Soil groundwater pollution fine investigation method

Abstract

The invention relates to the technical field of water source pollution investigation, and particularly discloses a soil groundwater pollution fine investigation method which comprises the steps of S1, pollution source investigation, S2, sampling design, S3, laboratory analysis, S4, ecological risk assessment, S5, restoration suggestion and S6, dynamic monitoring. According to the invention, through machine learning and big data analysis, historical pollution data, field sampling data and spatial geographic information can be effectively integrated to form an accurate pollution source distribution map, which not only facilitates identification of pollution hot spot areas, but also can predict the pollutant diffusion trend through a data-driven model to provide dynamic prediction of future pollution evolution; according to the method, high-precision positioning of a complex pollution area is realized, blind sampling and invalid investigation are reduced, the resource utilization efficiency is improved, the area where pollutants are possibly diffused in the future can be predicted, prevention and control measures are taken in advance, and further environmental damage is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of water source pollution investigation, and in particular relates to a fine investigation method for soil and groundwater pollution. Background Art

[0002] In the field of detailed investigation of soil and groundwater pollution, although traditional methods such as field sampling, laboratory analysis, and pollution assessment based on empirical models have played a certain role in identifying pollution sources and assessing the extent of pollution, they have some limitations.

[0003] Traditional methods are highly dependent on manual sampling, which is not only time-consuming and laborious, but also often difficult to fully reflect the spatial distribution characteristics of pollutants due to the limited and uneven distribution of sampling points, resulting in inaccurate identification of pollution hotspots and the risk of missing high-risk areas; traditional analysis methods are unable to cope with massive, multi-dimensional pollution data, and it is difficult to effectively integrate historical pollution data, on-site real-time monitoring data, and spatial geographic information, thus limiting the accuracy and timeliness of pollution source distribution maps. This information island phenomenon makes pollution prediction and trend analysis lack scientific basis, making it difficult to accurately predict the future diffusion trend of pollutants, which in turn affects the timeliness and effectiveness of prevention and control measures.

[0004] In response to this, the inventors proposed a detailed investigation method for soil and groundwater pollution to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for fine investigation of soil and groundwater pollution to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for fine investigation of soil and groundwater pollution comprises the following steps:

[0008] S1. Investigate pollution sources, investigate historical data, preliminarily delineate pollution source areas, use geographic information system (GIS) to integrate existing pollution data, create pollution source distribution maps, use machine learning and big data analysis technology to process historical data, environmental factors and pollutant distribution data, build pollution source prediction models, and identify high-risk areas based on prediction results as target areas for subsequent key investigations;

[0009] S2. Sampling design: Based on the results of the pollution source prediction model, design a sampling point layout plan, focusing on high-risk areas; during the sampling process, collect surface and deep soil samples to ensure that pollution information of different soil layers is covered; combined with hydrogeological conditions, collect groundwater samples in areas with shallow groundwater levels or near pollution sources; in the sampling design, combine high-throughput sequencing technology to sample microbial communities in soil and water to capture the impact of pollution on microbial ecosystems;

[0010] S3. Laboratory analysis: using chemical analysis methods to detect the concentration of pollutants in soil and groundwater; using high-throughput sequencing technology to analyze the composition and diversity of microbial communities in soil and groundwater; combining experimental analysis data with microbial gene data to analyze the diffusion patterns of pollutants, the correlation between microorganisms and pollutants, and predicting the diffusion trend of pollutants;

[0011] S4. Ecological risk assessment: Use big data analysis methods, combined with experimental data, pollutant concentrations, and microbial community changes to build a pollutant diffusion model to predict the diffusion path and rate of pollutants in soil and groundwater; apply a comprehensive ecological risk assessment model based on pollutant concentrations and ecological factors in different regions, and determine the ecological risk level of different regions by evaluating the impact of pollutants on the surrounding ecosystems; combine the results of microbial genome analysis to evaluate the degree of damage of pollutants to microbial communities and further quantify ecological risks;

