Heavy metal pollution source identification and ecological risk inversion method based on columnar sediment

Abstract

The invention provides a columnar sediment-based heavy metal pollution source identification and ecological risk inversion method. The method comprises the following five steps of: 1, collecting and preprocessing columnar sediment; 2, determining the physical and chemical properties of the columnar sediment; thirdly, heavy metal sources are analyzed based on multivariate statistics; 4, heavy metal pollution level and ecological risk assessment based on multiple indexes; and 5, a comprehensive identification module of a heavy metal pollution risk layer. According to the method, unified standards are established to perform layered identification on pollution intensity and ecological risk levels of different depth layers in the sediment, and a complete method system from data acquisition, pollution index calculation, pollution source identification, risk level output to section horizon division is formed. The method has a good application prospect in the technical field of environmental monitoring and environmental evaluation.

Description

[Technical Field]

[0001] This invention relates to a method for identifying heavy metal pollution sources and assessing ecological risks based on columnar sediments. Based on the vertical distribution characteristics of heavy metals in river columnar sediments, and combined with multivariate statistical analysis and multi-risk index comprehensive evaluation methods, it achieves comprehensive assessment of heavy metal pollution sources and ecological risks, belonging to the field of environmental monitoring and environmental assessment technology. [Background Technology]

[0002] Heavy metal pollution has become a prominent environmental problem threatening the safety of global aquatic ecosystems. Due to its high toxicity, high mobility, and non-biodegradability, it has attracted widespread attention. Once heavy metals such as Co, Ni, Sb, V, and Sr enter aquatic systems such as rivers and lakes, they can accumulate at the water-sediment interface for a long time and be released again through resuspension or desorption processes, causing continuous harm to aquatic ecosystems and human health.

[0003] Traditional identification and assessment of heavy metal pollution in aquatic waters largely rely on surface sediments, water samples, or bottom sediment sampling and analysis. While these methods can reflect the state of pollution at a specific point in time, they struggle to provide systematic historical accumulation information, hindering the determination of long-term pollutant evolution trends and the tracing of pollution source types. In recent years, an increasing number of studies have pointed out that sediments, as "historical recorders" of pollutants, can be used to reflect the intensity and source changes of pollution input at different time periods through their vertical distribution characteristics. Especially in watershed-scale environmental studies, columnar sediment samples, due to their continuous, stable, and stratified characteristics, have become important carriers for historical inversion and source tracing of heavy metal pollution. Columnar sediment samples can be obtained by stratifying them into different depths, representing relatively different depositional stages, and changes in metal element concentrations can reveal the spatiotemporal processes of pollution accumulation.

[0004] Currently, many mature methods exist for qualitatively identifying and quantitatively assessing soil heavy metal pollution and related risks. Single or composite indices are commonly used to determine the degree of heavy metal pollution, including the geoaccumulation index (Igeo), the potential ecological risk index (PERI), enrichment factors, and the pollution load index (PLI). For pollution source identification, commonly used techniques include Pearson correlation analysis, principal component analysis (PCA), factor analysis (FA), and source apportionment models (such as PMF). However, despite the good performance of these methods, few researchers have collectively integrated them into a comprehensive framework for studying heavy metal pollution and its ecological risks.

[0005] Furthermore, current technologies have not yet established a unified standard for stratifying and identifying the pollution intensity and ecological risk level of different depth layers in sediments, nor have they formed a complete methodological system from data collection, pollution index calculation, pollution source identification, risk level output to profile stratigraphic division. This leads to problems such as strong subjectivity, fragmented operational procedures, and difficulty in standardizing results in the ecological risk identification process.

[0006] In summary, there is an urgent need to develop an integrated method based on columnar sediments for heavy metal pollution source identification and ecological risk stratification assessment. This method should systematically integrate vertical pollutant concentration data, environmental factor analysis, and risk level standards to achieve structured inversion of pollutant evolution trajectories and rapid identification of high-risk pollution layers. Especially in the absence of long-term water quality monitoring data or in transboundary watershed environmental governance, constructing a pollution source tracing and risk identification method based on columnar sediment information has significant practical implications and widespread application value. [Summary of the Invention]

[0007] This invention aims to provide a technical methodology system for identifying the pollution types, causes, and risk levels of heavy metals in river sedimentary environments based on vertical sedimentary stratification data, using multivariate statistical analysis and multiple pollution assessment indicators, thereby forming a system for spatiotemporal inversion and risk zoning of pollutants.

