A method for delineating water environmental health risks in areas with black rock geological background

Abstract

The present invention discloses a method for delineating water environment health risks in areas with a black rock geological background. The method achieves a comprehensive assessment of human health risks by analyzing the degree of exposure of the population to different types of pollutants, and comprehensively assesses ecosystem health risks by obtaining ecological diversity data of sampling points in a target study area using environmental DNA technology. The method predicts the future potential risks of different types of pollutants to multiple sampling points by constructing a coupling model of a risk prediction big data model and a migration and diffusion model. The method can comprehensively utilize the human health risks and ecosystem health risks of multiple sampling points, the prediction results of future potential risks, environmental data, different types of pollutants, and the corresponding concentrations of different types of pollutants, and combine it with the spatial analysis function of ArcGIS to achieve accurate delineation and visualization of the overall water environment health risks.

Description

Technical Field

[0001] The present invention relates to the fields of environmental science and water environment protection, and in particular to a method for delineating water environment health risks in areas with black rock geological backgrounds. Background Art

[0002] Black rock formations, due to their unique chemical weathering processes and the potential generation of acidic substances, often contain heavy metals (such as cadmium and zinc) and other non-metallic elements (such as arsenic and selenium). Furthermore, due to their rich deposits of metal and non-metallic minerals, black rock formations host numerous historical or abandoned mining areas, as well as emerging pollutants (such as antibiotics, perfluorinated compounds, microplastics, and endocrine disruptors) introduced by various human activities in the surrounding areas. These substances may enter water bodies through the hydrological cycle, posing a threat to the ecological environment and human health, necessitating an urgent assessment of water environmental health risks. While some methods currently exist for assessing the health risks of single elements in geological environments, they are unable to comprehensively assess the potential risks of heavy metals, non-heavy metals, and emerging pollutants to a region. Furthermore, existing assessment methods often lack the ability to predict the migration and diffusion of pollutants in water bodies, making it difficult to scientifically and objectively assess and display potential risk areas. Therefore, it is crucial to develop a method that can accurately delineate and visualize water environmental health risks in black rock formations. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes a method for delineating water environment health risks in black rock geological background areas, and the technical solution adopted is:

[0004] A method for delineating water environment health risks in areas with black rock geological backgrounds, the method comprising the following specific steps:

[0005] S1. Collect environmental data from multiple sampling points in the target study area, and obtain the different types of pollutants and their corresponding concentrations at different sampling points. The types of pollutants include heavy metal elements, non-heavy metal elements, and emerging pollutants.

[0006] S2. Develop a water environment risk classification method to assess the exposure of the population to pollutants at the sampling points and the ecosystem health risks and human health risks caused by pollutant exposure. The water environment risk classification method includes the ecosystem health risk classification method and the human health risk classification method.

[0007] S3. Based on machine learning, a risk prediction model is constructed. The risk prediction model is a coupling model of the risk prediction big data model and the migration and diffusion model. The details are as follows:

[0008] BS1. Build a big data model for risk prediction:

[0009] as1. Obtain historical environmental data to form an original data set;

[0010] as2. Preprocess the original data set, including data cleaning, missing value processing, and outlier processing;

[0011] as3. Perform correlation analysis on the features in the preprocessed original dataset to obtain features that have a significant impact on risk prediction;

[0012] as4. Use Bootstrap sampling to randomly extract multiple sub-datasets from the processed original data set, build a decision tree for each sub-dataset, and randomly select a part of the features at each node for splitting;

[0013] as5. Repeat the above steps until the specified number of decision trees are generated and a CSV file is generated;

[0014] BS2. Construct a migration and diffusion model:

[0015] bs1. Obtain environmental data to build a migration and diffusion model;

[0016] bs2. Preprocess environmental data, including data cleaning, missing value processing, outlier processing, and interpolation and extension;

[0017] bs3. Set and adjust migration and diffusion model parameters, including time step, attenuation coefficient, and hydrological condition parameters;