[0012] S5. Remediation recommendations: Integrate pollutant concentrations, microbial community changes, and ecological risk assessment results to form a comprehensive pollution status report; formulate a phased remediation priority based on the predicted pollution spread trend, giving priority to high-risk areas; Based on the results of the ecological risk assessment, in areas where pollution has a greater impact on the microbial ecosystem, use bioremediation technology combined with bioaugmentation to restore the balance of the microbial community;

[0013] S6. Dynamic monitoring: During the restoration process, soil and groundwater samples will be collected regularly, and high-throughput sequencing technology will continue to be used to monitor changes in microbial communities and evaluate the progress of restoration. The monitoring data will be input into the previously constructed machine learning model, and the pollution spread prediction model will be continuously optimized through big data analysis technology to improve prediction accuracy. According to the latest ecological risk assessment results, the restoration plan will be adjusted to ensure the gradual recovery of the ecosystem.

[0014] Preferably, the historical data includes land use, industrial activities, and waste disposal records.

[0015] Preferably, the chemical analysis method includes liquid chromatography and gas chromatography, and the pollutants in the soil and groundwater include volatile organic compounds (VOCs) and heavy metals.

[0016] Preferably, in step S1, the machine learning and big data analysis technology uses a linear regression model to predict changes in pollutant concentrations. The expression of the linear regression model is:

[0017]

[0018] in predicted pollutant concentrations;

[0019] β0: intercept term of the model;

[0020] β1,β2,…,βn: regression coefficients, representing the effect of input variables x1,x2,…,xn on the output The weight of influence;

[0021] x1,x2,…,xn: input variables, representing different influencing factors, including groundwater flow rate, soil properties, and pollutant characteristics.

[0022] Preferably, in step S3, the diffusion trend of pollutants is predicted by using Kriging interpolation, and the expression is:

[0023]

[0024] Estimate the pollutant concentration at location x0;

[0025] Z(xi): the pollutant concentration at the known location xi;

[0026] λi: weight coefficient, solved by the covariance matrix, indicating the influence of position xi on the estimated value;

[0027] n: number of known sample points.

[0028] Preferably, the big data analysis method in step S4 uses cluster analysis K-means to identify different pollution patterns or distribution characteristics in the pollution area, and the expression is:

[0029]

[0030] Where J: objective function, total clustering distance;

[0031] k: the number of clusters, that is, the number of clusters into which the data is divided;

[0032] xj: data point belonging to the i-th cluster Ci;

[0033] μi: the centroid of the ith cluster, i.e. the center point;

[0034] ||xj-μi|| 2 : The Euclidean distance between a data point xj and the centroid μi of the cluster to which it belongs.

[0035] Preferably, the diffusion of pollutants in groundwater in the pollutant diffusion model in step S4 is represented by the convection-diffusion equation, specifically:

[0036]

[0037] Where C: pollutant concentration;

[0038] t: time;

[0039] D: dispersion coefficient, which describes how quickly a pollutant spreads in groundwater;

[0040] v: groundwater velocity;

[0041] x: spatial coordinate;

[0042] λ: The decay coefficient of the pollutant, which represents the decay rate of the pollutant through biological degradation or chemical reaction.

[0043] Preferably, the ecological risk assessment model in step S4 uses a pollution index method to calculate the risk, which is expressed as an ecological risk index based on the ratio of pollutant concentration to the baseline value:

[0044]

[0045] Among them, the potential ecological risk index Ei of a single pollutant is expressed as:

[0046]

[0047] Where ERI: Comprehensive Ecological Risk Index, which indicates the ecological risk of the entire survey area;

[0048] Ei: potential ecological risk index of a single pollutant;

[0049] Ti: toxicity coefficient of pollutants, reflecting the degree of harm of pollutants to the ecosystem;

[0050] Ci: actual concentration of pollutants;

[0051] Cr: baseline value of the pollutant;

[0052] n: number of pollutant types.

[0053] Preferably, the ecological risk assessment in step S6 assesses the impact of pollution on ecosystem services and calculates the loss of ecosystem services, expressed as:

[0054]

[0055] Where ESL: total ecosystem service loss;

[0056] ESi,b: functional level of the i-th ecosystem service before pollution;

[0057] ESi,a: functional level of the i-th ecosystem service after pollution;

[0058] Ai: The area covered by the i-th service.