[0008] This invention is a method for identifying heavy metal pollution sources and assessing ecological risks based on columnar sediments, specifically including the following steps:

[0009] Step 1: Collection and Pretreatment of Columnar Sediments

[0010] Representative sampling points, such as the watershed outlet, were selected for columnar sediment collection. At the collection site, the columnar sediments were divided into sections at specific vertical intervals (determined based on the actual height of the sediments). All sediment samples were immediately placed in polyethylene bags, sealed, and stored in a 4°C refrigerator before being brought back to the laboratory for processing as soon as possible. All sediment samples were then air-dried in a soil drying chamber. Gravel, coarse debris, plant roots, and other impurities were removed. The samples were then carefully ground in an agate mortar and pestle, and passed through a 100-mesh nylon sieve. After sieving, the sediment samples were refrigerated for analysis.

[0011] Step 2: Determination of physicochemical properties of columnar sediments

[0012] Based on the pretreated sediment column sample from step one, the sediment was digested using the HNO3–HF–HClO4 method. The specific steps were as follows: 0.25 g of sediment sample (accurate to 0.0001 g) was weighed and placed in a digestion vessel, and moistened with a small amount of ultrapure water. In an acid-proof fume hood, 10 mL of HNO3, 10 mL of HF, and 5 mL of HClO4 solution were added sequentially, and the sample and digestion solution were thoroughly mixed. The digestion vessel was placed in the digestion vessel support and then placed in the microwave digestion apparatus. After confirming that the temperature and pressure sensors were working properly, microwave digestion was performed, heating until almost all white fumes were emitted and the contents were viscous. The digestion vessel was cooled to room temperature (20–25 °C), and the analyte was transferred to a centrifuge tube and brought to a final volume of 50 mL. Heavy metals were determined using inductively coupled plasma atomic emission spectrometry (ICP-AES). Throughout the experiment, standard substances, reagent blanks, and parallel samples were used for quality control.

[0013] Step 3: Analysis of Heavy Metal Sources Based on Multivariate Statistics

[0014] Based on the heavy metal detection results of the sediment samples from step two, statistical analysis and tests were performed using SPSS Statistics 26 software, including principal component analysis, Pearson correlation analysis, and cluster analysis, to trace the source of different heavy metal elements and determine their main origins. The main formulas are as follows:

[0015] 1) Data Preprocessing: Before statistical analysis, the data (such as Co, Sr, Sb, Sr, V, etc.) need to be standardized to eliminate the influence of differences in dimensions and magnitudes. This invention selects the commonly used Z-standardization method, which is publicized as follows:

[0016]

[0017] Where Z is the standardized data value, X is the original data value, μ is the mean of the population data, and σ is the standard deviation of the population data.

[0018] 2) Pearson correlation analysis: This method can determine the correlation between various heavy metal elements and preliminarily identify elements with similar spatial distributions or potential common sources. The specific formula is as follows:

[0019]

[0020] Among them, X l and Y l Let X and Y be the l-th values ​​in the data samples X and Y, respectively. Let X′ and Y′ be the average values ​​of the data samples X and Y, respectively. Let n be the number of samples. Let r be the correlation coefficient, which ranges from -1 to 1. The larger the absolute value of r, the stronger the correlation between X and Y.

[0021] 3) Principal Component Analysis: This method reduces the dimensionality of multiple variables (heavy metal elements) into several principal components, extracting the main factors controlling the distribution of heavy metals (such as natural weathering, industrial pollution, agricultural activities, etc.). The specific formula is as follows:

[0022] PC k =ak1Z1+ak2Z2+…+ak j Z j (3)

[0023] Among them, PC k For the k-th principal component, ak j Z represents the load (coefficient) of the j-th variable in the k-th principal component. j Let j be the j-th variable after standardization.

[0024] 4) Cluster Analysis: This method categorizes different elements into several groups based on element concentration similarity, thereby identifying groups of elements with similar origins or migration behaviors. This invention employs a commonly used hierarchical clustering method, using Euclidean distance as the metric and the shortest distance method for merging. The specific formula is as follows:

[0025]

[0026] D(A,B)=min{d(x i ,x j (5)

[0027] Where, x i and x j It is the standardized value of the k-th value in the samples of elements i and j, d(x) i ,x j The distance between sample elements i and j is denoted as , where a smaller value indicates greater similarity. D(A,B) represents the two closest points, x... i ∈A,x j ∈B.

