Soil groundwater pollutant migration simulation method and system

Abstract

The invention is applied to the technical field of water pollution treatment, and discloses a soil groundwater pollutant migration simulation system which comprises a data acquisition module, a model construction module, a boundary condition setting module, a model solving module and a result verification module. According to the soil groundwater pollutant migration simulation system, by integrating soil physical properties, groundwater geology conditions, pollutant characteristics and the like and applying a scientific model construction and numerical calculation method, the concentration distribution mode of pollutants at different time and space positions is accurately predicted, the trend and range of pollution diffusion are mastered in advance, and the migration simulation of the soil groundwater pollutants is realized. Accurate data support is provided for formulating targeted pollution prevention and control and treatment strategies, and in an industrial pollution site repair project, the expansion direction and speed of pollution plume can be determined according to a simulation result, so that the range and key area of the repair project are reasonably planned, excessive treatment or insufficient treatment is avoided, and the repair efficiency is improved. The treatment efficiency is improved; the treatment cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution control, and in particular to a soil and groundwater pollutant migration simulation method and system. Background Art

[0002] With the acceleration of industrialization and urbanization, soil and groundwater pollution problems are becoming increasingly serious and have become one of the environmental hotspots of global concern. The migration process of soil and groundwater pollutants is complex and is affected by the interaction of multiple factors, such as the physical properties of the soil, the hydrogeological conditions of the groundwater, and the characteristics of the pollutants themselves. Accurately understanding the migration patterns of pollutants in the soil and groundwater system is crucial for pollution prevention and control, environmental remediation, and risk assessment. Traditional monitoring methods can often only obtain pollutant concentration information at a specific time and place, and it is difficult to comprehensively and dynamically characterize the migration process of pollutants. Therefore, using mathematical models to simulate the migration of soil and groundwater pollutants has become an important research and practical means. By constructing a simulation system, various relevant parameter data can be integrated, and appropriate mathematical models and calculation methods can be used to predict and analyze the concentration distribution of pollutants at different times and spatial locations. This helps to formulate effective pollution control strategies in advance and reduce the potential harm of pollution to the ecological environment and human health. Summary of the invention

[0003] The purpose of the present invention is to provide a soil and groundwater pollutant migration simulation method and system to solve the problem that traditional monitoring means proposed in the above background technology are difficult to comprehensively and dynamically characterize the migration process of pollutants.

[0004] To achieve the above object, the present invention provides the following technical solution: a soil and groundwater pollutant migration simulation system, comprising:

[0005] Data acquisition module: used to collect soil physical property parameters, groundwater hydrogeological parameters, and initial concentration and characteristic data of pollutants;

[0006] Model building module: electrically connected to the data acquisition module, and building a soil and groundwater pollutant migration model based on the data collected by the data acquisition module, wherein the model includes an adsorption sub-model, a diffusion sub-model and a water flow sub-model;

[0007] Boundary condition setting module: electrically connected to the model building module, setting the boundary conditions of the soil and groundwater pollutant migration model;

[0008] Model solving module: electrically connected to the boundary condition setting module, using a numerical calculation method to solve the soil groundwater pollutant migration model to obtain the concentration distribution data of pollutants at different time and space positions;

[0009] Result verification module: electrically connected to the model solving module, compares and verifies the pollutant concentration distribution data calculated by the model with the actual monitoring data. If the error exceeds the preset range, the model optimization module is triggered to adjust and optimize the model parameters until the error meets the requirements.

[0010] Preferably, the data acquisition module includes a soil parameter acquisition unit, a groundwater parameter acquisition unit and a pollutant parameter acquisition unit, which are respectively used to collect parameters of corresponding types.

[0011] Preferably, the model optimization module adopts a gradient-based optimization algorithm or an intelligent optimization algorithm when performing parameter optimization. The model optimization module is electrically connected to the result verification module, receives a trigger signal from the result verification module, and adjusts and optimizes the model parameters in the model construction module.

