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

Abstract

The present application relates to the technical field of environmental monitoring, and particularly relates to a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, which comprises a parameter acquisition module, a coupled resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy evaluation module. The parameter acquisition module is used for obtaining hydrological dynamic parameters, sediment heavy metal occurrence forms and biological disturbance intensity indexes; the coupled resuspension simulation module fuses hydrodynamic shear stress and biological irrigation flux to simulate resuspension concentration distribution; the heavy metal form migration calculation module combines redox sensitive factor fields and exchangeable heavy metal concentration to calculate dissolved heavy metal dynamic release flux; and the ecological risk entropy evaluation module introduces a dual evaluation mechanism of acute and chronic toxicity, constructs a pollution risk entropy matrix and generates a three-dimensional pollution risk entropy cloud chart. The present application is suitable for dynamic evaluation and early warning of resuspension pollution risk in water bodies such as near-shore, estuary and lake.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, and particularly relates to a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data. BACKGROUND

[0002] In the sedimentary environment of water bodies such as oceans, estuaries and lakes, sediments are not only important accumulation of heavy metals, but also potential pollution release sources under external disturbance. In recent years, with the change of bottom hydrodynamic conditions and the increasing disturbance of benthic organisms caused by human activities, the originally stable heavy metals in sediments are easily activated into water phase in the resuspension process, causing the release and diffusion of heavy metals, and thus causing secondary pollution risk to the aquatic ecosystem. The intensity of heavy metal resuspension release is controlled by multiple factors such as hydrodynamic conditions, sediment heavy metal occurrence form, biological disturbance intensity and water redox environment, and its dynamic change has obvious nonlinearity and spatial heterogeneity.

[0003] However, the existing technology for evaluating sediment heavy metal resuspension pollution mostly adopts the mode of fixed-point sampling + experimental extraction + empirical formula estimation, which has problems such as serious model simplification, poor dynamic simulation capability and insufficient spatial coverage. On the one hand, most methods ignore the role of biological disturbance in the resuspension mechanism and fail to couple benthic organism behavior with hydrodynamic process; on the other hand, the dynamic description of heavy metal redox migration mechanism is lacking, which cannot truly reflect the release behavior of different occurrence forms. In addition, the traditional pollution risk evaluation method is mostly based on concentration threshold judgment, lacks the quantification of risk uncertainty by information entropy such as entropy value, and is difficult to realize continuous dynamic visual analysis across scales and time periods. SUMMARY

[0004] The present application provides a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, which proposes a systematic solution capable of integrating multi-dimensional data acquisition, high-fidelity simulation calculation and risk entropy value expression to improve the scientificity, forward-looking nature and operability of resuspension pollution identification.

[0005] A sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, comprising 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;

[0006] The parameter acquisition module is used to obtain hydrological dynamic parameters, sediment heavy metal occurrence form data and biological disturbance intensity index of the target water area;

[0007] The coupled resuspension simulation module receives the hydrological dynamic parameters and biological disturbance intensity index, and generates a dynamic suspended concentration distribution map through a hydrodynamic-biological disturbance coupling model;

[0008] The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and the heavy metal occurrence form data of the sediment, and calculates the dynamic release flux of the dissolved heavy metal in combination with the redox sensitive factor;

[0009] The ecological risk entropy value evaluation module receives the dynamic release flux and generates a three-dimensional pollution risk entropy value cloud map by fusing the biological toxicity threshold value.

[0010] Optionally, the parameter acquisition module comprises:

[0011] Acquiring physical environment data: obtaining the latitude and longitude coordinates, water depth data and water temperature data of the target water area through the multi-parameter water quality monitoring buoy;

[0012] Extracting hydrodynamic parameters: based on the physical environment data, using an acoustic Doppler current profiler to measure the flow profile and turbulent kinetic energy at a sampling frequency of ≥10Hz, and calculating the bed shear stress;

[0013] Obtaining sediment heavy metal occurrence form data: at the determined latitude and longitude coordinate point, using a column sampler to collect a sediment column sample, after freeze-drying, using the BCR sequential extraction method to separate the exchangeable state, carbonate combined state, iron and manganese oxide combined state and residual state heavy metals, and using an inductively coupled plasma mass spectrometer to measure the content of each form;

