An analysis method, system and device for heavy metal input contribution of farmland soil around a mining area

Abstract

This application discloses a method, system, and equipment for analyzing the input contribution of heavy metals in farmland soil surrounding a mining area. The method includes: acquiring the heavy metal concentration and coordinate information of multiple sampling points in the study area; selecting the heavy metal with the highest pollution level as the characteristic pollutant of the study area; matching the distribution characteristics of the selected characteristic pollutant on different soil heavy metal carriers, selecting the input pathway of the characteristic pollutant based on the matching degree, determining the input contribution of the two input pathways to the characteristic pollutant, and identifying the inundation depth of the study area using a hydrodynamic model; constructing a statistical model based on a random forest algorithm with the input contribution of the two input pathways to the characteristic pollutant, the sampling point coordinate information, and the inundation depth as independent variables to obtain the input pathway of each characteristic pollutant in the study area; and using a positive definite factor matrix model to quantitatively analyze the relative contribution of each characteristic pollutant's input pathway.

Description

Technical Field

[0001] This invention belongs to the field of environmental pollution prevention and control technology, and relates to a method for rapidly assessing the risk of groundwater pollution in areas affected by mining and metallurgical activities. Specifically, it relates to the analysis method, system and equipment for the contribution of heavy metal input to farmland soil around mining areas. Background Technology

[0002] Mining waste generated during lead-zinc ore extraction and processing continuously releases large amounts of heavy metals into farmland soils through natural processes such as surface runoff, rain leaching, and weathering, posing a serious threat to human health and ecosystem security. Pb, Zn, As, and associated elements Cd, Cr, and Cu from mining operations migrate to surrounding farmland soils primarily through atmospheric deposition and hydraulic processes, causing soil pollution. Hydraulic transport of heavy metals is a significant source of heavy metals in soils of mining areas, but current research on hydraulic processes mainly focuses on wastewater irrigation. For metal mining areas located along riverbanks, the secondary distribution of pollutants by sediment carried by flooding is often overlooked. Increasing research indicates that with global climate change, the frequency and intensity of severe flood events may increase in the future. Therefore, it is necessary to analyze the impact of flooding on heavy metals in farmland soils surrounding mining areas along riverbanks, providing support for mineral extraction and heavy metal pollution management. Summary of the Invention

[0003] In response to the above-mentioned situation, this application proposes an analytical method, apparatus, equipment, and medium for analyzing the contribution of heavy metal input to farmland soil surrounding mining areas, in order to overcome or at least partially overcome the shortcomings of the prior art.

[0004] In a first aspect, embodiments of this application provide a method for analyzing the contribution of heavy metal input to farmland soil surrounding a mining area, the method comprising:

[0005] The study obtained heavy metal concentrations and sampling point coordinates from multiple sampling points of four soil metal carriers in the study area: farmland soil, river sediments, atmospheric deposition, and natural background soil.

[0006] The cumulative index was calculated to obtain the degree of heavy metal pollution in farmland soil, and the heavy metal with the highest degree of pollution was selected as the characteristic pollutant of the study area.

[0007] The distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers are matched, and the input pathways of characteristic pollutants are selected based on the matching degree. The input pathways include flooding and atmospheric deposition.

[0008] By calculating the input flux of heavy metals in farmland soil in the study area through two input pathways—flooding and atmospheric deposition—the contribution of the two input pathways to the input of characteristic pollutants was determined.

[0009] Using hydrodynamic models to identify the inundation depth of the study area;

[0010] By using the input contributions of the two input pathways to the characteristic pollutants, the sampling point coordinates, and the inundation depth as independent variables, a statistical model based on the random forest algorithm was constructed to obtain the input pathways of each characteristic pollutant in the study area.

[0011] The relative contribution of each characteristic pollutant to the input pathway was quantitatively analyzed using a positive definite factor matrix model.

[0012] Secondly, embodiments of this application also provide an analysis system for the contribution of heavy metal input to farmland soil surrounding a mining area, the device comprising:

[0013] The sample acquisition unit acquires heavy metal concentrations and sampling point coordinates from multiple sampling points of four soil metal carriers in the study area: farmland soil, river sediments, atmospheric deposition, and natural background soil.

[0014] The screening unit calculates the cumulative index to obtain the heavy metal pollution level of farmland soil, and selects the heavy metal with the highest pollution level as the characteristic pollutant of the study area.

[0015] The matching unit matches the distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers, and selects the input pathway of the characteristic pollutants based on the matching degree. The input pathway includes flooding and atmospheric deposition.

[0016] In the comparative unit, the input flux of heavy metals in farmland soil in the study area was calculated through two input pathways: flooding and atmospheric deposition, to determine the input contribution of the two input pathways to characteristic pollutants.

[0017] The first model calculation unit uses a hydrodynamic model to identify the inundation depth of the study area;

[0018] The second model calculation unit uses the input contributions of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the inundation depth as independent variables to construct a statistical model based on the random forest algorithm, thereby obtaining the input pathways of each characteristic pollutant in the study area.

