Method for predicting reactive migration of organic pollutants in soil and underground water

Abstract

The invention relates to a method for predicting reactive migration of organic pollutants in soil and underground water, which comprises the following steps of: establishing an organic pollutant classification database based on molecular structure characteristics of the organic pollutants; classifying the target organic pollutants according to the organic pollutant classification database, and determining migration types of the target organic pollutants; establishing a double-site adsorption model, and distinguishing a rapid adsorption site and a slow adsorption site; for reactive migration pollutants, establishing a reactive pollutant conversion model considering an oxidation-adsorption competitive effect; considering the influence of iron and manganese oxides on the migration behavior of the organic pollutants, and establishing a complete migration prediction model; optimizing model parameters through experimental data; and applying the optimized model to predict the migration behavior of the organic pollutants in the soil-underground water system.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and groundwater environmental assessment and management in contaminated sites, and in particular to a method for predicting the reactive migration of organic pollutants in soil and groundwater. Background Art

[0002] Due to processes such as oil extraction, transportation, storage, or accidental leaks, large amounts of petroleum hydrocarbons seep into the ground, causing petroleum hydrocarbon contamination of groundwater and soil. The damage caused by oil pollution is quite widespread, affecting birds, seabed organisms, green plants, people, and marine resources. In addition, its unique physical and chemical properties and environmental limitations such as burial conditions make the treatment and remediation of petroleum hydrocarbon pollutants more difficult. Currently, there are many remediation technologies for oil-contaminated soil, each with its own characteristics. Traditional remediation technologies include physical treatment, chemical treatment, and biological treatment. Considering the high cost of physical treatment technology, the susceptibility of chemical methods to secondary pollution, and the long cycle of biological treatment technology, in actual operations, a combination of multiple methods is often used to leverage their strengths and overcome their weaknesses, improve remediation results, and avoid the limitations of a single method. In recent years, the purpose of various enhanced bioremediation technologies is to improve the sustainability, extensiveness and efficiency of bioremediation. However, due to the limitations of environmental conditions and bioavailability, as well as the problems of insufficient number and low activity of organisms, natural bioremediation is usually unable to achieve complete degradation of petroleum hydrocarbon pollution or it is difficult to complete the remediation requirements within a certain period of time. The remediation time is long and the effect is not ideal. The migration and diffusion of petroleum hydrocarbon pollutants are difficult to inhibit. Various means are often required to enhance the microbial remediation process of pollution.

[0003] The migration, distribution, and degradation of organic pollutants within the soil-water system of a site have been thoroughly studied. This allows for the development of scientific, specific, and targeted remediation plans to address organically contaminated sites. Implementation of these measures requires an understanding of the migration, transformation, and spatial distribution trends of organic matter within the soil-groundwater system, highlighting the importance of understanding the migration and distribution trends of organic matter in the remediation of organically contaminated sites.

[0004] CN115798570A discloses a method for predicting the migration and distribution of organic matter driven by microbial field coupling. This method predicts the migration trends of organic matter in contaminated sites by constructing a microbial field model based on multiple Monod equations and coupling it with hydrodynamic, temperature, and chemical fields. This method emphasizes the dynamic response of microorganisms to pollutant transformation, but fails to account for the influence of pollutant molecular structure on migration behavior, nor does it consider the regulatory effects of surface-active minerals such as iron and manganese oxides on reactivity.

[0005] CN116148437A proposes a method for predicting the migration and degradation of soil organic matter based on time-dependent dynamics. This method calculates the distribution of pollutants in the three soil phases (air, water, and solid) using the distribution coefficient, comprehensively considers volatilization, leaching, and biodegradation, establishes a pollutant concentration-flux model, and introduces a NAPL discrimination mechanism. However, this method primarily targets surface ecosystem processes and fails to detail the physical mechanisms of pollutant adsorption-reaction behavior in porous media, nor does it cover the impact paths of complex underground interfaces such as mineral coatings.

[0006] CN119830794A discloses a method and system for simulating the migration of soil and groundwater pollutants. This system incorporates three sub-models: adsorption, diffusion, and flow. Combined with iterative correction based on monitoring data, it improves the practicality of model predictions. However, the adsorption model, based on traditional Langmuir or Freundlich isotherms, fails to capture the nonlinear reaction kinetics of reactive pollutants and does not consider the competitive relationship between oxidation and adsorption, limiting the ability to analyze the linkage mechanism of migration and transformation.

[0007] Existing models do not adequately characterize the mineral interfaces in soil and groundwater environments, making it difficult to accurately describe the effects of mineral surface morphology, active site distribution, and interfacial charge distribution on pollutant migration. Existing models do not adequately consider the molecular structural characteristics of pollutants, making it difficult to predict their migration behavior based on their molecular structural characteristics, limiting the scope of application of the models. Existing models do not adequately study the oxidation-adsorption competition effect in soil and groundwater environments, making it difficult to accurately describe the transformation process of reactive pollutants. The parameter calibration methods of existing models are imperfect, making it difficult to accurately determine model parameters based on experimental data, affecting prediction accuracy. Existing models make it difficult to accurately predict the differences in migration behavior of different types of organic pollutants (highly reactive and lowly reactive) in soil and groundwater environments.

[0008] Due to the complexity of the soil environment, many factors need to be considered when constructing the model. The solutions used in existing technologies cannot effectively target the changing soil environment. The soil remediation raw materials and processing methods used are complex and the processing environment is harsh. Therefore, different and easy-to-implement remediation solutions are needed for different contaminated sites.