[0014] Quantifying the biological disturbance intensity index: at the sediment sampling point, obtaining the biological abundance per unit area through the benthic organism trawl, and measuring the biological irrigation rate through the pore water tracing experiment, and multiplying the biological abundance and the biological irrigation rate to generate the biological disturbance intensity index;

[0015] Generating a physical environment data package: integrating the physical environment data, the hydrodynamic parameters, the sediment heavy metal occurrence form data and the biological disturbance intensity index into a structured data package, and outputting to the coupled resuspension simulation module and the heavy metal form migration calculation module.

[0016] Optionally, the coupled resuspension simulation module comprises:

[0017] Initializing the hydro-biological disturbance coupling model: receiving the hydrodynamic parameters and the biological disturbance intensity index output by the parameter acquisition module, configuring the model calculation domain grid (resolution ≤5m×5m) and the time step (≤1 hour);

[0018] Calculating the hydrodynamic shear stress field: based on the hydrodynamic parameters, solving the three-dimensional Navier-Stokes equation through the k-ω turbulence model, and outputting the time series of the bed shear stress field;

[0019] Generating a biological disturbance flux field: based on the biological disturbance intensity index, simulating the benthic organism activity using the random walk algorithm, outputting the biological pore network and the biological irrigation flux field;

[0020] Performing coupled resuspension calculation: input the bed shear stress field and the biological irrigation flux field into the hydrodynamic-biological disturbance coupled model, and calculate the sediment resuspension rate through the shear-pore coupling equation;

[0021] Generating dynamic suspended concentration distribution map: spatiotemporal integration of the resuspension rate, combined with the advection-diffusion equation to simulate the suspended matter transport process, output the dynamic suspended concentration distribution map in grid units, and transmit to the heavy metal speciation migration calculation module.

[0022] Optionally, the heavy metal speciation migration calculation module comprises:

[0023] Receiving input data set: obtaining the dynamic suspended concentration distribution map output by the coupled resuspension simulation module, and the heavy metal occurrence form data output by the parameter acquisition module;

[0024] Extracting exchangeable state heavy metal content: separating the exchangeable state heavy metal component from the heavy metal occurrence form data of the sediment, and generating a spatially distributed exchangeable state heavy metal concentration matrix;

[0025] Generating redox sensitive factor field: real-time acquisition of oxidation-reduction potential and pH value through the microelectrode array arranged in the target water area, calculating the oxidation-reduction sensitive factor according to the coupling relationship between the oxidation-reduction potential and the pH value, and outputting the spatiotemporally continuous oxidation-reduction sensitive factor field.

[0026] Optionally, the heavy metal speciation migration calculation module further comprises:

[0027] Calculating dynamic release flux of dissolved state heavy metal: based on the dynamic suspended concentration distribution map, the exchangeable state heavy metal concentration matrix and the oxidation-reduction sensitive factor field, the diffusion effect and the oxidation-reduction release effect are simultaneously processed through the migration flux calculation model, and the gridded dynamic release flux of dissolved state heavy metal is output;

[0028] Generating dynamic release flux data set: integrating the dynamic release flux of dissolved state heavy metal into a three-dimensional data structure of spatial coordinates-time step-heavy metal elements according to time sequence, and transmitting to the ecological risk entropy evaluation module.

[0029] Optionally, the ecological risk entropy evaluation module comprises:

[0030] Receiving dynamic release flux data set: obtaining the dynamic release flux data set output by the heavy metal speciation migration calculation module;

[0031] Loading biological toxicity threshold: extracting the biological toxicity threshold of each heavy metal element for the main aquatic organisms in the target water area from the ecological toxicity database, wherein the biological toxicity threshold includes acute toxicity threshold and chronic toxicity threshold;

[0032] Computing pollution risk entropy value: based on the dynamic release flux data set and the biological toxicity threshold, the risk quantification is carried out grid by grid, time step by time step and element by element through the entropy value calculation model, and a pollution risk entropy value matrix is generated;

[0033] Superimposed time cumulative effect: the pollution risk entropy value matrix is integrated in the time dimension, the cumulative duration ratio of the entropy value exceeding the threshold within 72 consecutive hours is calculated, and a time-enhanced pollution risk entropy value field is generated;

[0034] Generating a three-dimensional pollution risk entropy value cloud chart: inputting the time-enhanced pollution risk entropy value field into a three-dimensional visualization engine, superimposing the time dimension (Z axis) and the entropy value intensity (color mapping) in the spatial dimension (X / Y coordinates), and outputting a three-dimensional pollution risk entropy value cloud chart.

