A soil and groundwater pollutant monitoring method and digital early warning platform

Abstract

The present invention discloses a soil and groundwater pollutant monitoring method and a digital early warning platform, which relates to the field of data processing technology. The method comprises the following steps: S1, obtaining a monitoring data set and an impact data set of soil and groundwater; S2, using the impact data set of groundwater and the topographic parameters of the soil to construct a migration flow model for soil and groundwater; S3, using the migration flow model and the monitoring data set of soil and groundwater to determine the heavy metal concentration of soil and groundwater at the next moment, and when the heavy metal concentration at the next moment exceeds a preset threshold, an early warning is issued. The present invention can accurately predict the heavy metal concentration of groundwater and improve the early warning accuracy by fusing multiple parameters and constructing a model, providing a scientific basis for environmental protection and water resources management, and promoting sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a soil and groundwater pollutant monitoring method and a digital early warning platform. Background Art

[0002] As a precious freshwater resource on the earth, groundwater has high ecological and economic value. However, with the development of the economy and society, the over-exploitation and irregular management of groundwater resources have led to increasingly prominent problems such as the decline of groundwater levels and the deterioration of water quality. The dynamic monitoring of groundwater resource quality has become the top priority for solving the current groundwater quality problem. The accumulation of heavy metals in the soil will destroy the ecological balance of the soil and affect the activity of soil microorganisms. Once the concentration of heavy metals in groundwater exceeds the safety limit, it will also pose a threat to human health and the environment. At present, although there are groundwater pollutant early warning methods based on heavy metal monitoring, they need to rely on equipment to collect water samples and detect them in real time, and there are problems such as slow detection process and inability to predict. Therefore, it is of great significance to develop a soil and groundwater pollutant monitoring method and early warning platform. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a soil and groundwater pollutant monitoring method and a digital early warning platform.

[0004] The technical solution of the present invention is: a soil and groundwater pollutant monitoring method comprises the following steps:

[0005] S1. Obtain soil and groundwater monitoring data sets and impact data sets;

[0006] S2, using the groundwater impact dataset and soil topographic parameters, construct a migration flow model for soil groundwater;

[0007] S3. Use the migration flow model and the soil and groundwater monitoring data set to determine the heavy metal concentration of the soil and groundwater at the next moment, and issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold.

[0008] Soil topographic parameters include soil porosity and bulk density.

[0009] Furthermore, in S1, the soil and groundwater monitoring dataset includes the heavy metal concentrations of soil and groundwater in the historical time period;

[0010] The soil-groundwater impact dataset includes soil-groundwater temperatures over historical time periods.

[0011] Furthermore, S2 includes the following sub-steps:

[0012] S21, dividing the soil at the current moment into grids, and taking each grid as a node;

[0013] S22, constructing a particle arrangement model for each node according to the soil aggregates contained in each node;

[0014] S23, determining the adjacent connectivity value of each node according to the particle arrangement model of each node;

[0015] S24. Based on the temperature of soil groundwater in the historical time period and the adjacent connectivity value between each node, a migration flow model is constructed for soil groundwater.

[0016] The beneficial effect of the above further scheme is: in the present invention, the groundwater temperature will indirectly affect the heavy metal concentration of the groundwater, such as the solubility of calcium bicarbonate is closely related to the pH value. When the pH value is low, the heavy metal ions bound to the carbonate may be released due to the dissolution of the carbonate, thereby increasing the concentration of heavy metals in the solution; on the contrary, when the pH value is high, the solubility of calcium bicarbonate decreases, and the stability of carbonate-bound heavy metals increases, which is conducive to reducing the concentration of heavy metals in the solution, and the temperature will affect the solubility of calcium bicarbonate. For example, silica sol has strong adsorption and can interact with substances such as heavy metal ions in groundwater, and temperature will also affect the adsorption of silica sol. The distribution of macropores of soil aggregates in the soil is also an important parameter. The present invention divides the soil at the current moment, one node corresponds to one grid, and one grid corresponds to a local soil. The water content of the pores has an important influence on the permeability and water retention of the soil. Therefore, the aggregate parameters and pore parameters of each node are used to evaluate the structural relationship between nodes, and then combined with the temperature value at the historical moment to form a migration flow model.