[0018] bs4. Obtain environmental data and divide the target study area into grids;

[0019] bs5. Insert the CSV file constructed from the risk prediction big data model as the dynamic boundary condition of the migration and diffusion model;

[0020] bs6. Generate Ad. file;

[0021] BS3. Import the Ad. file into the coupled model, generate the Sim file, and complete the model coupling;

[0022] S4. Input the degree of human exposure to pollutants at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain prediction results of potential future human health risks and ecosystem health risks;

[0023] S5. Construct a risk coordinate system, input the human health risks and ecosystem health risks of multiple sampling points, and the predicted future potential human health risks and ecosystem health risks into the risk coordinates respectively, and obtain the water environment health risks and future potential water environment health risks of the sampling points.

[0024] In a further preferred embodiment of the technical solution of the present invention, the average daily exposure through oral intake and skin exposure is used as an evaluation index for the exposure degree of heavy metal elements and non-heavy metal element pollutants to the human population at the sampling point in step S2, specifically:

[0025] ;

[0026] refers to the average daily exposure through oral intake; CW refers to the pollutant content in the drinking water of the area; IR refers to the drinking water intake; EF refers to the exposure frequency; ED refers to the exposure duration, that is, the average life expectancy of the permanent population in the area in a certain year; BW refers to the average body weight; AT refers to the average exposure time;

[0027] ;

[0028] Refers to the average daily exposure through skin exposure; SA refers to the skin contact surface area; PC refers to the skin permeability coefficient of chemical substances; ET refers to the short-term exposure time; CF is the volume conversion factor.

[0029] In a further preferred embodiment of the technical solution of the present invention, the human health risks in step S2 include human health risks caused by exposure to heavy metal elements and non-heavy metal elements and human health risks caused by exposure to new pollutants;

[0030] Human health risks caused by exposure to heavy metals and non-heavy metals :

[0031] ;

[0032] Human health risks caused by heavy metals and non-heavy metals;

[0033] R is the total carcinogenic risk of all carcinogenic elements;

[0034] HI is the total health risk of all non-carcinogenic elements;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] is the carcinogenic risk of the i-th carcinogen through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the i-th carcinogenic element through skin contact; Refers to the average daily exposure of the i-th carcinogenic element through oral intake; Refers to the average daily exposure of the i-th carcinogenic element through skin exposure; is the carcinogenic intensity coefficient; L is the average life expectancy of the permanent population in the area in a certain year;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] is the health risk of the i-th non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the i-th non-carcinogenic element; is the health risk of skin contact of the i-th non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements;

[0045] Human health risks posed by exposure to new pollutants :

[0046] ;

[0047] ;

[0048] is the risk entropy of the i-th new pollutant;

[0049] is the detected concentration of the i-th new pollutant;

[0050] is the drinking water equivalent value of the i-th new pollutant;

[0051] ;

[0052] is the daily acceptable intake of the i-th new pollutant; BW is the average body weight; HQ is the highest risk; DWI is the amount of water consumed by the human body through drinking water every day; is the gastrointestinal absorption rate of the i-th new pollutant in the gastrointestinal tract; is the exposure frequency of human body to the i-th new pollutant each year.

[0053] In a further embodiment of the technical solution of the present invention, the method for classifying human health risks in step S2 is as follows:

[0054] According to the sampling point The highest risk level corresponding to the value and RQ value is taken as the human health risk level;

[0055] When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are the preset maximum acceptable human health risk level thresholds and negligible human health risk level thresholds caused by exposure to heavy metal elements and non-heavy metal elements, respectively;

[0056] When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are respectively the maximum acceptable human health risk level threshold and the negligible human health risk level threshold caused by the preset new pollutant exposure.