[0035] Optionally, the migration flux calculation model comprises a diffusion effect calculation unit and a redox release effect calculation unit, the diffusion effect calculation unit corrects the diffusion coefficient according to the pore characteristics of the sediment, and the redox release effect calculation unit is used to establish a coupling response relationship between the oxidation-reduction potential and the pH value.

[0036] Optionally, the entropy value calculation model comprises a double evaluation mechanism, specifically:

[0037] Acute risk evaluation mechanism: calculating the instantaneous risk entropy value based on the acute toxicity threshold in the biological toxicity threshold;

[0038] Chronic risk evaluation mechanism: calculating the cumulative risk entropy value based on the chronic toxicity threshold in the biological toxicity threshold;

[0039] Taking the maximum value of the calculation results of the acute risk evaluation mechanism and the chronic risk evaluation mechanism as the comprehensive pollution risk entropy value.

[0040] Optionally, when the three-dimensional pollution risk entropy value cloud chart is generated, the following visualization operations are performed:

[0041] Time dimension segmentation: dividing the continuous monitoring period into equal length time zones;

[0042] Risk level mapping: defining a blue-yellow-red three-color scale according to the pollution risk entropy value range, corresponding to the safety, warning and danger levels respectively;

[0043] Dynamic early warning trigger: if the entropy value of the same spatial position maintains the danger level for multiple time zones in succession, an early warning marker is automatically generated.

[0044] The beneficial effects of the present application are:

[0045] The present application, through the parameter acquisition module system integrates multi-parameter water quality monitoring buoy, acoustic Doppler current profiler (ADCP), microelectrode array and biological disturbance quantification device, realizes multi-dimensional data acquisition of water power, biological disturbance intensity, heavy metal occurrence form and oxidation-reduction state of sediments in the target water area. The sampling accuracy reaches the spatial resolution of meter level and the time resolution of hour level, which can dynamically reflect the real physical-chemical environmental state of the pollution occurrence area, thereby effectively avoiding the misjudgment caused by the spatial sparseness of the traditional single-point sampling method, and improving the continuity and representativeness of pollution perception.

[0046] The present application, by introducing the k-omega turbulence model nested Navier-Stokes equation and combining the random walk algorithm to simulate the benthic biological disturbance behavior, realizes the joint driving of shear stress field and biological irrigation flux field in the same model for the first time, couples the calculation of sediment resuspension rate, and deduces the dynamic suspended concentration distribution map through the advection-diffusion process. Compared with the resuspension model based on water power in the prior art, the mechanism can more comprehensively reflect the non-uniform disturbance effect caused by biological activity, and greatly improves the accuracy and dynamic response ability of the simulation of dissolved heavy metals.

[0047] In the ecological risk entropy evaluation module, the present application constructs a pollution risk entropy matrix based on acute and chronic toxicity thresholds, superimposes short-term burst and medium-term cumulative effect through a time-enhanced cumulative mechanism, further generates a three-dimensional pollution risk entropy cloud chart by color grading and dynamic warning markers, and intuitively displays the risk evolution process and spatial distribution. The mechanism not only can accurately locate the risk hotspot area, but also can identify the chronic risk diffusion process caused by high-frequency disturbance in advance, and significantly improves the dynamic response ability and visual auxiliary decision-making efficiency of the environmental management department to the resuspended pollution event. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0049] Figure 1 The system flowchart of the embodiment of the present application is shown in the figure.

[0050] Figure 2 The heavy metal form migration calculation module flowchart of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0051] The application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted here that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the application.