[0012] A soil and groundwater pollutant migration simulation method comprises the following steps:

[0013] Data collection steps: Collect soil physical property parameters, groundwater hydrogeological parameters, and initial concentration and characteristic data of pollutants;

[0014] Model building step: building a soil and groundwater pollutant migration model based on the collected data, the model includes an adsorption sub-model describing the adsorption effect of soil media on pollutants, a diffusion sub-model describing the diffusion effect of pollutants in groundwater, and a water flow sub-model describing the influence of groundwater flow on pollutant migration;

[0015] Boundary condition setting step: setting the boundary conditions of the soil groundwater pollutant migration model, including water flow and pollutant flux conditions at the upper boundary, lower boundary, and lateral boundary;

[0016] Model solving steps: using numerical calculation methods to solve the soil and groundwater pollutant migration model to obtain the concentration distribution data of pollutants at different time and spatial positions;

[0017] Result verification step: Compare and verify the pollutant concentration distribution data calculated by the model with the actual monitoring data. If the error exceeds the preset range, adjust and optimize the model parameters until the error meets the requirements.

[0018] Preferably, the physical property parameters of the soil include soil porosity, soil particle density, and soil permeability.

[0019] Preferably, the hydrogeological parameters of the groundwater include groundwater level depth, groundwater hydraulic gradient, and aquifer thickness.

[0020] Preferably, the adsorption sub-model uses the Freundlich adsorption isotherm or the Langmuir adsorption isotherm to describe the adsorption behavior of pollutants on the surface of soil particles.

[0021] Preferably, the diffusion sub-model is constructed based on Fick's law, taking into account the effects of molecular diffusion and mechanical dispersion on the diffusion of pollutants.

[0022] Preferably, the water flow sub-model uses Darcy's law to describe the flow of groundwater in soil pores.

[0023] Preferably, the numerical calculation method includes finite difference method, finite element method or boundary element method.

[0024] Compared with the prior art, the beneficial effects of the present invention are: the soil and groundwater pollutant migration simulation method:

[0025] 1. By integrating soil physical properties, underground hydrogeological conditions, and pollutant characteristics, and using scientific model construction and numerical calculation methods, we can accurately predict the concentration distribution of pollutants at different times and spatial locations, grasp the trend and scope of pollution diffusion in advance, and provide accurate data support for the formulation of targeted pollution prevention and control and governance strategies. In industrial pollution site remediation projects, the expansion direction and speed of the pollution plume can be determined based on simulation results, so as to rationally plan the scope and key areas of the remediation project, avoid excessive or insufficient governance, improve governance efficiency, and reduce governance costs;

[0026] Furthermore, by simulating the migration process of pollutants under different scenarios, we can quantitatively analyze the potential risks of pollution to the surrounding ecological environment, groundwater resources and human health. In the simulation around urban landfills, we can predict the impact of heavy metals and organic pollutants on nearby soil, water ecosystems and crop growth over a long period of time, providing a basis for formulating reasonable land use planning and environmental protection measures, and ensuring regional ecological security and residents' health.

[0027] 2. The simulation system can provide a prediction of the pollutant concentration distribution in the entire time and space domain. By comparing and verifying the simulation results with the actual monitoring data, the weak links in the monitoring network can be found, and the layout of the monitoring points can be optimized. In large-scale polluted sites, it is found that the simulation results are quite different from the monitoring data in certain areas. This may mean that the monitoring points in these areas are not set reasonably or the monitoring frequency is not enough. Therefore, the monitoring plan can be adjusted in a targeted manner to improve the representativeness and accuracy of the monitoring data and have a more comprehensive understanding of the pollution situation.

[0028] Furthermore, it can provide estimated information on pollutant concentrations in areas or time periods where actual monitoring is impossible, conduct simulation forecasts in advance in areas with complex geological conditions and difficult to reach, or in areas that may be affected by future planning projects, and provide early warning of potential pollution risks, providing forward-looking information support for environmental management and planning, and enhancing the initiative and preventive nature of environmental protection work. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The present invention provides a technical solution: a soil and groundwater pollutant migration simulation system.

[0031] Example 1: Simulation of soil and groundwater contamination migration around a municipal landfill

[0032] 1. Site Overview

[0033] The soil and groundwater around large urban landfills are potentially affected by a variety of pollutants, the main pollutants including heavy metals and some organic pollutants. The soil in the area where the landfill is located is mainly sandy loam, the groundwater level is about 8 meters deep, and the aquifer thickness is about 15 meters.

[0034] 2. Data Collection

[0035] Soil parameter acquisition unit:

[0036] After field sampling and laboratory analysis, the soil porosity was determined to be 0.35 and the soil particle density was 2.6 g / cm 3 The soil permeability is 5×10 -5 cm / s.

