A GMS collaborative GIS open-pit mine backfilling groundwater health risk prediction method

Abstract

The present application belongs to the technical field of numerical simulation of underground water in mine, and discloses a GMS cooperated with GIS health risk prediction method for backfilling underground water in open-pit mine. The present application combines with geological data around mining area, carries out fine 3D geological modeling through GCAD, EVS, Tin and Solid module in GMS, and on the basis of analyzing hydrogeological conditions of mining area, generalizes boundary conditions and aquifer, determines source and sink, hydrogeological parameters, establishes relatively accurate 3D underground water seepage model of open-pit mine by GMS, and identifies and verifies the model combining with underground water field monitoring data; determines each parameter of solute transport model through static leaching test, dynamic adsorption test and multi-parameter free energy relationship model, establishes solute transport equation of biological, chemical and seepage mutual action by multiple regression linear analysis, outputs solute transport result of open-pit mine backfilling pollutant in different stress periods of simulation period by MT3DMS, disperses solute transport result by Scatter in GMS and Field Calculator based on Python algorithm, and visually expresses in GIS combining with health risk prediction model, so as to solve the problem that potential health risk of backfilling material to underground water pollution in the process of open-pit mine backfilling mining or backfilling treatment is difficult to predict.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation technology of groundwater in mines, specifically a method for predicting the health risk of groundwater in open-pit mine backfill using GMS and GIS. Background Technology

[0002] Using solid waste generated from open-pit mining as backfill material plays a crucial role in both open-pit mine remediation and the comprehensive utilization of solid waste. However, solid waste may contain pollutants such as heavy metals and organic pollutants. After being backfilled into the open-pit mine, the solid waste material is soaked by groundwater and leached by rainfall, and the pollutants will gradually infiltrate the surrounding water environment, posing certain risks to the lives and water safety of residents in the vicinity of the mine. Therefore, accurate prediction of groundwater health risks is of great practical significance for selecting appropriate backfill materials and backfilling schemes to remediate open-pit mines and protect the physical and mental health of residents in the vicinity of the mine.

[0003] GMS is one of the most comprehensive groundwater numerical simulation software programs internationally. It allows for the import and export of various geometric objects (Tin, Mesh, Borhole) via interactive geographic information through projection files (.prj), providing a good data interface with GIS and enabling the visualization of pollutant solute transport results. However, it cannot intuitively and efficiently determine and visualize the health risk level and extent of groundwater pollution.

[0004] With the support of computer technology, GIS can accurately depict geographic information in Earth's space and can import various mathematical models. It can compare and analyze environmental changes based on measured data and visualize the results. However, it cannot predict and analyze complex groundwater solute transport, such as open-pit mine groundwater pollution and seawater intrusion.

[0005] Through on-site groundwater sampling and experimental analysis, the human health risk assessment model can evaluate the carcinogenic and non-carcinogenic risks of groundwater. However, due to the complex groundwater environment in open-pit mines and the hidden nature of groundwater pollution, this model cannot predict the potential health risks of already polluted groundwater in the future. Furthermore, due to the types and quantities of solid waste backfilled in open-pit mines, the uncertainty of backfilling schemes, and the complexity of the geological environment of the mining area, the health risks of groundwater caused by solid waste backfilling in open-pit mines are often difficult to predict.

[0006] Therefore, new technological methods are needed to accurately predict and intuitively represent the health risks of groundwater backfilling with open-pit mine solid waste. Summary of the Invention

[0007] To address the problems mentioned in the background art, the present invention aims to provide a method for predicting the health risks of groundwater in open-pit mine backfill using GMS and GIS. This method uses GMS to predict solute transport in pollutants from open-pit mine backfill, discretizes the analysis results, imports them into a health risk assessment model using a Field Calculator, and then imports them into GIS for interpolation calculation and visualization. This allows for accurate prediction and analysis of the health risks of groundwater pollution in open-pit mine backfill, and provides a clear representation of the health risk levels of groundwater pollution in different geographical areas surrounding the open-pit mine.

[0008] A method for predicting the health risk of groundwater in open-pit mine backfill using GMS collaborative GIS includes the following steps:

[0009] Step 1: Based on the hydrogeological data of the surrounding mining area and the on-site monitoring of groundwater, establish a groundwater seepage model for open-pit mines. The model is identified and verified by the manual trial calculation parameter adjustment method and the PEST automatic adjustment method.