[0028] Step 4: Assessment of Heavy Metal Pollution Levels and Ecological Risks Based on Multiple Indicators

[0029] Based on the heavy metal detection results of the sediment samples from step two, the pollution index method (PI), the potential ecological hazard index method (PERI), and the geoaccumulation index method (I) were used respectively. geo The assessment of heavy metal pollution levels and ecological risk evaluation are conducted. The specific formulas for each index are shown below:

[0030] 1) Pollution Index Method

[0031]

[0032] Among them, (Metal) SampleRepresents the concentration of heavy metals in sediments; (Metal) Background It is the average value of the same metal at the background level. The PI is divided into low pollution (PI ≤ 1), moderate pollution (1 < PI ≤ 3), and severe pollution (PI > 3). To further illustrate the pollution status of the sediments, the present invention also calculates the heavy metal pollution load index (PLI), and its calculation formula is as follows:

[0033]

[0034] Where, PI n Is the pollution index of the nth heavy metal element. According to the pollution degree, the PLI can be divided into unpolluted (1 ≥ PLI), unpolluted to moderately polluted (1 ≤ PLI ≤ 2), moderately polluted (2 ≤ PLI ≤ 3), moderately to severely polluted (3 ≤ PLI ≤ 4), severely polluted (4 ≤ PLI ≤ 5), and extremely severely polluted (PLI > 5).

[0035] 2) Potential ecological risk index method

[0036] PERI = ∑E r i (8)

[0037] E r i = T r i × C f i (9)

[0038]

[0039] [[ID=​​​​​​​​​​​​​​​​​​​​​​

[0043] Table 2 shows the pollution coefficients and potential ecological indexes for individual pollution indices, and the classification of ecological risk levels.

[0044]

[0045] 3) Geocumulative Index Method

[0046]

[0047] In the formula, I geo For the cumulative index of land; C i denoted as , where is the heavy metal content in the sediment; k is a constant introduced to account for background value fluctuations caused by regional differences, typically k = 1.5; B i This represents the background value of heavy metals in sediments. The classification criteria for the geoaccumulation index are shown in Table 3.

[0048] Table 3 shows the data based on I. geo The cumulative index classification standard

[0049]

[0050] Step 5: Comprehensive Identification Module for Heavy Metal Pollution Risk Layer

[0051] Based on the calculation results of the three indices in step four, the pollution layer type is automatically classified using the empirical rules in Table 4: when any indicator exceeds the high pollution threshold, it is determined to be a high-risk layer; when it is in the medium range, it is determined to be a medium-risk layer; and when it is below the standard background value, it is a low-risk layer.

[0052] Table 4 shows the comprehensive identification module for heavy metal pollution risks based on multiple indicators.

[0053]

[0054] The advantages and beneficial effects of this invention are as follows:

[0055] 1) The method has a clear process and a systematic technical path: This invention constructs a closed-loop method chain covering data acquisition, physicochemical measurement, statistical source tracing, pollution assessment and risk classification, forming a complete, logically rigorous and reusable technical system, avoiding the drawbacks of scattered methods and fragmented indicators in existing research.

[0056] 2) The pollution stratification identification module is highly innovative and provides good guidance for decision-making: It innovatively introduces a "pollution risk layer identification module" based on empirical rules, which realizes the automatic division of risk levels of different layers in the vertical profile. It can quickly identify high-risk layers and priority treatment layers, providing scientific support for refined treatment and remediation strategies.

[0057] 3) Supports standardization and layer output: It can output intuitive results such as pollution stratification tables and risk distribution layers, which facilitates environmental management units to carry out spatial decision support and pollution control zone delineation work, and has practical operability and management value. [Attached Image Description]

[0058] Figure 1 This is a flowchart of the heavy metal pollution source identification and ecological risk inversion method described in this invention.

[0059] Figure 2 This is a schematic diagram of the sampling points for columnar sediments.

[0060] Figure 3 This is a vertical distribution map of heavy metal content in columnar sediments.

[0061] Figure 4 This is a graph representing the source analysis of heavy metals based on multivariate statistics.

[0062] Figure 5 This is a diagram for assessing the level and risk of heavy metal pollution based on multiple indicators. [Specific Implementation Methods]

[0063] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The embodiments described below are only for explaining the present invention and do not limit the scope of application of the present invention.

