Black rock system geological background area water environment health risk demarcation method

Abstract

The invention discloses a black rock geological background area water environment health risk demarcation method, and the method achieves the comprehensive assessment of human health risks through analyzing the exposure degrees of different types of pollutants to people. Acquiring ecological diversity data of sampling points of a target research area by using an environmental DNA technical means to comprehensively evaluate the health risk of the ecological system; future potential risk prediction of different types of pollutants on a plurality of sampling points is realized by constructing a coupling model of a risk prediction big data model and a migration diffusion model; according to the method, human health risks and ecological system health risks of multiple sampling points, prediction results of future potential risks, environmental data, different pollutant types and concentrations corresponding to the different pollutant types can be comprehensively utilized, and the spatial analysis function of ArcGIS is combined, so that the overall water environment health risks are accurately delimited and visualized.

Description

Technical Field

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

[0002] Due to its unique chemical weathering process and the acidic substances that may be produced, the black rock geological background area often contains heavy metal elements (such as cadmium, zinc, etc.) and other non-metallic elements (such as arsenic, selenium, etc.). In addition, due to the rich metal and non-metallic minerals, there are a large number of historical or abandoned mining areas in the black rock area, as well as new pollutants (such as antibiotics, perfluorinated compounds, microplastics, endocrine disruptors, etc.) caused by various human activities in the surrounding area. These substances may enter the water body through the water cycle, posing a threat to the ecological environment and human health, and it is urgent to assess the health risks of the water environment. At present, although there are some methods for assessing the health risks of single elements in the geological environment, it is impossible to comprehensively assess the potential risks of heavy metal elements, non-heavy metal elements and new pollutants to the area. At the same time, the existing assessment methods usually lack the predictability of the migration and diffusion process of pollutants in the water body, making it difficult to scientifically and objectively assess and display potential risk areas. Therefore, it is particularly important to design a method that can accurately delineate and visually express the health risks of the water environment in the black rock geological background area. 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: A method for delineating water environment health risks in a black rock geological background area, the method for delineating water environment health risks comprises the following specific steps: S1. Collect environmental data from multiple sampling points in the target study area, obtain environmental data to obtain different types of pollutants at different sampling points and the corresponding concentrations of different types of pollutants; where pollutant types include heavy metal elements, non-heavy metal elements, and new pollutants; S2. Construct a water environment risk classification method to assess the exposure of pollutants at sampling points to the population 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. Build a risk prediction model based on machine learning; S4. Input the exposure degree of pollutants to the population at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain the prediction results of potential human health risks and ecosystem health risks in the future; 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 of the sampling points and the future potential water environment health risks.

[0004] In a further preferred embodiment of the technical solution of the present invention, the average daily exposure through oral ingestion and skin exposure is used as an evaluation index of the exposure degree of heavy metal elements and non-heavy metal element pollutants at the sampling points to the population in step S2, specifically: ; 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 contact time; ; It 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.

[0005] 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; 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 ith carcinogenic element through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the ith 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 ith non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the ith non-carcinogenic element; is the health risk of skin contact of the ith non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements; Human health risks from 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 ith new pollutant in the gastrointestinal tract; is the exposure frequency of human body to the i-th new pollutant each year.

[0006] In a further preferred embodiment of the technical solution of the present invention, the method for classifying human health risks in step S2 is specifically as follows: According to the sampling point The highest risk level of the two corresponding risk levels 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 preset maximum acceptable human health risk level threshold and the negligible human health risk level threshold caused by exposure to new pollutants.

[0007] In a further preferred embodiment of the technical solution of the present invention, the method for dividing ecosystem health risks in step S2 is specifically 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 it is in 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 factor, 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 the detection time of the current i-th pollutant; ; is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the ith pollutant in history; is the attenuation coefficient of the ith 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.

[0008] In a further preferred embodiment of the technical solution of the present invention, the risk prediction model in step S3 is a coupling model of a risk prediction big data model and a migration and diffusion model; BS1. Constructing a risk prediction big data model: 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 data set to obtain features that have an important impact on risk prediction; as4. Use Bootstrap sampling method 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 migration 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 extension; bs3. Set and adjust the 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 built by 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 coupling model, generate the Sim file, and complete the model coupling.