[0010] The hydrogeological data surrounding the mining area includes the natural geographical overview, geological overview, and hydrological conditions of the mining area; the natural geographical overview includes natural geography and transportation, topography, meteorological conditions, and surface water system; the geological overview includes the geological structure and stratigraphic lithology of the mining area; the hydrological conditions include the distribution of groundwater aquifers and impermeable layers in the mining area, groundwater flow trends, and groundwater recharge and discharge conditions in the mining area.

[0011] The process of establishing and validating a groundwater seepage model for a mining area includes:

[0012] S1. Simplify and establish a three-dimensional geological structure model of the open-pit mine;

[0013] S2. Establish a three-dimensional mathematical model of groundwater seepage in open-pit mines;

[0014] S3. Generalize the aquifer and boundary conditions of open-pit mines;

[0015] S4. Convert the three-dimensional geological structure model of the open-pit mine into a numerical model, and perform spatial and temporal discretization on the model;

[0016] S5. Determine the source and sink terms of the numerical model, including atmospheric precipitation replenishment, groundwater evaporation and discharge, river replenishment and discharge, lateral runoff replenishment and discharge, and mine drainage, and input them into the model.

[0017] S6. Determine the seepage simulation parameters for the numerical model, and import the permeability coefficient, specific yield, storage coefficient, and porosity of each aquifer into the model;

[0018] S7. Determine the initial conditions by combining on-site monitoring of groundwater in the mining area, and identify and verify the model: Conduct on-site monitoring of groundwater around the open-pit mine area, compare the simulation results with the monitoring data for error analysis, and correct the seepage model parameters and source-sink parameters by manual trial calculation parameter adjustment method and PEST automatic adjustment method.

[0019] Step 2: Select backfill materials for open-pit mines, study the properties of backfill materials, conduct static leaching tests on backfill materials, and determine the pollution source intensity of backfill materials based on the backfill plan and backfill quantity;

[0020] The backfill materials for open-pit mines are selected from solid wastes such as coal gangue, waste rock, shale slag, steel slag, and fly ash generated by mining areas and nearby industrial enterprises. Static leaching tests are used to determine the types of pollutants in numerical simulation, including heavy metals and organic pollutants in solid waste. The quantity of backfill materials is then determined in conjunction with the open-pit mine backfill plan, and the pollution source strength is determined in conjunction with the seepage model.

[0021] Step 3: Determine the pollutant solute transport parameters through dynamic adsorption experiments and multi-parameter free energy relationship models (pp-LFERs), and establish mathematical models of pollutant solute transport under biodegradation, isothermal adsorption, and hydrodynamic dispersion.

[0022] Pollutant solute transport parameters, including biodegradation coefficient, isothermal partition coefficient, retardation factor, hydrodynamic dispersion, etc.;

[0023] A solute transport equation involving biological, chemical, and seepage effects was established using multiple linear regression analysis:

[0024]

[0025] λ=-0.02319+0.00009C+0.00014K d -0.7378K+0.0173ρ (2)

[0026] Wherein, C(x,y,z,t) — pollutant concentration (kg·m³) -3 );D xx D xy D xz D yy D yx D yz D zz D zy D zx —Three-dimensional spatial coordinate components of the hydrodynamic dispersion coefficient tensor (m) 2 ·d -1 );W e —Injection water intensity (d -1 );C e —W eContains pollutant concentration (kg·m³) -3 );W o —Water extraction intensity (d) -1 ); I—Source and sink items ((kg·m -3 ); R d —Restriction factor; n—Porosity; u x u y u z — Actual velocity component of water flow (m·d) -1 G—Study domain; λ—Biodegradation coefficient; K d —Isothermal distribution coefficient (m 3 ·mg -1 K—permeability coefficient (m·d) -1 ); ρ—density of medium (kg·m³) -3 ).

[0027] Step 4: Perform iterative calculations in GMS using the preprocessed conjugate gradient algorithm (PCG), and conduct solute transport simulation and prediction analysis of backfill material contaminants using the implicit GCG solution and the third-order TVD method in MT3DMS.

[0028] The solute transport simulation parameters determined in step 3 were imported into MT3DMS. Preprocessed conjugate gradient algorithm (PCG) was used in GMS to accelerate iterative calculations. The number of iterations was set to 100 and the residual was set to 0.1m. The dispersion term was calculated in MT3DMS using implicit GCG solution and the convection term was processed using third-order TVD method. The transport direction, distance and concentration of pollutants in different backfill materials were simulated and analyzed at different stress periods throughout the simulation period.