[0019] The third model calculation unit uses a positive definite factor matrix model to quantitatively analyze the relative contribution of each characteristic pollutant to the input pathway.

[0020] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0021] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0022] This study focuses on heavy metals in farmland soils surrounding the mining area. The pollution levels of different heavy metals were calculated using geoaccumulation indices. The three most heavily polluted heavy metals in agricultural soils were selected, and their distribution characteristics in different carriers were superimposed to reveal the common source relationships among these carriers. The input fluxes of heavy metals to farmland soils in the mining area were calculated through flooding and atmospheric deposition, allowing for a comparison of the contributions of these two pathways. A hydrodynamic model was used to identify the potential flooding range and depth in the mining area, pinpointing key areas susceptible to heavy metal pollution. A random forest model was employed to assess the importance of natural and anthropogenic factors influencing heavy metal content in agricultural soils, identifying the main input pathways. Finally, a positive definite factor matrix model was used to quantitatively analyze the input contributions of different pollution pathways to heavy metals in farmland soils in the mining area. The results provide a reliable basis for future research on heavy metal pollution in mining areas. The mechanisms discovered in this study may be widespread and contribute to the study of the impact of hydrodynamic transport on the mining environment under current extreme climatic conditions. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 A flowchart illustrating an embodiment of the present application shows a method for analyzing the contribution of heavy metal input to farmland soil surrounding a mining area.

[0025] Figure 2 The distribution characteristics of the heavy metal accumulation index are shown.

[0026] Figure 3 The distribution characteristics of heavy metals in farmland soil, river sediments, atmospheric deposition, and natural background soil are shown. Ellipses represent the 95% confidence intervals of heavy metal distribution in different carriers.

[0027] Figure 4 A schematic diagram illustrating the correlation between the flooded area and the pollution caused by characteristic pollutants is shown.

[0028] Figure 5 The results of the random forest importance assessment are shown.

[0029] Figure 6 This demonstrates the use of the PMF model to analyze the sources of heavy metals in soil.

[0030] Figure 7A schematic diagram of the structure of an analysis system for the contribution of heavy metal input to farmland soil surrounding a mining area, according to an embodiment of this application, is shown.

[0031] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0034] This invention discloses an analytical method for the contribution of heavy metal input to farmland soil surrounding a mining area, comprising: obtaining heavy metal concentration and coordinate information from multiple sampling points of four carriers—farmland soil, river sediments, historical atmospheric deposition, and natural background soil—in the mining area study region; calculating the geocumulative index to obtain the pollution level of heavy metals in farmland soil, and selecting the three heavy metal elements with the highest pollution levels as characteristic pollutants in the mining area study region; plotting the distribution characteristics of characteristic pollutants in different soil carriers (farmland soil, sediments, atmospheric deposition, and natural soil), and superimposing them to determine the homology between different carriers; calculating and comparing the input fluxes of heavy metals in farmland soil in the study region through flood inundation and atmospheric deposition, respectively; using a hydrodynamic model to simulate flood inundation and identify the potential inundation range and depth in the study region; constructing a random forest model to identify the main environmental impact factors of heavy metals in the soil of the study region, and determining the main input pathways of the three characteristic pollutants; and using a positive definite factor matrix model to quantitatively analyze the relative contributions of different pollution pathways. The above scheme can provide a reliable basis for identifying the impact of flooding on heavy metals in farmland soil in mining areas, while also providing a new perspective for source apportionment of farmland soil and providing theoretical support for decision-makers in future mining and heavy metal pollution management.

[0035] See Figure 1 This invention provides an analytical method for identifying the contribution of flood inundation to heavy metal input in farmland soils surrounding mining areas, comprising the following steps:

[0036] Step 101: Obtain the heavy metal concentration, coordinate information and environmental influencing factors of samples from different sampling points within the study area. The samples include samples collected from farmland soil, samples collected from river sediments, samples collected from natural soil, and samples collected from atmospheric dustfall.

[0037] The samples came from different soil metal carriers and from different locations on the soil metal carriers, including farmland soil, river sediments, natural soil, and atmospheric dustfall.

[0038] The study obtained heavy metal concentrations from multiple samples, including farmland soil, river sediments, atmospheric dustfall, and natural soil, as well as the coordinate information of the sampling points for each sample. The sampling points are the locations corresponding to samples collected from different soil metal carriers.

[0039] Step 101 also includes obtaining environmental impact factors of the mining area study region.

[0040] In one embodiment, in order to obtain the heavy metal concentration of river sediments in the soil heavy metal carrier, N sediment samples were collected in the river sediment area of ​​the mining study area. For example, the river sediment area mainly irrigates rivers and ditches. A total of 46 sediment samples (0-5cm) were collected at different sampling points in the river sediment area.

[0041] In one embodiment, in order to obtain the heavy metal concentration in farmland soil in the soil heavy metal carrier, N farmland soil samples were collected from different sampling points in the farmland soil area of ​​the mining area study area. For example, the farmland soil area selected the sides of rivers and ditches and the extended areas. A total of 112 farmland surface soil samples (0-20cm) were collected from different sampling points in the farmland soil area.