[0009] 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: 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; Take y=x as the water environment health risk baseline, and use the risk coordinates (low risk for human health, low risk for ecosystem health) and (medium risk for human health, medium risk for ecosystem health) as the dividing points to divide the water environment health risk baseline from the origin into low, medium and high risk areas for water environment health; 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) are expressed in the water environment health risk coordinate system and projected onto the risk baseline; If the projection of the risk coordinates of the sampling point on the risk baseline falls into 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 into 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 future potential water environment health risk is high risk.

[0010] 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.

[0011] An electronic device comprises 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.

[0012] A computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of any of the above methods.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention achieves a comprehensive assessment of human health risks by analyzing the degree of exposure of people to different types of pollutants, and conducts a comprehensive assessment of ecosystem health risks by obtaining ecological diversity data from sampling points in the target research area using environmental DNA technology.

[0014] 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.

[0015] 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 concentrations corresponding to different types of pollutants, and combine them with the spatial analysis function of ArcGIS to achieve accurate delineation and visualization of the overall water environment health risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for delineating water environment health risk in a black rock geological background area of ​​the present invention; Figure 2 It 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

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following Figure 1-2 It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] like Figure 1 As shown, a method for delineating water environment health risks in a black rock geological background area in this embodiment, the water environment health risk delineation method comprises the following specific steps: S1. Collect environmental data from multiple sampling points in the target study area, obtain environmental data to obtain different types of pollutants at different sampling points and the corresponding concentrations of different types of pollutants; where pollutant types include heavy metal elements, non-heavy metal elements, and new pollutants; S2. Construct a water environment risk classification method to assess the exposure of pollutants at sampling points to the population 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. Build a risk prediction model based on machine learning; S4. Input the exposure degree of pollutants to the population at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain the prediction results of potential human health risks and ecosystem health risks in the future; 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 of the sampling points and the future potential water environment health risks.

[0019] Environmental data also include geological data, hydrological data and ecological data of the target study area.

[0020] In step S2, the average daily exposure through oral ingestion and skin exposure is used as an evaluation index for the exposure degree of heavy metal elements and non-heavy metal element pollutants at the sampling point to the population, specifically: ; Refers to the average daily exposure through oral intake, mg·kg-1·day-1; CW refers to the pollutant content in the drinking water of the area, mg·L-1; IR refers to 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 contact time; ; 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.

[0021] 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 ith carcinogenic element through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the ith 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 ith non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the ith non-carcinogenic element; is the health risk of skin contact of the ith non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements.

[0022] Human health risks from 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 ith new pollutant in the gastrointestinal tract; is the exposure frequency of human body to the i-th new pollutant each year.

[0023] The human health risk classification method in step S2 is specifically: According to the sampling point The highest risk level of the two corresponding risk levels 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 preset maximum acceptable human health risk level threshold and the negligible human health risk level threshold caused by exposure to new pollutants.

[0024] In the current study, in 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 to be low risk.

[0025] 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 as low and the human health risk RQ risk level of the new pollutant is judged as high, then the human health risk level of the sampling point is judged as high.

[0026] The ecosystem health risk classification method in step S2 is specifically 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 it is in the range of [0.5, 1) ∪ (1, 1.5), it is judged as medium risk for ecosystem health; When the risk is in the range of (0, 0.5) ∪ (1.5, +∞), it is judged to be a high risk to ecosystem health.

[0027] ; is the current water environment ecosystem health risk factor, is the historical water environment ecosystem health risk factor.

[0028] ; 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 the detection time of the current i-th pollutant; ; is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the ith pollutant in history; is the attenuation coefficient of the ith 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.

[0029] 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 ecosystem functions such as nutrient cycling and material transformation are evaluated.

[0030] like Figure 2As shown, in step S3, a risk prediction model is constructed based on machine learning, and the risk prediction model is a coupling model of a risk prediction big data model and a migration and diffusion model; BS1. Constructing a risk prediction big data model: 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 data set to obtain features that have an important impact on risk prediction; as4. Use Bootstrap sampling method 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 migration 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 extension; bs3. Set and adjust the 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 built by 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 coupling model, generate the Sim file, and complete the model coupling.