[0059] Preferably, the big data analysis technology in step S6 uses a Monte Carlo simulation method to predict different scenarios of pollutant diffusion, ecological risks and restoration effects, expressed as:

[0060]

[0061] Where X: expected value of simulation results;

[0062] f(xi): function of input variable xi, representing a randomly generated pollutant diffusion or ecological risk assessment model;

[0063] n: Number of simulations. The more simulations you have, the more reliable the results will be.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] (1) Through machine learning and big data analysis, the present invention can effectively integrate historical pollution data, on-site sampling data and spatial geographic information to form an accurate pollution source distribution map, which not only helps to identify pollution hotspots, but also can predict the diffusion trend of pollutants through data-driven models and provide dynamic predictions of future pollution evolution; it achieves high-precision positioning of complex pollution areas, reduces blind sampling and invalid investigations, improves resource utilization efficiency, and can predict areas where pollutants may spread in the future, so as to take prevention and control measures in advance and avoid further environmental damage.

[0066] (2) The present invention uses high-throughput sequencing technology to analyze microbial communities, which can accurately capture the microscopic effects of pollutants on ecosystems, especially the changes in microbial communities under the long-term influence of heavy metals and organic pollutants. High-throughput sequencing can also help identify the natural degradation potential of microorganisms for pollutants and guide bioremediation plans; it improves the comprehensiveness of ecological risk assessment, not only focusing on the physical and chemical properties of pollutants, but also considering their potential effects on ecosystems, such as microorganisms; it provides a theoretical basis for bioremediation technology, and by screening specific degradation microorganisms, it can further optimize the remediation process and improve remediation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a method for fine investigation of soil and groundwater pollution according to the present invention;

[0068] Figure 2A block diagram of the historical data composition of the present invention;

[0069] Figure 3 It is a block diagram of the chemical analysis method of the present invention;

[0070] Figure 4 It is a block diagram of the pollutant composition of the present invention;

[0071] Figure 5 It is a block diagram of the pollutant diffusion model of the present invention;

[0072] Figure 6 It is a frame diagram of the pollution status report of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0074] Embodiment 1:

[0075] See also Figures 1 to 6 As shown, a method for fine investigation of soil and groundwater pollution comprises the following steps:

[0076] S1. Investigate pollution sources, investigate historical data, including land use, industrial activities, and waste disposal records, preliminarily delineate pollution source areas, use geographic information system (GIS) to integrate existing pollution data, create pollution source distribution maps, use machine learning and big data analysis technology to process historical data, environmental factors, and pollutant distribution data, build a pollution source prediction model, and based on the prediction results, identify high-risk areas as target areas for subsequent key investigations;

[0077] S2. Sampling design: Based on the results of the pollution source prediction model, design a sampling point layout plan, focusing on high-risk areas; during the sampling process, collect surface and deep soil samples to ensure that pollution information of different soil layers is covered; combined with hydrogeological conditions, collect groundwater samples in areas with shallow groundwater levels or near pollution sources; in the sampling design, combine high-throughput sequencing technology to sample microbial communities in soil and water to capture the impact of pollution on microbial ecosystems;

[0078] S3. Laboratory analysis, using chemical analysis methods, liquid chromatography, gas chromatography, to detect the concentration of pollutants in soil and groundwater, including volatile organic compounds (VOCs) and heavy metals; using high-throughput sequencing technology to analyze the composition and diversity of microbial communities in soil and groundwater; through changes in microbial communities, identify the long-term impact of pollution on the environment, and infer the evolution history and potential sources of pollutants; combine experimental analysis data with microbial gene data to analyze the diffusion law of pollutants, the correlation between microorganisms and pollutants, and predict the diffusion trend of pollutants;