[0064] This invention provides a method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments, such as... Figure 1 As shown, it includes the following steps:

[0065] Step 1: Collection and Pretreatment of Columnar Sediments

[0066] This invention uses the Orkhon River Basin in Mongolia as the study area, selecting downstream points for columnar sediment sampling to better represent the pollution situation in the basin. The sampling points are shown in the attached figure. Figure 2 As shown. A columnar sediment column with a height of 16 cm was collected. At the collection site, the columnar sediment column was divided into sections at 1 cm vertical intervals, resulting in 16 sediment samples. All sediment samples were immediately placed in polyethylene bags after collection, sealed, and stored in a refrigerator at 4°C before being brought back to the laboratory for processing as soon as possible. All sediment samples were placed in a soil drying chamber for natural air drying. Impurities such as gravel, coarse debris, and plant roots were removed from the samples. After careful grinding in an agate mortar, the samples were passed through a 100-mesh nylon sieve. The sieved sediment samples were then refrigerated for analysis.

[0067] Step 2: Determination of physicochemical properties of columnar sediments

[0068] Based on the pretreated sediment column sample from step one, the sediment was digested using the HNO3–HF–HClO4 method. The specific steps were as follows: 0.25 g of sediment sample (accurate to 0.0001 g) was weighed and placed in a digestion vessel, and moistened with a small amount of ultrapure water. In an acid-proof fume hood, 10 mL of HNO3, 10 mL of HF, and 5 mL of HClO4 solution were added sequentially, and the sample and digestion solution were thoroughly mixed. The digestion vessel was placed in the digestion vessel support and then placed in the microwave digestion apparatus. After confirming that the temperature and pressure sensors were working properly, microwave digestion was performed, heating until almost all white fumes were emitted and the contents were viscous. The digestion vessel was cooled to room temperature (20–25 °C), and the analyte was transferred to a centrifuge tube and brought to a final volume of 50 mL. Heavy metals were determined using inductively coupled plasma atomic emission spectrometry (ICP-AES). Throughout the experiment, standard substances, reagent blanks, and parallel samples were used for quality control. The experimental results are as follows: Figure 3 As shown. Figure 3 Figure (a) shows the vertical distribution characteristics of total nitrogen (TN), which has a high concentration in the depth range of 8–18 cm, with a peak of 1.2 g / kg and large fluctuations. Figure 3 Figure (b) shows the vertical variation of total phosphorus (TP), which reaches its maximum concentration (700 mg / kg) at 10 cm. Figure 3 Figure (c) in the diagram represents the vertical characteristics of Co. Figure 3 Figure (d) in the diagram represents the vertical characteristics of Sb. Figure 3 Figure (f) in the diagram represents the vertical characteristics of V. Figure 3 The (g) diagram in the figure represents the vertical characteristics of T. Figure 3 The (h) diagram shows the vertical characteristics of Fe. These five elements exhibit a relatively consistent trend: their content is relatively low and fluctuates little below 6–8 cm, rises rapidly from 4–6 cm to reach a peak, and then gradually decreases near the surface. Figure 3 The Sr content shown in Figure (e) is generally high (>350 mg / kg), but shows local decreases at 4 cm and 15 cm. Figure 3 Figure (i) shows the vertical characteristics of Ni, which has the largest fluctuation range, with the highest value reaching 22 mg / kg.