[0029] Step 5: Based on the different types of pollutants in the backfill materials of open-pit mines, and combined with the physical characteristics, diet and living habits of different groups of residents in the surrounding areas of the mine, establish a health risk prediction model;

[0030] Based on the pollutant types and source strengths determined in step 2, the drinking and shower water consumption of different population groups in the area is determined according to the physical characteristics, diet and living habits of residents around the mining area. Referring to parameters such as pollutant exposure route dose, non-carcinogenic and carcinogenic unit hazard levels in the health risk assessment of the U.S. Environmental Protection Agency (US EPA) and the International Commission on Radiation Protection (ICRP), non-carcinogenic and carcinogenic risk indices are determined, a health risk prediction model for groundwater pollution in open-pit mine backfill is established, and the overall risk level is divided according to the acceptable levels of non-carcinogenic and carcinogenic risks.

[0031] Step 6: Discretize the solute transport results of open-pit mine backfill pollutants using Scatter in GMS, and import the discretized results into GIS using Field Calculator based on Python algorithm, combined with the health risk prediction model. This enables GMS to work with GIS to predict the groundwater health risks caused by solid waste backfill in the complex and variable groundwater environment of open-pit mines.

[0032] The solute transport results of pollutants from open-pit mine backfill materials are discretized by assigning numerical scatter points to them using the Scatter function in GMS. The coordinates of the numerical scatter points are then converted into CGCS2000 projected coordinates. Using the health risk prediction model established in step 5, a pre-logic script is set up using the Field Calculator based on the Python algorithm. The discretized solute transport results are then imported into GIS, enabling GMS and GIS to work together to accurately predict the groundwater health risks caused by solid waste backfilling in open-pit mines with complex geological environments and hidden and difficult-to-monitor groundwater pollution.

[0033] Step 7: In GIS, use the inverse distance weight (IDW) interpolation method to interpolate and visualize the health risk prediction results in the simulated domain;

[0034] After importing the health risk prediction data into GIS in step 6, the environmental variable range is set to the same domain as the solute transport simulation. Combined with the health risk level classification standard, the prediction results are visualized using the inverse distance weight (IDW) interpolation method.

[0035] The beneficial effects of this invention are as follows:

[0036] This invention proposes a GMS-integrated GIS method for predicting the health risks of groundwater in open-pit mine backfill. An accurate groundwater seepage model for open-pit mines was established. Through experiments and multi-parameter free energy relation models (pp-LFERs), various parameters of the solute transport model were determined. A solute transport equation and a health risk prediction model based on the combined effects of biological, chemical, and seepage factors were established. The discretized solute transport results, combined with the health risk prediction model, were visualized in GIS using a Python-based Field Calculator. This method solves the problem of unpredictable potential health risks from groundwater pollution caused by backfill materials during open-pit mine backfilling or remediation. Based on the GIS visualization output, the method can accurately classify the health risk levels of the surrounding area and provide important reference for selecting harmless backfill materials for open-pit mine backfilling remediation, ensuring the safety of drinking water for residents around the mine, and regionalized and precise prevention and control of groundwater pollution. Attached Figure Description

[0037] Figure 1This is a flowchart of the method for predicting health risks from groundwater pollution during open-pit mine backfilling, as described in this invention.

[0038] Figure 2 This is a solute transport concentration distribution diagram of Cr, a pollutant in open-pit mine backfill, in an embodiment of the present invention.

[0039] Figure 3 This is a regional distribution map showing the predicted health risk levels of groundwater pollution during open-pit mine backfilling, as described in this embodiment of the invention. Detailed Implementation

[0040] To better illustrate the content and advantages of this invention, a specific embodiment of the invention is further described using the example of groundwater health risk prediction for solid waste backfilling in an open-pit mine in Liaoning Province, in conjunction with the accompanying drawings. The following examples are for illustrative purposes only and do not limit the scope of the invention.

[0041] A method for predicting the health risks of groundwater in open-pit mine backfill using GMS-coordinated GIS, such as Figure 1 As shown, it includes the following steps:

[0042] Step 1: Based on the hydrogeological data of the surrounding mining area and the on-site monitoring of groundwater, establish a groundwater seepage model for open-pit mines. The model is identified and verified by the manual trial calculation parameter adjustment method and the PEST automatic adjustment method.

[0043] The hydrogeological data surrounding the mining area includes the natural geographical overview, geological overview, and hydrological conditions of the mining area; the natural geographical overview includes natural geography and transportation, topography, meteorological conditions, and surface water system; the geological overview includes the geological structure and stratigraphic lithology of the mining area; the hydrological conditions include the distribution of groundwater aquifers and impermeable layers in the mining area, groundwater flow trends, and groundwater recharge and discharge conditions in the mining area.

