Sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data

Abstract

The invention relates to the technical field of environment monitoring, in particular to a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, which comprises a parameter acquisition module, a coupling resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy evaluation module. The parameter acquisition module is used for acquiring hydrodynamic parameters, sediment heavy metal occurrence forms and biological disturbance intensity indexes; the coupling resuspension simulation module fuses hydrodynamic shear stress and biological irrigation flux and simulates resuspension concentration distribution; the heavy metal form migration calculation module combines the redox sensitive factor field and the exchangeable heavy metal concentration to calculate the dynamic release flux of the dissolved heavy metal; the ecological risk entropy evaluation module introduces an acute and chronic toxicity dual evaluation mechanism, constructs a pollution risk entropy matrix and generates a three-dimensional pollution risk entropy cloud picture. The method is suitable for dynamic evaluation and early warning of resuspension pollution risks in water bodies such as offshore water bodies, river mouths and lakes.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a sediment heavy metal resuspension pollution evaluation system based on multidimensional data. Background Art

[0002] In aquatic sedimentary environments such as oceans, estuaries, and lakes, sediments are not only important reservoirs for heavy metals but also potential sources of pollution release under external disturbances. In recent years, with changes in bottom hydrodynamic conditions and increasing disturbance of benthic organisms caused by human activities, heavy metals previously stably stored in sediments are easily stimulated into the aqueous phase during resuspension, causing their re-release and diffusion, thus posing a secondary pollution risk to aquatic ecosystems. The intensity of heavy metal resuspension and release is controlled by multiple factors, such as hydrodynamic conditions, the form of heavy metals in sediments, the intensity of bioturbation, and the redox environment of the water body. Its dynamic changes exhibit significant nonlinearity and spatial heterogeneity.

[0003] However, existing technologies for evaluating sediment heavy metal resuspension pollution often rely on a combination of fixed-point sampling, experimental extraction, and empirical formula estimation. This approach suffers from severe model simplification, poor dynamic simulation capabilities, and insufficient spatial coverage. On the one hand, most methods ignore the role of bioturbation in the resuspension mechanism and fail to couple benthic biological behavior with hydrodynamic processes. On the other hand, they lack a dynamic characterization of the redox migration mechanism of heavy metals and are unable to truly reflect the release behavior of different occurrence forms. Furthermore, traditional pollution risk assessment methods are mostly based on concentration threshold judgments and lack information entropy indicators such as entropy to quantify risk uncertainty. Furthermore, they struggle to achieve continuous dynamic visualization analysis across scales and time periods. Summary of the Invention

[0004] The present invention provides a sediment heavy metal resuspension pollution assessment system based on multidimensional data, and proposes a systematic solution that can integrate multidimensional data acquisition, high-fidelity simulation calculation and risk entropy value expression to improve the scientificity, foresight and operability of resuspension pollution identification.

[0005] A sediment heavy metal resuspension pollution assessment system based on multidimensional data includes a parameter acquisition module, a coupled resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy value assessment module, wherein; The parameter acquisition module is used to obtain hydrodynamic parameters, sediment heavy metal occurrence morphology data and bioturbation intensity index of the target water area; The coupled resuspension simulation module receives the hydrodynamic parameters and the bioturbation intensity index and generates a dynamic suspended concentration distribution map through a hydrodynamic-bioturbation coupling model; The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and sediment heavy metal occurrence form data, and calculates the dynamic release flux of dissolved heavy metals in combination with the redox sensitive factor; The ecological risk entropy value evaluation module receives the dynamic release flux and integrates the biological toxicity threshold to generate a three-dimensional pollution risk entropy value cloud map.