[0009] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the Invention

[0010] In response to the shortcomings of the existing technology, the present invention aims to provide a method for predicting the reactive migration of organic pollutants in soil and groundwater. By comprehensively considering the physical and chemical properties of the medium, organic pollutants, iron and manganese oxides and other factors in the soil-groundwater system of the study area, an accurate simulation prediction of the migration and distribution of organic matter in the soil-groundwater system of the contaminated site can be made, and it can be effectively applied in actual engineering to provide accurate implementation timing and targets for the remediation of contaminated sites.

[0011] In order to solve at least part of the above technical problems, the present invention discloses a method for predicting the reactive migration of organic pollutants in soil and groundwater, which comprises the following steps:

[0012] Establish an organic pollutant classification database based on the molecular structure characteristics of organic pollutants;

[0013] Classify target organic pollutants according to the organic pollutant classification database and determine their migration types;

[0014] A dual-site adsorption model was established to distinguish between fast adsorption sites and slow adsorption sites;

[0015] For reactive migration pollutants, a reactive pollutant transformation model considering the oxidation-adsorption competition effect is established;

[0016] Considering the impact of iron and manganese oxides on the migration behavior of organic pollutants, a complete migration prediction model was established;

[0017] Optimize model parameters through experimental data;

[0018] The optimized model was used to predict the migration behavior of organic pollutants in the soil-groundwater system.

[0019] This method establishes a classification database based on the molecular structure characteristics of organic pollutants, and combined with the classification of migration types, it can accurately identify the differences in the environmental behavior of pollutants and provide a targeted basis for subsequent model construction. The dual-site adsorption model effectively simulates the non-equilibrium characteristics of the dynamic adsorption process of pollutants on the surface of soil particles by distinguishing between fast and slow adsorption sites, avoiding the over-simplification of adsorption dynamics by traditional single-site models. For reactive migration pollutants, the introduction of a conversion model of oxidation-adsorption competition effect can quantify the dynamic balance between the oxidation reaction and adsorption fixation of pollutants, thereby more realistically reflecting their environmental fate. After the influence of iron and manganese oxides is incorporated into the model, the synergistic control of natural minerals on the migration of pollutants is taken into account, solving the problem of traditional models ignoring the coupling effect of chemical transformation and adsorption mediated by oxides. By optimizing the model parameters through experimental data, the adaptability of the model to the complex conditions of the actual site is further improved, and ultimately a high-precision prediction of the migration behavior of organic pollutants is achieved.

[0020] According to a preferred embodiment, the establishment of the organic pollutant classification database includes the following steps:

[0021] Obtain molecular structure data of organic pollutants;

[0022] Obtain migration behavior data of organic pollutants;

[0023] Establish a correlation model between molecular structure data and migration behavior data.

[0024] This method establishes a theoretical foundation for pollutant classification by modeling the correlation between molecular structure data and migration behavior data. Molecular structure data (such as functional group type and hydrophobicity) directly reflect the pollutant's physicochemical properties, while migration behavior data (such as adsorption capacity and diffusion rate) reveal its dynamic response in the environmental medium. This correlation model frees the classification process from reliance on subjective experience and instead bases it on quantifiable structure-behavior relationships, significantly improving the objectivity and universality of the classification.

[0025] According to a preferred embodiment, the target organic pollutants are classified based on the functional group type, dissociation constant and hydrophobicity parameters, thereby dividing the target organic pollutants into reactive migration type and non-reactive migration type, wherein the reactive migration type of pollutants refers to pollutants containing easily oxidized and reduced functional groups and easily undergoing chemical reactions under environmental conditions; the non-reactive migration type of pollutants refers to pollutants with high chemical stability that mainly migrate through physical adsorption.

[0026] The classification method based on functional group type, dissociation constant and hydrophobicity parameters can analyze the reactive nature of pollutants at the molecular level. Pollutants containing easily oxidized and reduced functional groups (such as aromatic amines and phenols) are classified as reactive migration types, and their migration behavior is significantly affected by redox conditions; while pollutants with high chemical stability (such as polychlorinated biphenyls) are classified as non-reactive migration types, and their migration mainly depends on physical adsorption. This classification method avoids the general division of pollutant behavior by traditional classification methods, thereby providing a clear direction for model selection: reactive pollutants need to give priority to the oxidation-adsorption competition effect, while non-reactive pollutants can be simplified to a migration process dominated by physical adsorption. This classification strategy significantly reduces the complexity of the model while improving the pertinence and accuracy of the prediction.

[0027] According to a preferred embodiment, the mathematical expression of the dual-site adsorption model is:

[0028]

[0029] Wherein, K is the total adsorption distribution coefficient, f is the proportion of fast sites, α is the mass transfer coefficient, S is the total adsorption amount, S1 is the adsorption amount of fast adsorption sites, S2 is the adsorption amount of slow adsorption sites, k1 is the fast adsorption rate constant, and C is the liquid phase concentration.

[0030] The mathematical expression of the dual-site adsorption model can dynamically describe the non-equilibrium process of adsorption and desorption of pollutants on the surface of soil particles by introducing the fast site ratio (f) and the mass transfer coefficient (α). The fast adsorption site (S1) simulates the instantaneous binding of pollutants to surface active sites, while the slow adsorption site (S2) reflects the slow kinetics of pollutant migration to inner pores or inactive sites. This model breaks through the traditional single-site adsorption model's assumption of uniform adsorption rate and is more in line with the characteristics of heterogeneity of adsorption sites in actual soils. By adjusting the parameters f and α, the model can flexibly adapt to the adsorption characteristics of different pollutants, thereby improving the dynamic resolution of migration behavior prediction.