[0044] The process of establishing and validating a groundwater seepage model for a mining area includes:

[0045] S1. Simplify and establish a three-dimensional geological structure model of the open-pit mine;

[0046] In this embodiment, the strata are generalized into five layers from top to bottom in the vertical direction. To ensure the accuracy of the model, virtual borehole data is extracted from the open-pit mine profile using GCAD and EVS (Earth Volumetric Studio), and geological modeling is performed using the Solid module in GMS to obtain a three-dimensional geological model.

[0047] S2. Establish a three-dimensional mathematical model of groundwater seepage in open-pit mines;

[0048] The seepage flow of groundwater around the mining area conforms to the law of conservation of mass and Darcy's law. Furthermore, the groundwater system exhibits heterogeneity, isotropy, and instability. The unsteady three-dimensional seepage model of groundwater in a porous medium can be represented by the following differential equation:

[0049]

[0050] H(x,y,z,t) t＝0 =H0(x,y,z),(x,y,z)∈G(2)

[0051]

[0052]

[0053] Where H(x,y,z,t) is the groundwater head (m); K xx K yy K zz —Coordinate components of the permeability coefficient tensor (m / d); S s —Water storage capacity in porous media (d -1 W—Source-Sink Term Strength (d) -1 G—the domain of study; H0(x,y,z,t)—the initial head (m) on the given domain of study; H1(x,y,z,t)—the given head (m) on the first type of boundary r1; q(x,y,z,t)—the given flow rate (m·d) on the second type of boundary r2. -1 ); cos(n,x), cos(n,y), cos(n,z) — cosine of the angle between the outward normal vector of the boundary and the positive direction of the coordinate axis.

[0054] S3, Generalized open-pit mine aquifer and boundary conditions;

[0055] In this embodiment, the aquifers in the mining area are generalized into five layers vertically: Quaternary unconfined aquifer, clastic rock pore water aquifer, oil shale first aquitard, gneiss fracture water weak aquifer, and granite bedrock aquitard. The Quaternary unconfined aquifer and bedrock aquitard are respectively generalized as upper and lower boundaries. The river to the north is generalized as a constant head boundary, and the rivers to the south and west, being seasonal rivers, are generalized as constant flow boundaries.

[0056] S4. Convert the three-dimensional geological structure model into a numerical model, and perform spatial and temporal discretization on the model;

[0057] In GMS, the three-dimensional geological structure model is converted into a numerical model by using the Solid and Tin modules and setting up a 3D Grid. The model is then spatially discretized, and the simulation period and stress period are set to perform time discretization of the numerical model.

[0058] S5. Determine the source and sink terms of the numerical model, including atmospheric precipitation replenishment, groundwater evaporation and discharge, river replenishment and discharge, lateral runoff replenishment and discharge, and mine drainage, and input them into the model.

[0059] The atmospheric precipitation recharge was determined based on the local rainfall monitoring data and infiltration recharge coefficient in the mining area over the years, and the data was imported through the Recharge module in GMS.

[0060] Atmospheric precipitation replenishment can be calculated using the following formula:

[0061] Q rain =λPA (5)

[0062] Among them, Q rain —Rainfall infiltration recharge (m³) 3 / d); P—average annual rainfall (mm); A—area of ​​the calculation area (km²) 2 ); λ—rainfall infiltration coefficient;

[0063] The evaporation rate is determined based on the meteorological data of the mining area over the years, and the parameters are imported through the Evaporation module in GMS.

[0064] The amount of water vapor discharged through evaporation can be calculated using the following formula:

[0065]

[0066] Among them, Q ET —Groundwater evaporation discharge (m³) 3 / d); E0—average evaporation in the study area over many years (mm / a); α—evaporation coefficient; D—average depth of groundwater (m); X—limiting evaporation depth of groundwater (m); A ET —Calculate the evaporation area (km²) 2 );

[0067] The replenishment and discharge volume of the river is determined based on the historical water level, water volume, and riverbed depth of the rivers surrounding the mining area, and the data is imported through the River function in GMS.

[0068] The river's replenishment and discharge can be calculated using the following formula:

[0069] Q river =KTW(7)

[0070] Among them, Q river —River replenishment (m³) 3 / d); K—riverbed permeability coefficient (m / d); T—riverbed thickness (m); W—river width (m);

[0071] Lateral recharge and discharge of groundwater in mining areas can be calculated using Darcy's Law:

[0072] Q lateral =KHLi(8)

[0073] Among them, Q lateral — Lateral groundwater recharge (m³) 3 / d); K—aquifer permeability coefficient (m / d); H—aquifer thickness (m); L—aquifer profile length (m); i—hydraulic gradient of the profile perpendicular to the aquifer;

[0074] The mine's drainage volume is based on the drainage volume of each pumping station in previous years, and the data is imported into GMS using the Well module.