[0052] As shown in Figures 1-2 , a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data includes a parameter acquisition module, a coupling resuspension simulation module, a heavy metal form migration calculation module, and an ecological risk entropy evaluation module, wherein;

[0053] The parameter acquisition module is used to obtain hydrodynamic parameters, sediment heavy metal occurrence form data, and biological disturbance intensity index of the target water area;

[0054] The coupling resuspension simulation module receives the hydrodynamic parameters and the biological disturbance intensity index, and generates a dynamic suspended concentration distribution map through a hydrodynamic-biological disturbance coupling model;

[0055] The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and the sediment heavy metal occurrence form data, and calculates the dynamic release flux of dissolved heavy metals in combination with the oxidation-reduction sensitive factor;

[0056] The ecological risk entropy evaluation module receives the dynamic release flux and generates a three-dimensional pollution risk entropy cloud map by fusing the biological toxicity threshold.

[0057] The parameter acquisition module includes:

[0058] I. Collect physical environment data: Deploy multi-parameter water quality monitoring buoys in the target water area to collect real-time latitude and longitude coordinates (GPS positioning accuracy better than ± 5 meters), water depth data (ultrasonic depth gauge, accuracy ± 0.1 m), and water temperature data (thermistor sensor, accuracy ± 0.2°C). This step provides a basic reference layer for subsequent hydrodynamic parameter collection.

[0059] II. Extract hydrodynamic parameters: Deploy an acoustic Doppler current profiler (ADCP) near the buoy, which sets at least 5 equally spaced measurement layers in the vertical direction with a sampling frequency ≥ 10 Hz, and the layer spacing is set to 0.3 m. The measured flow velocity profile data are used to analyze the turbulence characteristics.

[0060] 1. Turbulence spectrum analysis: Use fast Fourier transform (FFT) to perform frequency domain analysis on the flow velocity time series, extract the main peak frequency in the turbulence kinetic energy spectrum (TKE spectrum), and use it to judge the water body kinetic energy concentration interval to assist in inverting the friction velocity .

[0061] 2. Bed shear stress calculation: The bed shear stress is calculated using the Reynolds stress method , which is given by: where, is the water density, is the friction velocity, which is obtained by fitting the vertical turbulent kinetic energy profile, and the fitting function is given by:

[0062] ;

[0063] where is the Reynolds stress covariance term, z is the depth of the measurement layer, and H is the total water depth.

[0064] Example: If the fitting obtains m / s for a measurement point, then the corresponding bed shear stress is:

[0065] ;

[0066] Three, Obtain heavy metal occurrence form data of sediments: Based on the longitude and latitude coordinates provided in the physical environment data, use a stainless steel column sampler to obtain columnar sediment samples (depth 0-30 cm) at specified points. The sampling samples are freeze-dried, ground and uniformly sieved (100 mesh) in the laboratory, and then the modified BCR sequential extraction method is used to separate four forms of heavy metal elements: exchangeable state, carbonate-bound state, iron-manganese oxide-bound state, and residual state.

[0067] The heavy metal content of each extraction solution is determined by inductively coupled plasma mass spectrometry (ICP-MS) with units of mg / kg. Blank, standard addition and parallel samples are set to ensure that the data quality meets the precision better than ±10% and the recovery rate of 90%-110%.

[0068] Four, Quantify biological disturbance intensity index: At the above-mentioned sediment sampling points, carry out benthic biological trawl sampling simultaneously. Repeat the trawl (0.1 m²) 3 times per unit area, and count the total abundance of benthic animals (number / m²). At the same time, set up tracer experiment units (with bromide or fluorescent tracer) in the same area, and calculate the unit area biological irrigation rate (unit: mL / cm² / h) through 30-minute tracer diffusion experiment.

[0069] Define the biological disturbance intensity index (BI) as:

[0070] ;

[0071] where A is the abundance of benthic organisms, and R is the biological irrigation rate.