[0017] Furthermore, in S22, the particle arrangement model of the node The expression is:

[0018] ;

[0019] Where N represents the total number of soil aggregates contained in the node, represents the structural particle size of the nth soil aggregate, represents the soil bulk density corresponding to the node, represents the soil porosity corresponding to the node, represents the index, Represents a logarithmic function.

[0020] Further, in S23, The adjacent connectivity value of the nodes The calculation formula is:

[0021] ;

[0022] In the formula, Indicates rounding up. Indicates The particle arrangement model of nodes, Indicates The particle arrangement model of the first node adjacent to the node, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The pore water content of each node, Indicates The pore water content of the first node adjacent to the node, Indicates The node adjacent to The pore water content of each node, Indicates The node adjacent to The pore water content of each node, It means to find the maximum value, Indicates The total number of nodes adjacent to a node.

[0023] Further, S24 includes the following sub-steps:

[0024] S241, calculating the global flow corresponding to the temperature of soil groundwater at each moment in the historical time period according to the adjacent connectivity value of each node;

[0025] S242. Add up the global flows at all times to form a migration flow model.

[0026] Further, in S241, Global flow at a moment The calculation formula is:

[0027] ;

[0028] In the formula, Indicates The node in The adjacent connectivity value at each moment is Indicates The node in The temperature at that moment, represents the index, Indicates the total number of nodes, Represents a constant.

[0029] Furthermore, in S3, the migration flow model value rounded up is used as the permeability coefficient of the Modflow model, and the heavy metal concentration of soil groundwater in the historical time period is input into the Modflow model. The Modflow model is solved using the conjugate gradient method to obtain the heavy metal concentration at the next moment. When the heavy metal concentration at the next moment exceeds the preset threshold, an early warning is issued.

[0030] The beneficial effect of the above further scheme is: in the present invention, the Modflow model adopts the three-dimensional finite difference method for simulation, which is suitable for fields such as soil and groundwater that require long-term monitoring, and the migration flow model generated by the temperature value can be used as a constraint parameter of the Modflow model to assist in predicting the heavy metal concentration at the next moment.

[0031] The beneficial effects of the present invention are as follows: the present invention analyzes and processes the temperature values ​​that affect the soil and groundwater environment, and combines the aggregate conditions of each local soil after grid division to obtain a migration flow model for the entire soil area, and the model value can assist in predicting the heavy metal concentration at the next moment; the present invention can accurately predict the heavy metal concentration of groundwater and improve the accuracy of early warning through multi-parameter fusion and model construction, thereby providing a scientific basis for environmental protection and water resources management and promoting sustainable development.

[0032] Based on the above method, the present invention also proposes a digital early warning platform for soil and groundwater pollutants, including a data set acquisition unit, a migration flow model construction unit and a monitoring and early warning unit;

[0033] The data set acquisition unit is used to acquire the monitoring data set and impact data set of soil and groundwater;

[0034] The migration flow model building unit is used to build a migration flow model for soil groundwater using the groundwater impact dataset and soil topographic parameters;

[0035] The monitoring and early warning unit is used to determine the heavy metal concentration of the soil and groundwater at the next moment by using the migration flow model and the monitoring data set of the soil and groundwater, and to issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold.

[0036] The beneficial effect of the present invention is that the digital early warning platform for soil and groundwater pollutants combines the monitoring data set and the impact data set of soil and groundwater to achieve accurate prediction of heavy metal concentrations, providing a scientific basis for environmental protection and water resources management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of the soil and groundwater contaminant monitoring methods;

[0038] Figure 2 This is a structural diagram of the digital early warning platform for soil and groundwater pollutants. DETAILED DESCRIPTION

[0039] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0040] like Figure 1 As shown, the present invention provides a method for monitoring soil and groundwater pollutants, comprising the following steps:

[0041] S1. Obtain soil and groundwater monitoring data sets and impact data sets;

[0042] S2, using the groundwater impact dataset and soil topographic parameters, construct a migration flow model for soil groundwater;

[0043] S3. Use the migration flow model and the soil and groundwater monitoring data set to determine the heavy metal concentration of the soil and groundwater at the next moment, and issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold.