[0031] 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: Import the calculated human health risk and ecosystem health risk data and surrounding environment data into the Sim. file in sequence; 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.

[0032] 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; Take y=x as the water environment health risk baseline, and use the risk coordinates (low risk for human health, low risk for ecosystem health) and (medium risk for human health, medium risk for ecosystem health) as the dividing points to divide the water environment health risk baseline from the origin into low, medium and high risk areas for water environment health; 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) are expressed in the water environment health risk coordinate system and projected onto the risk baseline; If the projection of the risk coordinates of the sampling point on the risk baseline falls into 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 into 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 future potential water environment health risk is high risk.

[0033] The water environment health risks and potential future water environment health risks, environmental data, different types of pollutants, and the corresponding concentrations of different types of pollutants at multiple sampling points are imported into ArcGIS to generate a visual water environment health risk delineation, as follows: Pre-process the water environment health risks and future potential water environment health risks, environmental data, different types of pollutants, and concentration data corresponding to different types of pollutants at multiple imported sampling points; 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; Using ArcGIS, different colors, patterns or labels can be set for different risk areas to generate intuitive and easy-to-understand risk distribution maps.

[0034] The parts not involved in the present invention are the same as the prior art or can be implemented by using the prior art.

[0035] 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 in form and details may be made 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 black rock geological background areas, 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, obtain environmental data to obtain different types of pollutants at different sampling points and the corresponding concentrations of different types of pollutants; where pollutant types include heavy metal elements, non-heavy metal elements, and new pollutants; S2. Construct a water environment risk classification method to assess the exposure of pollutants at sampling points to the population 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. Build a risk prediction model based on machine learning; S4. Input the exposure degree of pollutants to the population at multiple sampling points and the ecosystem health risks and human health risks caused by pollutant exposure into the risk prediction model to obtain the prediction results of potential human health risks and ecosystem health risks in the future; 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 of the sampling points and the future potential water environment health risks.

2. The method for delineating water environment health risk in a black rock geological background area 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 index for the exposure degree of heavy metal elements and non-heavy metal element pollutants at the sampling point to the population, 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 risk in a black rock geological background area 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 ith carcinogenic element through different exposure pathways; is the carcinogenic risk of oral intake of the i-th carcinogenic element; is the carcinogenic risk of the ith 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 ith non-carcinogenic element through different exposure pathways; is the health risk of oral intake of the ith non-carcinogenic element; is the health risk of skin contact of the ith non-carcinogenic element; Refers to the average daily reference dose of non-carcinogenic elements; Human health risks from 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 ith 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 risk in a black rock geological background area according to claim 3 is characterized by: The human health risk classification method in step S2 is specifically: According to the sampling point The highest risk level of the two corresponding risk levels 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 exposure to new pollutants.

5. The method for delineating water environment health risk in a black rock geological background area according to claim 1 is characterized by: The ecosystem health risk classification method in step S2 is specifically 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 it is in 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 factor, 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 the detection time of the current i-th pollutant; ； is the historical ecosystem integrity index; is the historical biodiversity index; is the concentration of the ith pollutant in history; is the attenuation coefficient of the ith 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 risk in a black rock geological background area according to claim 1 is characterized by: The risk prediction model in step S3 is a coupling model of the risk prediction big data model and the migration diffusion model; BS1. Constructing a risk prediction big data model: 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 data set to obtain features that have an important impact on risk prediction; as4. Use Bootstrap sampling method 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 migration 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 extension; bs3. Set and adjust the 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 built by 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 coupling model, generate the Sim file, and complete the model coupling.

7. The method for delineating water environment health risk 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; Take y=x as the water environment health risk baseline, and use the risk coordinates (low risk for human health, low risk for ecosystem health) and (medium risk for human health, medium risk for ecosystem health) as the dividing points to divide the water environment health risk baseline from the origin into low, medium and high risk areas for water environment health; 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) are expressed in the water environment health risk coordinate system and projected onto the risk baseline; If the projection of the risk coordinates of the sampling point on the risk baseline falls into 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 into 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 future potential water environment health risk is high risk.

8. The method for delineating water environment health risk in a black rock geological background area 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 types of pollutants from multiple sampling points into ArcGIS to generate a visual water environment health risk delineation.

9. 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.

10. 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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