[0072] Example:

[0073] If the biological abundance of a sampling point is 800 individuals / m2 and the irrigation rate is 0.02 mL / cm2 / h, then:

[0074] ;

[0075] V. Generating physical environment data package: The physical environment data, hydrodynamic parameters, sediment heavy metal occurrence form data and biological disturbance intensity index obtained in the above steps are uniformly formatted into a structured data package in JSON or XML standard format, which is transmitted to the coupled resuspension simulation module and heavy metal form migration calculation module as input data for subsequent modeling and calculation.

[0076] The coupled resuspension simulation module includes:

[0077] I. Initialization of hydrodynamic-biological disturbance coupled model: First, receive the hydrodynamic parameters and biological disturbance intensity index output by the parameter acquisition module. In the model initialization stage, establish a two-dimensional spatial grid and time step parameter.

[0078] The calculation domain space is divided into regular grids, and the grid resolution is set to ;

[0079] The time step is used to ensure the timeliness and numerical stability of the simulation.

[0080] The boundary conditions of the model include water flow inlet velocity boundary, free surface boundary and bottom boundary, among which the shear and disturbance flux input interface is set at the bottom.

[0081] II. Calculation of hydrodynamic shear stress field: The k-w turbulence model is embedded in the three-dimensional Navier-Stokes equation to solve the hydrodynamic field, and the following momentum conservation expression is established:

[0082] ;

[0083] Where: is the velocity vector field, p is the pressure, is the dynamic viscosity, is the turbulent stress tensor, which is provided by the k-ω model.

[0084] Based on the solution, the time series of bottom shear stress of each grid cell along the model bottom is extracted, which is used to drive the resuspension process.

[0085] III. Generation of biological disturbance flux field: The random walk algorithm is used to simulate the activity path of benthic organisms in the sediment. Each biological individual is set as a particle source, and its disturbance diffusion path conforms to the characteristics of Brownian motion. The biological disturbance space structure is represented in the form of biological pore network. The biological irrigation flux per unit area is defined as:

[0086] ;

[0087] where, is the bio-irrigation flux, is the flux proportionality factor (empirical value range 0.1-0.3, calibrated according to experiments), is the bio-disturbance intensity index corresponding to the grid cell, provided by the parameter acquisition module.

[0088] Example: if a point , set , then:

[0089] ;

[0090] Four, execute the coupling resuspension calculation: input and into the hydrodynamic-biological disturbance coupling model synchronously, and calculate the resuspension rate per unit time per unit area based on the shear-pore coupling equation:

[0091] ;

[0092] where, is the resuspension rate, is the coupling model coefficient, representing the contribution ratio of shear driving and biological disturbance to resuspension, is the sediment starting shear stress threshold, and n is the empirical index.

[0093] The model can be calibrated by experiments or historical data.

[0094] Example:

[0095] if a point Pa, Pa, , , then:

[0096] ;

[0097] Five, generate a dynamic suspended concentration distribution map: take the obtained resuspension rate as a source term, embed it into the advection-diffusion equation to solve the suspended particle concentration field , the equation is:

[0098] ;

[0099] where C is the suspended particle concentration, D is the horizontal and vertical diffusion coefficient, and h is the mixed layer height.

[0100] Through numerical integration, the dynamic suspended concentration distribution diagram of the entire model domain at continuous time is obtained, which is output in the form of a three-dimensional array and transmitted to the heavy metal form migration calculation module.

[0101] The heavy metal form migration calculation module includes:

[0102] I. Receive input data set: This module synchronously receives the dynamic suspended concentration distribution diagram and the heavy metal occurrence form data of the sediment, including the concentration of heavy metal elements in different forms and the spatial coordinates of the sampling points.

[0103] Through spatial interpolation or grid mapping function, the spatial coordinates of the two are uniformly processed to ensure that the data used for calculation correspond to the same resolution spatial grid.

[0104] II. Extract exchangeable state heavy metal content: Extract the exchangeable state heavy metal component from the heavy metal occurrence form data of the sediment to form a two-dimensional concentration matrix matching the suspended concentration diagram , 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.

[0105] III. Generate redox sensitive factor field: Deploy a microelectrode array (spacing ≤ 5m) in the target water area, and collect the oxidation-reduction potential and pH value in real time, and generate a time and space continuous redox sensitive factor field through time averaging and spatial interpolation.