[0079] S4. Ecological risk assessment: Use big data analysis methods, combined with experimental data, pollutant concentrations, and microbial community changes to build a pollutant diffusion model to predict the diffusion path and rate of pollutants in soil and groundwater; apply a comprehensive ecological risk assessment model based on pollutant concentrations and ecological factors in different regions to determine the ecological risk level of different regions by assessing the impact of pollutants on surrounding ecosystems, including plants, soil microorganisms, and aquatic organisms; combine the results of microbial genome analysis to assess the degree of damage of pollutants to microbial communities and further quantify ecological risks;

[0080] S5. Remediation recommendations: Integrate pollutant concentrations, microbial community changes, and ecological risk assessment results to form a comprehensive pollution status report; formulate a phased remediation priority based on the predicted pollution spread trend, giving priority to high-risk areas; Based on the results of the ecological risk assessment, in areas where pollution has a greater impact on the microbial ecosystem, use bioremediation technology combined with bioaugmentation to restore the balance of the microbial community;

[0081] S6. Dynamic monitoring: During the restoration process, soil and groundwater samples will be collected regularly, and high-throughput sequencing technology will continue to be used to monitor changes in microbial communities and evaluate the progress of restoration. The monitoring data will be input into the previously constructed machine learning model, and the pollution spread prediction model will be continuously optimized through big data analysis technology to improve prediction accuracy. According to the latest ecological risk assessment results, the restoration plan will be adjusted to ensure the gradual recovery of the ecosystem.

[0082] From the above, we can see that through machine learning and big data analysis, we can effectively integrate historical pollution data, on-site sampling data and spatial geographic information to form an accurate pollution source distribution map, which not only helps to identify pollution hotspots, but also predict pollutant diffusion trends through data-driven models, and provide dynamic predictions of future pollution evolution.

[0083] This method achieves high-precision positioning of complex polluted areas, reduces blind sampling and ineffective investigations, improves resource utilization efficiency, and can predict areas where pollutants may spread in the future, so that prevention and control measures can be taken in advance to avoid further environmental damage.

[0084] Embodiment 2:

[0085] refer to Figures 1 to 6 As shown in the figure, machine learning and big data analysis technology use linear regression model to predict changes in pollutant concentrations. The expression of the linear regression model is:

[0086]

[0087] in predicted pollutant concentrations;

[0088] β0: intercept term of the model;

[0089] β1,β2,…,βn: regression coefficients, representing the effect of input variables x1,x2,…,xn on the output The weight of influence;

[0090] x1, x2, …, xn: input variables, representing different influencing factors, including groundwater flow rate, soil properties, and pollutant characteristics;

[0091] The linear regression model is used to analyze the impact of various environmental factors on pollutant concentrations, thereby predicting future changes in pollution concentrations. The regression coefficient is obtained by fitting the data. The model helps explain which factors have a greater impact on the distribution and diffusion of pollutants.

[0092] Specifically, the diffusion trend of pollutants is predicted using Kriging interpolation, and the expression is:

[0093]

[0094] Estimate the pollutant concentration at location x0;

[0095] Z(xi): the pollutant concentration at the known location xi;

[0096] λi: weight coefficient, solved by the covariance matrix, indicating the influence of position xi on the estimated value;

[0097] n: number of known sample points;

[0098] Kriging interpolation is used to predict the spatial distribution of pollutants in the entire survey area based on limited sampling points. Taking spatial autocorrelation into account can improve the accuracy of pollutant concentration prediction and is particularly suitable for scenarios with sparse spatial data.

[0099] Specifically, the big data analysis method uses cluster analysis K-means to identify different pollution patterns or distribution characteristics in the pollution area, expressed as:

[0100]

[0101] Where J: objective function, total clustering distance;

[0102] k: the number of clusters, that is, the number of clusters into which the data is divided;

[0103] xj: data point belonging to the i-th cluster Ci;

[0104] μi: the centroid of the ith cluster, i.e. the center point;

[0105] ||xj-μi|| 2 : The Euclidean distance between the data point xj and the centroid μi of its cluster;

[0106] K-means is used to analyze the distribution patterns of different pollution sources, cluster the data based on the concentration, spatial location and other environmental factors of pollutants, identify polluted areas with similar characteristics, and allocate investigation and remediation resources more effectively.