[0006] Optionally, the parameter acquisition module includes: Collect physical environment data: obtain the latitude and longitude coordinates, water depth data and water temperature data of the target water area through multi-parameter water quality monitoring buoys; Extraction of hydrodynamic parameters: Based on physical environmental data, an acoustic Doppler current profiler was used to measure the velocity profile and turbulent kinetic energy at a sampling frequency of ≥10 Hz, and to calculate the bottom shear stress. Obtaining sediment heavy metal speciation data: Sediment columns were collected at defined latitude and longitude coordinates using a columnar sampler. After freeze-drying, the exchangeable, carbonate-bound, iron-manganese oxide-bound, and residual heavy metals were separated using the BCR continuous extraction method. The content of each speciation was determined using inductively coupled plasma mass spectrometry. Quantitative bioturbation intensity index: At the sediment sampling point, the biomass per unit area is obtained by benthic trawling, and the bioirrigation rate is determined by pore water tracer experiments. The bioturbation intensity index is generated by multiplying the biomass by the bioirrigation rate. Generate physical environment data package: Integrate physical environment data, hydrological dynamic parameters, sediment heavy metal occurrence form data and bioturbation intensity index into a structured data package, and output it to the coupled resuspension simulation module and heavy metal form migration calculation module.

[0007] Optionally, the coupled resuspension simulation module includes: Initialize the hydrodynamic-bioturbation coupling model: receive the hydrodynamic parameters and bioturbation intensity index output by the parameter acquisition module, configure the model computational domain grid (resolution ≤ 5m × 5m) and time step (≤ 1 hour); Calculation of hydrodynamic shear stress field: Based on the hydrodynamic parameters, the three-dimensional Navier-Stokes equations are solved using the k-ω turbulence model to output a time series of bottom shear stress field. Generate bioturbation flux field: Based on the bioturbation intensity index, a random walk algorithm is used to simulate benthic biological activities and output the biological pore network and bioirrigation flux field; Perform coupled resuspension calculations: Input the bottom shear stress field and the bioirrigation flux field into the hydrodynamic-bioturbation coupling model and calculate the sediment resuspension rate using the shear-pore coupling equation; Generate a dynamic suspended concentration distribution map: perform spatiotemporal integration of the resuspension rate, combine the advection-diffusion equation to simulate the suspended matter transport process, output a dynamic suspended concentration distribution map in grid units, and transmit it to the heavy metal form migration calculation module.

[0008] Optionally, the heavy metal form migration calculation module includes: Receive input data sets: obtain the dynamic suspended concentration distribution map output by the coupled resuspension simulation module and the sediment heavy metal occurrence form data output by the parameter acquisition module; Extraction of exchangeable heavy metal content: Separation of exchangeable heavy metal components from sediment heavy metal occurrence data to generate a spatially distributed exchangeable heavy metal concentration matrix; Generate redox sensitive factor field: The redox potential and pH value are collected in real time through a microelectrode array deployed in the target water area, the redox sensitive factor is calculated based on the coupling relationship between the redox potential and the pH value, and a spatiotemporally continuous redox sensitive factor field is output.

[0009] Optionally, the heavy metal form migration calculation module further includes: Calculation of dynamic release flux of dissolved heavy metals: Based on the dynamic suspended concentration distribution map, the exchangeable heavy metal concentration matrix and the redox sensitive factor field, the migration flux calculation model simultaneously processes the diffusion and redox release effects, and outputs the gridded dynamic release flux of dissolved heavy metals; Generate a dynamic release flux data set: Integrate the dynamic release flux of dissolved heavy metals into a three-dimensional data structure of spatial coordinates-time steps-heavy metal elements according to the time series, and transmit it to the ecological risk entropy value assessment module.