[0057] In a further embodiment of the technical solution of the present invention, the ecosystem health risk classification method in step S2 is as follows:

[0058] Changing trends in ecosystem health risk levels When it is equal to 1, it is judged as low risk to ecosystem health;

[0059] When the risk is within the range of [0.5, 1)∪(1,1.5), it is judged as medium risk for ecosystem health;

[0060] When it is in the range of (0, 0.5)∪(1.5, +∞), it is judged as a high risk to ecosystem health;

[0061] ;

[0062] is the current water environment ecosystem health risk coefficient, is the historical water environment ecosystem health risk factor;

[0063]

[0064] is the current ecosystem integrity index; is the current biodiversity index; is the current concentration of the i-th pollutant; is the attenuation coefficient of the current i-th pollutant; is the time interval between the sampling time and detection time of the current i-th pollutant;

[0065] ;

[0066] is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the i-th pollutant in history; is the attenuation coefficient of the i-th pollutant in history; is the time interval between the sampling time and the detection time of the i-th pollutant in history;

[0067] ;

[0068] The integrity of aquatic plants and animals in the current aquatic ecosystem; is the current flow rate; is the current water temperature; is the current pH; is the current total dissolved solids;

[0069] ;

[0070] The integrity of aquatic plants and animals in historical aquatic ecosystems; is the historical flow rate; is the historical water temperature; is the historical pH; is the historical total dissolved solids;

[0071] ;

[0072] is the relative abundance of the current i-th biological species;

[0073] ;

[0074] is the relative abundance of the i-th biological species in history.

[0075] In a further optimization of the technical solution of the present invention, a water environment health risk coordinate system is constructed in step S5, specifically:

[0076] The horizontal axis of the coordinate system is the human health risk level, which is low, medium, and high risk to human health from the origin to the right, and the vertical axis is the ecosystem health risk level, which is low, medium, and high risk to ecosystem health from the origin upward;

[0077] Taking y=x as the water environment health risk baseline, and using the risk coordinates (low risk to human health, low risk to ecosystem health) and (medium risk to human health, medium risk to ecosystem health) as the dividing points, the water environment health risk baseline is divided from the origin into low, medium, and high risk areas;

[0078] Express the risk coordinates of the sampling points (human health risk level, ecosystem health risk level) or (future potential human health risk level, future potential ecosystem health risk level) in the water environment health risk coordinate system and project them onto the risk baseline;

[0079] If the projection of the risk coordinates of the sampling point on the risk baseline falls within the low-risk area, the water environment health risk or future potential water environment health risk is low risk.

[0080] If the projection of the risk coordinates of the sampling point on the risk baseline falls within the medium risk area, the water environment health risk or future potential water environment health risk is medium risk.

[0081] If the projection of the risk coordinates of the sampling point on the risk baseline falls into the high-risk area, the water environment health risk or the potential future water environment health risk is high risk.

[0082] Further optimization of the technical solution of the present invention also includes importing the water environment health risks and future potential water environment health risks, environmental data, different types of pollutants, and concentrations corresponding to different types of pollutants at multiple sampling points into ArcGIS to generate a visual water environment health risk delineation.

[0083] An electronic device includes a processor and a memory, wherein the memory stores a computer program that can be run on the processor, and the processor executes the steps of any one of the above methods when running the computer program.

[0084] A computer-readable storage medium stores a computer program, and the computer program is used by a processor to execute the steps of any of the above methods.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] 1. This invention achieves a comprehensive assessment of human health risks by analyzing the degree of exposure of the human population to different types of pollutants, and conducts a comprehensive assessment of ecosystem health risks by using environmental DNA technology to obtain ecological diversity data from sampling points in the target study area.

[0087] 2. The present invention realizes the future potential risk prediction of different types of pollutants to multiple sampling points by constructing a coupling model of risk prediction big data model and migration and diffusion model.