[0107] Specifically, the diffusion of pollutants in groundwater in the pollutant diffusion model is expressed by the convection-diffusion equation, which is:

[0108]

[0109] Where C: pollutant concentration;

[0110] t: time;

[0111] D: dispersion coefficient, which describes how quickly a pollutant spreads in groundwater;

[0112] v: groundwater velocity;

[0113] x: spatial coordinate;

[0114] λ: attenuation coefficient of pollutants, which indicates the attenuation rate of pollutants through biological degradation or chemical reactions;

[0115] The above equation describes the changes of pollutants in groundwater over time and their spread in space. It combines the diffusion of pollutants: controlled by the dispersion coefficient D, convection: controlled by the groundwater flow velocity v, and the attenuation of pollutants: represented by the degradation coefficient λ. During the investigation, the pollutant concentration data obtained through sampling is substituted into the equation to estimate the future trend of pollutant diffusion and provide a basis for pollution control and remediation.

[0116] Specifically, the ecological risk assessment model uses the pollution index method to calculate the risk, which is expressed as an ecological risk index based on the ratio of pollutant concentration to the baseline value:

[0117]

[0118] Among them, the potential ecological risk index Ei of a single pollutant is expressed as:

[0119]

[0120] Where ERI: Comprehensive Ecological Risk Index, which indicates the ecological risk of the entire survey area;

[0121] Ei: potential ecological risk index of a single pollutant;

[0122] Ti: toxicity coefficient of pollutants, reflecting the degree of harm of pollutants to the ecosystem;

[0123] Ci: actual concentration of pollutants;

[0124] Cr: baseline value of the pollutant;

[0125] n: number of pollutant types;

[0126] The model quantifies the comprehensive ecological risks of different pollutants. By calculating the ecological risk index of each pollutant and combining its toxicity coefficient, it evaluates the potential harm of pollutants to the ecosystem. The comprehensive risk assessment results help decision makers prioritize high-risk areas.

[0127] Specifically, ecological risk assessment evaluates the impact of pollution on ecosystem services and calculates the loss of ecosystem services, expressed as:

[0128]

[0129] Where ESL: total ecosystem service loss;

[0130] ESi,b: functional level of the i-th ecosystem service before pollution;

[0131] ESi,a: functional level of the i-th ecosystem service after pollution;

[0132] Ai: the area covered by the i-th service;

[0133] This formula is used to quantify the damage caused by pollution to ecosystem services, including water purification, soil nutrient cycling, and biological habitats. Combined with ecological risk assessment, it provides a quantitative basis for the economic and environmental value of restoration work.

[0134] Specifically, the big data analysis technology uses the Monte Carlo simulation method to predict different scenarios of pollutant diffusion, ecological risks and remediation effects, which are expressed as:

[0135]

[0136] Where X: expected value of simulation results;

[0137] f(xi): function of input variable xi, representing a randomly generated pollutant diffusion or ecological risk assessment model;

[0138] n: number of simulations. The more simulations, the more reliable the results.

[0139] Monte Carlo simulation is used to deal with the uncertainty of complex systems in pollutant diffusion and risk assessment; through multiple simulation calculations, the probability range of pollutant diffusion and risk distribution can be obtained.

[0140] From the above, we can see that analyzing microbial communities through high-throughput sequencing technology can accurately capture the microscopic impact of pollutants on ecosystems, especially the changes in microbial communities under the long-term influence of heavy metals and organic pollutants. High-throughput sequencing can also help identify the natural degradation potential of microorganisms for pollutants and guide bioremediation programs.

[0141] It improves the comprehensiveness of ecological risk assessment, not only focusing on the physical and chemical properties of pollutants, but also considering their potential impacts on ecosystems, such as microorganisms; it provides a theoretical basis for bioremediation technology, and further optimizes the remediation process and improves remediation efficiency by screening specific degradation microorganisms.