[0010] Optionally, the ecological risk entropy value evaluation module includes: Receive dynamic release flux dataset: obtain the dynamic release flux dataset output by the heavy metal speciation migration calculation module; Loading biological toxicity thresholds: extracting the biological toxicity thresholds of major aquatic organisms in target waters to each heavy metal element from the ecotoxicity database, the biological toxicity thresholds including acute toxicity thresholds and chronic toxicity thresholds; Calculate pollution risk entropy: Based on the dynamic release flux dataset and biological toxicity threshold, the entropy calculation model is used to quantify the risk on a grid-by-grid, time-step-by-time, and element-by-element basis to generate a pollution risk entropy matrix. Superimpose the time cumulative effect: Integrate the pollution risk entropy matrix in the time dimension, calculate the cumulative duration of the entropy exceeding the threshold within 72 consecutive hours, and generate a time-enhanced pollution risk entropy field; Generate a three-dimensional pollution risk entropy cloud map: Input the time-enhanced pollution risk entropy field into the three-dimensional visualization engine, superimpose the time dimension (Z axis) and entropy intensity (color mapping) on ​​the spatial dimension (X / Y coordinates), and output a three-dimensional pollution risk entropy cloud map.

[0011] Optionally, the migration flux calculation model includes a diffusion calculation unit and a redox release calculation unit, the diffusion calculation unit corrects the diffusion coefficient according to the pore characteristics of the sediment, and the redox release calculation unit is used to establish a coupled response relationship between the redox potential and the pH value.

[0012] Optionally, the entropy calculation model includes a dual evaluation mechanism, specifically: Acute risk assessment mechanism: Calculate the instantaneous risk entropy value based on the acute toxicity threshold in the biological toxicity threshold; Chronic risk assessment mechanism: Calculate the cumulative risk entropy value based on the chronic toxicity threshold in the biological toxicity threshold; The maximum value of the calculation results of the acute risk assessment mechanism and the chronic risk assessment mechanism is taken as the comprehensive pollution risk entropy value.

[0013] Optionally, when the three-dimensional pollution risk entropy value cloud map is generated, the following visualization operations are performed: Time dimension segmentation: divide the continuous monitoring period into time segments of equal length; Risk level mapping: Define the blue-yellow-red color scale based on the pollution risk entropy value range, corresponding to the safety, warning, and danger levels respectively; Dynamic warning trigger: If the entropy value of the same spatial location maintains a dangerous level for multiple consecutive time periods, a warning mark will be automatically generated.

[0014] Beneficial effects of the present invention: This system integrates a multi-parameter water quality monitoring buoy, an acoustic Doppler current profiler (ADCP), a microelectrode array, and a bioturbation quantification device through a parameter acquisition module system. This system enables multidimensional data collection on the hydrodynamics of target waters, bioturbation intensity, sediment heavy metal speciation, and redox state. With sampling accuracy reaching meter-level spatial resolution and hour-level temporal resolution, it dynamically reflects the true physical and chemical state of the pollution area, effectively avoiding the misjudgment caused by traditional single-point sampling methods due to spatial sparseness, and improving the continuity and representativeness of pollution perception.

[0015] This study, by incorporating the k-ω turbulence model into the Navier-Stokes equations and combining it with a random walk algorithm to simulate benthic bioturbation behavior, achieves the first joint drive of shear stress and bioirrigation flux fields within the same model. This coupled calculation of sediment resuspension rates generates a dynamic suspended concentration profile based on advection-diffusion processes. Compared to existing resuspension models based solely on hydrodynamic forces, this mechanism more comprehensively reflects the heterogeneous disturbance effects caused by biological activity, significantly improving the accuracy and dynamic response of dissolved heavy metal release simulations.

[0016] In the ecological risk entropy assessment module, this invention constructs a pollution risk entropy matrix based on dual acute and chronic toxicity thresholds. A time-enhanced accumulation mechanism superimposes short-term burst and medium-term cumulative effects. Furthermore, color grading and dynamic early warning markers are used to generate a three-dimensional pollution risk entropy cloud map, visually displaying the risk evolution process and spatial distribution. This mechanism not only accurately locates risk hotspots but also proactively identifies the chronic risk diffusion process caused by high-frequency disturbances, significantly improving environmental management departments' dynamic response capabilities to resuspended pollution incidents and the efficiency of visually aided decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a system flow diagram of an embodiment of the present invention; Figure 2 Schematic diagram of the heavy metal form migration calculation module flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] like Figure 1-Figure 2 As shown, a sediment heavy metal resuspension pollution evaluation system based on multidimensional data includes a parameter acquisition module, a coupled resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy value evaluation module, wherein; The parameter acquisition module is used to obtain the hydrodynamic parameters of the target water area, sediment heavy metal occurrence data and bioturbation intensity index; The coupled resuspension simulation module receives hydrodynamic parameters and bioturbation intensity index and generates a dynamic suspended concentration distribution map through the hydrodynamic-bioturbation coupling model; The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and sediment heavy metal occurrence form data, and calculates the dynamic release flux of dissolved heavy metals in combination with the redox sensitive factor; The ecological risk entropy value evaluation module receives the dynamic release flux and integrates the biological toxicity threshold to generate a three-dimensional pollution risk entropy value cloud map.

