Ecological pollution assessment method and visualization system fusing hydrogeological factors

Abstract

This invention discloses an ecological pollution assessment method and visualization system that integrates hydrogeological factors, relating to the field of ecological and environmental assessment technology. The method includes constructing potential ecological risk indices for soil and water bodies and performing spatial interpolation, then performing three-dimensional coupling and fusion based on topographic and hydrological connectivity. Topographic factors such as slope, runoff direction, and surface runoff are incorporated into the pollution migration weight model. The spatial correlation structure of the risk index at sampling points is characterized based on a semi-variogram, ensuring that the contribution weight of each sampling point to the interpolation point has a clear statistical basis. Based on the three-dimensional coupled pollution model, geographical attributes and ecological sensitivity levels are introduced for weighted correction, so that the assessment results not only reflect pollution intensity but also simultaneously reflect the regional ecological carrying capacity, thus providing systematic support for the refined pollution assessment and management of ecological and environmental zones.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental pollution assessment technology, specifically an ecological pollution assessment method and visualization system that integrates hydrogeological factors. Background Technology

[0002] Existing spatial assessment methods for heavy metal pollution in the ecological environment mostly rely on statistical analysis and two-dimensional spatial interpolation of sampling point data. Their assessment goal is to obtain a spatial distribution map of pollutant concentrations or ecological risk indices within the study area. However, in actual ecological environments, heavy metal pollution is not statically distributed but continuously migrates under the influence of rainfall runoff, slope erosion, and groundwater infiltration. For example, in mountainous mining areas or hilly agricultural areas, heavy metals in the soil of uphill areas often accumulate along the slope towards low-lying areas with rainfall runoff, eventually entering valleys or water systems. Furthermore, there is a clear exchange of substances between soil and water bodies. For instance, heavy metals in the soil may enter rivers or lakes through surface runoff, while pollution in water bodies may be re-accumulated in bottom sediments or riparian soils through sedimentation.

[0003] Conventional ecological assessment methods mostly analyze soil and water pollution independently, and rely solely on two-dimensional interpolation results for spatial representation (spatial visualization of pollution), lacking systematic modeling of topographically driven migration processes. This results in pollution distribution results that primarily reflect statistical spatial differences, failing to explain the actual geomorphic control mechanisms of pollution formation, and thus easily leading to insufficient explanatory power in complex topographic regions. Summary of the Invention

[0004] (a) Technical problems to be solved This invention provides an ecological pollution assessment method and visualization system that integrates hydrogeological factors, which can reflect the ecological coupling relationship between soil pollution, water pollution and topographic structure, and perform three-dimensional visualization of the pollution space based on the topographic structure.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an ecological pollution assessment method integrating hydrogeological factors, comprising the following steps: The soil heavy metal content and water heavy metal concentration of each type of sampling point in the study area were obtained respectively. After data cleaning and standardization, the potential ecological risk index of soil and water body of each type of sampling point were calculated respectively. A digital elevation model was constructed based on elevation data and latitude and longitude within the study area; Using the potential ecological risk index of soil and water at each sampling point as variables, based on the spatial relationship of each sampling point in the digital elevation model, a semivariogram of soil and a semivariogram of water are constructed respectively. By calculating the spatial correlation of the potential ecological risk index of each type of sampling point, the contribution weight of the potential ecological risk index to the interpolation point is quantified. Based on the Kriging interpolation method, and based on the contribution weight of each sampling point to the interpolation point, the potential ecological risk index of each sampling point is spatially interpolated to generate two-dimensional continuously distributed soil pollution field and water pollution field respectively. Based on the topographic features and hydrological connectivity of the study area, a topographically driven pollution migration weight function is constructed to quantify the influence weights of slope, confluence direction, and surface runoff on the spatial migration of heavy metals. Combined with a digital elevation model, the soil pollution field and the water pollution field are coupled and fused to construct a three-dimensional coupled pollution model that includes lateral diffusion and vertical transport characteristics. For each spatial unit in the three-dimensional coupled pollution model, a corresponding weight coefficient is assigned according to the ecological sensitivity level of the geographical attributes of its location to perform weighted correction of the three-dimensional coupled pollution, thereby obtaining a comprehensive ecological pollution risk assessment result that reflects the dual constraints of pollution intensity and ecological carrying capacity.

