Method and system for evaluating heavy metal pollution of sediment in sedimentary environment

Abstract

The invention provides a method and a system for evaluating heavy metal pollution of sediment in a sedimentary environment. The method comprises the following steps: obtaining heavy metal content data of a solid phase and a liquid phase of sediment through multiple layers of sediment samples in a target water body sedimentary area; constructing a migration distribution curve of heavy metals in the bottom mud in different depth layers according to the content data of the heavy metals, extracting enrichment characteristics of the bottom mud heavy metals in vertical distribution according to the migration distribution curve, and generating spatial diffusion distribution of bottom mud heavy metal pollution according to the enrichment characteristics; determining the migration activity coefficient of the heavy metal in the bottom mud in a natural state according to the environmental parameters of the bottom mud, the change trend of the heavy metal concentration gradient in spatial diffusion distribution and the heavy metal form; and evaluating the re-release risk level of heavy metal pollution in the target water body sedimentary area according to the migration activity coefficient. By adopting the scheme, heavy metal pollution assessment can be promoted from static total amount analysis to dynamic behavior risk prediction, so that dynamic quantification of bottom mud heavy metal migration activity and accurate early warning of re-release risk are realized.

Description

Technical Field

[0001] This application relates to the field of heavy metal pollution assessment technology, and more specifically, to a method and system for assessing heavy metal pollution in sedimentary sediments. Background Technology

[0002] The assessment of heavy metal pollution in sedimentary environments refers to the method of collecting sediment samples from water bodies, determining the content and form of heavy metal elements, and evaluating the degree of pollution and potential ecological risks. The assessment results are a key scientific basis for carrying out water environment management and ecological restoration.

[0003] Existing methods for assessing heavy metal pollution in sediments mostly focus on measuring the total amount of heavy metals in surface sediments or a few deep layers, and classifying pollution levels statically based on background values ​​or quality standards. However, current technologies primarily focus on detecting the total amount of heavy metals in the solid phase of the sediment, without simultaneously analyzing the content and migration characteristics of heavy metals in pore water (liquid phase). This results in a single dimension of pollution data, failing to reflect the true pollution situation under the synergistic effects of the solid and liquid phases. Regarding spatial distribution characterization, existing technologies rely heavily on surface or a small number of discrete deep samples, making it impossible to accurately capture the spatial diffusion patterns of pollution. In the risk assessment phase, the inability to accurately quantify the speciation and re-release activity of heavy metals in complex sedimentary environments leads to discrepancies between the heavy metal pollution risk assessment results and the actual situation, thus failing to meet the practical needs of precise pollution control and efficient remediation. Therefore, how to elevate heavy metal pollution assessment from static total amount analysis to dynamic behavioral risk prediction, in order to achieve dynamic quantification of heavy metal migration activity in sediments and accurate early warning of re-release risks, has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for assessing heavy metal pollution in sedimentary environment sediments, which can elevate the assessment of heavy metal pollution from static total amount analysis to the level of dynamic behavioral risk prediction.

[0005] In a first aspect, this application provides a method for assessing heavy metal pollution in sedimentary sediments, comprising the following steps: Multi-layer sediment samples were obtained from the sedimentation area of ​​the target water body, and pore water was extracted and particle size was separated from the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment. Based on the heavy metal content data, migration and distribution curves of heavy metals in sediment at different depths are constructed, and enrichment characteristics of heavy metals in sediment in vertical distribution are extracted from the migration and distribution curves. Based on the enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in sediment of the target water body is generated. By monitoring environmental parameters of sediment through an intelligent sensor network, and based on the monitored environmental parameters, the changing trend of heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation, the migration activity coefficient of heavy metals in sediment under natural conditions is determined. The risk level of re-release of heavy metal pollution in the sedimentary area of ​​the target water body is assessed based on the migration activity coefficient.

[0006] In conjunction with the first aspect, in one possible implementation, the extraction of pore water and particle size separation of the multilayer sediment sample to obtain heavy metal content data in the solid and liquid phases of the sediment specifically includes: Centrifuge each independent layer of sediment sample in the multi-layer sediment sample to obtain pore water samples from each independent layer. Particle size analysis was performed on the solid phase samples of sediment from the same layer after centrifugation to classify the solid phase components of sediment into multiple particle size ranges. The heavy metal content data of the sediment solid and liquid phases were determined by analyzing solid components of all particle sizes and pore water samples from each independent layer.

[0007] In conjunction with the first aspect, in one possible implementation, constructing migration distribution curves of heavy metals in sediment at different depths based on the heavy metal content data specifically includes: Based on the heavy metal content data, the total heavy metal concentration and its corresponding depth of each independent layer are extracted. By using the depth of all independent layers and the corresponding total heavy metal concentration as data nodes, migration and distribution curves of heavy metals in sediment at different depth layers are generated.

[0008] In conjunction with the first aspect, in one possible implementation, generating the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body based on the enrichment characteristics specifically includes: The surface enrichment factor and peak depth in the enrichment features are used as spatial interpolation attributes; The spatial diffusion distribution of heavy metal pollution in the sediment of the target water body is generated based on the spatial interpolation attribute.

[0009] In conjunction with the first aspect, in one possible implementation, determining the migration activity coefficient of heavy metals in sediment under natural conditions based on monitored environmental parameters, the changing trends of heavy metal concentration gradients in the spatial diffusion distribution, and the speciation of heavy metals specifically includes: The factors influencing the activity of heavy metals in sediment were determined based on environmental parameters and heavy metal speciation obtained from monitoring. The vertical concentration gradient level is determined by the changing trend of the heavy metal concentration gradient in the spatial diffusion distribution; The migration activity coefficient of heavy metals in sediment under natural conditions is determined based on the activity influencing factor and the vertical concentration gradient level.