[0042] In one embodiment, to obtain the heavy metal concentration of natural background soil in the soil heavy metal carrier, N natural background soil samples were collected from different sampling points in the natural soil area (natural background soil) of the mining area study region. For example, the natural soil area was selected in a region more than 300m away from the road, and 31 natural background soil samples were collected from this area. To reduce the impact of the environment on the soil, clean metal tools were used to remove the topsoil from 0 to 20 cm during sampling, collecting the natural background soil below 20 cm as samples. Similarly, two natural background soil samples were randomly collected around each sampling point, uniformly mixed to form a composite sample for subsequent processing and analysis.

[0043] In one embodiment, to obtain the heavy metal concentration of atmospheric dust in the soil heavy metal carrier, N atmospheric dust samples were collected from different sampling points in the atmospheric dust sampling area within the mining area study area. For example, the atmospheric dust sampling area was located on the roof beams of a long-uninhabited house or attic, and seven historical atmospheric dust sampling points were set up in the atmospheric dust sampling area. Dust deposited at each sampling point was collected using a clean brush, and after removing foreign matter, the samples were placed in numbered plastic bags for subsequent experimental analysis.

[0044] The heavy metal concentrations of farmland soil samples, river sediment samples, natural background soil samples, and atmospheric dust samples were calculated respectively, thereby obtaining the heavy metal concentrations of soil heavy metal carriers in the study area.

[0045] It should be noted that two or more samples are randomly collected around each sampling point and uniformly mixed to form a composite sample. The composite sample is used for subsequent processing and analysis. The number of sampling points and the number of samples randomly collected near each sampling point are not limited in this application.

[0046] Step 102: Select the metal with the highest degree of pollution in the soil metal carrier as the characteristic pollutant.

[0047] This application utilizes the geocumulative index to calculate the degree of heavy metal pollution in farmland soil, selecting N heavy metals with the highest pollution levels as characteristic pollutants. Specifically, it includes: obtaining the degree of heavy metal pollution based on the heavy metal concentration of the soil heavy metal carrier; and further includes: identifying and removing outliers in the obtained heavy metal concentration using a box plot method, and then calculating the degree of heavy metal pollution using the geocumulative index on the heavy metal concentration data.

[0048] This application uses the geoaccumulation index to evaluate the degree of heavy metal pollution in soil. The geoaccumulation index assesses the degree of heavy metal pollution by comparing the current concentrations of heavy metals in soil metal carriers such as water, atmospheric dust, soil (farmland soil, natural soil), and river sediments with their original concentrations. The formula for calculating the geoaccumulation index is as follows:

[0049] I geo =log2C i / 1.5×B i (1)

[0050] Among them, I geo C represents the degree of heavy metal pollution. i It is the current concentration of heavy metal i, B i This represents the background concentration of heavy metal i in the soil's metal carriers. 1.5 is a correction factor, indicating the natural fluctuations in heavy metal concentrations within the environment, such as... Figure 2As shown.

[0051] Calculations showed that 93.75% of Cd, 87.50% of Pb, and 82.14% of Zn were at or above the moderate pollution level. In contrast, Cu, As, and Cr were relatively less polluted, with 31.25% of Cu, 78.57% of As, and 100% of Cr at levels of no pollution or below moderate pollution. Overall, the soil in the study area showed varying degrees of heavy metal pollution, with Cd, Pb, and Zn exhibiting the most severe pollution. Therefore, Cd, Pb, and Zn were selected as the characteristic pollutants for the mining area.

[0052] Step 103: Match the distribution characteristics of the selected characteristic pollutants on different soil heavy metal carriers, and select the input pathways of the characteristic pollutants based on the matching degree. The input pathways include flooding and atmospheric deposition.

[0053] Step 103 also includes plotting the distribution characteristics of the characteristic pollutants in different soil heavy metal carriers and overlaying the distribution characteristics of the selected characteristic pollutants in different soil heavy metal carriers.

[0054] Step 103 further includes:

[0055] Calculate the confidence interval probability value of the distribution characteristics of characteristic pollutants falling into the soil heavy metal carriers;

[0056] Input pathways for characteristic pollutants are selected based on probability values.

[0057] This application uses characteristic pollutant fingerprinting to analyze pollution sources. It superimposes the distribution characteristics of selected characteristic pollutants on different heavy metal carriers to compare the homology between different carriers. Specifically, it includes: performing a logarithmic transformation (base 10) on the heavy metal concentrations of different soil heavy metal carriers to eliminate the influence of orders of magnitude, thereby obtaining soil sample points. It selects the three heavy metals with the highest pollution levels in farmland soil (i.e., the characteristic pollutants selected in step 102) and plots their distribution characteristics on different heavy metal carriers, specifically by plotting a scatter plot. The distribution characteristics represent the distribution of the characteristic pollutants or heavy metals in the soil sample points. These soil sample points are numerical points obtained after processing the heavy metal concentration data. Based on this, it calculates the confidence intervals (95%) for the distribution of heavy metals in soil (including farmland soil and natural background soil), sediment, and atmospheric dustfall. Figure 3 As shown, the characteristic heavy metal fingerprints of the main pollution sources were finally obtained, and the homology between different heavy metal carriers was obtained.