[0088] 3. The present invention can comprehensively utilize the human health risks and ecosystem health risks of multiple sampling points, the prediction results of future potential risks, environmental data, different types of pollutants, and the corresponding concentrations of different pollutant types, and combine them with the spatial analysis function of ArcGIS to achieve the precise delineation and visualization of the overall water environment health risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is a flow chart of a method for delineating water environment health risks in black rock geological background areas according to the present invention;

[0090] Figure 2 This is a coupling model construction diagram of a method for delineating water environment health risks in a black rock geological background area according to the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following Figure 1-2 It should be understood that the specific embodiments described herein are only used to illustrate the present invention and are not intended to limit the present invention.

[0092] like Figure 1 As shown in the figure, a method for delineating water environment health risks in a black rock geological background area in this embodiment, the specific steps of the water environment health risk delineation method are as follows:

[0093] S1. Collect environmental data from multiple sampling points in the target study area, and obtain the different types of pollutants and their corresponding concentrations at different sampling points. The types of pollutants include heavy metal elements, non-heavy metal elements, and emerging pollutants.

[0094] S2. Develop a water environment risk classification method to assess the exposure of the population to pollutants at the sampling points and the ecosystem health risks and human health risks caused by pollutant exposure. The water environment risk classification method includes the ecosystem health risk classification method and the human health risk classification method.

[0095] S3. Build a risk prediction model based on machine learning;

[0096] S4. Input the degree of human exposure to pollutants at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain prediction results of potential future human health risks and ecosystem health risks;

[0097] S5. Construct a risk coordinate system, input the human health risks and ecosystem health risks of multiple sampling points, and the predicted future potential human health risks and ecosystem health risks into the risk coordinates respectively, and obtain the water environment health risks and future potential water environment health risks of the sampling points.

[0098] Environmental data also include geological data, hydrological data and ecological data of the target study area.

[0099] In step S2, the average daily exposure through oral ingestion and skin exposure is used as an evaluation indicator of the exposure level of heavy metal elements and non-heavy metal element pollutants to the human population at the sampling point, specifically:

[0100] ;

[0101] Refers to the average daily exposure through oral intake, mg·kg-1·day-1; CW refers to the pollutant content in drinking water in the area, mg·L-1; IR refers to drinking water intake; EF refers to exposure frequency; ED refers to exposure duration, that is, the average life expectancy of the permanent population in the area in a certain year; BW refers to average body weight; AT refers to average exposure time;

[0102] ;

[0103] It refers to the average daily exposure through skin exposure, mg·kg-1·day-1; SA refers to the skin contact surface area; PC refers to the skin permeability coefficient of chemical substances, cm / h; ET refers to the short-term exposure time; CF is the volume conversion factor.

[0104] The human health risks in step S2 include the human health risks caused by exposure to heavy metal elements and non-heavy metal elements and the human health risks caused by exposure to new pollutants;

[0105] Human health risks caused by exposure to heavy metals and non-heavy metals :

[0106] ;

[0107] Human health risks caused by heavy metals and non-heavy metals;

[0108] R is the total carcinogenic risk of all carcinogenic elements;

[0109] HI is the total health risk of all non-carcinogenic elements;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] is the carcinogenic risk of the i-th carcinogen through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the i-th carcinogenic element through skin contact; Refers to the average daily exposure of the i-th carcinogenic element through oral intake; Refers to the average daily exposure of the i-th carcinogenic element through skin exposure; is the carcinogenic intensity coefficient; L is the average life expectancy of the permanent population in the area in a certain year;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] is the health risk of the i-th non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the i-th non-carcinogenic element; is the health risk of skin contact of the i-th non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements.

[0120] Human health risks posed by exposure to new pollutants :

[0121] ;

[0122] ;

[0123] is the risk entropy of the i-th new pollutant;

[0124] is the detected concentration of the i-th new pollutant;

[0125] is the drinking water equivalent value of the i-th new pollutant;

[0126] ;

[0127] is the daily acceptable intake of the i-th new pollutant; BW is the average body weight; HQ is the highest risk; DWI is the amount of water consumed by the human body through drinking water every day; is the gastrointestinal absorption rate of the i-th new pollutant in the gastrointestinal tract; is the exposure frequency of human body to the i-th new pollutant each year.