[0075] S6. Determine the seepage simulation parameters and import the permeability coefficient, specific yield, storage coefficient, and porosity of each aquifer into the model;

[0076] In GMS, the permeability coefficient, specific yield, storage coefficient, and porosity of each aquifer are partitioned using the Map module, and the partition parameters are imported into the numerical model.

[0077] S7. Determine the initial conditions and identify and verify the model: Compare and analyze the error between the simulation results and the actual hydrological data of previous years. Correct the seepage model parameters and source-sink parameters by manual trial calculation and PEST automatic adjustment method to make the groundwater seepage model fit more accurately.

[0078] In this embodiment, water level data from January 10, 2016, is selected and imported into the model through the 2D Scatter module in GMS. The initial water level is then calculated using the Kriging interpolation method.

[0079] In GMS, the seepage model is run using the RUN MODFLOW command. After the run is complete, the numerical model is identified and verified.

[0080] In this embodiment, the complete hydrological year of 2016 was used as the simulation period, with each month representing a stress period. The numerical simulation results were identified and compared with actual monitoring well data. Initial adjustments were made using manual trial calculations, followed by automatic parameter tuning via the PEST automatic tuning module in GMS. The model after parameter tuning was validated using the complete hydrological year of 2017 as the simulation period and each month representing a stress period. The water level observation error was limited to within 1m, and a 95% confidence level was set. After parameter tuning, the error between 98% of the observation wells and the actual values ​​was within the error limit, and the absolute error met the accuracy requirements of the numerical simulation, indicating that the parameter tuning was reasonable.

[0081] Step 2: Select backfill materials for open-pit mines, study the properties of backfill materials, conduct static leaching tests on backfill materials, and determine the pollution source intensity of backfill materials based on the backfill plan and backfill quantity;

[0082] The backfill materials for open-pit mines are selected from solid wastes such as coal gangue, waste rock, shale slag, steel slag, and fly ash generated by mining areas and nearby industrial enterprises. In this embodiment, the backfill materials are coal gangue, shale slag, and steel slag. Static leaching tests are used to determine the types of pollutants in numerical simulation, including heavy metals and organic pollutants in solid waste. In this embodiment, the pollutants are Fe, Cr, and the organic pollutant naphthalene (Nap). The quantity of backfill materials is then determined in conjunction with the open-pit mine backfill scheme, and the pollution source strength is determined in conjunction with the seepage model.

[0083] Step 3: Determine the pollutant solute transport parameters through dynamic adsorption experiments and multi-parameter free energy relationship models (pp-LFERs), and establish mathematical models of pollutant solute transport under biodegradation, isothermal adsorption, and hydrodynamic dispersion.

[0084] Pollutant solute transport parameters, including biodegradation coefficient, isothermal partition coefficient, retardation factor, and hydrodynamic dispersion, are determined by dynamic adsorption tests for Fe and Cr in various lithological strata of the mining area. The isothermal partition coefficient for organic matter Nap is determined by a multi-parameter free energy relationship model (pp-LFERs). The retardation factor is calculated using the isothermal partition coefficient, and the hydrodynamic dispersion is determined in conjunction with the mining area.

[0085] A solute transport equation involving biological, chemical, and seepage effects was established using multiple linear regression analysis:

[0086]

[0087] λ=-0.02319+0.00009C+0.00014K d -0.7378K+0.0173ρ (10)

[0088] C(x,y,z,t)| t＝0 =C0(x,y,z),(x,y,z)∈G (11)

[0089]

[0090]

[0091] Wherein, C(x,y,z,t) — pollutant concentration (kg·m³) -3 );D xx D xy D xz D yy D yx D yz D zz D zy D zx—Three-dimensional spatial coordinate components of the hydrodynamic dispersion coefficient tensor (m) 2 ·d -1 );W e —Injection water intensity (d -1 );C e —W e Contains pollutant concentration (kg·m³) -3 );W o —Water extraction intensity (d) -1 ); I—Source and sink items ((kg·m -3 ); R d —Restriction factor; n—Porosity; u x u y u z — Actual velocity component of water flow (m·d) -1 G—Study domain; λ—Biodegradation coefficient; K d —Isothermal distribution coefficient (m 3 ·mg -1 K—permeability coefficient (m·d) -1 ); ρ—density of medium (kg·m³) -3 C0(x,y,z,t) — Initial concentration (kg·m³) over a given study domain -3 C1(x,y,z,t) — the given head concentration (kg·m³) on the first kind boundary r1. -3 f(x,y,z,t) — a given diffusion flux (kg·m²) on the second-kind boundary r². -3 ); cos(n,x), cos(n,y), cos(n,z) — cosine of the angle between the outward normal vector of the boundary and the positive direction of the coordinate axis.