[0031] According to a preferred embodiment, the fast site ratio f and the mass transfer coefficient α are obtained by the following steps:

[0032] Batch adsorption experiments were conducted to determine the adsorption isotherms of organic pollutants at different concentrations;

[0033] The Langmuir or Freundlich isotherm is used to fit the experimental data and obtain the adsorption parameters;

[0034] Conduct column chromatography experiments to determine the breakthrough curves of organic pollutants;

[0035] The fast site ratio f and mass transfer coefficient α were determined by breakthrough curve fitting.

[0036] By obtaining adsorption isotherms through batch adsorption experiments and fitting adsorption parameters using the Langmuir / Freundlich model, the distribution characteristics of pollutants at the solid-liquid interface can be quantified. The breakthrough curve of the column chromatography experiment further reveals the dynamic adsorption behavior of pollutants during migration, providing an experimental basis for determining the proportion of fast sites (f) and the mass transfer coefficient (α). This technical approach converts the microscopic adsorption mechanism into macroscopic model parameters through experimental-model coupling, avoiding the subjectivity of parameter estimation. The direct input of experimental data significantly improves the reliability of the model parameters, thereby enhancing the environmental relevance of the prediction results.

[0037] According to a preferred embodiment, the mathematical expression of the reactive pollutant conversion model considering the oxidation-adsorption competition effect is:

[0038]

[0039] Where D is the hydrodynamic diffusion coefficient, v is the pore flow velocity, ρ is the medium density, θ is the porosity, R is the reaction term, and x is the transmission distance.

[0040] The conversion model considering the oxidation-adsorption competition effect is constructed by introducing the reaction term (R) and the degradation rate (μ l 、μ s ), which can simultaneously characterize the physical migration (convection-diffusion) and chemical transformation (oxidation, adsorption and fixation) of pollutants. and convection terms describes the spatial distribution of pollutants in groundwater, while The term quantifies the dynamic contribution of solid phase adsorption. The degradation term (μ l C.μ s S) further simulates the chemical transformation of pollutants in the liquid and solid phases, thus fully covering the entire life cycle of pollutants, from migration and adsorption to degradation. This model breaks through the limitations of traditional single-transport models and realizes a multi-process coupled simulation of the environmental behavior of pollutants.

[0041] According to a preferred embodiment, the liquid phase degradation rate μ l and solid phase degradation rate μ s Obtain it in the following steps:

[0042] The oxidation products and adsorption amount of organic pollutants at the mineral interface were determined by isotope tracer method;

[0043] Establish the correlation equation between oxidation reaction rate and adsorption sites;

[0044] Determination of liquid phase degradation rate μ by model parameter optimization l and solid phase degradation rate μ s The optimal value of .

[0045] The isotope tracer method can accurately track the path and rate of oxidation reactions by labeling pollutants and their oxidation products. By measuring the adsorption amount at the mineral interface and combining the correlation equation between the oxidation reaction rate and the adsorption site, the competitive relationship between oxidation and adsorption can be analyzed. This method avoids the dependence on the hypothesis of reaction mechanism in traditional degradation rate estimation and directly infers the model parameters based on experimental data. The parameter optimization process is carried out by iteratively adjusting μ l and μ s , making the model output highly consistent with experimental observations, thereby significantly improving the accuracy of degradation rate calculations.

[0046] According to a preferred embodiment, for non-reactive pollutants, the degradation term μ l C and μ s S is set to zero, and the reactive pollutant conversion model considering the oxidation-adsorption competition effect is simplified to:

[0047]

[0048] For non-reactive pollutants, the simplified model is removed by removing the degradation term (μ l C.μ s S), focusing on physical migration and adsorption processes, avoiding redundant calculations of chemical transformations. This simplification strategy significantly reduces model complexity and improves computational efficiency while ensuring prediction accuracy. For pollutants with high chemical stability (such as polycyclic aromatic hydrocarbons), this model can quickly assess their migration range and residence time, thereby providing an efficient tool for risk management of non-reactive contaminated sites. The simplification of the model does not sacrifice key physical mechanisms (such as convection-diffusion and two-site adsorption), so it can still accurately reflect the environmental behavior of pollutants.

[0049] According to a preferred embodiment, for non-reactive pollutants under low pH conditions, the conversion model expression of reactive pollutants considering the dual porosity effect and the oxidation-adsorption competition effect is:

[0050]

[0051] Among them, C m is the solute concentration in the movable water region, C im is the solute concentration in the immovable water region, θ m is the movable water content, θ im is the immovable water content, and α is the mass transfer coefficient.

[0052] The dual porosity effect model is introduced under low pH conditions to distinguish the movable water (C m ) and immovable water (C im ) area, which can more realistically simulate the migration behavior of pollutants in heterogeneous media. The solute concentration in the movable water area (C m ) is driven by convection-diffusion, while the concentration in the immovable water region (C im ) establishes a dynamic equilibrium with the movable water area through the mass transfer coefficient (α). This model breaks through the traditional single-pore model's assumption of homogeneous water flow fields and is particularly suitable for complex scenarios with differentiated soil pore structures under low pH conditions. By incorporating the dual-porosity effect, the model can more accurately predict the retention and diffusion characteristics of pollutants in groundwater, providing theoretical support for pollution risk assessment in acidic environments.

[0053] According to a preferred embodiment, considering the influence of iron and manganese oxides on the migration behavior of organic pollutants includes the following steps:

[0054] Two modified media, quartz sand coated with iron-manganese oxide composites and quartz sand grown with iron-manganese oxide in situ, were prepared;

[0055] The migration behavior of organic pollutants in two modified media was studied by column migration experiments.

[0056] Establish a relationship model between the content of iron and manganese oxides and the adsorption parameters of organic pollutants;

[0057] Integrate factors affecting iron and manganese oxides into migration prediction models.

