Method, system and equipment for analyzing heavy metal input contribution of farmland soil around mining area

Abstract

The invention discloses a method, a system and equipment for analyzing input contribution of heavy metals in farmland soil around a mining area. The method comprises the following steps: acquiring heavy metal concentrations of a plurality of sampling points in a research area and coordinate information of the sampling points; selecting the heavy metal with the highest pollution degree as a characteristic pollutant of the research area; matching the distribution characteristics of the selected specific pollutants on different soil heavy metal carriers, selecting input ways of the specific pollutants based on the matching degree, determining input contributions of the two input ways to the specific pollutants, and identifying the submerging depth of the research area by using a hydrodynamic model; constructing a statistical model based on a random forest algorithm by taking the input contributions of the two input approaches to the characteristic pollutants, the coordinate information of the sampling points and the submerging depth as independent variables, and obtaining the input approach of each characteristic pollutant in the research area; and using a positive definite factor matrix model to quantitatively analyze the relative contribution of the input path of each characteristic pollutant.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental pollution prevention and control, and relates to a method for rapidly assessing the risk of groundwater pollution in an area affected by mining and smelting activities, and specifically to an analysis method, system and equipment for analyzing the contribution of heavy metal input to farmland soil around a mining area. Background Art

[0002] Mining waste generated by lead and zinc mining and processing activities continuously releases a large amount of heavy metals into farmland soil under natural effects such as surface runoff, rainfall leaching and weathering, posing a serious threat to human health and ecosystem safety. Pb, Zn, As and associated elements Cd, Cr and Cu generated by mining mainly migrate to surrounding farmland soil through atmospheric deposition and hydraulic action, causing surrounding soil pollution. Heavy metals transmitted by hydraulic action are an important source of heavy metals in soil in mining areas, but current research on hydraulic action mainly focuses on sewage irrigation. For metal mining areas distributed on both sides of the river, the secondary distribution of pollutants by sediment carried by flooding is often overlooked. More and more research results show 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 soil around mining areas on both sides of the river to provide support conditions for mineral mining and heavy metal pollution management. Summary of the invention

[0003] In view of the above situation, the embodiments of the present application propose an analysis method, device, equipment and medium for the contribution of heavy metal input to farmland soil around mining areas, so as to overcome or at least partially overcome the shortcomings of the prior art.

[0004] In a first aspect, the present application provides a method for analyzing the contribution of heavy metal input to farmland soil around a mining area, the method comprising:

[0005] Obtain the heavy metal concentrations and sampling point coordinate information of multiple sampling points of four soil metal carriers: farmland soil, river sediments, atmospheric deposition, and natural background soil in the study area;

[0006] The heavy metal pollution degree of farmland soil is obtained by calculating the cumulative index, and the heavy metal with the highest pollution degree is selected as the characteristic pollutant of the study area;

[0007] Matching the distribution characteristics of the selected characteristic pollutants on different soil heavy metal carriers, and selecting the input pathways of the characteristic pollutants based on the matching degree, wherein the input pathways include flooding and atmospheric deposition;

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

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

[0010] The input contribution of the two input pathways to characteristic pollutants, sampling point coordinate information, and flooding depth were used as independent variables to construct a statistical model based on the random forest algorithm to obtain the input pathways of each characteristic pollutant in the study area.

[0011] The positive definite factor matrix model was used to quantitatively analyze the relative contribution of the input pathways of each characteristic pollutant.

[0012] In a second aspect, the embodiment of the present application further provides a system for analyzing the contribution of heavy metal input to farmland soil around a mining area, the device comprising:

[0013] The sample acquisition unit obtains the heavy metal concentrations and sampling point coordinate information of multiple sampling points of four soil metal carriers, namely farmland soil, river sediment, atmospheric deposition and natural background soil in the study area;

[0014] Screening units, calculating the cumulative index to obtain the heavy metal pollution degree of farmland soil, and selecting the heavy metal with the highest pollution degree as the characteristic pollutant of the study area;

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

[0016] For the comparison unit, the input flux of heavy metals from flooding and atmospheric deposition to farmland soil in the study area was calculated 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 flooding depth of the study area;

[0018] The second model calculation unit takes the input contribution of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the flooding depth as independent variables, and constructs a statistical model based on the random forest algorithm to obtain 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 the input pathways of each characteristic pollutant.

[0020] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform any of the above methods.

