Method for predicting behavior of microbial chemotaxis based on reactive solute transport model
By constructing a reactive solute transport model in porous media, quantifying the intensity of microbial chemotaxis and coupling it with the convection term, the problem of insufficient coupling between microbial chemotaxis and solute transport in existing models is solved, and accurate prediction of microbial and solute co-transport is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- OCEAN UNIV OF CHINA
- Filing Date
- 2026-03-10
- Publication Date
- 2026-06-09
AI Technical Summary
Existing reactive solute transport models are difficult to effectively couple microbial chemotaxis and solute transport in porous media, resulting in large prediction biases. They lack reasonable chemotaxis parameter quantification methods and kinetic coupling designs, making it impossible to accurately predict the co-transport patterns of microorganisms and solutes.
By conducting migration experiments in porous media, breakthrough curves of microorganisms and target reactive solutes were obtained. A reactive solute transport model was constructed, chemotaxis intensity was quantified, and a convection-like term was introduced. The functional relationship between chemotaxis rate and solute concentration gradient was established, forming a model coupled with chemotaxis parameters. This model was then embedded into multiphysics simulation software for prediction.
This study achieves a deep integration of microbial chemotactic migration behavior and solute transport, accurately predicts the co-transport patterns of microorganisms and solutes in porous media, makes up for the shortcomings of existing models, and improves prediction accuracy.
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Figure CN122168714A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of microbial migration behavior prediction technology in porous media, specifically a method for predicting microbial chemotactic migration behavior based on a reactive solute transport model. Background Technology
[0002] The chemotactic migration behavior of microorganisms in porous media environments such as soil and filter sand columns is deeply intertwined with solute transport processes. Their synergistic effects have a crucial impact on engineering practices such as in-situ bioremediation of contaminated sites and control of solute pollution in groundwater. Accurately predicting the synergistic transport patterns of microorganisms and reactive solutes in porous media is a core technical requirement for optimizing bioremediation technologies and controlling solute pollution in groundwater. Reactive solute transport models, as a core tool for quantifying solute migration and transformation processes in porous media, can couple convection, dispersion, adsorption, and solute reactions in porous media to accurately characterize the spatiotemporal distribution of solutes. They have been widely used in the field of porous media environment simulation, and their theoretical framework provides a natural fit for integrating microbial chemotactic migration mechanisms, becoming a key technical foundation for analyzing microbial chemotactic migration behavior in this scenario.
[0003] Currently, research on chemotactic migration of microorganisms in porous media, especially in the quantitative characterization based on reactive solute transport models, still faces significant technical shortcomings. Existing studies are mostly limited to the observation of microbial chemotactic behavior under single conditions, failing to conduct coupled studies with the actual transport characteristics of reactive solutes, making it difficult to match the complex scenarios of solute transport and microbial chemotactic interactions in actual porous media. Although existing reactive solute transport models have mature solute transport quantification capabilities, they have not been optimized for the specificity of microbial chemotactic behavior in porous media. They lack reasonable quantification methods for chemotactic parameters and targeted coupling designs of chemotactic kinetics and solute transport processes. They have not yet achieved a deep integration of microbial chemotactic kinetics and reactive solute transport processes, and cannot effectively characterize the multiple interactions between microbial chemotaxis, porous media, and solute transport, thus making it difficult to accurately predict the synergistic transport patterns of microorganisms and solutes in porous media. Summary of the Invention
[0004] The purpose of this invention is to provide a method for predicting microbial chemotactic migration behavior based on a reactive solute transport model, in order to solve the problems of insufficient coupling and large prediction bias in existing reactive solute transport models.
[0005] The technical solution of this invention is: A method for predicting microbial chemotactic migration behavior based on a reactive solute transport model includes the following steps: Through migration experiments of model functional microorganisms in porous media, breakthrough curves of microorganisms and target reactive solutes were obtained. A reactive solute transport model was constructed based on solute transport theory. The model parameters were calibrated using the breakthrough curves of microorganisms and target reactive solutes. The solute concentration distribution was obtained through the constructed reactive solute transport model. The capillary method was used to quantify the chemotactic intensity of model functional microorganisms to target solutes, and a functional relationship between chemotactic rate and solute concentration gradient was established. Substituting the obtained solute concentration distribution into the established functional relationship, the chemotaxis rate of microorganisms at different locations and times in the porous medium was obtained, and a convection-diffusion equation with a convection-like term was introduced to simulate microbial migration. A coupling relationship between chemotactic parameters and reactive solute transport models was constructed to form a reactive solute transport model coupled with chemotactic parameters. This model was then embedded into a multiphysics simulation software. The basic physicochemical parameters of the porous medium, hydrodynamic parameters, initial boundary parameters of the research object, and microbial-porous medium interface parameters were input. The reactive solute transport model coupled with chemotactic parameters was used to predict the migration behavior of microorganisms under different initial conditions.