[0058] The preparation of two modified media, iron-manganese oxide composite coating and in-situ growth, simulated the existence form and distribution characteristics of oxides in natural soil. Column migration experiments revealed the synergistic effect of oxides on pollutant adsorption and oxidation by comparing the migration behavior of pollutants in the two media. The established relationship model between iron-manganese oxide content and adsorption parameters can quantify the degree of oxide inhibition on pollutant migration, thus providing key input for model parameterization. Integrating oxide influencing factors into the migration prediction model compensates for the traditional model's neglect of the role of natural minerals and significantly improves the model's ability to simulate oxide-mediated migration processes in actual contaminated sites.

[0059] Compared with the prior art, the present invention has the following beneficial technical effects:

[0060] Improved prediction accuracy: By characterizing the mineral interface properties in soil and groundwater environments and considering their differentiated effects on different types of organic pollutants, the method of the present invention significantly improves the accuracy of predictions.

[0061] Enhanced model applicability: By classifying pollutants based on molecular structure, the method of the present invention is adaptable to different types of pollutants and environmental conditions. For example, the method of the present invention can accurately predict the migration behavior of reactive pollutants containing phenolic hydroxyl groups (such as BPA) and non-reactive pollutants containing carboxyl groups (such as PFOA), while existing technologies are generally only applicable to specific types of pollutants.

[0062] Improved Reaction Mechanism Description: By considering the oxidation-adsorption competition effect, the method of the present invention improves the description of the transformation mechanism of reactive pollutants and enhances the model's mechanistic explanatory power. For example, the method of the present invention accurately describes the entire process of BPA transformation at the soil-mineral interface: oxidation reaction generates free radicals, forms macromolecular polymers through coupling, and ultimately stabilizes it on the mineral surface through covalent bonds. Existing techniques generally only consider the simple adsorption process.

[0063] Optimized parameter calibration method: Through parameter optimization based on experimental data, the present method improves the accuracy and reliability of model parameters. For example, the Levenberg-Marquardt algorithm, combined with global search and local optimization, avoids the problem of being trapped in a local optimal solution. Existing technologies often use simple trial-and-error methods or a single optimization algorithm.

[0064] Providing decision support: By visualizing and validating prediction results, the method provides a scientific basis for groundwater contamination risk assessment and remediation decisions. For example, by generating spatiotemporal distribution maps and breakthrough curves of pollutant concentrations, the migration patterns of pollutants can be intuitively displayed, providing decision support for contaminated site remediation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of the method for predicting the reactive migration of organic pollutants in soil and groundwater provided by the present invention;

[0066] Figure 2 A flow chart for establishing an organic pollutant classification database in the present invention;

[0067] Figure 3 Flow chart for obtaining parameters of the dual-site adsorption model in the present invention;

[0068] Figure 4 This is a flow chart for studying the effects of iron and manganese oxides on the migration behavior of organic pollutants in the present invention;

[0069] Figure 5 This is a comparison chart of the migration behaviors of bisphenol A and perfluorooctanoic acid in different media in the application example of the present invention. DETAILED DESCRIPTION

[0070] The following is a detailed description with reference to the accompanying drawings.

[0071] Organic pollutants often exhibit strong migration capabilities in groundwater systems due to their good water solubility and ionizability. These pollutants, including bisphenol A (BPA) and perfluorooctanoic acid (PFOA), are widely present in industrial wastewater, domestic sewage, and agricultural runoff, posing a potential threat to the ecological environment and human health. The molecular form of organic matter changes with changes in pH. Accurately predicting the migration and distribution patterns of organic matter in groundwater systems at different soil-mineral interfaces under different pH conditions is of great significance for pollution risk assessment, remediation plan design, and long-term monitoring.

[0072] Soil minerals, common redox-active media in the subsurface, possess abundant functional groups and active sites on their surfaces, which can influence the environmental fate of organic pollutants through adsorption and oxidation. However, the heterogeneity of soil mineral interfaces results in significant differences in their mechanisms of action on different types of organic pollutants, and the impact of this variability on pollutant migration behavior has not been fully studied.

[0073] Example 1

[0074] like Figure 1As shown, the present invention discloses a method for predicting the reactive migration of organic pollutants in soil and groundwater, comprising the following steps:

[0075] S1. Establish an organic pollutant classification database based on the molecular structure characteristics of organic pollutants.

[0076] Preferably, if Figure 2 As shown in the figure, the establishment of the organic pollutant classification database may include the following steps:

[0077] S1.1. Obtain molecular structural data of organic pollutants (through experimental determination, literature search, or computational simulation);

[0078] S1.2. Obtain migration behavior data of organic pollutants (through column chromatography experiments, batch adsorption experiments, etc.);

[0079] S1.3. Establish a correlation model between molecular structure data and migration behavior data (using regression analysis or machine learning algorithms).

[0080] Furthermore, molecular structure data includes, but is not limited to, molecular formula, molecular weight, type and number of functional groups, etc. Physicochemical properties include, but are not limited to, logKow (octanol-water partition coefficient), pKa (dissociation constant), water solubility, redox potential, etc. Migration behavior data includes, but is not limited to, adsorption coefficient, desorption coefficient, degradation rate, diffusion coefficient, etc. For example, the data categories and corresponding data sources of the organic pollutant classification database can be shown in the following table:

[0081]

[0082] S2. Classify the target organic pollutants according to the organic pollutant classification database and determine their migration type.

[0083] Preferably, organic pollutants can be classified based on functional group type, dissociation constant and hydrophobicity parameters. Specifically, pollutants can be divided into reactive migration type and non-reactive migration type. Among them, reactive migration type pollutants refer to pollutants containing easily oxidized and reduced functional groups (such as phenolic hydroxyl groups, amino groups, etc.) that are prone to chemical reactions under environmental conditions; non-reactive migration type pollutants refer to pollutants with high chemical stability that mainly migrate through physical adsorption.