[0010] In conjunction with the first aspect, in one possible implementation, assessing the risk level of re-release of heavy metal pollution in the target water body's sedimentary area based on the migration activity coefficient specifically includes: Preset the risk threshold range for heavy metal pollution in the sedimentary area of ​​the target water body; The risk level of re-release of heavy metal pollution in the target water body sedimentation area is determined based on the risk threshold range and the migration activity coefficient.

[0011] In conjunction with the first aspect, one possible implementation also includes: dividing the sediment area within the target water body sedimentation zone into zones based on the re-release risk level, screening out high-risk pollution areas, and then generating a remediation priority map of the target water body sedimentation zone.

[0012] Secondly, this application provides an assessment system for heavy metal pollution in sedimentary sediments, comprising: The acquisition module is used to acquire multi-layer sediment samples from the sedimentation area of ​​the target water body, and to extract pore water and perform particle size stratification on the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment. The processing module is used to construct migration and distribution curves of heavy metals in sediment at different depths based on the heavy metal content data, extract the enrichment characteristics of heavy metals in sediment in vertical distribution from the migration and distribution curves, and generate the spatial diffusion distribution of heavy metal pollution in sediment of the target water body based on the enrichment characteristics. The processing module is also used to monitor environmental parameters of the sediment through a smart sensor network, and determine the migration activity coefficient of heavy metals in the sediment under natural conditions based on the monitored environmental parameters, the changing trend of heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation. The execution module is used to assess the risk level of re-release of heavy metal pollution in the sedimentary area of ​​the target water body based on the migration activity coefficient.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described method for assessing heavy metal pollution in sedimentary sediments.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned method for assessing heavy metal pollution in sedimentary sediments.

[0015] The technical solution provided in this application has the following beneficial effects: In this application, multi-layer sediment samples from the target water body's sedimentary area are obtained, and pore water extraction and particle size stratification are performed on the multi-layer sediment samples to obtain heavy metal content data in the solid and liquid phases of the sediment. Based on the heavy metal content data, migration distribution curves of heavy metals in the sediment at different depths are constructed, and the enrichment characteristics of heavy metals in the sediment's vertical distribution are extracted from the migration distribution curves. Based on the enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body's sedimentary area is generated. Environmental parameters of the sediment are monitored through an intelligent sensor network. Based on the monitored environmental parameters, the changing trends of heavy metal concentration gradients in the spatial diffusion distribution, and the speciation of heavy metals, the migration activity coefficient of heavy metals in the sediment under natural conditions is determined. The risk level of re-release of heavy metal pollution in the target water body's sedimentary area is assessed based on the migration activity coefficient.

[0016] Therefore, in this application, firstly, the enrichment characteristics of heavy metals in the vertical distribution of sediment are extracted from the migration distribution curve. Based on these enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body is generated. This allows for the identification of accumulation zones, migration zones, and diffusion interfaces of heavy metals in the vertical direction, thereby revealing their migration direction and energy gradient in the sedimentary system. This achieves a shift from static concentration detection to dynamic migration behavior characterization, enabling pollution assessment to have predictive and spatiotemporal resolution capabilities. Secondly, based on the monitored environmental parameters and the changing trends of heavy metal concentration gradients and heavy metal speciation in the spatial diffusion distribution, the migration activity coefficient of heavy metals in sediment under natural conditions is determined. This quantifies the potential activity of heavy metals transforming from sediment to water in the natural environment, giving the assessment method dynamic response characteristics and enabling quantitative characterization of heavy metal migration activity in sediment. Finally, This method assesses the risk level of heavy metal re-release in the sedimentary area of ​​the target water body based on the migration activity coefficient. It can comprehensively transform complex multi-source environmental influencing factors into graded risk indicators, enabling a forward-looking judgment on the potential pollution release capacity of sediments in different areas. Furthermore, based on the re-release risk level, the sedimentary areas within the target water body's sedimentary area are divided into zones for management, high-risk pollution areas are screened, and a remediation priority map of the target water body's sedimentary area is generated. This allows dynamic risk prediction results to be directly transformed into actionable remediation instructions, achieving a seamless transition from static monitoring to dynamic management of heavy metal pollution. This upgrades the heavy metal pollution assessment system to a dynamic behavioral risk early warning level. In summary, this scheme can elevate heavy metal pollution assessment from static total amount analysis to dynamic behavioral risk prediction, enabling dynamic quantification of heavy metal migration activity in sediments and accurate early warning of re-release risks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a method for assessing heavy metal contamination in sedimentary sediments according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of a migration distribution curve according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of migration activity coefficients according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an assessment system for heavy metal contamination in sedimentary environment according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a method for assessing heavy metal pollution in sedimentary sediments according to some embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a method for assessing heavy metal pollution in sedimentary sediments according to some embodiments of this application. The method for assessing heavy metal pollution in sedimentary sediments mainly includes the following steps: In step 101, multi-layer sediment samples from the target water body sedimentation area are obtained, and pore water is extracted and particle size is stratified in the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment.