[0142] Embodiment three:

[0143] This design is applied to pollution monitoring in industrial areas and chemical parks. In industrial areas or chemical parks, due to historical accumulation and daily production activities, soil and groundwater may be damaged by various pollutants such as heavy metals and organic solvents. By using machine learning and big data analysis technology, combined with historical pollution data, on-site real-time monitoring data and high-resolution spatial geographic information, the distribution map of pollution sources can be accurately drawn, and the diffusion trend of pollutants can be dynamically predicted.

[0144] It helps management departments to identify pollution hotspots in a timely manner, deploy isolation zones in advance, optimize sampling points and monitoring networks, effectively reduce blind inspections and ineffective sampling, and improve the pertinence and efficiency of pollution control. At the same time, high-throughput sequencing technology is used to analyze the changes in microbial communities in the park, evaluate the microscopic impact of pollution on the ecosystem, and provide a scientific basis for the formulation of bioremediation plans, thereby avoiding the limited and uneven distribution of existing sampling points, which often make it difficult to fully reflect the spatial distribution characteristics of pollutants, resulting in inaccurate identification of pollution hotspots and the omission of high-risk areas.

[0145] Furthermore, the land and groundwater in an industrial area have been polluted by volatile organic compounds (VOCs) and heavy metals (such as lead, cadmium, mercury, etc.) for a long time. The area was once the location of chemical plants and metal processing plants, with a complex distribution of pollution sources. The goal is to assess the current status of pollution, predict the trend of pollution spread, assess ecological risks, and propose remediation suggestions through investigation.

[0146] 1. Investigate the source of pollution

[0147] Data source:

[0148] Historical pollution emission records: A chemical plant produced a large amount of volatile organic compounds between 2010 and 2020.

[0149] Historical soil and groundwater monitoring data:

[0150] VOCs concentration range: 0.5-1.5mg / L;

[0151] Heavy metal concentration is: 200-500mg / kg.

[0152] Preliminary prediction model:

[0153] Based on historical data and GIS analysis, the machine learning model predicted the most likely areas of concentrated pollution sources. The results showed that the concentration of pollutants in the southeast region was higher and the pollution spread was mainly along the direction of groundwater flow (southwest direction).

[0154] 2. Sampling Design

[0155] Sampling point distribution:

[0156] According to the prediction model, 30 sampling points were set up in the southeast region and the downstream area of ​​groundwater flow, including 10 soil sampling points and 20 groundwater sampling points, with a sampling point depth of 1-10 meters.

[0157] High-throughput sequencing sampling:

[0158] Soil and groundwater microbial community samples were collected at each sampling point to analyze their diversity and structural changes.

[0159] 3. Laboratory Analysis

[0160] Pollutant test results:

[0161] The highest VOCs concentration in soil is 2.5 mg / L, and the highest VOCs concentration in groundwater is 1.8 mg / L.

[0162] The soil concentrations of heavy metals (lead and cadmium) are 400 mg / kg and 250 mg / kg respectively, both exceeding the national standards.

[0163] Microbial community analysis:

[0164] High-throughput sequencing showed that the diversity of microbial communities in the polluted areas decreased by 30%, and some bacterial species sensitive to pollutants disappeared, indicating that the soil and water ecosystems were significantly affected.

[0165] Machine Learning and Data Processing:

[0166] Using a linear regression machine learning algorithm, combined with pollutant concentration and microbial community change data, the model predicts that pollutants may continue to spread to an area about 500 meters southwest in the next five years.

[0167] 4. Ecological risk assessment

[0168] Pollutant dispersion prediction:

[0169] The convection-diffusion model is used to simulate the diffusion path of pollutants, combined with the groundwater velocity v = 0.005m / s and the diffusion coefficient D = 0.001m 2 / s, the results show that pollutants will further spread to the downstream area of ​​300 meters in the next two years.

[0170] Ecological risk assessment:

[0171] The comprehensive ecological risk index (ERI) is calculated based on the toxicity coefficient Ti and pollutant concentration of the pollutants. The potential ecological risk index EVOC of VOCs is 3.6, the ecological risk index EPb of heavy metal lead is 5.0, and the overall ecological risk index ERI = 19.8, which is medium-high risk.