[0006] In some feasible embodiments, within the study area, based on the soil topography distribution and water structure within the region, multiple soil sampling points and water sampling points are set up to collect soil heavy metal content data and water heavy metal concentration data corresponding to each type of sampling point; at the same time, the spatial coordinate information of each sampling point is obtained.

[0007] In some feasible embodiments, the collected soil heavy metal content data and water heavy metal concentration data are subjected to outlier detection, missing value correction and consistency verification, respectively; after data cleaning, the content and concentration data of different types of heavy metals are processed into dimensionless data using a standardization method.

[0008] In some feasible embodiments, the standardized soil heavy metal content data and water heavy metal concentration data are obtained, and the ecotoxicity factors corresponding to each heavy metal are obtained. For each sampling point and detected heavy metal elements Calculate the risk coefficient of a single heavy metal element. : ; in, Sampling points Detected heavy metal elements content, Heavy metals The reference base value, Heavy metals Ecotoxic factors; For sampling points The potential ecological risk index is obtained by summing all detected heavy metal elements: ; The potential ecological risk index of soil and the potential ecological risk index of water body were obtained for each type of sampling point.

[0009] In some feasible embodiments, the location coordinates of each type of sampling point in the digital elevation model of the study area are obtained, as well as the potential ecological risk index of the soil and the potential ecological risk index of the water body are obtained. Construct soil semivariograms respectively and the semivariogram of water bodies : ; ; in, The spatial spacing between sampling points and These represent the number of point pairs for soil sampling points and water sampling points, respectively. and Soil sampling points and The potential ecological risk index of soil; and Water sampling points and The potential ecological risk index of water bodies.

[0010] In some feasible embodiments, a spherical model is selected to fit the semivariograms of the soil and water bodies respectively, to obtain the covariance function between each sampling point and any interpolation point in space: ; in, For the preset noise figure, Sampling points and interpolation points The distance between them; then the following actions were performed on the soil and water bodies respectively: ; in, Assigning contribution weights to each interpolation point; performing weighted summation calculations on each interpolation point to obtain its corresponding estimated potential ecological risk index value. : ; in, The potential ecological risk index for the corresponding sampling points is used to generate continuous soil pollution fields and water pollution fields within the study area.

[0011] In some feasible embodiments, based on the digital elevation model, hydro-topographic analysis is performed on the topography of the study area to extract driving factors describing the direction of surface water flow and the trend of pollution migration, including slope, aspect and confluence direction, and to construct a topographic-driven pollution migration weight function to quantify the modulating effect of topographic conditions on the lateral migration capacity of pollution.

[0012] In some feasible embodiments, the potential ecological risk index of each point in the corresponding topographic region in the two-dimensional soil pollution field and water pollution field is corrected based on the topographic migration weight. A vertical weighting function was constructed by combining digital elevation model to quantify the vertical migration pattern of heavy metal pollution among surface, soil and shallow water; a three-dimensional coupled pollution model of the study area was constructed by integrating these components.

[0013] An ecological pollution visualization system, comprising: The data input module is used to import the digital elevation model of the study area, and to perform data cleaning and standardization processing on the soil heavy metal content and water heavy metal concentration data of each set sampling point in the study area, and to calculate the soil potential ecological risk index and water potential ecological risk index of each point accordingly. The visualization module is used to receive the potential ecological risk index of soil and water at each point, generate a two-dimensional continuously distributed soil pollution field and water pollution field based on the Kriging interpolation method, receive the digital elevation model, and construct a three-dimensional coupled pollution model that includes lateral diffusion and vertical transport characteristics. The risk assessment module imports the ecological sensitivity level of each geographical attribute area within the study area and assigns it a corresponding weight coefficient to perform weighted correction of three-dimensional coupled pollution, thereby obtaining a comprehensive ecological pollution risk assessment result that reflects the dual constraints of pollution intensity and ecological carrying capacity.

[0014] (III) Beneficial Effects: Compared with the prior art, this invention has the following beneficial effects: This invention breaks through the limitations of traditional single-medium independent assessment by constructing potential ecological risk indices for soil and water bodies separately and performing spatial interpolation, and then performing three-dimensional coupling and fusion based on topography and hydrological connectivity. It can systematically depict the migration, diffusion and accumulation process of pollution between different media, making the ecological pollution assessment results closer to the real environmental process.