[0092] Step 4: Perform iterative calculations in GMS using the preprocessed conjugate gradient algorithm (PCG), and conduct solute transport simulation and prediction analysis of backfill material contaminants using the implicit GCG solution and the third-order TVD method in MT3DMS.

[0093] The solute transport simulation parameters determined in step 3 were imported into MT3DMS. Preprocessed conjugate gradient algorithm (PCG) was used in GMS to accelerate iterative calculations. The number of iterations was set to 100 and the residual was set to 0.1m. The dispersion term was calculated in MT3DMS using implicit GCG solution and the convection term was processed using third-order TVD method. The transport direction, distance and concentration of pollutants in different backfill materials were simulated and analyzed at different stress periods throughout the simulation period.

[0094] Step 5: Based on the different types of pollutants in the backfill materials of open-pit mines, and combined with the physical characteristics, diet and living habits of different groups of residents in the surrounding areas of the mine, establish a health risk prediction model;

[0095] Based on the pollutant types and source strengths determined in step 2, the drinking and shower water consumption of different groups of people in the area is determined according to the physical characteristics, diet and living habits of the residents around the mining area. Referring to the parameters such as pollutant exposure route dose, non-carcinogenic and carcinogenic unit hazard in the health risk assessment of the US Environmental Protection Agency (US EPA) and the International Commission on Radiation Protection (ICRP), the non-carcinogenic and carcinogenic risk indices are determined, and a health risk prediction model for backfill material pollutants of Fe, Cr and NaP is established.

[0096] The formula for calculating the health risk prediction model is as follows:

[0097] HI = HQ oral +HQ der (14)

[0098] HQ = CE / RFD(15)

[0099] HC=HM oral +HM der (16)

[0100]

[0101]

[0102]

[0103] Among them, HI—the overall non-carcinogenic risk index of pollutants; HQ oral —Non-carcinogenic risk index via oral intake; HQ der —Non-carcinogenic risk index via skin contact; RFD—Non-carcinogenic contaminant risk per unit (mg·kg) -1 ·d -1 ); HC—Overall carcinogenic risk index of pollutants; HM oral —Carcinogenic risk index via oral intake; HM der —Carcinogenic risk index via skin contact; CSF—Carcinogenic contaminant risk per unit (mg·kg) -1 ·d -1 u—mean age of local residents (a); CE—exposure dose (mg·kg) -1 ·d -1 It can be divided into oral exposure dose (CDI) or skin contact dose (DAD); C—concentration of non-carcinogenic or carcinogenic contaminants (mg·L⁻¹) -1 ); IR—Intake rate (L·d) -1 BW—Mean body weight (kg); EF—Annual exposure frequency (d·a) -1); ED—exposure time (a); AT—non-carcinogenic or carcinogenic exposure time (d); SA—total skin surface area (cm²) 2 ); EV—daily exposure frequency (d -1 P—skin permeability coefficient (cm·h) -1 );t p —Skin contact duration (h);

[0104] Based on the acceptable risk levels recommended by the U.S. Environmental Protection Agency (US EPA) and the International Commission on Radiation Protection (ICRP), the risks are divided into five levels, from I to V, as shown in Table 1.

[0105] Table 1

[0106] Risk level Evaluation criteria range Risk level Ⅰ [1e-7,1e-6] Low risk Ⅱ [1e-6,1e-5] Lower risk III [1e-5, 1e-4] Medium risk Ⅳ [1e-4,1e-3] Higher risk Ⅴ [1e-3,1e-1] High risk

[0107] Based on the health risk prediction model, the formulas for calculating the total risk index of non-carcinogenic pollutants Fe and Nap, and carcinogenic pollutant Cr for different receptor populations in children, adult men, and adult women are shown in Table 2.

[0108] Table 2

[0109]

[0110]

[0111] Step 6: Discretize the solute transport results of open-pit mine backfill pollutants using Scatter in GMS, and import the discretized results into GIS using Field Calculator based on Python algorithm, combined with the health risk prediction model. This enables GMS to work with GIS to predict the groundwater health risks caused by solid waste backfill in the complex and variable groundwater environment of open-pit mines.