[0172] Microbial community ecological risks:

[0173] Combined with the loss rate of microbial community diversity, further evaluation showed that the impact of pollutants on microbial communities was very significant, especially in groundwater microbial systems.

[0174] 5. Repair suggestions

[0175] Pollution Status Report:

[0176] Based on pollutant concentrations, diffusion models, and ecological risk assessments, the report pointed out that the concentrations of groundwater and soil pollutants in the area exceeded the standard, and the ecological risks were high, especially the impact on microbial communities was significant.

[0177] Repair suggestions:

[0178] Due to the serious impact of pollution on microbial systems, bioremediation technology is given priority in remediation plans to accelerate the remediation process by introducing specific microbial species that degrade pollutants.

[0179] It is also recommended to adopt soil tillage and groundwater extraction treatment technologies to reduce the further spread of pollutants.

[0180] 6. Dynamic monitoring

[0181] Monitoring data:

[0182] Dynamic monitoring of soil and groundwater is carried out every 6 months, including detection of pollutant concentrations and analysis of changes in microbial communities.

[0183] Model optimization:

[0184] Based on the newly collected data, the Bayesian statistical model is applied to continuously optimize the pollutant diffusion prediction model. By updating the prior probability with new data, the model gradually improves the prediction accuracy.

[0185] During the restoration process, the model showed that the spread of pollution slowed down and the diversity of microbial communities gradually recovered. It is expected that the ecosystem of the contaminated area can be restored within three years.

[0186] From the above, we can see that by combining a variety of emerging technologies, such as high-throughput sequencing, machine learning, and comprehensive ecological risk assessment models, the pollution status of soil and groundwater was comprehensively assessed, effective remediation strategies were proposed, and the remediation process was continuously optimized through dynamic monitoring. This method not only improves the accuracy of pollution investigations, but also significantly accelerates the pollution remediation process while reducing long-term harm to the environment.

[0187] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for fine investigation of soil and groundwater pollution, characterized in that: The following steps are involved: S1. Investigate pollution sources, investigate historical data, preliminarily delineate pollution source areas, use geographic information system (GIS) to integrate existing pollution data, create pollution source distribution maps, use machine learning and big data analysis technology to process historical data, environmental factors and pollutant distribution data, build pollution source prediction models, and identify high-risk areas based on prediction results as target areas for subsequent key investigations; S2. Sampling design: design sampling point layout plan based on the pollution source prediction model results, focusing on high-risk areas; During the sampling process, surface and deep soil samples are collected to ensure that pollution information of different soil layers is covered; based on the hydrogeological conditions, groundwater samples are collected in areas with shallow groundwater levels or near pollution sources; In the sampling design, high-throughput sequencing technology is combined to sample the microbial communities in soil and water to capture the impact of pollution on the microbial ecosystem; S3. Laboratory analysis: using chemical analysis methods to detect the concentration of pollutants in soil and groundwater; using high-throughput sequencing technology to analyze the composition and diversity of microbial communities in soil and groundwater; combining experimental analysis data with microbial gene data to analyze the diffusion patterns of pollutants, the correlation between microorganisms and pollutants, and predicting the diffusion trend of pollutants; S4. Ecological risk assessment: Use big data analysis methods, combined with experimental data, pollutant concentrations, and microbial community changes to build a pollutant diffusion model to predict the diffusion path and rate of pollutants in soil and groundwater; apply a comprehensive ecological risk assessment model based on pollutant concentrations and ecological factors in different regions, and determine the ecological risk level of different regions by evaluating the impact of pollutants on the surrounding ecosystems; combine the results of microbial genome analysis to evaluate the degree of damage of pollutants to microbial communities and further quantify ecological risks; S5. Remediation recommendations: Integrate pollutant concentrations, microbial community changes, and ecological risk assessment results to form a comprehensive pollution status report; based on the predicted pollution spread trend, formulate a phased remediation priority sequence, giving priority to high-risk areas; Based on the results of ecological risk assessment, bioremediation technology is used in combination with bioaugmentation to restore the balance of microbial communities in areas where pollution has a greater impact on microbial ecosystems; S6. Dynamic monitoring: During the restoration process, soil and groundwater samples will be collected regularly, and high-throughput sequencing technology will continue to be used to monitor changes in microbial communities and evaluate the progress of restoration. The monitoring data will be input into the previously constructed machine learning model, and the pollution spread prediction model will be continuously optimized through big data analysis technology to improve prediction accuracy. According to the latest ecological risk assessment results, the restoration plan will be adjusted to ensure the gradual recovery of the ecosystem.