[0021] In practice, obtaining multi-layered sediment samples from the target water body's sedimentary area can be achieved in the following ways: Multiple spatially representative assessment points can be established within the sedimentary area by examining the hydrogeological conditions and pollution history of the sedimentary zone; in-situ non-destructive collection of columnar samples using gravity samplers or piston samplers is performed to ensure the integrity of the sediment-water interface and maintain the natural stratification structure of the sediments; immediately after collection, the obtained complete columnar samples are precisely cut into layers at predetermined vertical intervals, such as every 2 cm, or based on actual geological stratification identified by sediment color and density variations, such as oxidation layers, transition layers, and reduction layers; the cut sediment samples are then processed... The samples were sealed and stored in inert containers pre-filled with high-purity nitrogen to isolate them from oxygen and prevent oxidation of sensitive components such as sulfides. All samples were then transported to the laboratory in a low-temperature environment below 4°C, protected from light, and subsequent analyses were completed within a specified timeframe to maximize the preservation of the original physical structure and geochemical state of the samples from collection to analysis. In a preferred embodiment, the gravity sampler may be equipped with a transparent inner liner to allow for direct observation and recording of sediment sequence characteristics on-site. In other embodiments, for special areas with shallow water depths or hard bottom sediments, box-type samplers may also be used for sample collection; no specific limitations are specified here.

[0022] It should be noted that the multilayer sediment samples in this application refer to a collection of sediment samples from multiple independent layers in the sedimentary area of ​​the target water body, which are used to characterize the heterogeneity of the vertical spatial distribution of heavy metals and other environmental parameters in the sediment.

[0023] In some embodiments, the pore water extraction and particle size stratification of the multilayer sediment sample to obtain heavy metal content data in the solid and liquid phases of the sediment can be achieved by the following steps: Centrifuge each independent layer of sediment sample in the multi-layer sediment sample to obtain pore water samples from each independent layer. Particle size analysis was performed on the solid phase samples of sediment from the same layer after centrifugation to classify the solid phase components of sediment into multiple particle size ranges. The heavy metal content data of the sediment solid and liquid phases were determined by analyzing solid components of all particle sizes and pore water samples from each independent layer.

[0024] In practice, the pore water sample from each independent layer of the multi-layer sediment sample is obtained by centrifugation. This can be achieved as follows: In the laboratory, each independent layer of sediment sample, collected on-site and sealed in layers, is quickly transferred to a centrifuge tube in a high-speed refrigerated centrifuge. The sample is then centrifuged continuously for 20 minutes at a speed of at least 3500 rpm in a constant low temperature and inert gas environment, such as 4°C and high-purity nitrogen. The strong centrifugal force generated allows for thorough separation of the sediment solid phase and the pore water. Subsequently, the supernatant is carefully extracted immediately using a disposable sterile syringe and vacuum filtered through a 0.45-micron aqueous microporous membrane. The filtered solution is collected in a polyethylene sample bottle that has been pre-soaked and cleaned with dilute nitric acid. The obtained filtrate is the pore water sample from that layer and must be immediately acidified and stored, with subsequent testing completed within 24 hours to minimize changes in sample properties.

[0025] In practice, particle size analysis of the solid phase sample of the same layer of sediment after centrifugation can be performed to classify the solid phase of sediment into multiple particle size fractions. This can be achieved by the following method: the solid phase sample of the same layer of sediment obtained after centrifugation is placed in an oven at a temperature below 60°C until constant weight is achieved, and then it is gently ground with an agate mortar to break up the aggregates while maintaining the original particle characteristics; an appropriate amount of the pretreated sample is put into the sample cell of the laser diffraction particle size analyzer and uniformly dispersed in the deionized water background solution under the action of a circulating pump; based on the Mie scattering theory model built into the instrument, the diffraction intensity distribution of the particle group at different angles is automatically analyzed, the equivalent diameter of each particle is calculated, and finally, according to the internationally accepted Udden-Wentworth particle size standard, the entire particle group is automatically divided and classified into multiple continuous particle size fractions such as clay (<4μm), silt (4-63μm), and sand (>63μm), while recording the mass percentage distribution of each particle size fraction.

[0026] In practice, determining the heavy metal content data of the sediment solid and liquid phases using all particle size fractions of sediment solid components and pore water samples from each independent layer can be achieved as follows: First, for pore water samples obtained from each independent layer, inductively coupled plasma mass spectrometry (ICP-MS) can be used for direct injection analysis. The dissolved concentrations of various target heavy metals, such as cadmium, lead, mercury, and arsenic, can be quantified using a standard curve method. Simultaneously, for each particle size fraction of sediment solid components identified through particle size analysis, a precise mass can be weighed and analyzed using a nitric acid-hydrofluoric acid-hydrogen peroxide system. A closed microwave digestion process was performed to completely transfer heavy metals from the sediment solid phase to the digestion solution. After cooling and volume adjustment, the total concentration of each heavy metal in the digestion solution was detected using inductively coupled plasma mass spectrometry. Finally, the above detection results, namely the heavy metal concentrations in the pore water samples of each independent layer and the heavy metal concentrations in the sediment solid phase components of each particle size in the same layer, were systematically integrated and recorded to form a heavy metal content dataset with depth-particle size-phase attributes, i.e., heavy metal content data of the sediment solid and liquid phases. Other methods may be used in other embodiments for determination, which are not limited here.

[0027] It should be noted that, in this application, the independent sediment sample refers to a sediment sample at a specific vertical position within a multi-layer sediment sample; the pore water sample refers to the liquid component separated from the independent sediment sample by centrifugation and filtration, which is used to carry and characterize dissolved heavy metals and other soluble substances present in the interparticle spaces of the sediment; the concentric sediment solid sample refers to the solid particulate matter remaining after pore water has been separated from the same independent sediment sample, which is used to retain the solid matrix of that sediment layer; the particle size sediment solid component refers to a subset of solid particles with a specific particle size range, which is used to distinguish particle groups with different adsorption and migration behaviors to reveal the enrichment pattern of heavy metals on particles of different sizes; the heavy metal content data refers to a structured dataset of heavy metal concentrations of all particle size sediment solid components and all independent sediment pore water samples, which is used to quantify the distribution information of heavy metals in the solid-liquid two-phase sediment and at different depths and particle sizes.