[0106] The coupling response relationship is as follows (based on the Nernst equation and empirical fitting):

[0107] ;

[0108] This factor comprehensively represents the intensity of redox-driven release at this location / time, and the smaller it is, the stronger the reducing environment and the greater the potential for releasing heavy metals.

[0109] IV. Calculate 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 diffusion and reaction release mechanisms.

[0110] 1. Diffusion calculation unit: Consider the effective diffusion coefficient under the influence of sediment porosity , pore structure , its expression is:

[0111] ;

[0112] where, The diffusion coefficient of molecules in free water (typical values ​​for Zn are...). ), The porosity of the sediment (generally taken as 0.4-0.6), The tortuosity is typically taken as 2-3.

[0113] The diffusion flux in each grid is calculated according to Fick's first law:

[0114] ;

[0115] (The vertical gradient can be approximated by a first-order difference);

[0116] 2. Redox Release Calculation Unit: Based on redox sensitive factors To adjust the parameters, the response of the dissolution and release of exchangeable metals was calculated. The release flux model is as follows:

[0117] ;

[0118] in, The reaction rate coefficient, As a redox inhibitor, it describes the ability of the environment to regulate metal release.

[0119] Comprehensive dynamic flux release calculation: Finally, at each grid point, the two fluxes are combined to obtain:

[0120] ;

[0121] V. Generate a dynamic flux release dataset: total flux at all times and all grid points. Integrated into a three-dimensional data structure:

[0122] ;

[0123] The structural dimensions are as follows:

[0124] Spatial coordinates : Corresponding grid number;

[0125] Time step : Corresponding time series

[0126] Each element: represents the release flux of a certain heavy metal element (such as Zn²⁺, Pb²⁺) at this point in time.

[0127] The data is encoded in NetCDF, HDF5, or GeoTIFF format and sent to the ecological risk entropy value assessment module.

[0128] The ecological risk entropy assessment module includes:

[0129] I. Receiving dynamic release flux dataset: receiving the three-dimensional data structure input by the module , wherein, represents a two-dimensional spatial grid coordinate, and k represents a time step, represents the dissolved release flux of the e-th heavy metal element in the k-th time step of the grid unit.

[0130] II. Loading biological toxicity threshold: extracting the acute toxicity threshold and the chronic toxicity threshold of the target water area typical aquatic organisms (such as crucian carp, water flea, and algae) to various heavy metal elements from the preset ecological toxicity database The toxicity response of multiple organisms takes the minimum threshold of the most sensitive species as the local threshold standard.

[0131] III. Calculating pollution risk entropy value: using an entropy value calculation model with double evaluation mechanism, including acute risk evaluation mechanism and chronic risk evaluation mechanism. The pollution risk entropy value of each space-time point The calculation process is as follows:

[0132] 1. Acute risk entropy value (instantaneous response): based on the comparison between the flux per unit time and the acute toxicity threshold, the acute risk entropy value is defined as:

[0133] ;

[0134] wherein, is the effective release concentration converted by unit volume, which is approximately , h is the effective mixing water layer thickness, if , then .

[0135] 2. Chronic risk entropy value (cumulative response): calculating the ratio between the cumulative concentration in the sliding time window (such as 24h) with the current time step as the starting point and the chronic toxicity threshold:

[0136] When the cumulative average ;

[0137] 3. Comprehensive pollution risk entropy value:

[0138] ;

[0139] All the entropy values are normalized to the range of [0, 10] to form the pollution risk entropy value matrix .

[0140] IV. Superimposing time cumulative effect: in order to identify the continuous pollution influence, the time-enhanced pollution risk entropy value field is defined, which is represented as:

[0141] ​​ ;

[0142] where, is the arbitrary starting time step, is the set entropy value warning threshold (e.g. 5), is the indicator function, judging whether exceeding the threshold or not;

[0143] The result is the proportion of the time length that the risk entropy value exceeds the threshold in the past 72 hours (range 0-1).