[0084] Furthermore, the criteria for classifying organic pollutants are as follows:

[0085] If the pollutant contains phenolic hydroxyl, amine, thiol and other easily oxidized and reduced functional groups, and the redox potential E° is less than 0.8V vs. Standard Hydrogen Electrode (SHE), it is classified as reactive migration;

[0086] If the molecular structure of the pollutant is stable (such as perfluorinated compounds) and the redox potential E°>1.5Vvs. Standard Hydrogen Electrode (SHE), it is classified as non-reactive migration;

[0087] For pollutants between the two, classification can be carried out based on experimental data.

[0088] According to the organic pollutant classification method of the present invention, its specific implementation method is as follows: The present invention establishes a migration type classification system for organic pollutants by combining the functional group characteristics, redox potential (E°) and environmental interface reactivity of the pollutants. For the target pollutants, a preliminary screening is first performed based on the type of functional groups in their molecular structure. If the pollutants contain functional groups that are prone to redox reactions, such as phenolic hydroxyl groups, amine groups, and thiol groups, their redox potential E° is further evaluated. When the E° value is lower than 0.8V (relative to the standard hydrogen electrode, SHE), it indicates that the pollutant is prone to electron transfer reactions with common oxidants (such as iron and manganese oxides) in the natural environment, such as generating free radicals or forming coupling products through hydroxyl oxidation, and is thus classified as a reactive migration type; if the molecular structure of the pollutant is highly stable (such as perfluorinated compounds) and its E° value is higher than 1.5V (such as the CF bond breakage of perfluorooctanoic acid PFOA needs to overcome the thermodynamic barrier of E°≈2.5V), it is difficult to be oxidized without the participation of a catalyst and belongs to the non-reactive migration type. The selection of the above classification thresholds (0.8V and 1.5V) is based on the redox potential range of typical iron-manganese oxides (such as δ-MnO2, birnessite, FeOOH) in a neutral water environment (Mn(IV) / Mn(II): approximately 0.8~1.0V; Fe(III) / Fe(II): approximately 0.77V), and is determined through systematic analysis in combination with experimental data on electron transfer reactions of pollutants at the interfaces of such oxides (such as hydroxyl oxidation kinetics, free radical generation efficiency) and thermodynamic calculation results. For pollutants with E° values ​​between 0.8 and 1.5V, their migration behavior needs to be further verified through empirical means such as adsorption experiments and degradation rate tests to ensure the accuracy and practicality of the classification. This method achieves a precise classification of organic pollutant migration types by combining quantitative standards (functional group identification + potential threshold) with experimental data, providing a theoretical basis and technical support for the formulation of pollution control strategies.

[0089] The above-mentioned standard hydrogen electrode (SHE) is a reference electrode commonly used in electrochemistry (potential is 0V).

[0090] In addition, further classification can be carried out according to the hydrophobicity parameter logKow and the acid-base parameter pKa:

[0091] When classifying according to the hydrophobicity parameter logKow, organic pollutants with logKow ≥ 3.5 can be classified as strongly hydrophobic pollutants, and organic pollutants with logKow < 3.5 can be classified as weakly hydrophobic pollutants;

[0092] When classifying according to the acid-base parameter pKa, organic pollutants with pKa ≤ 4.5 can be classified as strongly acidic pollutants, organic pollutants with pKa ≥ 9.5 can be classified as strongly basic pollutants, and organic pollutants with 4.5 < pKa < 9.5 can be classified as neutral pollutants.

[0093] S3. Establish a two-site adsorption model to distinguish fast adsorption sites and slow adsorption sites.

[0094] Preferably, the mathematical expression of the two-site adsorption model is:

[0095] S = S1 + S2,

[0096]

[0097] where K is the total adsorption distribution coefficient, f is the fast-site proportion, α is the mass transfer coefficient, S is the total adsorption amount, S1 is the adsorption amount of the fast adsorption site, S2 is the adsorption amount of the slow adsorption site, k1 is the fast adsorption rate constant, and C is the liquid-phase concentration.

[0098] Furthermore, as Figure 3 shown, the fast-site proportion f and the mass transfer coefficient α can be obtained through the following steps:

[0099] S3.1. Conduct batch adsorption experiments to measure the adsorption isotherms of organic pollutants at different concentrations;

[0100] S3.2. Use the Langmuir or Freundlich isotherm to fit the experimental data to obtain adsorption parameters;

[0101] S3.3. Conduct column chromatography experiments to measure the breakthrough curves of organic pollutants;

[0102] S3.4. Determine the fast-site proportion f and the mass transfer coefficient α through fitting the breakthrough curves.

[0103] Preferably, in the HYDRUS software, the present invention can use the nonlinear least squares fitting method to fit the breakthrough curves of column experiments at different time nodes, and extract f and α according to the two-site model equation.

[0104] S4. For reactive migration pollutants, establish a reactive pollutant transformation model considering the oxidation-adsorption competition effect.

[0105] Preferably, the "oxidation-adsorption competition effect" in the present invention refers to the following: during the migration of pollutants in iron-manganese oxide-coated porous media, surface active sites can participate in both adsorption and oxidation reactions, resulting in competition for interface site occupancy and interference with reaction pathways, with the two being coupled. BPA contains phenolic hydroxyl groups, which generate phenoloxyl radicals in the presence of MnO2 and FeOOH, followed by coupled polymerization and condensation reactions, with the products being covalently fixed to the mineral surface. XPS characterization shows a decrease in the Fe(III) ratio, confirming the occurrence of oxidation reactions. LC-MS analysis detects dimer and trimer products (m / z = 453.2, 668.4), confirming the oxidation pathway. Stable isotope tracing can also be used to label the C element of BPA to distinguish pollutants that are adsorbed, oxidized, or co-precipitated by the mineral, achieving accurate quantification of the oxidation-adsorption competition effect.