[0028] In step 102, migration and distribution curves of heavy metals in sediment at different depths are constructed based on the heavy metal content data. The enrichment characteristics of heavy metals in sediment in vertical distribution are extracted from the migration and distribution curves. Based on the enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in sediment of the target water body is generated.

[0029] In some embodiments, reference Figure 2As shown, this figure is an exemplary flowchart for determining migration distribution curves in some embodiments of this application. The following steps can be used to construct migration distribution curves of heavy metals in sediment at different depths based on the heavy metal content data: Based on the heavy metal content data, the total heavy metal concentration and its corresponding depth of each independent layer are extracted. By using the depth of all independent layers and the corresponding total heavy metal concentration as data nodes, migration and distribution curves of heavy metals in sediment at different depth layers are generated.

[0030] In specific implementation, the extraction of the total heavy metal concentration and corresponding depth of each independent layer based on the heavy metal content data can be achieved in the following way: First, the depth value of each independent layer, which was clearly recorded during the acquisition, is directly read from the structured record of the heavy metal content data; then, for all particle size sediment solid phase components belonging to the same independent layer, the measured heavy metal concentrations of each component are weighted and summed according to the mass ratio of that particle size component to calculate the average heavy metal concentration of the sediment solid phase of that layer, and this calculated value is used as the total heavy metal concentration of that layer; finally, a sequence of depth-total heavy metal concentration data pairs is output, where each data pair uniquely corresponds to an independent layer, thus obtaining the total heavy metal concentration and corresponding depth of each independent layer.

[0031] In specific implementation, the depth of all independent layers and the corresponding total heavy metal concentration are used as data nodes to generate migration distribution curves of heavy metals in sediment at different depths. This can be achieved as follows: The depth-total heavy metal concentration data pairs are imported into data processing software, such as OriginLab or Python's SciPy library. A coordinate system is established with depth as the ordinate (Y-axis) and total heavy metal concentration as the abscissa (X-axis). Subsequently, a cubic spline interpolation algorithm is used to perform smooth curve fitting on all data nodes. This algorithm ensures that the curve passes through all data nodes and guarantees the continuity of the first and second derivatives, thus generating a continuous and smooth curve. This curve serves as the migration distribution curve of heavy metals in sediment at different depths, revealing the continuous trend, peak position, and gradient variation characteristics of heavy metal concentration with depth on the vertical profile of the sediment. In other embodiments, linear interpolation or polynomial fitting algorithms can also be used to generate the curve; this is not limited here.

[0032] It should be noted that, in this application, a data node refers to a data pair consisting of the depth value of a single independent layer and its corresponding total heavy metal concentration value; the migration distribution curve in this application refers to a continuous and smooth curve generated based on all data nodes, which is used to visualize and make the discrete heavy metal concentration vertical data continuous, so as to intuitively reveal the overall trend, spatial distribution law and migration characteristics of heavy metals in the sediment column profile with depth.

[0033] In specific implementation, the enrichment characteristics of heavy metals in the vertical distribution of sediment can be extracted from the migration distribution curve in the following way: First, the migration distribution curve is mathematically analyzed in data processing software. By finding the extreme point where the first derivative of the curve is zero and the second derivative is negative, or by directly using a peak finding algorithm, the global maximum concentration point on the migration distribution curve is accurately identified, and the depth corresponding to the maximum point is recorded as the peak depth. At the same time, the heavy metal concentration value is directly read from the starting point of the migration distribution curve (i.e., at the sediment-water interface) as the surface concentration, and the ratio of this surface concentration to the regional geochemical background value of the heavy metal determined by historical data or control area samples is calculated. The result is the surface enrichment factor. Finally, the two obtained quantitative parameters, namely the peak depth and the surface enrichment factor, are used together to characterize the enrichment characteristics of heavy metals in the vertical distribution of sediment. Other methods can also be used to determine this in other embodiments, which are not limited here.

[0034] It should be noted that the enrichment characteristics in this application refer to a set of parameters characterizing the intensity and distribution pattern of heavy metals in the vertical profile of sediment, which are used to reveal the spatial structure of heavy metal pollution in the vertical direction; the surface enrichment factor refers to the ratio of surface heavy metal concentration to regional background concentration, which is used to quantify the degree of heavy metal pollution accumulation in the sediment surface; and the peak depth refers to the vertical depth corresponding to the maximum concentration of heavy metals on the migration distribution curve, which is used to indicate the spatial location of the maximum enrichment area of ​​heavy metals in the sediment profile.

[0035] In some embodiments, generating the spatial diffusion distribution of heavy metal pollution in sediments of a target water body based on the enrichment characteristics can be achieved through the following steps: The surface enrichment factor and peak depth in the enrichment features are used as spatial interpolation attributes; The spatial diffusion distribution of heavy metal pollution in the sediment of the target water body is generated based on the spatial interpolation attribute.

[0036] In specific implementation, the surface enrichment factor and peak depth in the enrichment features can be used as spatial interpolation attributes in the following way: In the geographic information system platform, an attribute table can be created for each evaluation point with known geographic coordinates in the sedimentary area of ​​the target water body under study. The values ​​of the surface enrichment factor and peak depth in the enrichment features can be entered and stored as two independent attribute fields of the spatial location of the evaluation point. In this way, the parameters describing the vertical distribution characteristics of heavy metals can be transformed into numerical spatial attributes that can be used to construct spatial surfaces, thus preparing data for generating spatial distribution maps.