[0044] Soil topographic parameters include soil porosity and bulk density.

[0045] In the embodiment of the present invention, in S1, the monitoring data set of soil and groundwater includes the heavy metal concentration of soil and groundwater in the historical time period;

[0046] The soil-groundwater impact dataset includes soil-groundwater temperatures over historical time periods.

[0047] In the embodiment of the present invention, heavy metals may be sampled such as arsenic, cadmium, chromium, lead, and mercury, and the specific indicator to be tested is used.

[0048] In this embodiment of the present invention, S2 includes the following sub-steps:

[0049] S21, dividing the soil at the current moment into grids, and taking each grid as a node;

[0050] S22, constructing a particle arrangement model for each node according to the soil aggregates contained in each node;

[0051] S23, determining the adjacent connectivity value of each node according to the particle arrangement model of each node;

[0052] S24. Based on the temperature of soil groundwater in the historical time period and the adjacent connectivity value between each node, a migration flow model is constructed for soil groundwater.

[0053] In the present invention, groundwater temperature will indirectly affect the concentration of heavy metals in groundwater. For example, the solubility of calcium bicarbonate is closely related to the pH value. When the pH value is low, heavy metal ions bound to carbonates may be released due to the dissolution of carbonates, thereby increasing the concentration of heavy metals in the solution; conversely, when the pH value is high, the solubility of calcium bicarbonate decreases, and the stability of carbonate-bound heavy metals increases, which is conducive to reducing the concentration of heavy metals in the solution, and temperature will affect the solubility of calcium bicarbonate. For example, silica sol has strong adsorption and can interact with substances such as heavy metal ions in groundwater, and temperature will also affect the adsorption of silica sol. The distribution of macropores of soil aggregates in soil is also an important parameter. The present invention divides the soil at the current moment, one node corresponds to one grid, and one grid corresponds to a local soil. The water content of the pores has an important influence on the permeability and water retention of the soil. Therefore, the aggregate parameters and pore parameters of each node are used to evaluate the structural relationship between nodes, and then combined with the temperature value at the historical moment to form a migration flow model.

[0054] In the embodiment of the present invention, in S22, the particle arrangement model of the node The expression is:

[0055] ;

[0056] Where N represents the total number of soil aggregates contained in the node, represents the structural particle size of the nth soil aggregate, represents the soil bulk density corresponding to the node, represents the soil porosity corresponding to the node, represents the index, Represents a logarithmic function.

[0057] In the embodiment of the present invention, in S23, The adjacent connectivity value of the nodes The calculation formula is:

[0058] ;

[0059] In the formula, Indicates rounding up. Indicates The particle arrangement model of nodes, Indicates The particle arrangement model of the first node adjacent to the node, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The pore water content of each node, Indicates The pore water content of the first node adjacent to the node, Indicates The node adjacent to The pore water content of each node, Indicates The node adjacent to The pore water content of each node, It means to find the maximum value, Indicates The total number of nodes adjacent to a node.

[0060] In this embodiment of the present invention, S24 includes the following sub-steps:

[0061] S241, calculating the global flow corresponding to the temperature of soil groundwater at each moment in the historical time period according to the adjacent connectivity value of each node;

[0062] S242. Add up the global flows at all times to form a migration flow model.

[0063] In the embodiment of the present invention, in S241, Global flow at a moment The calculation formula is:

[0064] ;

[0065] In the formula, Indicates The node in The adjacent connectivity value at each moment is Indicates The node in The temperature at that moment, represents the index, Indicates the total number of nodes, Represents a constant.

[0066] In an embodiment of the present invention, in S3, the migration flow model value rounded up is used as the permeability coefficient of the Modflow model, the heavy metal concentration of soil groundwater in the historical time period is input into the Modflow model, and the Modflow model is solved using the conjugate gradient method to obtain the heavy metal concentration at the next moment. When the heavy metal concentration at the next moment exceeds the preset threshold, an early warning is issued.

[0067] In the present invention, the Modflow model is simulated using a three-dimensional finite difference method, which is suitable for fields such as soil and groundwater that require long-term monitoring. The migration flow model generated by the temperature value can be used as a constraint parameter of the Modflow model to assist in predicting the heavy metal concentration at the next moment.