[0015] By characterizing the spatial correlation structure of the risk index of sampling points based on the semi-variogram function, the contribution weight of each sampling point to the interpolation point has a clear statistical basis, which improves the accuracy and continuity of the two-dimensional pollution field construction and enhances the reliability of the spatial assessment results.

[0016] By introducing weighted corrections based on geographical attributes and ecological sensitivity levels on the basis of a three-dimensional coupled pollution model, the assessment results not only reflect the pollution intensity but also simultaneously reflect the regional ecological carrying capacity, achieving a comprehensive risk assessment under the dual constraints of pollution level and ecological vulnerability, and enhancing the guiding value of the assessment results for ecological management decisions. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an ecological pollution assessment method that integrates hydrogeological factors, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of an ecological pollution visualization system provided in an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the repair difficulty assessment provided in an embodiment of the present invention; Figure 4 This is a three-dimensional schematic diagram of the ecological risk index provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of three-dimensional coupling contamination provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0020] Most of the conventional assessment methods mentioned above analyze soil and water pollution independently, neglecting the significant ecological coupling relationship that usually exists between soil pollution, water pollution, and topographic structure. Furthermore, there is a practical problem related to engineering implementation: existing geoscientific data processing software has limited capabilities in representing the coupling relationship between topography and pollution.

[0021] Taking commonly used geoprocessing software as an example, while software such as Surfer, ArcGIS, and Grapher can perform interpolation analysis and two-dimensional contour mapping, they mainly focus on planar spatial representation and are difficult to intuitively display the three-dimensional spatial distribution structure of pollution under complex terrain conditions. Furthermore, in practical applications, researchers often need to perform data conversion and joint mapping between multiple software programs such as MapGIS and Surfer. This not only increases the complexity of the operational process but also makes the pollution analysis process lack a unified visualization environment.

[0022] Furthermore, in the interpolation modeling process, the variogram model of algorithms such as Kriging interpolation usually needs to be selected and adjusted based on human experience. When the geological structure of the study area is complex or the spatial heterogeneity is strong, this experience-based approach is difficult to effectively adapt to the actual environmental conditions.

[0023] Combination Figures 1 to 5 The present invention provides an ecological pollution assessment method and visualization system that integrates hydrogeological factors. In an ecosystem that includes soil and water, heavy metal pollution is not a single-medium problem, but a multi-factor coupled process involving water, soil, and topography. Soil is the main accumulation medium for heavy metals, while water is the main carrier for the migration and diffusion of heavy metals.

[0024] Therefore, in the embodiments of this invention, pollution information from both soil and water is acquired simultaneously to avoid bias in the assessment results towards a single environmental element. Simultaneously, the digital elevation model (DEM) and spatial attribute data of the study area are acquired, providing a foundation for the subsequent construction of a terrain-driven pollution migration model.

[0025] Specifically, within the study area, taking Jiangyuan District of Baishan City, Jilin Province as an example, geographical coordinates (latitude and longitude) and elevation data (DEM) were obtained. Soil and water sampling points were established to acquire heavy metal content data in the soil and heavy metal concentration data in the water, including the contents of elements such as Cd, As, Pb, Zn, Cu, Cr, Ni, and Hg. Simultaneously, the spatial coordinates of the sampling points and the digital elevation data of the study area, specifically latitude, longitude, and elevation information, were acquired. In addition, geospatial attribute data such as land use type, ecological functional zoning, and vegetation cover of the study area were also obtained as the basic input for subsequent spatial modeling and ecological constraint analysis.

[0026] Considering that actual monitoring data usually have sampling errors and that different heavy metals have different dimensions, it is necessary to perform quality control and standardization on the raw data to ensure the reliability of subsequent analysis.

[0027] In some embodiments of the present invention, the interquartile range (IQR) method is used to detect outlier elemental concentrations for numerical anomaly processing. This processing can eliminate samples with extremely high or low concentrations, retaining 95% of the normal data distribution. For multiple sampling data at the same coordinate point, median aggregation is used.

[0028] Q1 = df[el].quantile(0.25) Q3 = df[el].quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5*IQR upper_bound = Q3 + 1.5*IQR df[el] = np.where((df[el]<lower_bound) | (df[el]> upper_bound), np.nan, df[el]) df.groupby(['longitude','latitude']).agg({'Cd':'median'}) The key technologies for data preprocessing are outlier detection and cleaning: def load_data(): df = pd.read_excel(DATA_PATH) df = df.replace([np.inf, -np.inf], np.nan) df = df.dropna(subset=ELEMENTS) print(f"Original data size: {len(df)}") After data cleaning, the data of different types of heavy metals are standardized to eliminate the dimensional differences between different elements, so that the heavy metal indicators are on a uniform numerical scale, providing a consistent basis for subsequent risk-weighted calculations.