[0058] For example, the distribution characteristics of selected characteristic pollutants (Cd, Pb) on different soil heavy metal carriers are matched, and the specific operation is as follows:

[0059] The lnCd values ​​for soil heavy metals range from -0.63 to 1.73, where lnCd refers to the range or distribution characteristics of soil sample points for the characteristic pollutant Cd. The lnPb values ​​range from 1.38 to 3.77, where lnPb refers to the range or distribution characteristics of soil sample points for the characteristic pollutant Pb. 91.1% of the soil sample points for the selected characteristic pollutants (Cd and Pb) fall within the confidence ellipse of river sediments (lnCd ranges from 0.18 to 2.62, and lnPb ranges from 1.84 to 4.01), where 91.1% represents the matching degree of the distribution characteristics of the selected characteristic pollutants on river sediments.

[0060] 7.8% of the soil samples of the selected characteristic pollutants (Cd, Pb) fall within the confidence ellipse of atmospheric dust (lnCd = 1.69–2.23, lnPb = 3.32–4.00), of which 7.8% represents the degree of matching of the distribution characteristics of the selected characteristic pollutants on atmospheric dust.

[0061] 5.3% of the soil samples for the selected characteristic pollutants (Cd, Pb) fall within the confidence ellipse of the natural background soil (lnCd ranges from -1.04 to -0.30, lnPb ranges from 1.10 to 2.09), where 5.3% represents the degree of matching of the distribution characteristics of the selected characteristic pollutants in the natural background soil.

[0062] The above data analysis shows that the distribution characteristics of Cd and Pb in the soil surrounding the mining area are highly similar to those of river sediments. Fingerprint analysis results indicate that the distribution characteristics of heavy metals in farmland soils highly overlap with those in river sediments, followed by atmospheric dustfall. The natural background values ​​show the lowest overlap with the distribution characteristics of heavy metals in soils, suggesting that heavy metals in soils may originate from two combined pollution pathways: river sediments and atmospheric deposition.

[0063] Step 104: Calculate the annual input flux of characteristic pollutants entering farmland soil through flooding and atmospheric dustfall, respectively, and determine the input contribution of the two pathways to characteristic pollutants in farmland soil.

[0064] This application calculated the input flux of heavy metals in soil through two pathways: flooding and atmospheric deposition, and then compared the contributions of different input pathways to heavy metals in soil.

[0065] Step 104 further includes:

[0066] Step 1040: Based on the historical atmospheric deposition data collected in the study area, calculate the annual input flux of characteristic pollutants (Cd, Pb, Zn) entering farmland soil through atmospheric deposition in the study area. The historical atmospheric deposition data refers to the annual deposition amount of atmospheric dust in the study area and the concentration of characteristic pollutants (Cd, Pb, Zn) in atmospheric deposition in the study area.

[0067] The annual input flux (Q) of heavy metals (e.g., characteristic pollutants (Cd, Pb, Zn)) entering farmland soil via atmospheric deposition a mg·m -2 ·a -1 The formula is as follows:

[0068] Q a =C i ×M i (2)

[0069] C i The concentration of heavy metal i, in mg / kg. -1 M i This refers to the total mass of sediment per unit area per year, for example, 114.67ga. -1 .

[0070] Step 1041: Calculate the single input flux of characteristic pollutants (Cd, Pb, Zn) into farmland soil through flooding within the study area based on the volume of floodwater retained per unit area of ​​farmland soil, the concentration of characteristic pollutants in river sediments, and the total mass of sediments carried by floodwater per unit volume.

[0071] Single input flux (Q) of heavy metals entering farmland soil via flooding f mg·m -2 The calculation formula for ) is as follows:

[0072] Q a =S i ×V×m i (3)

[0073] S i The concentration of heavy metal i, in mg / kg. -1 V represents the volume of floodwater that a unit area of ​​farmland soil can retain, for example, 0.2 m³. 3 ;m i The total mass of sediment carried by the flood per unit volume, for example, 20 kg m³. -3 .

[0074] The flooding pathway refers to the process by which sediments carried by floods enter farmland soil.

[0075] Step 1042: Based on the calculated annual input flux of characteristic pollutants entering farmland soil via atmospheric deposition, calculate how many years of atmospheric deposition are needed to reach the current concentration of the characteristic pollutants in farmland soil.

[0076] Based on the calculated single-input flux of heavy metals entering farmland soil via flooding, it is estimated how many floods are needed to reach the current concentration of the characteristic pollutants in the farmland soil.