[0037] Groundwater parameter acquisition unit:

[0038] By setting up multiple water level monitoring wells around and inside the landfill, it was monitored that the groundwater level depth fluctuated between 7.5 and 8.5 meters, the average hydraulic gradient was 0.002, and the thickness of the aquifer was determined to be 15 meters through detailed geological surveys.

[0039] Pollutant parameter collection unit:

[0040] Soil and groundwater sampling and analysis were carried out at different locations and distances around the landfill. The initial concentration of lead, cadmium and mercury was measured to be 150 μg / L, 20 μg / L and 5 μg / L, respectively, 50 meters away from the edge of the landfill. For volatile fatty acids, the initial concentration can reach up to 1000 mg / L. The adsorption characteristics of heavy metals were described by the Langmuir adsorption isotherm, and the relevant parameters were fitted through laboratory adsorption experiments.

[0041] 3. Model Construction

[0042] Based on the collected data, a soil and groundwater pollutant migration model is constructed, including:

[0043] Adsorption sub-model: For heavy metal pollutants, the Langmuir adsorption isotherm was used to describe their adsorption behavior on the surface of sandy loam particles. The parameters of the Langmuir equation were obtained through experimental fitting, including the maximum adsorption amount, adsorption equilibrium constant, etc. Among them, the maximum adsorption amount of lead was 500μg / g, and the adsorption equilibrium constant was 0.1L / μg.

[0044] Diffusion submodel: Based on Fick's law, the influence of molecular diffusion and mechanical diffusion on the diffusion of pollutants is considered. The molecular diffusion coefficient is determined to be 1×10 -6 cm 2 / s, and the mechanical diffusion coefficient is 5×10 -5 cm 2 / s.

[0045] Water flow submodel: Darcy's law is used to describe the flow of groundwater in the pores of sandy loam. Combined with the acquired groundwater level depth, hydraulic gradient and other parameters, the water flow velocity is calculated to be approximately 0.01 cm / s.

[0046] 4. Boundary Condition Setting

[0047] Upper boundary: set as the precipitation infiltration and atmospheric evaporation boundary. According to meteorological data, the average annual precipitation is 800 mm, the precipitation infiltration coefficient is 0.3, and the evaporation intensity varies between 0.1-0.3 mm / d depending on the season. The infiltration flux and evaporation flux conditions are determined accordingly.

[0048] Lower boundary: set as the aquiclude boundary, assuming that the flux of pollutants at the aquiclude is zero, that is, the pollutants cannot continue to migrate downward through the aquiclude.

[0049] Lateral boundary: Different types of boundaries are set according to the topography and hydrogeological conditions around the landfill. The side close to the mountain is set as a zero flux boundary, as it is believed that less pollutants flow in and out in this direction. The side close to the river is set as an exchange boundary with the river water. The exchange flux conditions are determined based on the river flow, water quality, and hydraulic connection with groundwater.

[0050] 5. Model Solution

[0051] The finite element method was used to solve the constructed soil and groundwater pollutant migration model. The surrounding area of ​​the landfill and the affected area were divided into several triangular units. The concentration distribution data of each pollutant at different time and spatial positions were gradually calculated according to the time step and space step. The concentration of each pollutant at different distances from the edge of the landfill was simulated and calculated at the 2nd, 5th, and 10th year after the operation of the landfill. In the 5th year, at 200 meters away from the edge of the landfill, the predicted lead concentration was 50μg / L, the cadmium concentration was 8μg / L, and the volatile fatty acid concentration was 200mg / L.

[0052] 6. Result Verification

[0053] The pollutant concentration distribution data calculated by the model were compared with the actual monitoring data at multiple long-term monitoring points set up around the landfill. At 200 meters from the edge of the landfill, in the fifth year, the model predicted that the lead concentration was 50μg / L, and the actual monitoring value was 55μg / L, with an error of (55-50) / 55×100%≈9.1%; the cadmium concentration was predicted to be 8μg / L, and the actual monitoring value was 9μg / L, with an error of (9-8) / 9×100%≈11.1%; the volatile fatty acid concentration was predicted to be 200mg / L, and the actual monitoring value was 220mg / L, with an error of (220-200) / 220×100%≈9.1%. If the error exceeds the preset range, i.e. ±15%, the model optimization module will be triggered.

[0054] 7. Model optimization (if necessary)

[0055] If the result verification finds that the error exceeds the preset range, the model parameters are adjusted and optimized using an intelligent optimization algorithm (such as the particle swarm optimization algorithm), and key parameters such as the Langmuir adsorption isotherm parameters in the adsorption sub-model and the diffusion coefficient in the diffusion sub-model are adjusted. Then, the model solution and result verification steps are performed again until the error meets the requirements.