[0006] Preferably, as a further improvement of the present invention, the specific operation of the migration experiment of the model functional microorganism in the porous medium includes the following steps: after pretreatment of high-purity round-grained quartz sand, a porous medium column is formed by wet filling; a functional microorganism suspension is prepared as influent; after adding the target reactive solute, it is pumped into the porous medium column; and the content of microorganisms and the content of the target reactive solute in the water are continuously detected.
[0007] Preferably, as a further improvement of the present invention, the reactive solute is nitrate, and the microorganism is a model functional microorganism with nitrate-reducing ability.
[0008] Preferably, as a further improvement of the present invention, the expression of the constructed reactive solute transport model is as follows: , In the formula, It is a mobile phase species i concentration, i represent ML -3 ; It is the hydrodynamic dispersion coefficient; R Indicates the reaction term.
[0009] Preferably, as a further improvement of the present invention, the microbial-mediated reaction expression is as follows: , In the formula, b This refers to the bacterial concentration in the mobile phase. S This represents the bacterial concentration in the sedimentary phase. The density of the porous medium; The porosity of the sand column; This is the yield coefficient; The maximum growth rate; This refers to the concentration of dissolved organic carbon in the mobile phase. Species of the mobile phase i The half-saturation constant, is the half-saturation constant of dissolved organic carbon in the mobile phase species.
[0010] Preferably, as a further improvement of the present invention, the established functional relationship between the chemotaxis rate and the solute concentration gradient is expressed as follows: , In the formula, V c Chemotaxis rate; v is the bacterial swimming speed; The effective chemotactic sensitivity coefficient of bacteria in porous media; K is the chemotactic receptor constant; c i .
[0011] Preferably, as a further improvement of the present invention, the expression for simulating the migration of chemotactic bacteria in a porous aqueous medium by introducing a convection-like term to describe the three-dimensional convection-diffusion equation for chemotaxis is shown below: , In the formula, b This refers to the bacterial concentration in the mobile phase. t For time; The bacterial hydrodynamic dispersion coefficient; u Pore flow velocity; The density of the porous medium; The porosity of the sand column; S This represents the bacterial concentration in the sedimentary phase. V c Chemotaxis rate.
[0012] Preferably, as a further improvement of the present invention, the change in the sedimentary phase microbial concentration S satisfies: , in, , , In the formula, The adhesion rate constant; The desorption rate constant; The Langmuir blocking function is related to the share of available porous media for deposition. To simulate the depth-dependent retention function of the super-exponential retention curve; SThis represents the bacterial concentration in the sedimentary phase. This represents the maximum bacterial concentration in the sedimentary phase. The coefficient representing the exponential decay of strain rate with depth is e, which is a natural constant.
[0013] Preferably, as a further improvement of the present invention, the model functional microorganism includes a wild-type strain or a defective mutant strain, and both are of the same strain. When the model functional microorganism is a defective mutant strain, the chemotactic rate... V c =0.
[0014] Preferably, as a further improvement of the present invention, the wild-type strain is Shewanella oneidensis MR-1, the defective mutant strain is Shewanella oneidensis MR-1 flagellate-deficient nonmotile mutant strain Δ flab .
[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. No longer limited to the observation of microbial chemotaxis under single conditions, but based on the characteristics of reactive solute transport, the breakthrough curves of microorganisms and target reactive solutes are obtained simultaneously through migration experiments. The actual process of solute transport is deeply integrated with the study of microbial chemotaxis. The constructed technical solution is fully matched to the complex scenario of solute transport and microbial chemotaxis interaction in actual porous media, making the research results of microbial chemotaxis and migration more in line with engineering practice and making up for the shortcomings of existing research being disconnected from actual application scenarios.