[0106] Preferably, the mathematical expression of the reactive pollutant conversion model considering the oxidation-adsorption competition effect is:

[0107]

[0108] Where D is the hydrodynamic diffusion coefficient, v is the pore flow velocity, ρ is the medium density, θ is the porosity, R is the reaction term, and x is the transmission distance.

[0109] Furthermore, the reaction term R can be determined by coupling the microscopic kinetic model with the macroscopic scale parameters. Specifically, based on the Langmuir-Hinshelwood mechanism, the adsorption and oxidation process of pollutant P on the mineral surface can be decomposed into two key steps:

[0110] First, the pollutant P occupies the active sites via Langmuir adsorption, with an occupancy rate θ P It is characterized by the following adsorption equilibrium formula:

[0111]

[0112] Where K is the adsorption constant, C is the concentration of liquid pollutants;

[0113] Secondly, in the adsorption state, the pollutant undergoes an oxidation reaction with the oxidant Mn(IV) to generate oxidation products and reduced Mn(II). The microscopic reaction rate r can be expressed as:

[0114]

[0115] Among them, k surf is the surface reaction rate constant, which is a key kinetic parameter describing the rate of chemical reaction (such as adsorption and oxidation) of pollutants on the mineral surface. It reflects the interaction between pollutants and oxidants (such as Mn4+ ) contacts and undergoes oxidation reaction on the mineral surface, which is the core indicator for characterizing surface reaction activity.

[0116] This expression reflects the coupling effect between the pollutant adsorption occupancy and the effective concentration of the oxidant, revealing the dynamic regulation mechanism of oxidation-adsorption competition on the reaction rate.

[0117] In order to achieve the transformation from microscopic interface reaction to macroscopic scale, the present invention further introduces the mineral specific surface area a s (Unit: m 2 / g) and mineral loading M s (Unit: g / L), the microscopic reaction rate r per unit surface area is expanded to the macroscopic reaction rate:

[0118] R=r·a s ·M s .

[0119] By substituting the expression for r and simplifying, we finally get the empirical fitting form:

[0120] R=μ l C+μ s S,

[0121] Among them, μ l With μ s Represent the degradation rate constants of the liquid phase and solid phase respectively, and C and S are the concentrations of pollutants in the liquid phase and solid phase.

[0122] The above formula not only retains the physical meaning of the microscopic mechanism (such as adsorption competition on μ l 、μ s ), and compatibility with the macroscopic convection-dispersion-reaction equation is achieved through parameterization.

[0123] In a specific implementation, the present invention can use the Inverse module of HYDRUS-1D software to perform inverse simulation, and embed the above-mentioned micro-scale rate expression into the macro-control equation. Through parameter optimization and experimental data calibration, the model can quantify the pollutant in the liquid phase (μ l ) and solid phase (μ s ) and dynamically reflects the oxidant Mn(IV) concentration, mineral specific surface area a s and the combined effect of the adsorption constant K on R. This method, driven by multi-scale coupling (microscopic interface reaction mechanism + macroscopic flux expression) and empirical data, achieves accurate prediction of the transformation behavior of reactive pollutants in complex environmental media, providing theoretical support for the formulation of pollution control strategies.

[0124] Furthermore, the liquid phase degradation rate μ l and solid phase degradation rate μs You can obtain it by following the steps below:

[0125] S4.1. Determine the oxidation products and adsorption amount of organic pollutants at the mineral interface by isotope tracer method;

[0126] S4.2. Establish an equation relating the oxidation reaction rate to the adsorption sites.

[0127] S4.3. Determine the liquid phase degradation rate μ by optimizing model parameters l and solid phase degradation rate μ s The optimal value of .

[0128] Preferably, for non-reactive pollutants, the degradation term μ l C and μ s S is set to zero, and the model is simplified to:

[0129]

[0130] For non-reactive pollutants (such as PFOA) at pH values ​​lower than the pKa (acidity dissociation constant) of ionic organic compounds (i.e., low pH conditions), the model expression is:

[0131]

[0132] Among them, C m is the solute concentration in the movable water region, is the solute concentration in the immovable water region, θ m is the movable water content, is the immovable water content, and α is the mass transfer coefficient.

[0133] S5. Consider the impact of iron and manganese oxides on the migration behavior of organic pollutants and establish a complete migration prediction model.

[0134] Preferably, if Figure 4 As shown in Figure 2, considering the impact of iron and manganese oxides on the migration behavior of organic pollutants includes the following steps:

[0135] S5.1. Preparation of two modified media: quartz sand coated with iron and manganese oxides (GM-C) and quartz sand with in-situ growth of iron and manganese oxides (GM-G);

[0136] S5.2. Study the migration behavior of organic pollutants in two modified media through column migration experiments;

[0137] S5.3. Establish a relationship model between iron and manganese oxide content and organic pollutant adsorption parameters;

[0138] In addition, multi-scale characterization techniques can be used between steps S5.2 and S5.3 to verify the migration mechanism, including scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and liquid chromatography-mass spectrometry (LC-MS).

[0139] Furthermore, after step S5.3, the following steps may be further included:

[0140] S5.4. Integrate the influencing factors of iron and manganese oxides into the migration prediction model.

[0141] Furthermore, quartz sand coated with iron-manganese oxide composites (GM-C) was prepared by direct coating of manganese oxide and goethite suspension, and quartz sand grown with iron-manganese oxide in situ (GM-G) was prepared by Fe 2+ / Mn 2+ The solution is generated by in-situ oxidation on the surface of quartz sand.