[0077] In one specific embodiment, the average concentrations of heavy metals Cd, Pb, and Zn in atmospheric deposition in the study area were 103.14, 5555.87, and 9466.41 mg / kg, respectively. -1 The average concentrations of heavy metals Cd, Pb, and Zn in river sediments were 42.31, 1645.14, and 3153.07 mg / kg, respectively. -1 (Table 1). In typical industrial cities in Hunan Province, where metal smelting is the main industry, the annual atmospheric dust deposition in mining areas is 89.46–114.67 g / m³. 2 Based on this data, and combined with historical atmospheric deposition data collected in this study, the annual input fluxes of characteristic pollutants (Cd, Pb, Zn) via atmospheric deposition pathways in the study area were calculated to be 11.83, 637.09, and 1085.51 mg·m³, respectively. -2 ·a -1 (Table 2). When calculating the flood input flux, this application assumes a 1m³ of paddy field construction standard in Hunan Province. 2 The floodwater volume that farmland can store is 0.2 m³. 3 At the same time, 1m 2 The mass of sediment carried by the flood ranges from 15 to 20 kg. Based on this assumption, this application calculates the input flux for each flood inundation as 169.24, 6580.56, and 12612.68 mg·m³. -2 (Table 2). For 1m 2 In terms of the total input to farmland soil (0–20 cm), to reach the current pollution level of the study area, it would require an input of 1417.5 mg of Cd, 75.4 g of Pb, and 106.9 g of Zn. This would require 8.4–11.5 floods or 98.2–119.5 years of continuous atmospheric deposition to reach the current pollution level. Therefore, this application hypothesizes that, in addition to atmospheric deposition, frequent flooding leading to the migration of heavy metals from sediments between land and water may be another important factor contributing to soil pollution in the study area.

[0078] Table 1

[0079] Concentration of heavy metals in sediments and atmospheric dust (mg / kg) -1 ).

[0080]

[0081] Table 2

[0082] Heavy metal input from a single flood and annual atmospheric deposition (mg m- 2 ).

[0083]

[0084] Step 105: Use a hydrodynamic model to identify the potential inundation extent and depth of the mining area under study;

[0085] Hydrodynamic models were used to determine the correlation between inundation areas and characteristic pollutant contamination within the study area. These models were also used to identify the potential inundation extent and depth within the mining area and to assess the soil's ability to carry pollutants.

[0086] Step 105 further includes:

[0087] The DFM model was selected, and a 30m high-resolution topographic map of the study area was imported into the DFM model. The highest flood peak height in the study area was set as a boundary condition to obtain the water system, inundation range, and inundation depth of the inundated area.

[0088] The inundation extent and depth of the acquired inundation area are overlaid with the geocumulative index of characteristic pollutants at different sampling points to obtain the correlation between the inundation area and the pollution of characteristic pollutants from a spatial distribution perspective. (See...) Figure 4 As shown.

[0089] The correlation between the flooded area and the pollution by the characteristic pollutants specifically includes:

[0090] The color of the patches in the flooded area represents the flooding depth; the darker the color, the higher the flooding height.

[0091] The color of the soil sample points indicates the geocumulative index of characteristic pollutants (such as Cd). The darker the color, the higher the degree of pollution of the characteristic pollutant Cd.

[0092] The higher the pollution level of the characteristic pollutant Cd, the more the sampling points showed a distribution trend of spreading to both sides along the water system of the flooded area. In areas with higher water depth (areas with higher flooding depth), the pollution level of the characteristic pollutant Cd was also higher.

[0093] Water systems act as conduits for transporting polluted sediments to farmland soil; the farther away from a water system, the less sediment the soil receives.

[0094] This application uses hydrodynamic models to identify the potential inundation extent and depth within the study area, and the correlation between inundated areas and characteristic pollutant pollution, thereby assessing the pollutant carrying capacity of farmland soils, specifically including:

[0095] This application uses the Deltt3D Flexible Mesh (DFM model) to simulate the extent and depth of land inundation in the study area under a 50-year flood event. The Land Flood Model (DFM model) combines regular and irregular triangular meshes and is a numerical model of surface runoff, with a mesh resolution of 50m. The DFM model domain covers the study area. A 30m high-resolution topographic map of the study area is imported into the DFM model to roughly reflect the influence of topography. A peak flood height of 61.03m is set as the boundary condition. Field investigations were conducted in the study area to verify the model's prediction results.

[0096] The DFM model is used to overlay the simulated inundation range and depth of the inundation area with the geocumulative index of characteristic pollutants at different sampling points, thereby obtaining the correlation between the inundation area and the pollution of characteristic pollutants from the perspective of spatial distribution.

[0097] The exemplary DFM model is used to overlay the simulated inundation area with the geocumulative index of soil heavy metal Cd at different sampling points, aiming to study the correlation between inundation areas and heavy metal Cd pollution from a spatial distribution perspective. Figure 4 The diagram shows the inundation depth and Cd pollution level in the simulated study area under a 50-year flood. The patch colors represent inundation depth, with darker colors indicating higher inundation levels. The soil sample colors represent the geoaccumulation index of Cd, with darker colors indicating higher Cd pollution levels. Figure 4 It is evident that soil sampling points exhibiting severe Cd contamination show a distribution trend of spreading laterally along the water system. Furthermore, areas with higher catchment depths also show higher levels of Cd contamination. This is consistent with the results of correlation analysis. Water systems act as conduits for transporting contaminated sediments to soil; the farther away from a water system, the less sediment the soil receives. Similarly, catchment depth reflects the soil's capacity to retain contaminated sediments; greater catchment depth indicates a greater amount of sediment that can be retained, thus posing a higher risk of heavy metal contamination.