[0021] The parameter acquisition module includes: 1. Collect physical environmental data: Deploy multi-parameter water quality monitoring buoys in the target waters to collect real-time latitude and longitude coordinates (GPS positioning accuracy better than ±5 meters), water depth data (ultrasonic depth meter, accuracy of ±0.1 meters), and water temperature data (thermistor sensor, accuracy of ±0.2°C). This step provides a basic reference layer for subsequent hydrodynamic parameter collection.

[0022] Second, extract hydrodynamic parameters: An acoustic Doppler current profiler (ADCP) was deployed near the buoy. The device measured at a sampling frequency of 10 Hz or higher, with at least five equally spaced vertical layers, each 0.3 m apart. The velocity profile data obtained was used to analyze turbulence characteristics.

[0023] 1. Turbulence spectrum analysis: Fast Fourier transform (FFT) is used to perform frequency domain analysis on the velocity time series, extracting the main peak frequency in the turbulent kinetic energy spectrum (TKE spectrum) to determine the concentrated area of ​​water kinetic energy and assist in inverting friction velocity. .

[0024] 2. Calculation of substrate shear stress: Calculate substrate shear stress using the Reynolds stress method , the calculation formula is: ,in, is the water density, is the friction velocity, which is obtained by fitting the vertical turbulent kinetic energy profile. The fitting function is as follows: ; in is the Reynolds stress covariance term, z is the depth of the measured layer, and H is the total water depth.

[0025] Example: If a point is fitted and obtained m / s, then the corresponding bottom shear stress is: ; III. Obtaining Sediment Heavy Metal Speciation Data: Based on the longitude and latitude coordinates provided in the physical environment data, sediment core samples (depth 0–30 cm) were obtained at designated locations using a stainless steel cylindrical sampler. Samples were freeze-dried, ground, and uniformly sieved (100 mesh) in the laboratory. A modified BCR continuous extraction method was then used to separate four heavy metal species: exchangeable, carbonate-bound, iron-manganese oxide-bound, and residual.

[0026] The heavy metal content of each extract was determined by inductively coupled plasma mass spectrometry (ICP-MS) in mg / kg. Blank, spiked, and parallel samples were set up in the experiment to ensure that the data quality met the requirements of precision better than ±10% and recovery rate of 90%–110%.

[0027] IV. Quantifying the Bioturbation Intensity Index: Benthic trawling sampling was conducted simultaneously at the aforementioned sediment sampling sites. Trawling was repeated three times per unit area (0.1 m²), and the total abundance of benthic fauna was calculated (number of individuals / m²). Simultaneously, tracer experimental units (equipped with bromide or fluorescent tracers) were deployed in the same area. A 30-minute tracer diffusion experiment was conducted to calculate the bioirrigation rate per unit area (unit: mL / cm² / h).

[0028] The bioturbation intensity index (BI) is defined as: ; Where A is the abundance of benthic organisms, R is the bioirrigation rate; Example: If the biological abundance at a sampling point is 800 individuals / m2 and the irrigation rate is 0.02 mL / cm2 / h, then: ; 5. Generate physical environment data package: The physical environment data, hydrodynamic parameters, sediment heavy metal occurrence form data and bioturbation intensity index obtained in the above steps are uniformly formatted into a structured data package, using JSON or XML standard format, and transmitted to the coupled resuspension simulation module and heavy metal form migration calculation module as input data for subsequent modeling and calculation.