[0128] The human health risk classification method in step S2 is specifically as follows:

[0129] According to the sampling point The highest risk level corresponding to the value and RQ value is taken as the human health risk level;

[0130] When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are the preset maximum acceptable human health risk level thresholds and negligible human health risk level thresholds caused by exposure to heavy metal elements and non-heavy metal elements, respectively;

[0131] When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are respectively the maximum acceptable human health risk level threshold and the negligible human health risk level threshold caused by the preset new pollutant exposure.

[0132] In the current study, regarding the scope of human health risks caused by exposure to new pollutants, the risk entropy RQ of new pollutants only has a maximum acceptable range. Considering that the toxic accumulation of new pollutants is greatly affected by long time series, in this embodiment, when the RQ value in the evaluation result is less than the maximum acceptable level range of RQ, it is judged as low risk.

[0133] If the human health risks caused by heavy metal elements and non-heavy metal elements at a sampling point in the target research area If the risk level is judged to be low and the human health risk RQ risk level of the new pollutant is judged to be high, then the human health risk level of the sampling point is judged to be high.

[0134] The ecosystem health risk classification method in step S2 is as follows:

[0135] Changing trends in ecosystem health risk levels When it is equal to 1, it is judged as low risk to ecosystem health;

[0136] When the risk is within the range of [0.5, 1)∪(1,1.5), it is judged as medium risk for ecosystem health;

[0137] When the risk is in the range of (0, 0.5)∪(1.5, +∞), it is judged to be a high risk to ecosystem health.

[0138] ;

[0139] is the current water environment ecosystem health risk coefficient, is the historical water environment ecosystem health risk coefficient.

[0140] ;

[0141] is the current ecosystem integrity index; is the current biodiversity index; is the current concentration of the i-th pollutant; is the attenuation coefficient of the current i-th pollutant; is the time interval between the sampling time and detection time of the current i-th pollutant;

[0142] ;

[0143] is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the i-th pollutant in history; is the attenuation coefficient of the i-th pollutant in history; is the time interval between the sampling time and the detection time of the i-th pollutant in history;

[0144] ;

[0145] The integrity of aquatic plants and animals in the current aquatic ecosystem; is the current flow rate; is the current water temperature; is the current pH; is the current total dissolved solids;

[0146] ;

[0147] The integrity of aquatic plants and animals in historical aquatic ecosystems; is the historical flow rate; is the historical water temperature; is the historical pH; is the historical total dissolved solids;

[0148] ;

[0149] is the relative abundance of the current i-th biological species;

[0150] ;

[0151] is the relative abundance of the i-th biological species in history.

[0152] Environmental DNA technology is used to obtain DNA fragments of phytoplankton and other organisms in the water environment of the study area. Species are identified through PCR amplification and sequence analysis, and high-throughput sequencing data is obtained after high-throughput sequencing. The number of sequences of different species is counted, and the richness data of different species and the integrity data of aquatic plants and animals in aquatic ecosystems are obtained. By calculating indicators such as species richness and species diversity index, the species diversity of the water ecosystem in the study area is evaluated. Organisms sensitive to specific pollutants are selected as indicator species, and their richness changes are analyzed. The impact of pollutants on the ecosystem is evaluated, the structure and function of microbial communities are analyzed, and the ecosystem functions such as nutrient cycling and material transformation are evaluated.

[0153] like Figure 2 As shown, in step S3, a risk prediction model is constructed based on machine learning. The risk prediction model is a coupling model of the risk prediction big data model and the migration and diffusion model;

[0154] BS1. Build a big data model for risk prediction:

[0155] as1. Obtain historical environmental data to form an original data set;

[0156] as2. Preprocess the original data set, including data cleaning, missing value processing, and outlier processing;

[0157] as3. Perform correlation analysis on the features in the preprocessed original dataset to obtain features that have a significant impact on risk prediction;