[0029] Given that the content or concentration of a single element cannot accurately reflect the overall ecological hazard under conditions of multiple metal coexistence, and that different elements exhibit significant differences in toxicity, bioaccumulation capacity, and ecological effects, it is essential to combine concentration information with toxicity weights to construct a unified indicator for characterizing potential ecological risks.

[0030] Meanwhile, the spatial distribution of pollution is not random, but exhibits significant spatial autocorrelation and clustering. Ignoring this spatial structure will lead to distorted interpolation results and affect the accuracy of spatial expansion.

[0031] Therefore, in the embodiments of the present invention, the standardized soil heavy metal content data and water heavy metal concentration data are used as inputs, and the ecotoxicity weight factor of each heavy metal element and the regional background benchmark value are combined to calculate the potential ecological risk coefficient of each sampling point. The following formula can be referred to here.

[0032] For each sampling point and detected heavy metal elements Calculate the risk coefficient of a single heavy metal element. : ; in, Sampling points Detected heavy metal elements content, Heavy metals The reference base value, Heavy metals Ecotoxicity factors / coefficients, such as the toxicity coefficient of Cd being 30 and the toxicity coefficient of Hg being 80.

[0033] For a single sampling point The potential ecological risk index is obtained by summing all detected heavy metal elements. : ; By summing up all elements, the potential ecological risk index of the soil at each soil sampling point and the potential ecological risk index of the water at each water sampling point are obtained.

[0034] In summary, this process transforms multi-element, multi-dimensional pollution information into a single comprehensive risk indicator, enabling a comparable expression of pollution intensity.

[0035] Then, using the potential ecological risk index of each sampling point as a variable, experimental semivariograms of the soil risk field and the water risk field were constructed based on their spatial location relationship to quantify the statistical law of the risk index changing with spatial distance.

[0036] Specifically, this involves obtaining the location coordinates of various types of sampling points in the digital elevation model of the study area, as well as obtaining the potential ecological risk index of soil and water bodies; and constructing soil semivariograms respectively. and the semivariogram of water bodies : ; ; in, The spatial spacing between sampling points and These represent the number of point pairs for soil sampling points and water sampling points, respectively. and Soil sampling points and The potential ecological risk index of soil; and Water sampling points and The potential ecological risk index of water bodies.

[0037] By fitting the experimental semivariogram to a model, key parameters such as nugget value, sill value, and range are obtained, a theoretical semivariogram model is established, and then a spatial correlation function of the potential ecological risk index is constructed.

[0038] Specifically, a spherical model is selected to fit the semivariograms of the soil and water bodies, respectively. The spherical model abruptly changes to the sill value at the range of variation, making it more suitable for scenarios with clear spatial boundaries. The spherical model is as follows:

[0039] ； ; in, This is the nugget effect (random noise); For sill values ​​(total variability); It represents the range of spatial correlation. Its asymptotic approximation of the sill value is suitable for describing variables that change continuously and gradually (such as soil heavy metal concentration).

[0040] Then, the covariance function between each sampling point and any interpolation point in space is obtained by fitting the data. This function can also be understood as a spatial correlation function. It characterizes the influence of different sampling points on any spatial location and serves as the mathematical basis for subsequent Kriging interpolation weight calculations. The formula for the covariance function is as follows:

[0041] ; in, Sampling points and interpolation points The distance between them.

[0042] Sampling points can only reflect the pollution status at discrete spatial locations, while ecological assessments require pollution distribution information at continuous spatial scales. Therefore, spatial interpolation methods must be used to extend the discrete sampling results into a continuous pollution distribution field.

[0043] On the other hand, in real ecological environments, pollution migration is strongly controlled by topography, manifesting as lateral diffusion along slope runoff and vertical transport driven by elevation gradients. Two-dimensional interpolation alone will fail to reflect the true migration paths and accumulation characteristics of pollution under complex terrain conditions.