[0029] The coupled resuspension simulation module includes: 1. Initialize the hydrodynamic-bioturbation coupling model: First, receive the hydrodynamic parameters and bioturbation intensity index output by the parameter acquisition module. In the model initialization stage, establish the two-dimensional spatial grid and time step parameters.

[0030] The computational domain space is divided into regular grids, and the grid resolution is set to ; Time step , used to ensure the timeliness and numerical stability of the simulation.

[0031] The model boundary conditions include the water inlet velocity boundary, the free surface boundary and the bottom bed boundary, among which the shear and disturbance flux input interfaces are set at the bottom bed.

[0032] 2. Calculation of the hydrodynamic shear stress field: The hydrodynamic field is solved by embedding the KW turbulence model in the three-dimensional Navier-Stokes equations to establish the following momentum conservation expression: ; in: is the velocity vector field, p is the pressure, is the dynamic viscosity, is the turbulent stress tensor, which is closed by the k-ω model.

[0033] Based on the solution results, the time series of bottom shear stress of each grid cell along the bottom of the model is extracted. , used to drive the resuspension process.

[0034] 3. Generate bioturbation flux field: Use a random walk algorithm to simulate the activity path of benthic organisms in the sediment. Set each individual organism as a particle source, and its disturbance diffusion path conforms to the characteristics of Brownian motion. The spatial structure of bioturbation is represented by a biological pore network. Define the bioirrigation flux per unit area as: ; in, is the bioirrigation flux, is the flux scaling factor (empirical value range 0.1-0.3, based on experimental calibration), is the bioturbation intensity index corresponding to the grid unit, provided by the parameter acquisition module.

[0035] Example: If a point ,set up ,but: ; 4. Perform coupled resuspension calculation: and The data are input into the hydrodynamic-bioturbation coupling model and the resuspension rate per unit area per unit time is calculated based on the shear-pore coupling equation. : ; in, is the resuspension rate, is the coupling model coefficient, representing the contribution ratio of shear drive and bioturbation to resuspension, is the sediment initiation shear stress threshold, and n is the empirical index.

[0036] The model can be calibrated through experimental fitting or historical data.

[0037] Example: If a point Pa, Pa, , ,but: ; 5. Generate dynamic suspension concentration distribution map: As a source term, it is embedded in the advection-diffusion equation to solve the suspended particle concentration field , the equation is: ; Where C is the concentration of suspended particulate matter, D is the horizontal and vertical diffusion coefficient, and h is the height of the mixing layer.

[0038] Through numerical integration, the dynamic suspended concentration distribution map of the entire model domain in continuous time is obtained, output in the form of a three-dimensional array and transmitted to the heavy metal form migration calculation module.

[0039] The heavy metal form migration calculation module includes: 1. Receive input data set: This module simultaneously receives dynamic suspended concentration distribution map and sediment heavy metal occurrence form data, including the concentrations of heavy metal elements in different forms and the spatial coordinates of sampling points.

[0040] The spatial coordinates of the two are unified through spatial interpolation or grid mapping function to ensure that the data used in the calculation corresponds to the spatial grid of the same resolution.

[0041] 2. Extraction of exchangeable heavy metal content: Extract exchangeable heavy metal components from sediment heavy metal occurrence data to form a two-dimensional concentration matrix that matches the suspended concentration map ,On this basis, the exchangeable state concentration tensor in each grid cell is constructed, ,which is used for the initial flux calculation of the subsequent diffusion-driven ,model.

[0042] 3. Generate a redox sensitive factor field: Deploy a microelectrode array (spacing ≤ 5m) in the target waters to collect redox potential in real time. The pH value is then averaged and interpolated to generate a spatiotemporally continuous redox sensitive factor field. .