[0158] as4. Use Bootstrap sampling to randomly extract multiple sub-datasets from the processed original data set, build a decision tree for each sub-dataset, and randomly select a part of the features at each node for splitting;

[0159] as5. Repeat the above steps until the specified number of decision trees are generated and a CSV file is generated;

[0160] BS2. Construct a migration and diffusion model:

[0161] bs1. Obtain environmental data to build a migration and diffusion model;

[0162] bs2. Preprocess environmental data, including data cleaning, missing value processing, outlier processing, and interpolation and extension;

[0163] bs3. Set and adjust migration and diffusion model parameters, including time step, attenuation coefficient, and hydrological condition parameters;

[0164] bs4. Obtain environmental data and divide the target study area into grids;

[0165] bs5. Insert the CSV file constructed from the risk prediction big data model as the dynamic boundary condition of the migration and diffusion model;

[0166] bs6. Generate Ad. file;

[0167] BS3. Import the Ad. file into the coupled model, generate the Sim file, and complete the model coupling.

[0168] The human health risks and ecosystem health risks affected by pollutants at the sampling points, as well as the surrounding environmental data, are input into the coupling model for risk prediction. The specific steps are as follows:

[0169] Import the calculated human health risk and ecosystem health risk data and surrounding environment data into the Sim. file in sequence;

[0170] Click Run Sim. file on the menu interface, adjust the parameters, and obtain the prediction results of the potential future human health risks and ecosystem health risks of the sampling point.

[0171] In step S5, a water environment health risk coordinate system is constructed, specifically:

[0172] The horizontal axis of the coordinate system is the human health risk level, which is low, medium, and high risk to human health from the origin to the right, and the vertical axis is the ecosystem health risk level, which is low, medium, and high risk to ecosystem health from the origin upward;

[0173] Taking y=x as the water environment health risk baseline, and using the risk coordinates (low risk to human health, low risk to ecosystem health) and (medium risk to human health, medium risk to ecosystem health) as the dividing points, the water environment health risk baseline is divided from the origin into low, medium, and high risk areas;

[0174] Express the risk coordinates of the sampling points (human health risk level, ecosystem health risk level) or (future potential human health risk level, future potential ecosystem health risk level) in the water environment health risk coordinate system and project them onto the risk baseline;

[0175] If the projection of the risk coordinates of the sampling point on the risk baseline falls within the low-risk area, the water environment health risk or future potential water environment health risk is low risk.

[0176] If the projection of the risk coordinates of the sampling point on the risk baseline falls within the medium risk area, the water environment health risk or future potential water environment health risk is medium risk.

[0177] If the projection of the risk coordinates of the sampling point on the risk baseline falls into the high-risk area, the water environment health risk or the potential future water environment health risk is high risk.

[0178] Import the water environment health risks and potential future water environment health risks, environmental data, different pollutant types, and the corresponding concentrations of different pollutant types at multiple sampling points into ArcGIS to generate a visual water environment health risk delineation, as follows:

[0179] Pre-process the imported water environment health risks and future potential water environment health risks, environmental data, different pollutant types, and concentration data corresponding to different pollutant types at multiple sampling points;

[0180] ArcGIS was used to interpolate the pollutant concentrations and water environment health risk data at the sampling points to generate a continuous spatial distribution map;

[0181] Using ArcGIS, different colors, patterns or labels are set for different risk areas to generate intuitive and easy-to-understand risk distribution maps.

[0182] The parts not involved in the present invention are the same as the existing technology or can be implemented by using the existing technology.