[0098] Step 106: Use a random forest model to assess the importance of environmental factors affecting soil heavy metal concentrations and determine the main input pathways of characteristic pollutants.

[0099] This application uses a random forest model to identify the main influencing factors of heavy metals in soil. As an extension of classification and regression tree models, the random forest model has stronger predictive power. When building the random forest model, each independent variable is associated with a %IncMSE output value. The higher the %IncMSE, the more important the variable is to the model's predictions.

[0100] Based on the floodplain identification results, i.e., the correlation between inundated areas and characteristic pollutant pollution, this application constructs a statistical model based on the random forest algorithm, using sediment heavy metal concentration, atmospheric dust heavy metal concentration, distance of sampling points from rivers, distance of sampling points from mining enterprises, and catchment depth as independent variables. This model calculates the importance of these factors to the heavy metal concentration in farmland soil within the study area and analyzes the main input pathways of Cd, Pb, and Zn in the soil of the study area. Figure 5 As shown, the results indicate that the concentration of Cd in sediments is the most important factor affecting soil Cd concentration, and the impact of river distance on soil Cd concentration is greater than that of enterprise distance. This suggests that flooding is the main input pathway for Cd in the soil of the study area, and sediments carried into the soil by floods are the main source of soil Cd. For Pb and Zn, atmospheric dustfall and enterprise distance have a greater impact on soil heavy metals than sediment and river distance, respectively, indicating that Pb and Zn in the soil of the study area mainly migrate to the soil in the form of atmospheric dustfall, and with the continuous input of atmospheric dustfall, Pb and Zn continuously accumulate in the soil.

[0101] Step 107: Use a positive definite factor matrix model to quantitatively analyze the relative contributions of various heavy metal input pathways.

[0102] This application uses the Positive Matrix Factorization (PMF) model to quantify the contributions of different heavy metal source pathways. The PMF model does not require measurement of source component spectra. As a positive definite matrix factorization (PMF) model, it estimates the error for each heavy metal content to reduce omissions in data processing, thereby improving accuracy and reliability. Uncertain and concentration data are input, and factor calculations are iterated at different frequencies to continuously adjust the parameters of the PMF model. The concentration data mentioned below refers to the heavy metal concentrations in the samples. When the source factor count is set to 3, the model exhibits the smallest and most stable Q value, indicating that the results at this setting better explain the various factors.

[0103] The PMF model decomposes the concentration data matrix X into a factor contribution matrix G, a factor distribution matrix, and a residual matrix e, as shown in Equation 4:

[0104]

[0105] Among them, X ij Let p represent the concentration of the j-th metal in the i-th sample, and G represent the number of principal factors. ik F represents the concentration of the k-th source in the i-th sample. kj Let represent the concentration of the j-th metal in the k-th source, and e ij That is the residual for each sample.

[0106] PMF (Proteinized Factor Mitigation) minimizes the objective function Q by continuously decomposing the concentration data matrix X, ultimately determining the contribution and characteristics of the factors. Q is calculated as follows:

[0107]

[0108] Where n is the number of samples, m is the number of metal types, and u ij This represents the uncertainty of the j-th metal in the i-th sample. When the heavy metal concentration is less than or equal to the detection limit (MDL), the calculation method is as follows:

[0109]

[0110] When the heavy metal concentration is greater than the detection limit, the calculation method is as follows:

[0111]

[0112] Where EF represents the error fraction in chemical analysis, and X is the concentration of heavy metals. In this application, the concentrations (U) of all heavy metals exceed the detection limits of the measuring instrument; therefore, Equation 7 is used to calculate the uncertainty.

[0113] like Figure 7 As shown, Factor 1 has a significantly stronger explanatory power for the heavy metal Cr than for other heavy metals, with a contribution rate of 89.2%. Factor 2 has a strong explanatory power for the heavy metal Cd, with a contribution rate of 74.6%. Factor 3 has the highest explanatory power for the heavy metal Pb, with a contribution rate of 73.7%. Combining the results of the above analyses, Factor 1 is identified as natural background, Factor 2 as flooding, and Factor 3 as atmospheric deposition.

[0114] The analytical results of this application can provide a reliable basis for identifying the impact of flooding on heavy metals in farmland soil in mining areas, while also providing a new perspective on source apportionment of farmland soil and providing theoretical support for decision-makers in future mining and heavy metal pollution management.

[0115] Figure 2 This diagram illustrates the structure of an analysis system for the contribution of heavy metal input to farmland soil surrounding a mining area, according to an embodiment of this application. Figure 2 It can be seen that the analysis system 300, which analyzes the contribution of heavy metal input to farmland soil surrounding the mining area, includes:

[0116] The sample acquisition unit acquires heavy metal concentrations and sampling point coordinates from multiple sampling points of four soil metal carriers in the study area: farmland soil, river sediments, atmospheric deposition, and natural background soil.