[0043] The coupling response relationship is as follows (based on the Nernst equation and empirical fitting): ; This factor comprehensively characterizes the intensity of redox-driven release at this location / time. The smaller the factor, the stronger the reducing environment and the greater the potential for heavy metal release.

[0044] 4. Calculation of the dynamic release flux of dissolved heavy metals: This process is completed by the migration flux calculation model, which includes a diffusion calculation unit and a redox release calculation unit to realize the simultaneous processing of dispersion and reaction release mechanisms.

[0045] 1. Diffusion calculation unit: considering sediment porosity , effective diffusion coefficient under the influence of pore structure , its expression is: ; in, is the molecular diffusion coefficient in free water (typical values ​​such as Zn are ), is the porosity of the sediment (generally 0.4-0.6), is the tortuosity (usually 2-3).

[0046] The diffusion flux under each grid is calculated according to Fick's first law: ; (The vertical gradient can be approximated by first-order difference); 2. Redox release calculation unit: Redox sensitive factor To adjust the parameters, the response calculation of the dissolution release of exchangeable metals is performed. The release flux model is as follows: ; in, is the reaction rate coefficient, It is a redox inhibitory factor that describes the ability of the environment to regulate metal release.

[0047] Comprehensive dynamic release flux calculation: Finally, at each grid point, the two fluxes are combined to obtain: ; 5. Generate dynamic release flux dataset: total flux at all times and all grid points is integrated into a three-dimensional data structure: ; The structural dimensions are as follows: Space coordinates : Corresponding grid number; Time step : corresponding to the time series, Each element: represents the release flux of a certain heavy metal element (such as Zn²⁺, Pb²⁺) at that point in time and space.

[0048] The data are encoded in NetCDF, HDF5 or GeoTIFF format and sent to the ecological risk entropy assessment module.

[0049] The ecological risk entropy value assessment module includes: 1. Receive the dynamic release flux dataset: Receive the three-dimensional data structure input by the module ,in, represents the two-dimensional space grid coordinates, k represents the time step, Indicates that the e-th heavy metal element is The dissolved phase release flux of the grid cell at the kth time step.

[0050] 2. Loading biological toxicity thresholds: Extracting the acute toxicity thresholds of typical aquatic organisms (such as crucian carp, water fleas, and algae) in the target waters to various heavy metal elements from the preset ecotoxicity database and chronic toxicity threshold , the toxicity response of multiple organisms takes the minimum threshold of the most sensitive species as the local threshold standard.

[0051] 3. Calculate the entropy value of pollution risk: adopt the entropy value calculation model of dual evaluation mechanism, including acute risk evaluation mechanism and chronic risk evaluation mechanism. The entropy value of pollution risk at each time and space point The calculation process is as follows: 1. Acute risk entropy value (instantaneous response): Based on the comparison between the unit time flux and the acute toxicity threshold, the acute risk entropy value is defined as: ; in, is the effective release concentration per unit volume, approximately ,h is the effective mixed water layer thickness, if ,but .

[0052] 2. Chronic risk entropy value (cumulative response): Calculate the ratio of the cumulative concentration within the sliding time window (e.g. 24 hours) starting from the current time step to the chronic toxicity threshold: , when the cumulative mean ; 3. Comprehensive pollution risk entropy value: ; All entropy values ​​are normalized to the range of [0,10] to form the pollution risk entropy matrix .

[0053] 4. Superimposed time cumulative effect: To identify the impact of continuous pollution, define the time-enhanced pollution risk entropy value field , expressed as: ; in, is any starting time step, is the set entropy value warning threshold (such as: 5), is the indicator function to determine whether the threshold is exceeded; The result is the proportion of time that the risk entropy value exceeds the threshold in the past 72 hours (range 0-1); 5. Generate a three-dimensional pollution risk entropy cloud map: Integrate with spatial coordinates and time steps to build dynamic 3D visualization models: The visualization process includes: Time dimension segmentation: Divide the continuous monitoring period (such as 7 days) into time segments of equal length (such as 1 segment every 12 hours, a total of 14 segments), and generate an independent layer for each segment.