[0056] Example 2: Simulation of soil and groundwater contamination migration in agricultural irrigation areas

[0057] 1. Site Overview

[0058] The long-term use of chemical fertilizers and pesticides in agricultural irrigation areas has caused a certain degree of pollution in the soil and groundwater. The soil in this area is mainly loam, the groundwater level is about 3 meters deep, and the aquifer thickness is about 8 meters.

[0059] 2. Data Collection

[0060] Soil parameter acquisition unit:

[0061] Field sampling and laboratory analysis revealed that the soil porosity was 0.42 and the soil particle density was 2.7 g / cm 3 The soil permeability is 3×10 -5 cm / s.

[0062] Groundwater parameter acquisition unit:

[0063] By setting up water level monitoring wells in the irrigation area, it was monitored that the groundwater level depth fluctuated between 2.5 and 3.5 meters, the average hydraulic gradient was 0.0015, and the aquifer thickness was determined to be 8 meters through geological surveys.

[0064] Pollutant parameter collection unit:

[0065] Sampling and analysis of different farmland plots in the irrigation area revealed that the main pollutants were nitrates (NO3 - ) and some pesticide residues (such as dichlorvos, etc.). Taking a typical farmland plot as an example, the initial concentration of nitrate was measured to be 200 mg / L, and the initial concentration of dichlorvos was 0.5 mg / L. The adsorption characteristics of nitrate were described by the Freundlich adsorption isotherm, and the relevant parameters were obtained by fitting the laboratory adsorption experiment.

[0066] 3. Model Construction

[0067] Based on the collected data, a soil and groundwater pollutant migration model is constructed, including:

[0068] Adsorption sub-model: For nitrate pollutants, the Freundlich adsorption isotherm is used to describe its adsorption behavior on the surface of loam particles. The parameters of the Freundlich equation, such as the adsorption coefficient, are obtained through experimental fitting. The adsorption coefficient is 0.5 (L / g)^(1 / n), where n is 2.

[0069] Diffusion submodel: Based on Fick's law, the influence of molecular diffusion and mechanical diffusion on the diffusion of pollutants is considered. The molecular diffusion coefficient is determined to be 8×10 -6 cm 2 / s, and the mechanical diffusion coefficient is 2×10 -5 cm 2 / s.

[0070] Water flow submodel: Darcy's law is used to describe the flow of groundwater in the pores of loam. Combined with the acquired parameters such as groundwater level depth and total hydraulic gradient, the water flow velocity is calculated to be approximately 0.005 cm / s.

[0071] 4. Boundary Condition Setting

[0072] Upper boundary: set as the irrigation water infiltration boundary. According to the local agricultural irrigation water situation, the irrigation volume is about 50 mm each time, and the irrigation cycle is once a week. The infiltration flux conditions are determined accordingly. At the same time, atmospheric evaporation is taken into account, and the evaporation intensity varies between 0.05-0.2 mm / d according to the season.

[0073] Lower boundary: set as the aquiclude boundary, assuming that the flux of pollutants at the aquiclude is zero, that is, the pollutants cannot continue to migrate downward through the aquiclude.

[0074] Lateral boundary: Based on the topography and hydrogeological conditions around the irrigation area, it is set as a zero flux boundary, assuming that pollutants flow in and out less in the lateral direction.

[0075] 5. Model Solution

[0076] The boundary element method was used to test the constructed soil and groundwater pollutant migration model. The irrigation area was divided into several boundary units. The concentration distribution data of each pollutant at different time and spatial positions were gradually calculated according to the time step and space step. The concentration of each pollutant at different distances from the irrigation water source after a planting season (about 6 months) was simulated and calculated. After the planting season, the concentration of nitrate was predicted to be 100 mg / L and the concentration of dichlorvos was 0.2 mg / L at 100 meters away from the irrigation water source.

[0077] 6. Result Verification

[0078] The concentration distribution data of each pollutant calculated by the model were compared and verified with the data actually monitored at multiple monitoring points set up in the irrigation area. At a distance of 100 meters from the irrigation water source, after the planting season, the model predicted that the concentration of nitrate was 100 mg / L, and the actual monitored value was 105 mg / L, with an error of (105-100) / 105×100%≈4.8%; the predicted concentration of dichlorvos was 0.2 mg / L, and the actual monitored value was 0.22 mg / L, with an error of (0.22-0.2) / 0.22×100%≈9.1%. If the error exceeds the preset range (such as ±15%), the model optimization module is triggered.