[0037] In specific implementation, the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body can be generated based on the aforementioned spatial interpolation attributes in the following manner: In the geographic information system software, the two spatial interpolation attribute fields of surface enrichment factor and peak depth can be selected respectively, and a gridded calculation can be performed using the Kriging spatial interpolation algorithm. This process first calculates a semi-variogram model of spatial autocorrelation based on the attribute values ​​of all evaluation points to determine the optimal interpolation parameters. Then, the attribute values ​​of each unevaluated location within the sediment of the target water body are predicted. Finally, two raster layers are generated, one visually representing the spatial distribution of the surface enrichment factor and the other the spatial distribution of the peak depth using color gradients. After overlaying and merging the two layers and symbolic refinement, a spatial diffusion distribution map that comprehensively characterizes the degree of heavy metal pollution and the vertical migration trend in the sediment is obtained, i.e., the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body. In other embodiments, inverse distance weighted interpolation or spline function interpolation algorithms can also be used to complete this process; no limitation is made here.

[0038] It should be noted that the spatial interpolation attribute in this application refers to a numerical parameter that can represent the characteristics of a specific spatial location and can be used for interpolation calculation. It is used to transform the feature data of discrete points into continuous fields that can be used for spatial surface modeling. The spatial diffusion distribution in this application refers to a spatial diffusion distribution map that reflects the degree of heavy metal pollution and the vertical migration trend. It is used to visually display the static spatial pattern of pollution in the sedimentation area of ​​the target water body and the potential migration paths and accumulation areas of heavy metals in the sediment.

[0039] In step 103, the environmental parameters of the sediment are monitored by a smart sensor network. Based on the monitored environmental parameters, the changing trend of the heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation, the migration activity coefficient of heavy metals in the sediment under natural conditions is determined.

[0040] In practice, monitoring environmental parameters of sediment through a smart sensor network can be achieved as follows: Multiple smart monitoring nodes are deployed in a grid pattern within the target water sedimentation area. Each node integrates an oxidation-reduction potential (ORP) sensing module and an organic matter content detection module. The ORP sensing module collects potential signals from different depth layers in real time via an iridium electrode array vertically inserted into the sediment. These depth layers may include the sediment-water interface, oxidation layer, and reduction layer. After temperature compensation, a precise ORP value is output. The organic matter content detection module employs near-infrared spectroscopy sensing technology, detecting potential signals from the sediment surface... The sediment layer emits near-infrared light and receives the reflectance spectrum. The organic matter content data is obtained by inverting the pre-established spectral characteristics and the organic matter content calibration model. As a preferred embodiment, the organic matter content detection module can also use laser-induced breakdown spectroscopy technology for high-precision measurement. All monitoring data are transmitted to the gateway device through the LoRa communication module built into the node, and finally uploaded to the cloud monitoring platform to form a continuous spatiotemporal sequence dataset, so as to obtain the environmental parameters of the sediment, including the redox potential value and the organic matter content value. Other methods can also be used in other embodiments, and this application does not limit them.

[0041] It should be noted that the environmental parameters in this application refer to a set of dynamic factors that characterize the physicochemical state of sediments and affect the migration activity of heavy metals, and are used to quantify the environmental conditions of the sedimentary zone of the target water body.

[0042] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the migration activity coefficient in some embodiments of this application. In this embodiment, the migration activity coefficient of heavy metals in sediment under natural conditions is determined based on the monitored environmental parameters, the changing trend of heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation. This can be achieved by the following steps: First, in step 1031, the activity influencing factors of heavy metals in the sediment are determined based on the environmental parameters and heavy metal speciation obtained from monitoring. Secondly, in step 1032, the vertical concentration gradient level is determined by the changing trend of the heavy metal concentration gradient in the spatial diffusion distribution; Finally, in step 1033, the migration activity coefficient of heavy metals in the sediment under natural conditions is determined based on the activity influencing factor and the vertical concentration gradient level.

[0043] In practice, determining the activity influencing factors of heavy metals in sediment based on monitored environmental parameters and heavy metal speciation can be achieved in the following way: First, based on historical environmental monitoring data and pollution migration mechanism studies of the target water body's sedimentation area, a mapping relationship between environmental parameters and speciation proportions and activity influencing factors can be established. Then, the redox potential value in the environmental parameters obtained from real-time monitoring is mapped as the first influencing factor; for example, a value of 0.8 is assigned when the potential value is below -100 mV, 0.5 is assigned when it is between -100 mV and 100 mV, and 0.2 is assigned when it is above 100 mV. Simultaneously, the organic matter content value in the environmental parameters obtained from real-time monitoring is mapped as the second influencing factor; for example, a value of 0.5 is assigned when the content is above 5%. The first, second, and third influencing factors are assigned a value of 0.7, 0.4 when the content is between 2% and 5%, and 0.1 when it is below 2%. Next, a third influencing factor is determined based on the proportion of acid-extractable heavy metals in the heavy metal speciation obtained through the Tessier continuous extraction method. For example, a value of 0.9 is assigned when the proportion is above 30%, 0.6 when it is between 15% and 30%, and 0.3 when it is below 15%. Finally, the arithmetic mean of the first, second, and third influencing factors is taken as the comprehensive activity influencing factor of heavy metals in the sediment. The mapping rules and thresholds can be calibrated and adjusted according to the type and pollution characteristics of the target water body. In other embodiments, other methods, such as expert scoring, can also be used for determination, which is not limited here.