[0054] Risk level mapping: using a three-color grading standard: Table 1: Risk level mapping table Entropy range Display Color Risk Level 0–3 blue Safety 3–6 yellow warn 6–10 red Danger The entropy value of each grid is mapped to a 3D layer via color.

[0055] 3. Dynamic warning trigger: If a The grid is within 3 consecutive time periods , then the system generates a dynamic flashing red warning mark at that point, with the coordinates and warning time information.

[0056] Visualization output format: x / y axis: spatial grid coordinates; z-axis: time dimension (in hours / time zones); Color coding: contamination risk level; Output format: WebGL dynamic visualization component, or static GeoTIFF + XML warning mark set.

[0057] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0058] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A sediment heavy metal resuspension pollution assessment system based on multidimensional data, characterized in that: It includes parameter acquisition module, coupled resuspension simulation module, heavy metal form migration calculation module and ecological risk entropy value evaluation module, among which; The parameter acquisition module is used to obtain hydrodynamic parameters, sediment heavy metal occurrence morphology data and bioturbation intensity index of the target water area; The coupled resuspension simulation module receives the hydrodynamic parameters and the bioturbation intensity index and generates a dynamic suspended concentration distribution map through a hydrodynamic-bioturbation coupling model; The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and sediment heavy metal occurrence form data, and calculates the dynamic release flux of dissolved heavy metals in combination with the redox sensitive factor; The ecological risk entropy value evaluation module receives the dynamic release flux and integrates the biological toxicity threshold to generate a three-dimensional pollution risk entropy value cloud map.

2. A sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 1, characterized in that: The parameter acquisition module includes: Collect physical environment data: obtain the latitude and longitude coordinates, water depth data and water temperature data of the target water area through multi-parameter water quality monitoring buoys; Extraction of hydrodynamic parameters: Based on physical environmental data, an acoustic Doppler current profiler was used to measure the velocity profile and turbulent kinetic energy at a sampling frequency of ≥10 Hz, and to calculate the bottom shear stress. Obtaining sediment heavy metal speciation data: Sediment columns were collected at defined latitude and longitude coordinates using a columnar sampler. After freeze-drying, the exchangeable, carbonate-bound, iron-manganese oxide-bound, and residual heavy metals were separated using the BCR continuous extraction method. The content of each speciation was determined using inductively coupled plasma mass spectrometry. Quantitative bioturbation intensity index: At the sediment sampling point, the biomass per unit area is obtained by benthic trawling, and the bioirrigation rate is determined by pore water tracer experiments. The bioturbation intensity index is generated by multiplying the biomass by the bioirrigation rate. Generate physical environment data package: Integrate physical environment data, hydrological dynamic parameters, sediment heavy metal occurrence form data and bioturbation intensity index into a structured data package, and output it to the coupled resuspension simulation module and heavy metal form migration calculation module.

3. A sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 2, characterized in that: The coupled resuspension simulation module includes: Initialize the hydrodynamic-bioturbation coupling model: receive the hydrodynamic parameters and bioturbation intensity index output by the parameter acquisition module, and configure the model calculation domain grid and time step; Calculation of hydrodynamic shear stress field: Based on the hydrodynamic parameters, the three-dimensional Navier-Stokes equations are solved using the k-ω turbulence model to output a time series of bottom shear stress field. Generate bioturbation flux field: Based on the bioturbation intensity index, a random walk algorithm is used to simulate benthic biological activities and output the biological pore network and bioirrigation flux field; Perform coupled resuspension calculations: Input the bottom shear stress field and the bioirrigation flux field into the hydrodynamic-bioturbation coupling model and calculate the sediment resuspension rate using the shear-pore coupling equation; Generate a dynamic suspended concentration distribution map: perform spatiotemporal integration of the resuspension rate, combine the advection-diffusion equation to simulate the suspended matter transport process, output a dynamic suspended concentration distribution map in grid units, and transmit it to the heavy metal form migration calculation module.