[0144] Five, generate a three-dimensional pollution risk entropy value cloud map: combine with spatial coordinates and time steps to build a dynamic three-dimensional visualization model:

[0145] The visualization process includes:

[0146] Time dimension segmentation: divide the continuous monitoring period (e.g. 7 days) into equal length time segments (e.g. 12 hours each, a total of 14 segments), and generate an independent layer for each segment.

[0147] Risk level mapping: use a three-color classification standard:

[0148] Table 1: Risk level mapping table

[0149] Entropy value range Display color Risk level 0–3 Blue Safe 3–6 Yellow Warning 6–10 Red Danger

[0150] Each grid entropy value is mapped to the three-dimensional layer by color.

[0151] 3. Dynamic warning trigger: if a certain grid exceeds the threshold for 3 consecutive time segments, then the system generates a dynamic flashing red warning marker at that point, with coordinate and warning time information.

[0152] Visualization output format:

[0153] x / y axis: spatial grid coordinates;

[0154] z-axis: time dimension (in hours / time segment units);

[0155] Color coding: pollution risk level;

[0156] Output format: WebGL dynamic visualization component, or static GeoTIFF + XML warning marker set.

[0157] The present application encompasses any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details by those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0158] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A system for evaluating the pollution of heavy metals resuspension in sediments based on multidimensional data, characterized by, The system comprises a parameter collection module, a coupling resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy evaluation module. The parameter collection module is used to obtain hydrodynamic parameters, sediment heavy metal occurrence form data and biological disturbance intensity index of a target water area. The coupling resuspension simulation module receives the hydrodynamic parameters and the biological disturbance intensity index, and generates a dynamic suspended concentration distribution map through a hydrodynamic-biological disturbance coupling model. The heavy metal form migration calculation module receives the dynamic suspended concentration distribution map and the sediment heavy metal occurrence form data, and calculates the dynamic release flux of dissolved heavy metals in combination with a redox sensitive factor. The ecological risk entropy evaluation module receives the dynamic release flux, and generates a three-dimensional pollution risk entropy cloud map by fusing a biological toxicity threshold value. The parameter collection module comprises: Collecting physical environment data: obtaining latitude and longitude coordinates, water depth data and water temperature data of the target water area through a multi-parameter water quality monitoring buoy; Extracting hydrodynamic parameters: based on the physical environment data, using an acoustic Doppler current profiler to measure the flow profile, turbulent kinetic energy at a sampling frequency of ≥10 Hz, and calculating the bottom bed shear stress; Obtaining sediment heavy metal occurrence form data: at a determined latitude and longitude coordinate point, using a columnar sampler to collect a sediment columnar sample, after freeze-drying, using the BCR continuous extraction method to separate exchangeable state, carbonate combined state, iron and manganese oxide combined state and residual state heavy metals, and using an inductively coupled plasma mass spectrometer to determine the content of each form; Quantifying the biological disturbance intensity index: at the sediment sampling point, obtaining the biological abundance per unit area through a benthic organism trawl, combining the biological irrigation rate determined through a pore water tracing experiment, and multiplying the biological abundance and the biological irrigation rate to generate the biological disturbance intensity index; Generating a physical environment data package: integrating the physical environment data, the hydrodynamic parameters, the sediment heavy metal occurrence form data and the biological disturbance intensity index into a structured data package, and outputting to the coupling resuspension simulation module and the heavy metal form migration calculation module; The coupling resuspension simulation module comprises: Initializing the hydrodynamic-biological disturbance coupling model: receiving the hydrodynamic parameters and the biological disturbance intensity index output by the parameter collection module, and configuring the model calculation domain grid and the time step; Calculating the hydrodynamic shear stress field: based on the hydrodynamic parameters, solving the three-dimensional Navier-Stokes equation through the k-ω turbulent flow model, and outputting the time series of the bottom bed shear stress field; Generating the biological disturbance flux field: based on the biological disturbance intensity index, using the random walk algorithm to simulate benthic organism activity, and outputting the biological pore network and the biological irrigation flux field; Performing coupling resuspension calculation: inputting the bottom bed shear stress field and the biological irrigation flux field into the hydrodynamic-biological disturbance coupling model, and calculating the sediment resuspension rate through the shear-pore coupling equation; Generating the dynamic suspended concentration distribution map: performing time and space integration on the resuspension rate, simulating the suspended matter transmission process through the advection-diffusion equation, and outputting the dynamic suspended concentration distribution map in grid units, and transmitting to the heavy metal form migration calculation module; The heavy metal form migration calculation module comprises: Receiving input dataset: obtaining the dynamic suspended concentration distribution map coupled with the output of the resuspension simulation module, and the heavy metal speciation data of the sediment output by the parameter acquisition module; Extracting exchangeable heavy metal content: separating the exchangeable heavy metal component from the heavy metal speciation data of the sediment to generate a spatially distributed exchangeable heavy metal concentration matrix; Generating redox sensitive factor field: real-time acquisition of redox potential and pH value through the microelectrode array arranged in the target water area, calculation of redox sensitive factor according to the coupling relationship between redox potential and pH value, and output of the spatially and temporally continuous redox sensitive factor field; The heavy metal speciation migration calculation module further comprises: Calculating the 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 diffusion effect and the redox release effect are simultaneously processed through the migration flux calculation model to output the grid-based dynamic release flux of dissolved heavy metals; Generating dynamic release flux dataset: integrating the dynamic release flux of dissolved heavy metals into a three-dimensional data structure of spatial coordinates-time step-heavy metal elements according to time sequence, and transmitting to the ecological risk entropy evaluation module; The ecological risk entropy evaluation module comprises: Receiving dynamic release flux dataset: obtaining the dynamic release flux dataset output by the heavy metal speciation migration calculation module; Loading biological toxicity threshold: extracting the biological toxicity threshold of each heavy metal element for the main aquatic organisms in the target water area from the ecological toxicity database, wherein the biological toxicity threshold includes acute toxicity threshold and chronic toxicity threshold; Calculating pollution risk entropy value: based on the dynamic release flux dataset and the biological toxicity threshold, the risk is quantified through the entropy calculation model on a grid-by-grid, time-step-by-time-step and element-by-element basis to generate a pollution risk entropy value matrix; Superimposing time cumulative effect: integrating the pollution risk entropy value matrix in time dimension to calculate the cumulative duration proportion of entropy value exceeding threshold within 72 consecutive hours to generate a time-enhanced pollution risk entropy value field; Generating three-dimensional pollution risk entropy value cloud chart: inputting the time-enhanced pollution risk entropy value field into the three-dimensional visualization engine, superimposing the time dimension and the entropy intensity in the spatial dimension, and outputting the three-dimensional pollution risk entropy value cloud chart.