[0044] In specific implementation, determining the vertical concentration gradient level based on the changing trend of heavy metal concentration gradient in the spatial diffusion distribution can be achieved in the following way: From the underlying data corresponding to the spatial diffusion distribution, for each assessment point (i.e., a basic spatial assessment unit with clear spatial coordinates within the target water body's sedimentary area), extract the surface concentration and the peak concentration at the peak depth. Calculate the ratio of the concentration difference between the two to the peak depth to obtain the vertical concentration gradient value of the assessment point, and normalize it to between 0 and 1. Then, classify the level according to the numerical range of this vertical concentration gradient value, i.e. The vertical concentration gradient value is compared with a preset gradient threshold range. For example, when the vertical concentration gradient value is greater than 0.5, the vertical concentration gradient level is assigned a high level of 0.9; when the vertical concentration gradient value is between 0.2 and 0.5, it is assigned a medium level of 0.5; when the vertical concentration gradient value is less than 0.2, it is assigned a low level of 0.1. The obtained level is then assigned as the vertical concentration gradient level. The gradient threshold range can be configured and adjusted according to the regional background during the evaluation process. Other methods can also be used to determine it in other embodiments, which are not limited here.

[0045] In specific implementation, the migration activity coefficient of heavy metals in sediment under natural conditions can be determined based on the activity influencing factor and the vertical concentration gradient level in the following manner: the activity influencing factor and the vertical concentration gradient level are weighted and summed. Based on the sensitivity analysis of the contribution to the risk of heavy metal re-release, the weight of the activity influencing factor can be set to 0.7, and the weight of the vertical concentration gradient level can be set to 0.3. The weighted calculation result is then normalized and limited to a value range of 0 to 1. The obtained value is then used as the migration activity coefficient of heavy metals in sediment under natural conditions. The closer the coefficient is to 1, the higher the migration ability and re-release risk of heavy metals; the closer it is to 0, the more stable it is. The weight settings of the activity influencing factor and the vertical concentration gradient level can also be adaptively adjusted according to the pollution characteristics and assessment requirements of the specific application scenario. Other methods can also be used in other embodiments, which are not limited here.

[0046] It should be noted that, in this application, the heavy metal speciation refers to the different chemical phases of heavy metals present in sediment, used to distinguish the chemical stability and bioavailability of heavy metals to characterize their potential migration capacity under changing environmental conditions; the activity impact factor refers to a numerical index that quantifies the degree to which environmental conditions promote the migration and transformation of heavy metals; the trend of heavy metal concentration gradient refers to the rate characteristics reflecting the vertical change of heavy metal concentration, used to indicate the migration direction and diffusion intensity of pollutants in the sediment profile; the vertical concentration gradient level refers to the standardized evaluation result obtained after quantifying and classifying the trend of vertical concentration gradient, used to transform continuous gradient values ​​into a level sequence with clear environmental significance for comprehensive risk assessment; and the migration activity coefficient refers to a standardized index characterizing the potential for heavy metals to release from sediment into water bodies under natural conditions, used to transform complex environmental behaviors into comparable values, providing a direct basis for risk assessment.

[0047] In step 104, the risk level of re-release of heavy metal pollution in the target water body sedimentation area is assessed based on the migration activity coefficient. In some embodiments, assessing the risk level of re-release of heavy metal pollution in the sedimentary area of ​​a target water body based on the migration activity coefficient can be achieved using the following steps: Preset the risk threshold range for heavy metal pollution in the sedimentary area of ​​the target water body; The risk level of re-release of heavy metal pollution in the target water body sedimentation area is determined based on the risk threshold range and the migration activity coefficient.

[0048] In specific implementation, the preset risk threshold range for heavy metal pollution in the sedimentary area of ​​the target water body can be achieved in the following way: referring to the relevant limits on heavy metal activity in the "Soil Environmental Quality Assessment Standard" and the "Sediment Quality Guidelines", and combining the environmental background characteristics of the target water body, the migration activity coefficient range of 0 to 1 is divided into three continuous intervals. For example, 0.00-0.35 is defined as the low-risk threshold interval, 0.35-0.65 is defined as the medium-risk threshold interval, and 0.65-1.00 is defined as the high-risk threshold interval. The divided intervals are used as the risk threshold ranges for heavy metal pollution in the sedimentary area of ​​the target water body. The preset risk threshold ranges are stored in the configuration file of the assessment system in the form of a data table. In some embodiments, they can also be calibrated and adjusted through the management interface according to the specific water body type. This application does not limit this.

[0049] In specific implementation, determining the re-release risk level of heavy metal pollution in the target water body sedimentation area based on the risk threshold interval and the migration activity coefficient can be achieved in the following way: a condition judgment module can be established in the assessment system to automatically compare the migration activity coefficient calculated for each assessment point with the risk threshold interval. That is, when the migration activity coefficient falls into the low-risk threshold interval, the system automatically outputs a low-risk level; when the migration activity coefficient falls into the medium-risk threshold interval, the system automatically outputs a medium-risk level; when the migration activity coefficient falls into the high-risk threshold interval, the system automatically outputs a high-risk level; finally, a spatial attribute dataset containing risk level information of all assessment points is generated, and this spatial attribute dataset is used as the re-release risk level of heavy metal pollution in the target water body sedimentation area. Other methods can also be used in other embodiments, which are not limited here.

[0050] It should be noted that the risk threshold range in this application refers to the numerical range of migration activity coefficients used to classify different risk levels. It is used to provide a clear risk level classification standard for continuous heavy metal migration activity coefficients, so as to map continuous values ​​to discrete risk levels. The re-release risk level in this application refers to the evaluation level of the potential possibility of heavy metals being released from sediments to overlying water.

[0051] In some embodiments, the method may further include: dividing the sediment area within the target water body sedimentation zone into zones based on the re-release risk level, screening out high-risk pollution areas, and then generating a remediation priority map of the target water body sedimentation zone.