[0142] S6. Optimize model parameters based on experimental data.

[0143] Preferably, the least squares method can be used to optimize the model parameters, and the optimal model parameter set can be determined by minimizing the error function between the model prediction value and the experimental observation value:

[0144]

[0145] Among them, p is the parameter vector, C oβs,i is the concentration of the i-th observation point, is the concentration of the i-th point predicted by the model, w i is the weight.

[0146] Preferably, the optimization algorithm may adopt the Levenberg-Marquardt algorithm, combining global search and local optimization to avoid falling into a local optimal solution.

[0147] S7. Use the optimized model to predict the migration behavior of organic pollutants in the soil-groundwater system.

[0148] Preferably, model validation may include the following sub-steps:

[0149] S7.1. Use cross-validation to evaluate the generalization ability of the model.

[0150] S7.2. Draw a scatter plot or Bland-Altman plot of the predicted and measured values.

[0151] S7.3. Calculate the model prediction error, including root mean square error or relative error.

[0152] Example 2

[0153] This embodiment is a further improvement of embodiment 1, and repeated contents will not be repeated here.

[0154] This example provides an application case for predicting the migration behavior of bisphenol A (BPA) and perfluorooctanoic acid (PFOA) in different media.

[0155] First, based on the molecular structure characteristics, BPA was identified as a reactive migration pollutant (containing phenolic hydroxyl groups, redox potential E°<0.8V) and PFOA was identified as a non-reactive migration pollutant (perfluorinated compound, redox potential E°>1.5V) from the established organic pollutant classification database.

[0156] Column migration experiments were then conducted to investigate the migration behavior of BPA and PFOA in three media: quartz sand, GM-C, and GM-G. The experimental conditions included different pH values ​​(2.5, 6.5, and 9.5), flow rates (0.5 mL / min and 1.0 mL / min), and concentrations (5 mg / L and 10 mg / L).

[0157] Figure 5 Breakthrough curves for BPA and PFOA in porous quartz sand; breakthrough curves and model fits for BPA and PFOA in porous quartz sand coated with iron-manganese oxides via a combination of coating (GM-C) and in-situ growth (GM-G). Data points represent measured values, while solid lines represent model predictions. Inflow conditions: 50 μM BPA / PFOA; pH 6.5 (BPA) and 2.5 (PFOA), with both BPA and PFOA exhibiting neutral molecular forms; flow rate 0.5 mL / min; V / Vp is the ratio of injection volume to pore volume; dark background areas indicate elution with H2O and ethanol, respectively.

[0158] For BPA, a dual-site adsorption model was adopted and coupled with reaction terms, taking into account the oxidation-adsorption competition effect. The model fitting results showed that the adsorption coefficient of BPA in GM-G medium (k = 40.59) was significantly higher than that in GM-C medium (k = 7.682), and the liquid phase degradation rate μ l In GM-C medium (1.263×10 -2 ) was significantly higher than that of GM-G medium (2.13×10 -4 ), which indicates that BPA is adsorbed in GM-G mainly through coordination bonding of Fe / Mn-OH sites, while the scarcity of active sites in GM-C leads to a sharp decrease in chemical bonding ability.

[0159] For PFOA, the dual-pore model can accurately describe its migration behavior under pH = 2.5. The model fitting results show that the adsorption coefficient of PFOA in GM-G medium (k = 45.52) is 6 times higher than that under neutral conditions, and the mass transfer coefficient (α = 8.82×10 -5 ) decreased abnormally, which indicated that under low pH conditions, PFOA was retained in the medium through a synergistic mechanism of strong electrostatic adsorption and physical retention.

[0160] The migration mechanism was verified by multi-scale characterization techniques: XPS detected an enhanced Fe-O bond signal and an increased Fe(II) ratio in the GM-G sample after BPA treatment, and LC-MS detected BPA polymerization products. These results confirmed that the oxidative fixation process of BPA involved a triple mechanism of free radical generation catalyzed by the mineral surface, free radical coupling polymerization, and covalent bonding; the Zeta potential test results were consistent with the trend of changes in the model parameters of PFOA, indicating that its migration kinetics were synergistically controlled by the molecular charge state, mineral surface electrical properties, and pore mass transfer efficiency.

[0161] Finally, the relative error between the model prediction results and the experimental observation values ​​was less than 5%, which proved the accuracy and reliability of the method of the present invention.

[0162] Example 3

[0163] This embodiment is a further improvement of Embodiment 1 and / or 2, and repeated contents will not be repeated here.

[0164] This embodiment provides a practical application case to predict the migration distribution of benzene series compounds in the soil-groundwater system around a petrochemical plant.

[0165] First, based on the molecular structure of benzene series, we identified them as non-reactive migrating pollutants from an established organic pollutant classification database. The specific classification is based on the following: benzene series have a stable molecular structure, a redox potential (E°) greater than 1.5V vs. the Standard Hydrogen Electrode (SHE), and migrate primarily via physical adsorption, resulting in high chemical stability.

[0166] Then, through field investigation, we obtained data on soil physical and chemical parameters, groundwater flow field, iron and manganese oxide content, etc. The data collected from the field investigation of the study area are shown in Tables 1 and 2 below.

[0167] Table 1 Soil physical and chemical parameters in the study area

[0168]

[0169]

[0170] Table 2 Groundwater flow field parameters

[0171] parameter Numerical range average value unit Groundwater velocity (v) 0.15～0.42 0.28 m / d Hydrodynamic dispersion coefficient (D) 0.08～0.25 0.15 <![CDATA[m 2 / d]]> Groundwater depth 2.5～4.8 3.6 m

[0172] Next, a migration prediction model for the region was established, and the model parameters were optimized using experimental data. Finally, the model calculations were used to obtain the predicted spatial distribution of BTEX in the region over the next five years. The prediction results show:

[0173] In areas with high iron and manganese oxide content (>3.0g / kg), the migration rate of benzene series compounds slows down significantly, and the pollutant concentration will drop to less than 30% of the initial value after 5 years.