[0044] In the process of generating a two-dimensional continuous pollution field using Kriging interpolation, a regularized spatial interpolation grid is constructed within the study area based on the established semi-variogram model. The sampling point weight coefficients are solved for each interpolation point, and the potential ecological risk estimate for that point is obtained by weighted summation.

[0045] Specifically, based on the contribution weights calculated above, a system of Kriging equations is established: ; in, As the contribution weight to the interpolation point, For Lagrange multipliers, The point to be interpolated.

[0046] Finally, a weighted summation calculation is performed on each interpolation point to obtain the corresponding estimated value of the potential ecological risk index. : ; Continuously distributed soil and water pollution fields within the study area were generated. This process represents a spatial transformation from point to surface representation, yielding a high-resolution pollution risk distribution map.

[0047] Subsequently, based on the digital elevation model, hydro-topographic analysis was conducted on the topography of the study area to extract key driving factors such as slope, aspect, confluence direction, and confluence accumulation.

[0048] By combining these topographic parameters, a topographically driven pollution migration weight function is constructed to quantify the modulating effect of different topographic conditions on pollution migration capacity, thereby forming a spatial migration weight field to characterize the migration trend of pollution driven by surface runoff.

[0049] Based on two-dimensional soil and water pollution fields, and combined with topographically driven migration weights, the two types of pollution fields are spatially coupled and fused: One approach is to apply lateral diffusion correction to the two-dimensional pollution field to characterize the migration process of pollution along the slope and water flow direction; Second, a vertical weighting function is introduced to characterize the vertical transport process of pollution between soil, surface, and shallow water.

[0050] Finally, a three-dimensional coupled pollution model incorporating lateral diffusion effects and vertical transport characteristics was constructed to achieve continuous representation and dynamic migration characterization of pollution in three-dimensional space. In summary, it can be understood that this stage, by introducing a terrain-driven mechanism, depicts the pollution migration path and accumulation area, constructs a three-dimensional pollution model that couples water and soil, and achieves a leap from static distribution to dynamic diffusion structure.

[0051] In embodiments of the present invention, it is considered that pollution intensity is not equivalent to ecological risk. The same level of pollution may lead to completely different ecological consequences in different ecological units. For example, ecological protection areas are highly sensitive to pollution; built-up areas have a higher tolerance for pollution; and farmland systems have a long-term cumulative risk of heavy metal pollution.

[0052] Therefore, in the embodiments of the present invention, it is necessary to combine pollution intensity with ecological sensitivity constraints in order to truly reflect the comprehensive risk level borne by the ecosystem.

[0053] Specifically, the study aims to acquire geospatial attribute data such as land use type, ecological function zoning, vegetation coverage, and slope grade of the study area, and to construct corresponding ecological sensitivity weight models to classify different geographical units into different levels of ecological vulnerability.

[0054] Subsequently, for each spatial unit in the three-dimensional coupled pollution model, an corresponding ecological weight coefficient is assigned according to its geographical attribute type, and the pollution risk value is weighted and corrected, thereby forming a comprehensive ecological pollution risk assessment result that reflects the dual constraints of pollution intensity and ecological vulnerability.

[0055] In addition, in some other embodiments of the present invention, the ecological vulnerability level obtained above can be combined to automatically generate differentiated ecological restoration strategy recommendations. Here, it is necessary to introduce the calculation formula for the Restoration Difficulty Index (RDI):

[0056] ; in, This is the slope difficulty factor (value 1 when slope > 15°, otherwise 0); C The water catchment center identification factor (1 for yes, 0 for no); P Normalized pollution index; R To normalize the potential ecological risk index; F The groundwater flow direction influencing factor (based on flow direction sine normalization). arrive These are the weighting coefficients, with default values ​​of 0.25, 0.15, 0.25, 0.25, and 0.10. A visualization of the repair difficulty is provided. Figure 3 .

[0057] refer to Figure 2 The diagram shows the principle block diagram of each module in the visualization system. The data input module serves as the front-end data hub of the system. Its core function is to transform raw, multi-source, and heterogeneous environmental monitoring data into a standardized input dataset with a unified structure, consistent scale, and direct participation in modeling calculations.

[0058] From an engineering execution perspective, the system's data input module receives and loads the following data: Digital Elevation Model (DEM) of the study area; Data on heavy metal content in soil at each sampling point; Heavy metal concentration data in water samples from each sampling point; Spatial coordinates of each sampling point.

[0059] By using a unified spatial coordinate datum, all data is mapped to a unified spatial reference system.