[0052] In some embodiments, the following steps can be used to manage the sediment areas within the target water body's sedimentation zone by region based on the re-release risk level and to screen out high-risk contaminated areas: Based on the re-release risk level, the assessment points in the target water body sedimentation area are spatially aggregated to obtain the boundary of the high-risk pollution area. High-risk pollution zones are generated based on the boundaries of the aforementioned high-risk pollution zones.

[0053] In specific implementation, the spatial aggregation of assessment points within the target water body sedimentation area based on the re-release risk level can be achieved by the following method to obtain the boundary of the high-risk pollution area: In the geographic information system software, firstly, all assessment points with a re-release risk level of high risk are selected through the attribute selection function; then, a spatial aggregation analysis tool can be used to set an appropriate distance tolerance value, such as 50 meters, and then all high-risk assessment points with a distance smaller than this distance tolerance value are automatically merged into the same polygon feature; wherein, this process will generate a minimum convex hull boundary or adaptive elliptical boundary that can surround all points in the cluster for each independent high-risk assessment point cluster, thereby obtaining the preliminary discrete boundary of the high-risk pollution area.

[0054] In specific implementation, the generation of high-risk pollution zones based on the boundaries of the high-risk pollution zones can be achieved in the following way: the obtained preliminary high-risk pollution zone boundary elements can be loaded into a geographic information system, and a layer fusion tool can be used, setting "risk level" as the fusion field; this tool will automatically identify and merge all adjacent or intersecting high-risk pollution zone boundaries, eliminating their internal overlapping parts and small gaps, forming one or more continuous and seamless polygonal areas; the finally generated polygonal area is the high-risk pollution zone used for subsequent remediation priority determination, and its spatial range completely covers all pollution deposition areas that need to be prioritized for control; other methods can also be used in other embodiments, which are not limited here.

[0055] It should be noted that the boundary of the high-risk pollution area in this application refers to the preliminary spatial boundary surrounding the high-risk assessment points, which is used to transform the discrete high-risk assessment points into a preliminary management unit with a clear spatial scope; the high-risk pollution area in this application refers to the continuous and seamless spatial scope formed by merging all adjacent high-risk pollution area boundaries, which is used to integrate scattered high-risk areas into a unified key control area, so as to clearly identify the core pollution spatial scope that requires priority remediation measures.

[0056] In practice, generating a remediation priority map for the target water body's sedimentary area can be achieved as follows: On a geographic information system platform where high-risk pollution areas have been delineated, an attribute table can be created for each high-risk pollution area, and its remediation priority index can be calculated. The remediation priority index is calculated by integrating the representative migration activity coefficient and the regional average heavy metal concentration of the high-risk pollution area. The migration activity coefficient is taken as the maximum value of all assessment points within the high-risk pollution area, and the regional average heavy metal concentration is extracted from the corresponding range of the spatial diffusion distribution map. These two parameters are normalized and then weighted and summed. The migration activity coefficient can be weighted at 0.6, and the heavy metal concentration at 0.4, thus obtaining a range from 0 to 1. The priority index within the assessment is used, where the weights can be pre-set based on expert experience and model sensitivity analysis, and can be dynamically adjusted according to management needs during the assessment process. Subsequently, all high-risk pollution areas are sorted in descending order based on this priority index, and the sorting results are visualized on an electronic map with different color depths, ultimately generating a remediation priority map that can intuitively reflect the urgency of remediation of each high-risk pollution area in the target water body sedimentation area. In a preferred embodiment, the visualization rendering can use a gradient color scheme from dark red (i.e., the highest priority) to light yellow (i.e., the relatively low priority). In other embodiments, the priority index can be assigned in segments or combined with the hydrological conditions of the sedimentation area for auxiliary correction, which is not limited in this application.

[0057] It should be noted that the remediation priority map in this application refers to a visualized spatial decision support map that reflects the urgency of remediation of each high-risk pollution zone within the sedimentation area of ​​the target water body. It is used to integrate complex risk assessment results with spatial distribution information into an intuitive hierarchical management guide, providing direct graphical decision-making basis for environmental managers to optimize the allocation of remediation resources and arrange action sequences.

[0058] Furthermore, in another aspect of this application, in some embodiments, this application provides an assessment system for heavy metal contamination of sedimentary sediments, referring to... Figure 4 The figure is a schematic diagram of the structure of an assessment system for heavy metal pollution in sedimentary environment according to some embodiments of this application. The assessment system 400 for heavy metal pollution in sedimentary environment includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire multi-layer sediment samples from the sedimentation area of ​​the target water body, and to extract pore water and perform particle size stratification on the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment. Processing module 402 in this application is mainly used to construct migration distribution curves of heavy metals in sediment at different depth layers based on the heavy metal content data, extract the enrichment characteristics of heavy metals in sediment in vertical distribution based on the migration distribution curves, and generate the spatial diffusion distribution of heavy metal pollution in sediment of the target water body based on the enrichment characteristics. The processing module 402 described in this application is also used to monitor the environmental parameters of the sediment through a smart sensor network, and determine the migration activity coefficient of heavy metals in the sediment under natural conditions based on the monitored environmental parameters, the changing trend of the heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation. The execution module 403 in this application is mainly used to assess the risk level of re-release of heavy metal pollution in the sedimentation area of ​​the target water body based on the migration activity coefficient.

[0059] The foregoing has detailed examples of methods and systems for assessing heavy metal pollution in sedimentary environments provided in this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0060] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for assessing heavy metal pollution in sedimentary sediments.

[0061] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the computer equipment used to implement the assessment method for heavy metal pollution in sedimentary sediments of this application. The assessment method for heavy metal pollution in sedimentary sediments in the above embodiments can be implemented through… Figure 5 The computer device 500 shown is used to implement this, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 may be a terminal device, a server or a chip.