[0174] In areas with low iron and manganese oxide content (<1.0g / kg), the migration rate of benzene series is faster, and pollutants may reach the groundwater surface and cause groundwater pollution.

[0175] Based on this prediction, targeted remediation strategies can be developed for contaminated sites, prioritizing high-risk areas with lower iron and manganese oxide content. Specific remediation recommendations include:

[0176] For areas where the iron and manganese oxide content is less than 1.0g / kg, in-situ chemical oxidation or bioremediation technology is used to block the migration path of pollutants to groundwater.

[0177] For areas where the iron and manganese oxide content is 1.0 to 3.0 g / kg, the natural attenuation monitoring method is used to regularly monitor changes in pollutant concentrations.

[0178] For areas where the iron and manganese oxide content is >3.0g / kg, natural attenuation can be considered, combined with necessary engineering measures to control pollution sources.

[0179] Through the application of this embodiment, the applicability and prediction accuracy of the method of the present invention in actual contaminated sites have been verified, providing a scientific basis for risk assessment and remediation decision-making of contaminated sites.

[0180] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" or "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as having to be set, so the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A method for predicting the reactive migration of organic pollutants in soil and groundwater, characterized in that: It includes the following steps: Establish an organic pollutant classification database based on the molecular structure characteristics of organic pollutants; Classify target organic pollutants according to the organic pollutant classification database and determine their migration types; A dual-site adsorption model was established to distinguish between fast adsorption sites and slow adsorption sites; For reactive migration pollutants, a reactive pollutant transformation model considering the oxidation-adsorption competition effect is established; Considering the impact of iron and manganese oxides on the migration behavior of organic pollutants, a complete migration prediction model was established; Optimize model parameters through experimental data; The optimized model was used to predict the migration behavior of organic pollutants in the soil-groundwater system.

2. The method according to claim 1, characterized in that The establishment of the organic pollutant classification database includes the following steps: Obtain molecular structure data of organic pollutants; Obtain migration behavior data of organic pollutants; Establish a correlation model between molecular structure data and migration behavior data.

3. The method according to claim 1 or 2, characterized in that The target organic pollutants are classified based on the functional group type, dissociation constant and hydrophobicity parameters, and are thus divided into reactive migration type and non-reactive migration type. Among them, reactive migration type pollutants refer to pollutants that contain easily oxidized and reduced functional groups and are prone to chemical reactions under environmental conditions; non-reactive migration type pollutants refer to pollutants with high chemical stability that mainly migrate through physical adsorption.

4. The method according to any one of claims 1 to 3, characterized in that The mathematical expression of the two-site adsorption model is: Wherein, K is the total adsorption distribution coefficient, f is the proportion of fast sites, α is the mass transfer coefficient, S is the total adsorption amount, S1 is the adsorption amount of fast adsorption sites, S2 is the adsorption amount of slow adsorption sites, k1 is the fast adsorption rate constant, and C is the liquid phase concentration.

5. The method according to any one of claims 1 to 4, characterized in that The fast site ratio f and mass transfer coefficient α are obtained by the following steps: Batch adsorption experiments were conducted to determine the adsorption isotherms of organic pollutants at different concentrations; The adsorption parameters were obtained by fitting the experimental data with the Langmuir or Freundlich isotherm. Conduct column chromatography experiments to determine the breakthrough curves of organic pollutants; The fast site ratio f and mass transfer coefficient α were determined by breakthrough curve fitting.

6. The method according to any one of claims 1 to 5, characterized in that The mathematical expression of the reactive pollutant transformation model considering the oxidation-adsorption competition effect is: Where D is the hydrodynamic diffusion coefficient, v is the pore flow velocity, ρ is the medium density, θ is the porosity, R is the reaction term, and x is the transmission distance.

7. The method according to any one of claims 1 to 6, characterized in that Liquid phase degradation rate μ l and solid phase degradation rate μ s Obtain it in the following steps: The oxidation products and adsorption amount of organic pollutants at the mineral interface were determined by isotope tracer method; Establish the correlation equation between oxidation reaction rate and adsorption sites; Determination of liquid phase degradation rate μ by model parameter optimization l and solid phase degradation rate μ s The optimal value of .

8. The method according to any one of claims 1 to 7, characterized in that For non-reactive pollutants, the degradation term μ l C and μ s S is set to zero, and the reactive pollutant conversion model considering the oxidation-adsorption competition effect is simplified to:

9. The method according to any one of claims 1 to 8, characterized in that For non-reactive pollutants under low pH conditions, the conversion model of reactive pollutants considering the dual porosity effect and the oxidation-adsorption competition effect is expressed as: Among them, C m is the solute concentration in the movable water region, C im is the solute concentration in the immovable water region, θ m is the movable water content, θ im is the immovable water content, and α is the mass transfer coefficient.

10. The method according to any one of claims 1 to 9, characterized in that Considering the impact of iron and manganese oxides on the migration behavior of organic pollutants includes the following steps: Two modified media, quartz sand coated with iron-manganese oxide composites and quartz sand grown with iron-manganese oxide in situ, were prepared; The migration behavior of organic pollutants in two modified media was studied by column migration experiments. Establish a relationship model between the content of iron and manganese oxides and the adsorption parameters of organic pollutants; Integrate factors affecting iron and manganese oxides into migration prediction models.

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