[0016] 2. The capillary method is used to accurately quantify the chemotactic intensity of functional microorganisms to target solutes. By establishing a functional relationship between chemotactic rate and solute concentration gradient, the chemotactic kinetic parameters are quantitatively and formulaically expressed. This provides a reasonable chemotactic parameter quantification method for reactive solute transport models and solves the problem of existing models lacking effective chemotactic parameter quantification methods.
[0017] 3. Based on the reactive solute transport model, a quantitative chemotactic rate is introduced as a key parameter into the convection-dispersion equation, which is similar to a convection term. This establishes a coupling relationship between the chemotactic motion parameters and the reactive solute transport model, achieving a deep integration of microbial chemotactic kinetics and reactive solute transport processes. This coupling design can effectively characterize the multiple interactions between microbial chemotaxis, porous media, and solute transport, changing the current situation where existing models cannot characterize the interactions among these three factors. This allows the model to provide a more comprehensive and realistic representation of complex transport processes in porous media.
[0018] 4. By using breakthrough curves, the parameters of the reactive solute transport model are calibrated and the accuracy of the coupled model is verified. Combined with the design of a deeply coupled model and quantitative chemotactic parameters, the coupled model can accurately predict the migration trajectory and concentration distribution of microorganisms in porous media under different initial conditions. It effectively captures the co-transport law of microorganisms and reactive solutes, fundamentally solving the problem of insufficient prediction accuracy of existing models, and providing reliable model support for the accurate analysis of the transport relationship between microorganisms and solutes in porous media. Attached Figure Description
[0019] Figure 1 This is a flowchart of the microbial chemotactic migration behavior prediction method based on a reactive solute transport model according to the present invention.
[0020] Figure 2 This is a schematic diagram of the apparatus used in the present invention to perform migration experiments in porous media.
[0021] Figure 3 The measured curves of the reaction solute were obtained in the migration experiment of the porous medium in this invention.
[0022] Figure 4 The graphs show the concentration curves of each solute fitted to the reactive solute transport model in this invention.
[0023] Figure 5 This is a graph showing the relationship between the chemotaxis intensity of microorganisms and the solutes in each reaction in this invention.
[0024] Figure 6 This is a comparison chart of the microbial penetration curve predicted by the coupling model in this invention and the measured curve. Detailed Implementation
[0025] The following is combined Figures 1-6 The specific embodiments of the present invention will be described in detail below. In the description of the invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.
[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the invention, unless otherwise stated, "a plurality of" means two or more.
[0027] It should be noted that the experiments involved in this invention use materials containing nitrates (NO3). − -N) Model functional microorganisms with reducing capacity, including wild-type strains or defective mutants; wherein, the wild-type strain is the first specified strain, the defective mutant is the second specified strain, and both are the same strain. The wild-type strain is used as the model for the following description. Shewanella oneidensis MR-1, the defective mutant strain is Shewanella oneidensis MR-1 flagellate-deficient nonmotile mutant strain Δ flab Taking this as an example, the construction and verification process of a reactive solute transport model with coupled chemotactic parameters is illustrated using the multiphysics coupling simulation software COMSOL.
[0028] Example 1 like Figures 1-5 As shown, this embodiment of the invention provides a method for predicting microbial chemotactic migration behavior based on a reactive solute transport model, in order to select model functional microorganisms with nitrate-reducing capabilities. Shewanella oneidensis MR-1, as the first chemotactic designation strain wild-type (WT), was cultured in LB medium with shaking until mid-log growth (OD). 600 (≈ 0.5), then the bacterial culture was centrifuged to collect the bacterial cells, and after thorough washing, it was resuspended in background solution (hydroxyethylpiperazine ethanethiolate buffer, pH=7.1). The specific steps are as follows: Step S1: Obtain the breakthrough curve: Obtain the breakthrough curve of the microorganism and the target reactive solute through the migration experiment of the model functional microorganism in the porous medium.