[0117] The screening unit calculates the cumulative index to obtain the heavy metal pollution level of farmland soil, and selects the heavy metal with the highest pollution level as the characteristic pollutant of the study area.

[0118] The matching unit matches the distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers, and selects the input pathway of the characteristic pollutants based on the matching degree. The input pathway includes flooding and atmospheric deposition.

[0119] In the comparative unit, the input flux of heavy metals in farmland soil in the study area was calculated through two input pathways: flooding and atmospheric deposition, to determine the input contribution of the two input pathways to characteristic pollutants.

[0120] The first model calculation unit uses a hydrodynamic model to identify the inundation depth of the study area;

[0121] The second model calculation unit uses the input contributions of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the inundation depth as independent variables to construct a statistical model based on the random forest algorithm, thereby obtaining the input pathways of each characteristic pollutant in the study area.

[0122] The third model calculation unit uses a positive definite factor matrix model to quantitatively analyze the relative contribution of each characteristic pollutant to the input pathway.

[0123] It should be noted that the above-mentioned analysis system for the contribution of heavy metal input to farmland soil around the mining area can implement the aforementioned analysis method based on the contribution of heavy metal input to farmland soil around the mining area, which will not be elaborated here.

[0124] This invention discloses an analytical method for identifying the contribution of flood inundation to heavy metal input in farmland soils surrounding a mining area. The method includes: acquiring heavy metal content and coordinate information from multiple sampling points across four carriers—farmland soil, river sediments, historical atmospheric deposition, and natural background soil—within the mining area study region; calculating the geocumulative index to determine the pollution level of heavy metals in farmland soils, and selecting the three heavy metal elements with the highest pollution levels as characteristic pollutants in the mining area study region; mapping the distribution characteristics of characteristic pollutants in different carriers (farmland soil, sediments, atmospheric deposition, and natural soils) and superimposing them to determine the homology between different carriers; calculating and comparing the input fluxes of heavy metals in farmland soils from both flood inundation and atmospheric deposition pathways; simulating flood inundation using a hydrodynamic model to identify the potential inundation range and depth in the study region; constructing a random forest model to identify the main environmental impact factors of heavy metals in the soil of the study region, and determining the main input pathways for the three characteristic pollutants; and quantitatively analyzing the relative contributions of different pollution pathways using a positive definite factor matrix model. The above scheme can provide a reliable basis for identifying the impact of flooding on heavy metals in farmland soil in mining areas, while also providing a new perspective for source apportionment of farmland soil and providing theoretical support for decision-makers in future mining and heavy metal pollution management.

[0125] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 8 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0126] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0127] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0128] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a logical analysis system for the contribution of heavy metal input to the soil of farmland surrounding the mining area. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0129] The above is as stated in this application. Figure 2The method for analyzing the contribution of heavy metal input to farmland soil surrounding a mining area, as disclosed in the illustrated embodiment, can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0130] The electronic device can also perform Figure 1 A method for analyzing the contribution of heavy metal input to farmland soils surrounding mining areas is presented, and the system for analyzing the contribution of heavy metal input to farmland soils surrounding mining areas is implemented. Figure 7 The functions of the embodiments shown are not described in detail here.

[0131] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 8 The method described in the embodiment is used to analyze the contribution of heavy metal input to the soil of farmland surrounding the mining area, and is specifically used to execute the aforementioned method.

[0132] 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.

[0133] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0137] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0138] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0140] 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.

[0141] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for analyzing the contribution of heavy metal input to farmland soil surrounding mining areas, characterized in that, include: The study obtained heavy metal concentrations and sampling point coordinates from multiple sampling points of four soil metal carriers in the study area: farmland soil, river sediments, atmospheric deposition, and natural background soil. The cumulative index was calculated to obtain the degree of heavy metal pollution in farmland soil, and the heavy metal with the highest degree of pollution was selected as the characteristic pollutant of the study area. The distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers are matched, and the input pathways of characteristic pollutants are selected based on the matching degree. The input pathways include flooding and atmospheric deposition. By calculating the input flux of heavy metals in farmland soil in the study area through two input pathways—flooding and atmospheric deposition—the contribution of the two input pathways to the input of characteristic pollutants was determined. Using hydrodynamic models to identify the inundation depth of the study area; By using the input contributions of the two input pathways to the characteristic pollutants, the sampling point coordinates, and the inundation depth as independent variables, a statistical model based on the random forest algorithm was constructed to obtain the input pathways of each characteristic pollutant in the study area. The relative contribution of each characteristic pollutant to the input pathway was quantitatively analyzed using a positive definite factor matrix model.

2. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 1, characterized in that, The step of selecting the metal with the highest pollutant level in the soil metal carrier as the characteristic pollutant further includes: The geocumulative index is used to calculate the degree of heavy metal pollution in soil metal carriers. The formula for calculating the geocumulative index is as follows: I geo = log2c i / 1.5 x B i (1) C i It is the current concentration of heavy metal i, B i It represents the background concentration of heavy metal i in the medium, with 1.5 as a correction factor, indicating the natural fluctuation of heavy metal content in the environment.

3. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 1, characterized in that, The step of matching the distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers to obtain the matching degree between the characteristic pollutants and the soil heavy metal carriers further includes: Data processing is performed based on the degree of heavy metal pollution of the selected characteristic pollutants; To obtain the distribution characteristics of the pollution levels of characteristic pollutants; Calculate the confidence intervals for the distribution of heavy metals in farmland soil, natural background soil, river sediments, and atmospheric dustfall; The matching degree between the characteristic pollutant and the soil heavy metal carrier is obtained based on the proportion of the distribution characteristics of the characteristic pollutant falling within the confidence interval.

4. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 1, characterized in that, The determination of the input contributions of the two pathways to characteristic pollutants in farmland soil further includes: Based on historical atmospheric deposition data collected within the study area, the annual input flux of characteristic pollutants entering farmland soil through atmospheric deposition within the study area was calculated. The historical atmospheric deposition data refers to the annual deposition amount of atmospheric dust within the study area and the concentration of characteristic pollutants in atmospheric deposition within the study area. The single input flux of characteristic pollutants entering farmland soil through flooding within the study area is calculated based on the volume of floodwater retained per unit area of ​​farmland soil, the concentration of characteristic pollutants in river sediments, and the total mass of sediments carried by floodwaters per unit volume. Based on the calculated annual input flux of characteristic pollutants entering farmland soil via atmospheric deposition, the number of years of atmospheric deposition required to reach the current concentration of the aforementioned characteristic pollutants in farmland soil is calculated. Based on the calculated single-input flux of heavy metals entering farmland soil via flooding, it is estimated how many floods are needed to reach the current concentration of the characteristic pollutants in the farmland soil.

5. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 1, characterized in that, The method of using hydrodynamic models to identify the inundation depth of the study area further includes: Select the DFM model, import the 30m high-resolution topographic map of the study area into the DFM model, and set the highest flood peak height of the study area as a boundary condition to obtain the water system, flood range and flood depth of the inundated area. The inundation range and depth of the inundated area are superimposed with the geocumulative index of characteristic pollutants at different sampling points to obtain the correlation between the inundated area and the pollution of characteristic pollutants from the perspective of spatial distribution.

6. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 4, characterized in that, Annual input flux Q of heavy metals into farmland soil through atmospheric deposition a Calculated using equation 2: Q a =C i ×M i (2) wherein Q a is the total amount of heavy metal i in the soil in mg·kg -2 ·a -1 ; C i is the concentration of heavy metal i element in mg·kg -1 ; M i is the total mass of the sediment per unit area in a year.

7. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding the mining area as described in claim 4, characterized in that, Single input flux Q of heavy metals into farmland soil through the flooding route f Calculated using equation 3: Q f =S i ×V×m i (3) Among them, Q f The unit is mg·m -2 S i The concentration of heavy metal i is expressed in mg·kg⁻¹. -1 V represents the volume of floodwater that can be retained per unit area of ​​farmland soil; m i This refers to the total mass of sediment carried by the flood per unit volume.

8. The analytical method for analyzing the contribution of heavy metal input to farmland soil surrounding a mining area as described in claim 1, characterized in that, The quantitative analysis of the relative contribution of each characteristic pollutant to the input pathway using a positive definite factor matrix model further includes: The positive definite factor matrix model is a PMF model, which decomposes the concentration data matrix X into a factor contribution matrix G, a factor distribution matrix F, and a residual matrix e, as shown in Equation 4: ； Among them, X ij Let p represent the concentration of the j-th metal in the i-th sample, and G represent the number of principal factors. ik F represents the concentration of the k-th source in the i-th sample. kj Let represent the concentration of the j-th metal in the k-th source, and e ij That is the residual for each sample; PMF minimizes the objective function Q by continuously decomposing the concentration data matrix X, and finally determines the contribution value of the factor, which is the input path.

9. An analytical system for analyzing the contribution of heavy metal input to farmland soil surrounding a mining area, characterized in that, include: The sample acquisition unit acquires heavy metal concentrations and sampling point coordinates from multiple sampling points of four soil metal carriers in the study area: farmland soil, river sediments, atmospheric deposition, and natural background soil. The screening unit calculates the cumulative index to obtain the heavy metal pollution level of farmland soil, and selects the heavy metal with the highest pollution level as the characteristic pollutant of the study area. The matching unit matches the distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers, and selects the input pathway of the characteristic pollutants based on the matching degree. The input pathway includes flooding and atmospheric deposition. In the comparative unit, the input flux of heavy metals in farmland soil in the study area was calculated through two input pathways: flooding and atmospheric deposition, to determine the input contribution of the two input pathways to characteristic pollutants. The first model calculation unit uses a hydrodynamic model to identify the inundation depth of the study area; The second model calculation unit uses the input contributions of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the inundation depth as independent variables to construct a statistical model based on the random forest algorithm, thereby obtaining the input pathways of each characteristic pollutant in the study area. The third model calculation unit uses a positive definite factor matrix model to quantitatively analyze the relative contribution of each characteristic pollutant to the input pathway.

10. An electronic device, comprising: processor; And a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 8.

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