[0069] Step 3: Analysis of Heavy Metal Sources Based on Multivariate Statistics

[0070] Based on the heavy metal detection results of the sediment samples from step two, statistical analysis and tests were performed using SPSS Statistics 26 software, including principal component analysis, Pearson correlation analysis, and cluster analysis, to trace the source of different heavy metal elements and determine their main sources. The relevant results are attached. Figure 4 As shown. Figure 4Figure (a) shows the Pearson correlation analysis between environmental factors and seven heavy metal elements. Ni shows a significant positive correlation with TN and TP, indicating that Ni concentration increases with increasing nutrients, reflecting the impact of human activities on Ni distribution. Ni shows a positive correlation with clay and silt grains, but a negative correlation with sand grains. This suggests that Ni concentration may be lower in areas with higher sand content, which is related to the physical properties of the sediments; clay and silt grains have a certain adsorption capacity for Ni, while sand grains have a smaller adsorption capacity for heavy metals. Furthermore, Co shows a high positive correlation with Ti, V, and Fe with TN, water content, clay, silt grains, and sand grains, possibly indicating a common source or similar sedimentary environments for these metals. Figure 4 Figure (b) shows the hierarchical cluster analysis of environmental factors and seven heavy metal elements. The heavy metal elements and environmental factors can be divided into three categories. The first category includes TN, water content, TP, silt grains, and Ni, indicating that these factors may have similar effects under environmental changes. The second category includes sand grains, Co, Ti, V, Fe, and Sb, indicating that they may be affected by similar environmental conditions. The third category is Sr, which may reflect their different sources or influencing factors in sediments, consistent with the results of the correlation analysis. Figure 4 Figure (c) shows the correlation analysis among the seven heavy metal elements. Co, V, Ti, and Fe showed strong correlations (p<0.01), with Fe and V exhibiting the strongest correlation at a coefficient of 0.99, indicating they may originate from similar geological processes or pollution sources. In contrast, Sb and Sr showed correlation coefficients less than 0.1 with other elements and environmental factors, indicating very weak correlations. However, the correlation between Sr and Ni was greater than 0.5, suggesting a relatively strong correlation. Furthermore, the correlation coefficients between Co and Sr, Sr and V, Sr and Fe, V and Ni, and Co and Ni ranged from 0.3 to 0.5, indicating weak correlations among these elements, possibly stemming from different geological or anthropogenic activities. Figure 4 Figure (d) shows the principal component analysis results of heavy metal elements. Co, V, Ti and Fe can be basically determined to mainly originate from the natural environment, while the Sr content is mainly affected by human activities.

[0071] Step 4: Assessment of Heavy Metal Pollution Levels and Ecological Risks Based on Multiple Indicators

[0072] Based on the heavy metal detection results of the sediment samples from step two, the pollution index method (PI), the potential ecological hazard index method (PERI), and the geoaccumulation index method (I) were used respectively. geo An assessment of heavy metal pollution levels and an ecological risk evaluation were conducted. The main results are attached. Figure 5 As shown. Among them, Figure 5Figure (a) shows the box plots of the PI and PLI for heavy metals. Based on the PI index, the pollution intensity of the four heavy metals is in the order of V > Sr > Co > Ni, all belonging to the category of severe pollution. Based on the PERI index, the ecological risk intensity of the four heavy metals is in the order of Sr > Co > Ni > V. Figure 5 Figure (b) shows the vertical distribution of PERI. The order of PERI values ​​for each element is: Sr>Co>Ni>V. All of them have a potential ecological hazard index of less than 40, which means they are all low potential ecological hazards. This indicates that these four heavy metals pose a low ecological hazard to the Orkhon River. Figure 5 Figure (c) in the middle represents I geo The vertical distribution map shows the heavy metal pollution levels from high to low: V>Sr>Co>Ni. Overall, I... geo The values ​​indicate that the sediments in the watershed are at a slightly polluted level, and the pollution level of shallow sediments is significantly higher than that of deep sediments.

[0073] Step 5: Comprehensive Identification Module for Heavy Metal Pollution Risk Layer

[0074] Based on the assessment results of different indices in step four, the standards in Table 4 were used for comprehensive identification of pollution risks. The results show that the heavy metal risk in the Orkhon watershed is at a medium pollution / medium risk level, especially in the 0-5 cm range.

Claims

1. A method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments, characterized in that, Includes the following steps: Step 1: Collection and Pretreatment of Columnar Sediments Select sampling points to collect columnar sediment samples; At the collection site, the columnar sediments were divided into slices at certain vertical intervals; Step 2: Determination of physicochemical properties of columnar sediments Based on the pretreated sediment column sample in step one, the sediment was digested using the HNO3–HF–HClO4 method. Step 3: Analysis of Heavy Metal Sources Based on Multivariate Statistics Based on the heavy metal detection results of the sediment samples in step two, statistical analysis and tests were performed using SPSS Statistics 26 software, including principal component analysis, Pearson correlation analysis and cluster analysis, to trace the source of different heavy metal elements and determine their origins. Step 4: Assessment of Heavy Metal Pollution Levels and Ecological Risks Based on Multiple Indicators Based on the heavy metal detection results of the sediment samples from step two, the Pollution Index (PI), Potential Ecological Hazard Index (PERI), and Geoaccumulation Index (I) were used respectively. geo Conduct assessments of heavy metal pollution levels and ecological risks; Step 5: Comprehensive Identification Module for Heavy Metal Pollution Risk Layer Based on the calculation results of the three indices in step four, the pollution layer type is automatically classified using empirical rules: when any indicator exceeds the high pollution threshold, it is determined to be a high-risk layer; when it is in the medium range, it is determined to be a medium-risk layer; and when it is below the standard background value, it is a low-risk layer.

2. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step one further includes: immediately placing all sediment samples into polyethylene bags after collection, sealing and storing them in a 4°C refrigerator, and bringing them back to the laboratory for processing as soon as possible; placing all sediment samples in a soil drying room for natural air drying, removing gravel, coarse debris, and plant roots from the samples, carefully grinding them in an agate mortar, passing them through a 100-mesh nylon sieve, and then refrigerating the sediment samples for testing.

3. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step two further includes: weighing 0.25g of sediment sample and placing it in a digestion vessel, then wetting it with a small amount of ultrapure water; in an acid-proof fume hood, adding 10mL of HNO3, 10mL of HF, and 5mL of HClO4 solution in sequence, and thoroughly mixing the sample and digestion solution; placing the digestion vessel in the digestion vessel support, and then placing it in the microwave digestion apparatus cavity; after confirming that the temperature and pressure sensors are working properly, performing microwave digestion, heating and digesting until white smoke disappears and the contents become viscous; cooling the digestion vessel to room temperature, then transferring the analyte to a centrifuge tube and making up to 50mL; and determining heavy metals using inductively coupled plasma atomic emission spectrometry (ICP-AES).

4. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step three further includes: before conducting statistical analysis, the data need to be standardized to eliminate the influence of differences in units and magnitudes. The Z-standardization method is selected, and its formula is as follows: Where Z is the standardized data value, X is the original data value, μ is the mean of the population data, and σ is the standard deviation of the population data.

5. A method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1 or 4, characterized in that: Step three further includes: Pearson correlation analysis: determining the correlation between various heavy metal elements and preliminarily identifying elements with similar spatial distributions or potential common sources; the specific formula is as follows: Among them, X l and Y l Let X and Y be the l-th values ​​in the data samples X and Y, respectively, and let X′ and Y′ be the average values ​​of the data samples X and Y, respectively. Let n be the number of samples; r is the correlation coefficient, which ranges from -1 to 1. The larger the absolute value of r, the stronger the correlation between X and Y.

6. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1 or 4, characterized in that: Step three further includes: Principal component analysis: reducing the dimensionality of multiple variables into several principal components to extract the main factors controlling the distribution of heavy metals; the specific formula is as follows: PC k =ak1Z1+ak2Z2+…+ak j From j (3) Among them, PC k For the k-th principal component, ak j Z represents the load of the j-th variable in the k-th principal component. j Let j be the j-th variable after standardization.

7. A method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1 or 4, characterized in that: Step three further includes: cluster analysis: based on the similarity of element concentration, different elements are divided into several categories to identify groups of elements with similar origins or migration behaviors; a hierarchical clustering method is used, with Euclidean distance as the metric and the shortest distance method for merging; the specific formula is as follows: D(A,B)=min{d(x i ,x j )} (5) Where, x i and x j It is the standardized value of the k-th value in the samples of elements i and j, d(x) i ,x j The distance between element samples i and j is denoted as ), where the smaller the value, the more similar the two samples are; D(A,B) represents the two closest points, x... i ∈A,x j ∈B.

8. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step four further includes: the pollution index method: Among them, (Metal) Sample Indicates the concentration of heavy metals in sediments; (Metal) Background This represents the average value of the same metal at the background level; PI is divided into low pollution, moderate pollution, and heavy pollution; the heavy metal pollution load index (PLI) is calculated using the following formula: Among them, PI n is the pollution index of the nth heavy metal element; according to the degree of pollution, PLI is divided into unpolluted, unpolluted to moderately polluted, moderately polluted, moderately to heavily polluted, heavily polluted and extremely heavily polluted.

9. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step four further includes: the potential ecological hazard index method: In the formula: PERI represents the comprehensive potential ecological hazard index; E r i Indicates a single potential ecological hazard index; C f i Indicates a single pollution index; T r i Indicates the toxicity coefficient of heavy metals; C i The concentration of the analyzed element in the sediment; C i BS Let i be the soil environmental background value of element i.

10. The method for identifying heavy metal pollution sources and inverting ecological risks based on columnar sediments according to claim 1, characterized in that: Step four further includes: the geocumulative index method: In the formula, I geo For the cumulative index of land; C i B represents the heavy metal content in the sediment; k is a constant introduced to account for background value fluctuations caused by regional differences; and B represents the heavy metal content in the sediment. i This represents the background value of heavy metals in the sediment.

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