[0029] Microbial migration experiments in porous media: First, high-purity round-grained quartz sand (SiO2 content greater than 99.8%, average particle size 425 μm) was pretreated, then wet-filled with the quartz sand to ensure uniform, saturated, and bubble-free packing. The porosity of the packed column was determined by gravimetric analysis and maintained at 0.40. All experiments were conducted in a constant temperature chamber (25℃), with upward movement as follows... Figure 2 As shown. Under stable experimental conditions, the flow rate was set to 5 m / d. Then, 10 pore volumes (PVs) of background solution were pumped into the saturated silica sand column to pre-equilibrate the column. Subsequently, a total of 3 PVs of background solution containing bacteria, 2 mM NaNO3, and 4 mM sodium lactate was pumped in using a three-way valve, followed by another 3 PVs of background solution. The eluent was collected periodically using an automatic fraction collector. The bacterial and solute concentrations in the effluent were measured, and a breakthrough curve reflecting the relative cell concentration as a function of the influent flow rate was plotted, as shown. Figure 3 (1) is shown, so as to facilitate subsequent model calibration.
[0030] Step S2: Construct and calibrate a reactive solute transport model: Construct a reactive solute transport model based on solute transport theory, calibrate the model parameters using the breakthrough curves of the obtained model functional microorganism and the target reactive solute, and obtain the solute concentration distribution through the constructed reactive solute transport model.
[0031] Specifically, based on the theory of solute transport in porous media and combined with the reaction characteristics of the target solute, a reactive solute transport model is constructed. The expression of the constructed reactive solute transport model is shown in the following equation: , in, It is a mobile phase species i concentration, i represent or or ML -3 ; It is the hydrodynamic dispersion coefficient, L 2 T -1 ; R ML represents the reaction term. -3 T -1 ; Microbial-mediated reactions can be described by Monod kinetics as shown in the following equation: , in, b The concentration of bacteria in the mobile phase, ML -3 ; S MM represents the bacterial concentration in the sedimentary phase. -1 , The density of the porous medium; The porosity of the sand column is a dimensionless parameter. This is the yield coefficient, dimensionless. For the maximum growth rate, T -1 ; The concentration of dissolved organic carbon (DOC) in the mobile phase, ML -3 ; Species of the mobile phase i The half-saturation constant, ML is the half-saturation constant of the mobile phase species DOC. -3 .
[0032] The parameters of the reactive solute transport model were calibrated using the breakthrough curves of the microorganisms and the target reactive solute obtained in step S1. After calibration, the solute concentration distribution at different locations and times in the porous medium was obtained by solving the calibrated reactive solute transport model. Figure 4As shown in (1), the calibrated model accurately depicts the transport of reactive solutes in the porous medium in this example.
[0033] Step S3: Establish the chemotaxis rate function relationship: Use the capillary method to quantify the chemotaxis intensity of the model functional microorganisms to the target solute, and establish the functional relationship between the chemotaxis rate and the solute concentration gradient.
[0034] Specifically, the capillary method was used to quantitatively analyze the chemotactic movement of functional bacteria toward the target solute, quantify and compare the chemotactic strength of the target microorganisms toward each reactant solute, and confirm the chemotactic effect of the target microorganisms on the target reactant solute. The chemotaxis is strongest, such as Figure 5 As shown, nitrate was used as the chemotactic inducer: a background solution containing 2 mM NaNO3 and 4 mM sodium lactate was prepared as the chemotactic assay solution; 0.4 mL of the chemotactic assay solution was drawn into a 1 mL syringe with a 22 G needle and inserted into the cell suspension. Both the syringe solution and the chemotactic assay solution contained 3 μg / mL chloramphenicol to inhibit cell proliferation. After standing for 6 h, the liquid in the syringe was carefully transferred to a centrifuge tube and diluted with buffer solution to a cell concentration of ~10⁻⁶. 3 -10 4 The concentration of cells / mL in the test solution was measured using the plate colony counting method, and the functional relationship between the chemotactic rate and the solute concentration gradient was established as shown in the following equation:
[0035] , In the formula, V c chemotaxis rate, LT -1 ; For bacterial swimming speed, LT -1 ; L represents the effective chemotactic sensitivity coefficient of bacteria in porous media. 2 T -1 ; ML is the chemokine receptor constant. -3 ; The concentration of the chemotactic inducer in the mobile phase is ML. -3 .
