: The following steps are involved: S1: Set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point; S2: For each depth monitoring area, the soil column was scanned regularly under in situ conditions using micro-CT to determine the permeability of the soil column at different humidity levels and to evaluate the impact of humidity changes on water flow and heavy metal migration pathways; S3: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms; S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the degree of deviation between the prediction model prediction results and the actual migration behavior; S5: If the degree of deviation is high, further analysis is conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed.

2. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 1 is characterized in that: In S2, after analyzing the influence of pore connectivity on the migration of heavy metals from the surface to the deep layer, the pore connectivity migration interference index is generated. The method for obtaining the pore connectivity migration interference index is as follows: The soil pore network is modeled as a weighted graph G(V,E), where: V is a node set representing pores, E is an edge set representing the connecting channels between pores, and the weight wij of each edge eij represents the migration resistance of heavy metals through channel i→j; micro-CT scanning is used to obtain the three-dimensional pore structure of the soil, and the geometric parameters of the pore network nodes and edges are extracted to construct a weighted graph G(V,E), where the starting node s is the initial position of heavy metal migration, and the end node t is the target position of migration; the shortest path algorithm is used to calculate the shortest path from the starting point s to the end point t, and the shortest path weight Wmin is: Wmin=min∑(i,j ) ∈Pwij; P is the set of all paths, initialize the distance from the starting point s to each node d(s,v) = ∞, and the starting point d(s,s) = 0; traverse the nodes, select the node u with the shortest distance among the unvisited nodes, and update the distance of u’s neighbor node v: d(s,v) = min(d(s,v),d(s,u)+w uv );until d(s,t)=W is calculated min , the pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the migration path of heavy metals: Where W ideal is the shortest path weight under ideal conditions.

3. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 2 is characterized by: In S3, the heavy metal proportion anomaly index is generated by analyzing the proportion of heavy metal chemical forms before and after humidity changes. The method for obtaining the heavy metal proportion anomaly index is as follows: Obtain time series data during humidity changes, including the proportion of each heavy metal chemical form, where t is the time point, k is the chemical form category, is the proportion of chemical form k at time t, select the sliding window size w, the window slides on the time series, each time moving a unit time Δt, the proportion of each chemical form k Calculate the rate of change within the sliding window [t, t+w-1] The relative change rate of chemical form k in the window [t, t+w-1], is the proportion of chemical form k at the end point of the window t+w-1; define the abnormal threshold τ: when When , it is marked as abnormal, and the cumulative abnormal weight W of chemical form k is calculated. k : is the indicator function, when The value is 1 when the chemical form is positive, otherwise it is 0; the accumulated abnormal weights of all chemical forms are normalized to calculate the heavy metal proportion abnormal index AS, which is expressed as: Where K is the total number of chemical form categories, max ( W k) is the maximum cumulative anomaly weight among all chemical forms k, used for normalization.

4. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 3 is characterized by: In S4, based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the degree of deviation between the prediction model prediction results and the actual migration behavior is evaluated, specifically: The pore connectivity migration interference index and the heavy metal proportion anomaly index were normalized, and the deviation degree between the prediction model prediction results and the actual migration behavior was calculated by the normalized pore connectivity migration interference index and the heavy metal proportion anomaly index.

5. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 4 is characterized by: The obtained deviation value between the prediction result of the prediction model and the actual migration behavior is compared with the deviation value reference threshold value under normal conditions preset according to historical data. If the deviation value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the deviation value reference threshold value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; If the deviation between the prediction model prediction result and the actual migration behavior is less than the deviation value reference threshold, it means that the deviation between the prediction model prediction result and the actual migration behavior is low, and a normal signal is generated at this time.

6. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 1 is characterized by: In S5, if the degree of deviation is high, further analysis is performed on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed, specifically: If the degree of deviation is high, that is, the deviation value between the prediction results of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation value reference threshold, the deviation values ​​between the prediction results of the prediction model generated within a subsequent fixed time period that are greater than or equal to the deviation value reference threshold and the actual migration behavior are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, different levels of warning signals are generated according to the analysis results, and corresponding processing is made.

7. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 6 is characterized by: If the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, a first-level warning signal is generated, indicating that the high deviation is persistent and stable, and emergency response measures need to be taken quickly; If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a secondary warning signal is generated, indicating that the degree of high deviation fluctuates greatly and requires key monitoring and model adjustment; If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a third-level warning signal is generated, indicating that the overall deviation is low but the fluctuation is large, and the model is optimized; If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, no warning signal is generated, indicating that the degree of deviation is low and stable, and routine monitoring continues.

8. An early warning system for analyzing the migration path of ecological pollution based on heavy metals in soil, used to implement the early warning method for analyzing the migration path of ecological pollution based on heavy metals in soil according to any one of claims 1 to 7, characterized in that: It includes monitoring point layout module, penetration assessment module, chemical form analysis module, deviation assessment module and early warning response module: Monitoring point layout module: set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point; Infiltration assessment module: For each depth monitoring area, micro-CT is used to regularly scan the soil column under in situ conditions to determine the permeability coefficient of the soil column at different humidity levels and evaluate the impact of humidity changes on water flow and heavy metal migration paths; Chemical form analysis module: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms; Deviation assessment module: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the deviation between the prediction model prediction results and the actual migration behavior is evaluated; Early warning response module: If the degree of deviation is high, further analysis will be conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and early warning signals of different levels will be generated based on the analysis results, and corresponding processing will be carried out.

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