[0183] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for delineating water environment health risks in areas with black rock geological backgrounds, characterized by: The specific steps of the water environment health risk delineation method are as follows: S1. Collect environmental data from multiple sampling points in the target study area, and obtain the different types of pollutants and their corresponding concentrations at different sampling points. The types of pollutants include heavy metal elements, non-heavy metal elements, and emerging pollutants. S2. Develop a water environment risk classification method to assess the exposure of the population to pollutants at the sampling points and the ecosystem health risks and human health risks caused by pollutant exposure. The water environment risk classification method includes the ecosystem health risk classification method and the human health risk classification method. S3. Based on machine learning, a risk prediction model is constructed. The risk prediction model is a coupling model of the risk prediction big data model and the migration and diffusion model. The details are as follows: BS1. Build a big data model for risk prediction: as1. Obtain historical environmental data to form an original data set; as2. Preprocess the original data set, including data cleaning, missing value processing, and outlier processing; as3. Perform correlation analysis on the features in the preprocessed original dataset to obtain features that have a significant impact on risk prediction; as4. Use Bootstrap sampling to randomly extract multiple sub-datasets from the processed original data set, build a decision tree for each sub-dataset, and randomly select a part of the features at each node for splitting; as5. Repeat the above steps until the specified number of decision trees are generated and a CSV file is generated; BS2. Construct a migration and diffusion model: bs1. Obtain environmental data to build a migration and diffusion model; bs2. Preprocess environmental data, including data cleaning, missing value processing, outlier processing, and interpolation and extension; bs3. Set and adjust migration and diffusion model parameters, including time step, attenuation coefficient, and hydrological condition parameters; bs4. Obtain environmental data and divide the target study area into grids; bs5. Insert the CSV file constructed from the risk prediction big data model as the dynamic boundary condition of the migration and diffusion model; bs6. Generate Ad. file; BS3. Import the Ad. file into the coupled model, generate the Sim file, and complete the model coupling; S4. Input the degree of human exposure to pollutants at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain prediction results of potential future human health risks and ecosystem health risks; S5. Construct a risk coordinate system, input the human health risks and ecosystem health risks of multiple sampling points, and the predicted future potential human health risks and ecosystem health risks into the risk coordinates respectively, and obtain the water environment health risks and future potential water environment health risks of the sampling points.

2. The method for delineating water environment health risks in black rock geological background areas according to claim 1 is characterized by: In step S2, the average daily exposure through oral ingestion and skin exposure is used as an evaluation indicator of the exposure level of heavy metal elements and non-heavy metal element pollutants to the human population at the sampling point, specifically: ； Refers to the average daily exposure via oral intake; CW refers to the pollutant content in the drinking water of the area; IR refers to the drinking water intake; EF refers to the exposure frequency; ED refers to the exposure duration, that is, the average life expectancy of the permanent population in the area in a certain year; BW refers to the average body weight; AT refers to the average exposure time; ； Refers to the average daily exposure through skin exposure; SA refers to skin contact surface area; PC refers to the skin permeability coefficient of the chemical substance; ET refers to the short-term exposure time; CF is the volume conversion factor.

3. The method for delineating water environment health risks in black rock geological background areas according to claim 2 is characterized by: The human health risks in step S2 include the human health risks caused by exposure to heavy metal elements and non-heavy metal elements and the human health risks caused by exposure to new pollutants; Human health risks caused by exposure to heavy metals and non-heavy metals : ； Human health risks caused by heavy metals and non-heavy metals; R is the total carcinogenic risk of all carcinogenic elements; HI is the total health risk of all non-carcinogenic elements; ； ； ； ； is the carcinogenic risk of the i-th carcinogen through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the i-th carcinogenic element through skin contact; Refers to the average daily exposure of the i-th carcinogenic element through oral intake; Refers to the average daily exposure of the i-th carcinogenic element through skin exposure; is the carcinogenic intensity coefficient; L is the average life expectancy of the permanent population in the area in a certain year; ； ； ； ； is the health risk of the i-th non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the i-th non-carcinogenic element; is the health risk of skin contact of the i-th non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements; Human health risks posed by exposure to new pollutants : ； ； is the risk entropy of the i-th new pollutant; is the detected concentration of the i-th new pollutant; is the drinking water equivalent value of the i-th new pollutant; ； is the daily acceptable intake of the i-th new pollutant; BW is the average body weight; HQ is the highest risk; DWI is the amount of water consumed by the human body through drinking water every day; is the gastrointestinal absorption rate of the i-th new pollutant in the gastrointestinal tract; is the exposure frequency of human body to the i-th new pollutant each year.