[0112] The solute transport results of pollutants from open-pit mine backfill materials are discretized by assigning numerical scatter points to them using the Scatter function in GMS. The coordinates of the numerical scatter points are then converted into CGCS2000 projected coordinates. Using the health risk prediction model established in step 5, a pre-logic script is set up using the Field Calculator based on the Python algorithm. The discretized solute transport results are then imported into GIS, enabling GMS and GIS to work together to accurately predict the groundwater health risks caused by solid waste backfilling in open-pit mines with complex geological environments and hidden and difficult-to-monitor groundwater pollution.

[0113] Step 7: In GIS, use the inverse distance weight (IDW) interpolation method to interpolate and visualize the health risk prediction results in the simulated domain;

[0114] After importing the health risk prediction data into GIS in step 6, interpolation calculations are performed in GIS using the Interpolation analysis tool in Spatial Analyst. The interpolation method adopted is the inverse distance weighting method (IDW). The environmental processing range is set to the same domain as the GMS numerical simulation, and the output cell size is set to 30. After interpolation calculation, the output is a visualization raster. The calculation results are divided into regions according to different risk levels, and the health risk prediction results can be visualized through GIS.

[0115] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for predicting the health risk of groundwater in open-pit mine backfill using GMS-coordinated GIS, characterized in that, Includes the following steps: Step 1: Based on the hydrogeological data of the surrounding mining area and the on-site monitoring of groundwater, establish a groundwater seepage model for open-pit mines. The model is identified and verified by the manual trial calculation parameter adjustment method and the PEST automatic adjustment method. Step 2: Select backfill materials for open-pit mines, study the physical and chemical properties of backfill materials, conduct static leaching tests on backfill materials, and determine the pollution source intensity of backfill materials based on the backfill plan and backfill volume; Step 3: Determine the pollutant solute transport parameters through dynamic adsorption experiments and multi-parameter free energy relationship models (pp-LFERs), and establish a mathematical model of pollutant solute transport under the combined effects of biodegradation, isothermal adsorption, and hydrodynamic dispersion. Step 4: Perform iterative calculations in GMS using the preprocessed conjugate gradient algorithm (PCG), and conduct solute transport simulation and prediction analysis of backfill material contaminants using the implicit GCG solution and the third-order TVD method in MT3DMS. Step 5: Based on the different types of pollutants in the backfill materials of open-pit mines, and combined with the physical characteristics, diet and living habits of different groups of residents in the surrounding areas of the mine, establish a health risk prediction model; Step 6: Discretize the solute transport results of open-pit mine backfill pollutants using Scatter in GMS, and import the discretized results into GIS using Field Calculator based on Python algorithm, combined with the health risk prediction model. This enables GMS to work with GIS to predict the groundwater health risks caused by solid waste backfill in the complex and variable groundwater environment of open-pit mines. Step 7: In GIS, use the inverse distance weight (IDW) interpolation method to interpolate and visualize the health risk prediction results in the simulation domain.

2. The method for predicting groundwater health risks in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... The hydrogeological data surrounding the mining area mentioned in step 1 includes the natural geographical overview, geological overview, and hydrological conditions of the mining area; the natural geographical overview includes natural geography and transportation, topography, meteorological conditions, and surface water system; the geological overview includes the geological structure and lithology of the mining area; the hydrological conditions include the distribution of groundwater aquifers and impermeable layers in the mining area, the flow trend of groundwater, and the recharge and discharge conditions of groundwater in the mining area.

3. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS collaborative GIS as described in claim 1, characterized in that... In step 1, based on hydrogeological data of the surrounding mining area and on-site groundwater monitoring, an open-pit mine groundwater seepage model is established. The model is identified and verified using a manual trial parameter adjustment method and a PEST automatic adjustment method. Specifically: S1. Simplify and establish a three-dimensional geological structure model of the open-pit mine; S2. Establish a three-dimensional mathematical model of groundwater seepage in open-pit mines; S3. Generalize the aquifer and boundary conditions of open-pit mines; S4. Convert the three-dimensional geological structure model of the open-pit mine into a numerical model, and perform spatial and temporal discretization on the model; S5. Determine the source and sink terms of the numerical model, including atmospheric precipitation replenishment, groundwater evaporation and discharge, river replenishment and discharge, lateral runoff replenishment and discharge, and mine drainage, and input them into the model. S6. Determine the seepage simulation parameters for the numerical model, and import the permeability coefficient, specific yield, storage coefficient, and porosity of each aquifer into the model; S7. Determine the initial conditions by combining on-site monitoring of groundwater in the mining area, and identify and verify the model: Conduct on-site monitoring of groundwater around the open-pit mine area, compare the simulation results with the monitoring data for error analysis, and correct the seepage model parameters and source-sink parameters by manual trial calculation parameter adjustment method and PEST automatic adjustment method.

4. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... Step 2 specifically involves selecting coal gangue, waste rock, shale slag, steel slag, and fly ash solid waste generated by mining areas and nearby industrial enterprises as backfill materials for open-pit mines. Static leaching tests are used to determine the types of pollutants in numerical simulation, including heavy metals and organic pollutants in solid waste. The quantity of backfill materials is then determined in conjunction with the open-pit mine backfill plan, and the pollution source strength is determined in conjunction with the seepage model.

5. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... In step 3, pollutant solute transport parameters, including biodegradation coefficient, isothermal partition coefficient, retardation factor, and hydrodynamic dispersion, are determined through dynamic adsorption experiments and multi-parameter free energy relationship models (pp-LFERs).

6. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS collaborative GIS as described in claim 1, characterized in that... In step 3, a solute transport equation involving the combined effects of biological, chemical, and seepage factors is established through multiple linear regression analysis: -λR d nC+I(x,y,z)∈G,t>0 λ=-0.02319+0.00009C+0.00014K d -0.7378K+0.0173p (2) Wherein, C(x,y,z,t) — pollutant concentration (kg·m³) -3 );D xx D xy D xz D yy D yx D yz D zz D zy D zx —Three-dimensional spatial coordinate components of the hydrodynamic dispersion coefficient tensor (m) 2 ·d -1 );W e —Injection water intensity (d -1 );C e —W e Contains pollutant concentration (kg·m³) -3 );W o —Water extraction intensity (d) -1 ); I—Source and sink items ((kg·m -3 ); R d —Restriction factor; n—Porosity; u x u y u z — Actual velocity component of water flow (m·d) -1 G—Study domain; λ—Biodegradation coefficient; K d —Isothermal distribution coefficient (m 3 ·mg -1 K—permeability coefficient (m·d) -1 ); ρ—density of medium (kg·m³) -3 ).

7. The method for predicting groundwater health risks in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... Step 4 specifically involves importing the solute transport simulation parameters determined in Step 3 into MT3DMS, using the preprocessed conjugate gradient algorithm (PCG) in GMS to accelerate iterative calculations, setting the number of iterations to 100 and the residual to 0.1m, calculating the dispersion term using the implicit GCG solution in MT3DMS, and processing the convection term using the third-order TVD method, and simulating and analyzing the transport direction, distance, and concentration of pollutants in different backfill materials during different stress periods throughout the simulation period.

8. The method for predicting groundwater health risks in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... Step 5 specifically involves determining the types and source strength of pollutants identified in Step 2, determining the drinking and shower water consumption of different population groups based on the physical characteristics, diet, and lifestyle of residents around the mining area, referring to the pollutant exposure pathway dose, non-carcinogenic and carcinogenic unit hazard parameters in the health risk assessment of the U.S. Environmental Protection Agency (EPA) and the International Commission on Radiation Protection (ICRP), determining the non-carcinogenic and carcinogenic risk indices, establishing a health risk prediction model for groundwater pollution from open-pit mine backfill, and classifying the overall risk level based on the acceptable levels of non-carcinogenic and carcinogenic risks.

9. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS collaborative GIS as described in claim 1, characterized in that... Step 6 specifically involves discretizing the solute transport results of pollutants from open-pit mine backfill materials by allocating them to numerical scatter points using the Scatter function in GMS, converting the coordinates of the numerical scatter points to CGCS2000 projected coordinates, and importing the discretized solute transport results into GIS using the health risk prediction model established in Step 5 and the pre-logic script set up with the Field Calculator based on the Python algorithm. This enables GMS and GIS to work together to accurately predict the groundwater health risks caused by solid waste backfilling in open-pit mines with complex geological environments and hidden and difficult-to-monitor groundwater pollution.

10. The method for predicting the health risk of groundwater in open-pit mine backfill using GMS-coordinated GIS as described in claim 1, characterized in that... Step 7 specifically involves importing the health risk prediction data into the GIS after step 6, setting the environmental variable range to the same domain as the solute transport simulation, and visualizing the prediction results using the inverse distance weight (IDW) interpolation method in conjunction with the health risk level classification standard.

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