[0060] The module then automatically performs outlier identification, missing value repair, and normalization and standardization of different heavy metal data.

[0061] In addition, the system has an embedded risk index calculation model that executes the following logic for each sampling point: Call the heavy metal toxicity coefficient library corresponding to the region; Combined with local background values ​​or standard limits; Automatically calculate the single-factor ecological risk coefficient for each element; The potential ecological risk index of soil and the potential ecological risk index of water are obtained by summing them up.

[0062] In conjunction with the assessment methods mentioned above, the toxicity weight coefficients are dynamically adjusted according to different ecological scenarios. For example, for agricultural ecological zones, the weights of Cd and As are increased; for drinking water source protection zones, the weights of Pb and Hg are increased; and for industrial zones, the weights of Cr and Ni are increased.

[0063] Regarding the visualization module in this system, taking the potential ecological risk index of the sampling points as input, it automatically calls the semi-variogram model and the Kriging interpolation engine to construct a regularized spatial interpolation grid in the study area, performs weight calculation and risk estimation for each interpolation point, and generates a continuously distributed two-dimensional soil pollution field and a two-dimensional water pollution field respectively.

[0064] The visualization module uses a two-dimensional pollution field as a basis and combines terrain migration weights to spatially couple and fuse the pollution distribution, ultimately generating a three-dimensional coupled pollution model containing lateral diffusion and vertical transport characteristics, which is then stored in the form of a three-dimensional voxel model.

[0065] In addition, the visualization module uses a 3D terrain engine to render EM terrain, 3D pollutants, and hydrological networks in real time. (See attached image for reference.) Figure 5 It enables functions such as rotation browsing, cross-sectional viewing, and hierarchical penetration display.

[0066] The risk assessment module mainly integrates pollution intensity with ecological carrying capacity, and outputs comprehensive ecological pollution risk zoning results that can be directly used for management decisions.

[0067] In conjunction with the above, the risk assessment module imports the land use type map, ecological function zoning map, and vegetation cover distribution map of the study area, and maps them uniformly into an ecological sensitivity level zoning layer.

[0068] Finally, for each spatial unit in the three-dimensional coupled pollution model, weight coefficients are automatically matched according to its ecological zone, and weighted correction is performed to obtain a comprehensive ecological pollution risk value. The model also supports automatic execution of a five-level risk zoning, the classification of which is shown in Table 1 below. The corresponding generated visualization model is also shown in Table 1. Figure 4 .

[0069] Table 1 Classification of Potential Ecological Risk Levels This module combines a dynamic adjustment strategy based on regional toxicity coefficients to achieve five levels of risk zoning (slight, moderate, strong, very strong, and extremely strong), and uses color coding to intuitively reflect the spatial distribution of ecological risks.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of protection of the present invention is defined by the claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention should also be included within the scope of protection of the present invention.

Claims

1. An ecological pollution assessment method integrating hydrogeological factors, characterized in that, Includes the following steps: The soil heavy metal content and water heavy metal concentration of each type of sampling point in the study area were obtained respectively. After data cleaning and standardization, the potential ecological risk index of soil and water body of each type of sampling point were calculated respectively. A digital elevation model was constructed based on elevation data and latitude and longitude within the study area; Using the potential ecological risk index of soil and water at each sampling point as variables, based on the spatial relationship of each sampling point in the digital elevation model, a semivariogram of soil and a semivariogram of water are constructed respectively. By calculating the spatial correlation of the potential ecological risk index of each type of sampling point, the contribution weight of the potential ecological risk index to the interpolation point is quantified. Based on the Kriging interpolation method, and based on the contribution weight of each sampling point to the interpolation point, the potential ecological risk index of each sampling point is spatially interpolated to generate two-dimensional continuously distributed soil pollution field and water pollution field respectively. Based on the topographic features and hydrological connectivity of the study area, a topographically driven pollution migration weight function is constructed to quantify the influence weights of slope, confluence direction, and surface runoff on the spatial migration of heavy metals. Combined with a digital elevation model, the soil pollution field and the water pollution field are coupled and fused to construct a three-dimensional coupled pollution model that includes lateral diffusion and vertical transport characteristics. For each spatial unit in the three-dimensional coupled pollution model, a corresponding weight coefficient is assigned according to the ecological sensitivity level of the geographical attributes of its location to perform weighted correction of the three-dimensional coupled pollution, thereby obtaining a comprehensive ecological pollution risk assessment result that reflects the dual constraints of pollution intensity and ecological carrying capacity.