2. The system for evaluating the pollution of heavy metals re-suspension in sediment based on multi-dimensional data according to claim 1, characterized in that, The migration flux calculation model comprises a diffusion effect calculation unit and a redox release effect calculation unit, the diffusion effect calculation unit corrects the diffusion coefficient according to the pore characteristics of the sediment, and the redox release effect calculation unit is used to establish the coupling response relationship between the redox potential and the pH value.

3. The system for evaluating the pollution of heavy metals re-suspension in sediment based on multi-dimensional data according to claim 1, characterized in that, The entropy calculation model comprises a double evaluation mechanism, specifically: Acute risk evaluation mechanism: calculating the instantaneous risk entropy value based on the acute toxicity threshold in the biological toxicity threshold; Chronic risk evaluation mechanism: calculating the cumulative risk entropy value based on the chronic toxicity threshold in the biological toxicity threshold; Taking the maximum value of the calculation results of the acute risk evaluation mechanism and the chronic risk evaluation mechanism as the comprehensive pollution risk entropy value.

4. The system for evaluating the pollution of heavy metals re-suspension in sediment based on multi-dimensional data according to claim 1, characterized in that, When generating the three-dimensional pollution risk entropy value cloud chart, the following visualization operations are performed: Time dimension segmentation: dividing the continuous monitoring period into equal length time zones; Risk level mapping: According to the pollution risk entropy value range, define blue-yellow-red three color levels, corresponding to safe, warning, dangerous levels respectively; Dynamic early warning trigger: If the entropy value of the same spatial position maintains the dangerous level for consecutive time zones, automatically generate a warning marker.

Images