4. The method for delineating water environment health risks in black rock geological background areas according to claim 3 is characterized by: The human health risk classification method in step S2 is specifically as follows: According to the sampling point The highest risk level corresponding to the value and RQ value is taken as the human health risk level; When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are the preset maximum acceptable human health risk level thresholds and negligible human health risk level thresholds caused by exposure to heavy metal elements and non-heavy metal elements, respectively; When the risk is high, When the risk is medium, When , it is judged as low risk; among them, 、 They are respectively the maximum acceptable human health risk level threshold and the negligible human health risk level threshold caused by the preset new pollutant exposure.

5. The method for delineating water environment health risks in black rock geological background areas according to claim 1 is characterized by: The ecosystem health risk classification method in step S2 is as follows: Changing trends in ecosystem health risk levels When it is equal to 1, it is judged as low risk to ecosystem health; When the risk is within the range of [0.5, 1) ∪ (1, 1.5), it is judged as medium risk for ecosystem health; When it is in the range of (0, 0.5)∪(1.5, +∞), it is judged as a high risk to ecosystem health; ； is the current water environment ecosystem health risk coefficient, is the historical water environment ecosystem health risk factor; ; is the current ecosystem integrity index; is the current biodiversity index; is the current concentration of the i-th pollutant; is the attenuation coefficient of the current i-th pollutant; is the time interval between the sampling time and detection time of the current i-th pollutant; ； is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the i-th pollutant in history; is the attenuation coefficient of the i-th pollutant in history; is the time interval between the sampling time and the detection time of the i-th pollutant in history; ； The integrity of aquatic plants and animals in the current aquatic ecosystem; is the current flow rate; is the current water temperature; is the current pH; is the current total dissolved solids; ； The integrity of aquatic plants and animals in historical aquatic ecosystems; is the historical flow rate; is the historical water temperature; is the historical pH; is the historical total dissolved solids; ； is the relative abundance of the current i-th biological species; ； is the relative abundance of the i-th biological species in history.

6. The method for delineating water environment health risks in black rock geological background areas according to claim 1 is characterized by: In step S5, a water environment health risk coordinate system is constructed, specifically: The horizontal axis of the coordinate system is the human health risk level, which is low, medium, and high risk to human health from the origin to the right, and the vertical axis is the ecosystem health risk level, which is low, medium, and high risk to ecosystem health from the origin upward; Taking y=x as the water environment health risk baseline, and using the risk coordinates (low risk to human health, low risk to ecosystem health) and (medium risk to human health, medium risk to ecosystem health) as the dividing points, the water environment health risk baseline is divided from the origin into low, medium, and high risk areas; Express the risk coordinates of the sampling points (human health risk level, ecosystem health risk level) or (future potential human health risk level, future potential ecosystem health risk level) in the water environment health risk coordinate system and project them onto the risk baseline; If the projection of the risk coordinates of the sampling point on the risk baseline falls within the low-risk area, the water environment health risk or future potential water environment health risk is low risk. If the projection of the risk coordinates of the sampling point on the risk baseline falls within the medium risk area, the water environment health risk or future potential water environment health risk is medium risk. If the projection of the risk coordinates of the sampling point on the risk baseline falls into the high-risk area, the water environment health risk or the potential future water environment health risk is high risk.

7. The method for delineating water environment health risks in black rock geological background areas according to claim 1 is characterized by: It also includes importing the water environment health risks and potential future water environment health risks, environmental data, different types of pollutants, and the corresponding concentrations of different pollutant types at multiple sampling points into ArcGIS to generate a visual water environment health risk delineation.

8. An electronic device comprising a processor and a memory, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor runs the computer program, the steps of the method according to any one of claims 1 to 7 are performed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is used by a processor to execute the steps of the method according to any one of claims 1 to 7.

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