[0068] Based on the above method, the present invention also proposes a digital early warning platform for soil and groundwater pollutants, such as Figure 2 As shown, it includes a data set acquisition unit, a migration flow model construction unit and a monitoring and early warning unit;

[0069] The data set acquisition unit is used to acquire the monitoring data set and impact data set of soil and groundwater;

[0070] The migration flow model building unit is used to build a migration flow model for soil groundwater using the groundwater impact dataset and soil topographic parameters;

[0071] The monitoring and early warning unit is used to determine the heavy metal concentration of the soil and groundwater at the next moment by using the migration flow model and the monitoring data set of the soil and groundwater, and to issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold.

[0072] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for monitoring soil and groundwater pollutants, characterized in that: The following steps are involved: S1. Obtain soil and groundwater monitoring data sets and impact data sets; S2, using the groundwater impact dataset and soil topographic parameters, construct a migration flow model for soil groundwater; S3. Determine the heavy metal concentration of the soil and groundwater at the next moment using the migration flow model and the soil and groundwater monitoring data set, and issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold; In S1, the monitoring data set of soil and groundwater includes the heavy metal concentrations of soil and groundwater in the historical period; The soil and groundwater impact data set includes the temperature of soil and groundwater in a historical period; The S2 comprises the following sub-steps: S21, dividing the soil at the current moment into grids, and taking each grid as a node; S22, constructing a particle arrangement model for each node according to the soil aggregates contained in each node; S23, determining the adjacent connectivity value of each node according to the particle arrangement model of each node; S24, constructing a migration flow model for soil groundwater based on the temperature of soil groundwater in the historical time period and the adjacent connectivity value between each node; In S22, the node particle arrangement model The expression is: ； Where N represents the total number of soil aggregates contained in the node, represents the structural particle size of the nth soil aggregate, represents the soil bulk density corresponding to the node, represents the soil porosity corresponding to the node, represents the index, represents the logarithmic function; In the S23, The adjacent connectivity value of the nodes The calculation formula is: ； In the formula, Indicates rounding up. Indicates The particle arrangement model of nodes, Indicates The particle arrangement model of the first node adjacent to the node, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The node adjacent to The particle arrangement model of nodes, Indicates The pore water content of each node, Indicates The pore water content of the first node adjacent to the node, Indicates The node adjacent to The pore water content of each node, Indicates The node adjacent to The pore water content of each node, It means to find the maximum value, Indicates The total number of nodes adjacent to a node; The S24 comprises the following sub-steps: S241, calculating the global flow corresponding to the temperature of soil groundwater at each moment in the historical time period according to the adjacent connectivity value of each node; S242, adding up the global flows at all times to form a migration flow model; In the above S241, Global flow at a moment The calculation formula is: ； In the formula, Indicates The node in The adjacent connectivity value at each moment is Indicates The node in The temperature at that moment, represents the index, Indicates the total number of nodes, represents a constant; In S3, the migration flow model value rounded up is used as the permeability coefficient of the Modflow model, the heavy metal concentration of soil groundwater in the historical time period is input into the Modflow model, and the Modflow model is solved using the conjugate gradient method to obtain the heavy metal concentration at the next moment. When the heavy metal concentration at the next moment exceeds the preset threshold, an early warning is issued.

2. A digital early warning platform for soil and groundwater pollutants, characterized in that: The digital early warning platform for soil and groundwater pollutants is used to execute the digital early warning method for soil and groundwater pollutants as claimed in claim 1, and the platform includes a data set acquisition unit, a migration flow model construction unit and a monitoring and early warning unit; The data set acquisition unit is used to acquire a monitoring data set and an impact data set of soil and groundwater; The migration flow model construction unit is used to construct a migration flow model for soil groundwater using the groundwater impact data set and soil topographic parameters; The monitoring and early warning unit is used to determine the heavy metal concentration of the soil and groundwater at the next moment by using the migration flow model and the monitoring data set of the soil and groundwater, and to issue an early warning when the heavy metal concentration at the next moment exceeds a preset threshold.

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