2. A soil and groundwater pollution detailed investigation method according to claim 1, characterized in that: The historical data include land use, industrial activities, and waste disposal records.

3. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: The chemical analysis method includes liquid chromatography and gas chromatography, and the pollutants in the soil and groundwater include volatile organic compounds (VOCs) and heavy metals.

4. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: In step S1, machine learning and big data analysis technology use a linear regression model to predict changes in pollutant concentrations. The expression of the linear regression model is: in predicted pollutant concentrations; β0: intercept term of the model; β1,β2,…,βn: regression coefficients, representing the effect of input variables x1,x2,…,xn on the output The weight of influence; x1,x2,…,xn: input variables, representing different influencing factors, including groundwater flow rate, soil properties, and pollutant characteristics.

5. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: In step S3, the diffusion trend of pollutants is predicted using Kriging interpolation, and the expression is: Estimate the pollutant concentration at location x0; Z(xi): the pollutant concentration at the known location xi; λi: weight coefficient, solved by the covariance matrix, indicating the influence of position xi on the estimated value; n: number of known sample points.

6. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: The big data analysis method in step S4 uses cluster analysis K-means to identify different pollution patterns or distribution characteristics in the pollution area, and the expression is: Where J: objective function, total clustering distance; k: the number of clusters, that is, the number of clusters into which the data is divided; xj: data point belonging to the i-th cluster Ci; μi: the centroid of the ith cluster, i.e. the center point; ||xj-μi|| 2 : The Euclidean distance between a data point xj and the centroid μi of the cluster to which it belongs.

7. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: In the pollutant diffusion model in step S4, the diffusion of pollutants in groundwater is represented by the convection-diffusion equation, which is specifically: Where C: pollutant concentration; t: time; D: dispersion coefficient, which describes how quickly a pollutant spreads in groundwater; v: groundwater velocity; x: spatial coordinate; λ: The decay coefficient of the pollutant, which represents the decay rate of the pollutant through biological degradation or chemical reaction.

8. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: The ecological risk assessment model in step S4 uses the pollution index method to calculate the risk, which is expressed as an ecological risk index based on the ratio of pollutant concentration to the baseline value: Among them, the potential ecological risk index Ei of a single pollutant is expressed as: Where ERI: Comprehensive Ecological Risk Index, which indicates the ecological risk of the entire survey area; Ei: potential ecological risk index of a single pollutant; Ti: toxicity coefficient of pollutants, reflecting the degree of harm of pollutants to the ecosystem; Ci: actual concentration of pollutants; Cr: baseline value of the pollutant; n: number of pollutant types.

9. The method for detailed investigation of soil and groundwater pollution according to claim 1 is characterized by: In step S6, the ecological risk assessment evaluates the impact of pollution on ecosystem services and calculates the loss of ecosystem services, which is expressed as: Where ESL: total ecosystem service loss; ESi,b: functional level of the i-th ecosystem service before pollution; ESi,a: functional level of the i-th ecosystem service after pollution; Ai: The area covered by the i-th service.

10. The method for detailed investigation of soil and groundwater pollution according to claim 1, characterized in that: In step S6, the big data analysis technology uses the Monte Carlo simulation method to predict different scenarios of pollutant diffusion, ecological risks and restoration effects, which are expressed as: Where X: expected value of simulation results; f(xi): function of input variable xi, representing a randomly generated pollutant diffusion or ecological risk assessment model; n: Number of simulations. The more simulations you have, the more reliable the results will be.

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