[0062] The processor 501 can be a general-purpose processor or a special-purpose processor. For example, the processor 501 can be a central processing unit (CPU). The CPU can be used to control the computer device 500, execute software programs, and process data from the software programs. The computer device 500 may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0063] For example, computer device 500 may be a chip, communication unit 505 may be the input and / or output circuit of the chip, or communication unit 505 may be the communication interface of the chip, and the chip may be a component of terminal device, network device or other device.

[0064] For example, computer device 500 may be a terminal device or a server, and communication unit 505 may be a transceiver of the terminal device or the server, or communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0065] The computer device 500 may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0066] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0067] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for assessing heavy metal pollution in sedimentary sediments.

[0070] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for assessing heavy metal pollution in sedimentary sediments, characterized in that, Includes the following steps: Multi-layer sediment samples were obtained from the sedimentation area of ​​the target water body, and pore water was extracted and particle size was separated from the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment. Based on the heavy metal content data, migration and distribution curves of heavy metals in sediment at different depths are constructed, and enrichment characteristics of heavy metals in sediment in vertical distribution are extracted from the migration and distribution curves. Based on the enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in sediment of the target water body is generated. By monitoring environmental parameters of sediment through an intelligent sensor network, and based on the monitored environmental parameters, the changing trend of heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation, the migration activity coefficient of heavy metals in sediment under natural conditions is determined. The risk level of re-release of heavy metal pollution in the sedimentary area of ​​the target water body is assessed based on the migration activity coefficient.

2. The method as described in claim 1, characterized in that, Pore ​​water extraction and particle size stratification were performed on the multilayer sediment samples to obtain heavy metal content data in the solid and liquid phases of the sediment, specifically including: Centrifuge each independent layer of sediment sample in the multi-layer sediment sample to obtain pore water samples from each independent layer. Particle size analysis was performed on the solid phase samples of sediment from the same layer after centrifugation to classify the solid phase components of sediment into multiple particle size ranges. The heavy metal content data of the sediment solid and liquid phases were determined by analyzing solid components of all particle sizes and pore water samples from each independent layer.

3. The method as described in claim 1, characterized in that, The specific steps for constructing migration and distribution curves of heavy metals in sediment at different depths based on the aforementioned heavy metal content data include: Based on the heavy metal content data, the total heavy metal concentration and its corresponding depth of each independent layer are extracted. By using the depth of all independent layers and the corresponding total heavy metal concentration as data nodes, migration and distribution curves of heavy metals in sediment at different depth layers are generated.

4. The method as described in claim 1, characterized in that, Based on the enrichment characteristics, the spatial diffusion distribution of heavy metal pollution in the sediment of the target water body's sedimentary area is specifically generated, including: The surface enrichment factor and peak depth in the enrichment features are used as spatial interpolation attributes; The spatial diffusion distribution of heavy metal pollution in the sediment of the target water body is generated based on the spatial interpolation attribute.

5. The method as described in claim 1, characterized in that, Based on the monitored environmental parameters, the changing trends of heavy metal concentration gradients in the spatial diffusion distribution, and the speciation of heavy metals, the migration activity coefficients of heavy metals in sediment under natural conditions are determined, specifically including: The factors influencing the activity of heavy metals in sediment were determined based on environmental parameters and heavy metal speciation obtained from monitoring. The vertical concentration gradient level is determined by the changing trend of the heavy metal concentration gradient in the spatial diffusion distribution; The migration activity coefficient of heavy metals in sediment under natural conditions is determined based on the activity influencing factor and the vertical concentration gradient level.

6. The method as described in claim 1, characterized in that, The assessment of the risk level of re-release of heavy metal pollution in the sedimentary area of ​​the target water body based on the migration activity coefficient specifically includes: Preset the risk threshold range for heavy metal pollution in the sedimentary area of ​​the target water body; The risk level of re-release of heavy metal pollution in the target water body sedimentation area is determined based on the risk threshold range and the migration activity coefficient.

7. The method as described in claim 1, characterized in that, Also includes: Based on the risk level of re-release, the sediment areas within the target water body sedimentation zone are divided into zones for management, high-risk pollution areas are screened out, and a remediation priority map of the target water body sedimentation zone is generated.

8. A system for assessing heavy metal pollution in sedimentary sediments, characterized in that, include: The acquisition module is used to acquire multi-layer sediment samples from the sedimentation area of ​​the target water body, and to extract pore water and perform particle size stratification on the multi-layer sediment samples to obtain heavy metal content data of the solid and liquid phases of the sediment. The processing module is used to construct migration and distribution curves of heavy metals in sediment at different depths based on the heavy metal content data, extract the enrichment characteristics of heavy metals in sediment in vertical distribution from the migration and distribution curves, and generate the spatial diffusion distribution of heavy metal pollution in sediment of the target water body based on the enrichment characteristics. The processing module is also used to monitor environmental parameters of the sediment through a smart sensor network, and determine the migration activity coefficient of heavy metals in the sediment under natural conditions based on the monitored environmental parameters, the changing trend of heavy metal concentration gradient in the spatial diffusion distribution, and the heavy metal speciation. The execution module is used to assess the risk level of re-release of heavy metal pollution in the sedimentary area of ​​the target water body based on the migration activity coefficient.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory being used to store computer programs, and the processor being used to call and run the computer programs from the memory, causing the computer device to perform the assessment method for heavy metal pollution of sedimentary sediments in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to perform the assessment method for heavy metal pollution in sedimentary sediments as described in any one of claims 1 to 7.

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