[0036] Step S4: Simulate microbial chemotactic migration: Substitute the reactive solute concentration distribution results obtained from the reactive solute transport model into the chemotactic rate function to obtain the microbial chemotactic rate at different locations and times in the porous medium; introduce a convection-like term to simulate the migration of chemotactic bacteria in the porous medium aqueous phase using a three-dimensional convection-diffusion equation to describe chemotaxis. , in,b It is the concentration of bacteria in the mobile phase, in ml. -3 ; t It is time, T; It is the bacterial hydrodynamic dispersion coefficient, L 2 T -1 ; u It is the pore flow velocity of pore water within a porous medium. u = (0, 0, That is, the model only considers the pore velocity in the vertical direction, LT -1 ; It is the density of the porous medium, ML -3 ; It is the porosity of the sand column, a dimensionless unit; S It refers to the bacterial concentration in the sedimentary phase, MM. -1 Defined as: in, T is the adhesion rate constant. -1 T is the desorption rate constant. -1 The Langmuir blocking function is a dimensionless parameter relating to the share of available porous media for deposition. This is a depth-dependent retention function commonly used to simulate super-exponential retention curves, with dimensionless parameters; and is defined as follows. , , in, S It refers to the bacterial concentration in the sedimentary phase, MM. -1 ; The maximum bacterial concentration in the sedimentary phase, i.e., the bacterial retention capacity, is MM. -1 ; MM is a coefficient characterizing the exponential decay of strain rate with depth. -1 .
[0037] Step S5: Couple the model and validate the predictions: Construct the coupling relationship between chemotactic parameters and the reactive solute transport model to form a reactive solute transport model coupled with chemotactic parameters. Embed the COMSOL reactive solute transport model. Validate the accuracy of the coupled model based on the breakthrough curve of cell relative concentration as a function of influent volume. Figure 6As shown in (1), after successful verification, input the basic physicochemical parameters of the porous media, the hydrodynamic parameters, the initial boundary parameters of the research object, and the microbial-porous media interface parameters. Among them, the basic physicochemical parameters of the porous media include porosity, the hydrodynamic parameters include pore flow velocity and hydrodynamic dispersion coefficient, the initial boundary parameters include the initial concentrations of microorganisms and target reactive solutes, and the microbial-porous media interface parameters include parameters such as adhesion rate constant and desorption rate constant. The reactive solute transport model coupled with chemotactic parameters is used to predict the transport of solutes under different initial solute concentrations and different pore flow velocities. Shewanella oneidensis Migration trajectory and concentration distribution of MR-1 in porous media.
[0038] Example 2 This embodiment follows the same experimental steps, model construction, and verification methods as Embodiment 1, the only difference being that the model functional microorganism is replaced with... Shewanella oneidensis MR-1 flagellate-deficient nonmotile mutant strain Δ flab As a second predicate strain for chemotaxis, replacing the wild-type bacterial strain, for the flagella-deficient nonmotile mutant Δ flab In terms of, with Shewanella oneidensis The difference with MR-1 lies in the chemotaxis rate. They lack chemotactic ability. Experiments and model simulations were conducted according to steps S1-S5 of Example 1, and breakthrough curves reflecting the change in relative cell concentration with water inflow were plotted as follows: Figure 3 As shown in (2), the solute concentration distribution at different locations and times in the porous medium is obtained by solving the calibrated reactive solute transport model, as follows: Figure 4 As shown in (2), the capillary method was used to quantitatively analyze the chemotactic movement of functional bacteria toward the target solute, quantify the chemotactic strength of the target microorganisms toward each reactant solute, and compare them to confirm the chemotactic strength of the target microorganisms toward the target reactant solute. The chemotaxis is strongest, such as Figure 5 As shown, the prediction results of the coupled model are compared with the measured breakthrough curves. Based on the breakthrough curves showing the change in relative cell concentration with influent flow rate, the accuracy of the coupled model is verified. Figure 6 As shown in (2), compared with Example 1 Figure 6 (1) Compared to the previous model, there was significantly less cell outflow, indicating that chemotaxis affects the distribution of microorganisms in porous media. The model of coupling microbial chemotaxis and reactive solutes in this invention is more accurate.
[0039] The above-disclosed embodiments are merely preferred embodiments of the present invention. However, the embodiments of the present invention are not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.