[0079] 7. Model optimization (if necessary)

[0080] If the result verification finds that the error exceeds the preset range, the gradient-based optimization algorithm is used to adjust and optimize the model parameters, and key parameters such as the Freundlich adsorption isotherm parameters in the adsorption sub-model and the diffusion coefficient in the diffusion sub-model are adjusted. Then the model solution and result verification steps are performed again until the error meets the requirements.

[0081] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and do not limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the protection scope of the present invention.

Claims

1. A soil and groundwater pollutant migration simulation system, characterized in that: include: Data acquisition module: used to collect soil physical property parameters, groundwater hydrogeological parameters, and initial concentration and characteristic data of pollutants; Model building module: electrically connected to the data acquisition module, and building a soil and groundwater pollutant migration model based on the data collected by the data acquisition module, wherein the model includes an adsorption sub-model, a diffusion sub-model and a water flow sub-model; Boundary condition setting module: electrically connected to the model building module, setting the boundary conditions of the soil and groundwater pollutant migration model; Model solving module: electrically connected to the boundary condition setting module, using a numerical calculation method to solve the soil groundwater pollutant migration model to obtain the concentration distribution data of pollutants at different time and space positions; Result verification module: electrically connected to the model solving module, compares and verifies the pollutant concentration distribution data calculated by the model with the actual monitoring data. If the error exceeds the preset range, the model optimization module is triggered to adjust and optimize the model parameters until the error meets the requirements.

2. According to the soil and groundwater pollutant migration simulation system described in claim 1, the data acquisition module includes a soil parameter acquisition unit, a groundwater parameter acquisition unit and a pollutant parameter acquisition unit, which are respectively used to collect parameters of corresponding types.

3. According to the soil and groundwater pollutant migration simulation system described in claim 1, the model optimization module adopts a gradient-based optimization algorithm or an intelligent optimization algorithm when performing parameter optimization, and the model optimization module is electrically connected to the result verification module, receives the trigger signal of the result verification module and adjusts and optimizes the model parameters in the model construction module.

4. A method for simulating the migration of pollutants in soil and groundwater based on the system described in any one of claims 1 to 3, characterized in that: The following steps are involved: Data collection steps: Collect soil physical property parameters, groundwater hydrogeological parameters, and initial concentration and characteristic data of pollutants; Model building step: building a soil and groundwater pollutant migration model based on the collected data, the model includes an adsorption sub-model describing the adsorption effect of soil media on pollutants, a diffusion sub-model describing the diffusion effect of pollutants in groundwater, and a water flow sub-model describing the influence of groundwater flow on pollutant migration; Boundary condition setting step: setting the boundary conditions of the soil groundwater pollutant migration model, including water flow and pollutant flux conditions at the upper boundary, lower boundary, and lateral boundary; Model solving steps: using numerical calculation methods to solve the soil and groundwater pollutant migration model to obtain the concentration distribution data of pollutants at different time and spatial positions; Result verification steps: Compare and verify the pollutant concentration distribution data calculated by the model with the actual monitoring data. If the error exceeds the preset range, adjust and optimize the model parameters until the error meets the requirements.

5. According to the soil and groundwater pollutant migration simulation method described in claim 4, the physical property parameters of the soil include soil porosity, soil particle density, and soil permeability.

6. According to the soil groundwater pollutant migration simulation method described in claim 4, the hydrogeological parameters of the groundwater include groundwater level depth, groundwater hydraulic gradient, and aquifer thickness.

7. According to the soil and groundwater pollutant migration simulation method described in claim 4, the adsorption sub-model uses the Freundlich adsorption isotherm or the Langmuir adsorption isotherm to describe the adsorption behavior of pollutants on the surface of soil particles.

8. According to the soil and groundwater pollutant migration simulation method described in claim 4, the diffusion sub-model is constructed based on Fick's law, taking into account the influence of molecular diffusion and mechanical diffusion on the diffusion of pollutants.

9. According to the soil and groundwater pollutant migration simulation method according to claim 4, the water flow sub-model uses Darcy's law to describe the flow of groundwater in soil pores.

10. A soil and groundwater pollutant migration simulation method according to claim 4, characterized in that: The numerical calculation method includes finite difference method, finite element method or boundary element method.