4. A sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 3, characterized in that: The heavy metal form migration calculation module includes: Receive input data sets: obtain the dynamic suspended concentration distribution map output by the coupled resuspension simulation module and the sediment heavy metal occurrence form data output by the parameter acquisition module; Extraction of exchangeable heavy metal content: Separation of exchangeable heavy metal components from sediment heavy metal occurrence data to generate a spatially distributed exchangeable heavy metal concentration matrix; Generate redox sensitive factor field: The redox potential and pH value are collected in real time through a microelectrode array deployed in the target water area, the redox sensitive factor is calculated based on the coupling relationship between the redox potential and the pH value, and a spatiotemporally continuous redox sensitive factor field is output.

5. The sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 4, characterized in that: The heavy metal form migration calculation module also includes: Calculation of dynamic release flux of dissolved heavy metals: Based on the dynamic suspended concentration distribution map, the exchangeable heavy metal concentration matrix and the redox sensitive factor field, the migration flux calculation model simultaneously processes the diffusion and redox release effects, and outputs the gridded dynamic release flux of dissolved heavy metals; Generate a dynamic release flux data set: Integrate the dynamic release flux of dissolved heavy metals into a three-dimensional data structure of spatial coordinates-time steps-heavy metal elements according to the time series, and transmit it to the ecological risk entropy value assessment module.

6. The sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 5, characterized in that: The ecological risk entropy value evaluation module includes: Receive dynamic release flux dataset: obtain the dynamic release flux dataset output by the heavy metal speciation migration calculation module; Loading biological toxicity thresholds: extracting the biological toxicity thresholds of major aquatic organisms in target waters to each heavy metal element from the ecotoxicity database, the biological toxicity thresholds including acute toxicity thresholds and chronic toxicity thresholds; Calculate pollution risk entropy: Based on the dynamic release flux dataset and biological toxicity threshold, the entropy calculation model is used to quantify the risk on a grid-by-grid, time-step-by-time, and element-by-element basis to generate a pollution risk entropy matrix. Superimpose the time cumulative effect: Integrate the pollution risk entropy matrix in the time dimension, calculate the cumulative duration of the entropy exceeding the threshold within 72 consecutive hours, and generate a time-enhanced pollution risk entropy field; Generate a three-dimensional pollution risk entropy cloud map: input the time-enhanced pollution risk entropy field into the three-dimensional visualization engine, superimpose the time dimension and entropy intensity on the spatial dimension, and output a three-dimensional pollution risk entropy cloud map.

7. The sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 5, characterized in that: The migration flux calculation model includes a diffusion calculation unit and a redox release calculation unit. The diffusion calculation unit corrects the diffusion coefficient according to the pore characteristics of the sediment, and the redox release calculation unit is used to establish a coupled response relationship between the redox potential and the pH value.

8. The sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 6, characterized in that: The entropy calculation model includes a dual evaluation mechanism, specifically: Acute risk assessment mechanism: Calculate the instantaneous risk entropy value based on the acute toxicity threshold in the biological toxicity threshold; Chronic risk assessment mechanism: Calculate the cumulative risk entropy value based on the chronic toxicity threshold in the biological toxicity threshold; The maximum value of the calculation results of the acute risk assessment mechanism and the chronic risk assessment mechanism is taken as the comprehensive pollution risk entropy value.

9. The sediment heavy metal resuspension pollution assessment system based on multidimensional data according to claim 6, characterized in that: When the three-dimensional pollution risk entropy value cloud map is generated, the following visualization operations are performed: Time dimension segmentation: divide the continuous monitoring period into time segments of equal length; Risk level mapping: Define the blue-yellow-red color scale based on the pollution risk entropy value range, corresponding to the safety, warning, and danger levels respectively; Dynamic warning trigger: If the entropy value of the same spatial location maintains a dangerous level for multiple consecutive time periods, a warning mark will be automatically generated.

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