Claims
1. A method for predicting the behavior of microorganism chemotactic migration based on a reactive solute transport model, characterized by, Includes the following steps: Through migration experiments of model functional microorganisms in porous media, breakthrough curves of microorganisms and target reactive solutes were obtained. A reactive solute transport model was constructed based on solute transport theory. The parameters of the reactive solute transport model were calibrated using the breakthrough curves of microorganisms and target reactive solutes. The solute concentration distribution was obtained through the constructed reactive solute transport model. The capillary method was used to quantify the chemotactic intensity of model functional microorganisms to target solutes, and a functional relationship between chemotactic rate and solute concentration gradient was established. Substituting the obtained solute concentration distribution into the established functional relationship, the chemotaxis rate of microorganisms at different locations and times in the porous medium was obtained, and a convection-diffusion equation with a convection-like term was introduced to simulate microbial migration. A coupling relationship between chemotactic parameters and reactive solute transport models was constructed to form a reactive solute transport model coupled with chemotactic parameters. This model was then embedded into a multiphysics simulation software. The basic physicochemical parameters of the porous medium, hydrodynamic parameters, initial boundary parameters of the research object, and microbial-porous medium interface parameters were input. The reactive solute transport model coupled with chemotactic parameters was used to predict the migration behavior of microorganisms under different initial conditions. 2.The method of claim 1, wherein the method is characterized by, The specific operation of the migration experiment of the model functional microorganisms in porous media includes the following steps: after pretreatment of high-purity round-grained quartz sand, a porous media column is formed by wet filling; a functional microorganism suspension is prepared as influent; after adding the target reactive solute, it is pumped into the porous media column; and the content of microorganisms and the content of the target reactive solute in the water are continuously detected.
3. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 2, characterized in that, The reactive solute is nitrate, and the microorganism is a model functional microorganism with nitrate-reducing ability.
4. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 3, characterized in that, The expression for the constructed reactive solute transport model is shown in the following equation: ， In the formula, Species of the mobile phase i The concentration; t For time; i for ; The hydrodynamic dispersion coefficient; R For the reaction term; The porosity of the sand column; u The pore flow velocity is [value missing].
5. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 4, characterized in that, The microbial-mediated reaction expression is shown in the following formula: ， In the formula, b This refers to the bacterial concentration in the mobile phase. S This represents the bacterial concentration in the sedimentary phase. The density of the porous medium; The porosity of the sand column; This is the yield coefficient; The maximum growth rate; This refers to the concentration of dissolved organic carbon in the mobile phase. Species of the mobile phase i The half-saturation constant, is the half-saturation constant of dissolved organic carbon in the mobile phase species.
6. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 5, characterized in that, The established functional relationship between the chemotaxis rate and the solute concentration gradient is expressed as follows: ， In the formula, V c Chemotaxis rate; v is the bacterial swimming speed; The effective chemotactic sensitivity coefficient of bacteria in porous media; K is the chemotactic receptor constant; c i .
7. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 6, characterized in that, The expression for simulating the migration of chemotactic bacteria in porous aqueous media by introducing a convection-like term to describe chemotaxis using a three-dimensional convection-diffusion equation is shown below: ， In the formula, b This refers to the bacterial concentration in the mobile phase. t For time; The bacterial hydrodynamic dispersion coefficient; u Pore flow velocity; The density of the porous medium; The porosity of the sand column; S This represents the bacterial concentration in the sedimentary phase. V c Chemotaxis rate.
8. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 7, characterized in that, The change in the microbial concentration S in the sedimentary phase satisfies: ， in, , , In the formula, This is the adhesion rate constant; The desorption rate constant; The Langmuir blocking function is related to the share of available porous media for deposition. To simulate the depth-dependent retention function of the super-exponential retention curve; S This represents the bacterial concentration in the sedimentary phase. This represents the maximum bacterial concentration in the sedimentary phase. The coefficient representing the exponential decay of strain rate with depth is e, which is a natural constant.
9. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 6, wherein the model functional microorganism includes wild-type strains or defective mutant strains, and both are of the same strain; when the model functional microorganism is a defective mutant strain, the chemotactic rate is... V c =0.
10. The method for predicting microbial chemotactic migration behavior based on a reactive solute transport model according to claim 9, characterized in that, The wild-type strain is Shewanella oneidensis MR-1, the defective mutant strain is Shewanella oneidensis MR-1 flagellate-deficient nonmotile mutant strain Δ flab .