2. The ecological pollution assessment method integrating hydrogeological factors according to claim 1, characterized in that, Within the study area, based on the soil topography and water structure within the region, multiple soil sampling points and water sampling points were set up to collect soil heavy metal content data and water heavy metal concentration data corresponding to each type of sampling point; at the same time, the spatial coordinate information of each sampling point was obtained.

3. The ecological pollution assessment method integrating hydrogeological factors according to claim 1, characterized in that, The collected heavy metal content data in soil and water were subjected to outlier detection, missing value correction, and consistency verification. After data cleaning, the content and concentration data of different types of heavy metals were dimensionless by standardization methods.

4. The ecological pollution assessment method integrating hydrogeological factors according to claim 1, characterized in that, Obtain standardized data on soil heavy metal content and water heavy metal concentration, and obtain ecotoxicity factors corresponding to each heavy metal; For each sampling point and detected heavy metal elements Calculate the risk coefficient of a single heavy metal element. : ； in, Sampling points Detected heavy metal elements content, Heavy metals The reference base value, Heavy metals Ecotoxic factors; For sampling points The potential ecological risk index is obtained by summing all detected heavy metal elements: ； The potential ecological risk index of soil and the potential ecological risk index of water body were obtained for each type of sampling point.

5. The ecological pollution assessment method integrating hydrogeological factors according to claim 4, characterized in that, Obtain the location coordinates of each type of sampling point in the digital elevation model of the study area, and obtain the potential ecological risk index of the soil and the potential ecological risk index of the water body; Construct soil semivariograms respectively and the semivariogram of water bodies : ； ； in, The spatial spacing between sampling points and These represent the number of point pairs for soil sampling points and water sampling points, respectively. and Soil sampling points and The potential ecological risk index of soil; and Water sampling points and The potential ecological risk index of water bodies.

6. The ecological pollution assessment method integrating hydrogeological factors according to claim 5, characterized in that, A spherical model was selected to fit the semivariograms of the soil and water bodies, respectively, to obtain the covariance function between each sampling point and any interpolation point in space: ； in, For the preset noise figure, Sampling points and interpolation points The distance between them; then the following actions were performed on the soil and water bodies respectively: ； in, Assigning contribution weights to each interpolation point; performing weighted summation calculations on each interpolation point to obtain its corresponding estimated potential ecological risk index value. : ； in, The potential ecological risk index for the corresponding sampling points is used to generate continuous soil pollution fields and water pollution fields within the study area.

7. The ecological pollution assessment method integrating hydrogeological factors according to claim 1, characterized in that, Based on the digital elevation model, hydro-topographic analysis was conducted on the topography of the study area to extract driving factors describing the direction of surface water flow and the trend of pollution migration, including slope, aspect and confluence direction, and a topographic-driven pollution migration weight function was constructed.

8. The ecological pollution assessment method integrating hydrogeological factors according to claim 7, characterized in that, Based on the terrain migration weight, the potential ecological risk index of each point in the corresponding terrain area in the two-dimensional soil pollution field and water pollution field is corrected. A vertical weighting function was constructed by combining a digital elevation model to quantify the vertical migration patterns of heavy metal pollution among the surface, soil, and shallow water. A three-dimensional coupled pollution model of the study area was constructed by fusion.

9. An ecological pollution visualization system, characterized in that, include: The data input module is used to import the digital elevation model of the study area, and to perform data cleaning and standardization processing on the soil heavy metal content and water heavy metal concentration data of each set sampling point in the study area, and to calculate the soil potential ecological risk index and water potential ecological risk index of each point accordingly. The visualization module is used to receive the potential ecological risk index of soil and water at each point, generate a two-dimensional continuously distributed soil pollution field and water pollution field based on the Kriging interpolation method, receive the digital elevation model, and construct a three-dimensional coupled pollution model that includes lateral diffusion and vertical transport characteristics. The risk assessment module imports the ecological sensitivity level of each geographical attribute area within the study area and assigns it a corresponding weight coefficient to perform weighted correction of three-dimensional coupled pollution, thereby obtaining a comprehensive ecological pollution risk assessment result that reflects the dual constraints of pollution intensity and ecological carrying capacity.

Images