[0021] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0022] The heavy metals in the farmland soil around the mining area were taken as the research object, and the pollution degree of different heavy metals was obtained by calculating the corresponding geoaccumulation index; the three heavy metals with the highest pollution degree in agricultural soil were selected, and the distribution characteristics of the three heavy metals in different carriers were superimposed to obtain the homologous relationship between different carriers; by calculating the input flux of heavy metals in the farmland soil of the mining area through flooding and atmospheric deposition, the contribution of the two pathways to heavy metals can be compared; the hydrodynamic model was used to identify the possible flooding range and depth of the mining area, and determine the key areas that are easily polluted by heavy metals; the random forest model was used to evaluate the importance of natural and human factors affecting the content of heavy metals in agricultural soil, and the main input pathways of heavy metals could be determined; finally, the positive definite factor matrix model was used to quantitatively analyze the input contribution of different pollution pathways to the heavy metals in the farmland soil of the mining area. The research results can provide a reliable basis for future research on heavy metal pollution in mining areas. The mechanism discovered in this study may be very common, which is helpful to study the impact of hydrodynamic transmission on the mining environment under the current extreme climate conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 A schematic flow chart showing a method for analyzing the input contribution of heavy metals to farmland soil around a mining area according to an embodiment of the present application;

[0025] Figure 2 Shows the cumulative exponential distribution characteristics of heavy metals.

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

[0027] Figure 4 Schematic diagram showing the correlation between flooded areas and pollution by characteristic pollutants.

[0028] Figure 5 Shows the results of random forest importance evaluation.

[0029] Figure 6 The PMF model is used to analyze the sources of heavy metals in soil.

[0030] Figure 7A schematic diagram showing the structure of a system for analyzing the input contribution of heavy metals to farmland soil around a mining area according to an embodiment of the present application;

[0031] Figure 8 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

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

[0034] The invention discloses an analysis method for the input contribution of heavy metals in farmland soil around a mining area, comprising: obtaining the heavy metal concentrations and coordinate information of multiple sampling points of four carriers, namely, farmland soil, river sediments, historical atmospheric deposition and natural background soil in a mining area; calculating the ground cumulative index to obtain the pollution degree of heavy metals in farmland soil, and selecting three heavy metal elements with the highest pollution degree as characteristic pollutants in the mining area; drawing 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 relationship between different carriers; respectively calculating the input flux of heavy metals in farmland soil in the study area through flood inundation and atmospheric deposition, and comparing them; using a hydrodynamic model to simulate flood inundation, and identifying the potential inundation range and inundation depth of the study area; constructing a random forest model to identify the main environmental influencing factors of heavy metals in soil in the study area, and respectively determining the main input pathways of 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, and at the same time provide a new perspective for the source analysis of farmland soil, and provide theoretical support for decision makers in future mineral mining and heavy metal pollution management.

[0035] See also Figure 1 The embodiment of the present invention provides an analysis method for identifying the contribution of flood inundation to the input of heavy metals into farmland soil around a mining area, which comprises the following steps:

[0036] Step 101, obtaining heavy metal concentrations, coordinate information, and environmental impact factors of samples at different sampling points in the study area, wherein the samples include samples collected from farmland soil, samples collected from river sediments, samples collected from natural soil, and samples collected from atmospheric dust;

[0037] The samples come from different soil metal carriers and from different locations of the soil metal carriers. The soil metal carriers include farmland soil, river sediments, natural soil, atmospheric dust, etc.

[0038] Obtain the heavy metal concentrations of multiple samples such as farmland soil, river sediments, atmospheric dust, natural soil, etc., and obtain the coordinate information of the sampling points of each of the multiple samples. The sampling points are the locations corresponding to the samples of different soil metal carriers.

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

[0040] In one embodiment, in order to obtain the heavy metal concentrations of river sediments in soil heavy metal carriers, N sediment samples were collected in the river sediment area of ​​the mining study area, such as the river sediment area mainly irrigation rivers and ditches, and a total of 46 sediment samples (0-5 cm) were collected at different sampling points in the river sediment area.

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

[0042] In one embodiment, in order to obtain the heavy metal concentration of natural background soil in the soil heavy metal carrier, N natural background soil samples were collected at different sampling points in the natural soil area (natural background soil) of the mining study area. For example, the natural soil area selected an area more than 300m away from the road, and 31 natural background soil samples were collected in the natural soil area. In order to reduce the impact of the environment on the soil, clean metal tools were used to remove 0 to 20 cm of topsoil during sampling, and natural background soil below 20 cm was collected as samples. Similarly, 2 natural background soil samples were randomly collected around each sampling point and evenly mixed to form a composite sample for subsequent processing and analysis.

[0043] In one embodiment, in order to obtain the heavy metal concentration of atmospheric dust in the soil heavy metal carrier, N atmospheric dust samples are collected at different sampling points in the atmospheric dust sampling area in the mining area study area. For example, the atmospheric dust sampling area is located on the beams of a house or attic that has been uninhabited for a long time, and 7 historical atmospheric dust sampling points are set in the atmospheric dust sampling area. These sampling points all use clean brushes to collect dust deposited at the sampling points, and after removing foreign matter, the samples are placed in plastic bags marked with serial numbers 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 and obtained respectively, and then the heavy metal concentrations of soil heavy metal carriers in the study area were obtained.

[0045] It should be noted that 2 or more samples are randomly collected around each sampling point, and 2 or more samples are randomly collected around each sampling point and evenly mixed to form a composite sample. The composite sample is used for subsequent processing and analysis. The number of sampling points in this application and the number of samples randomly collected near each sampling point are not limited here.

[0046] Step 102, selecting the metal with the highest degree of contamination in the soil metal carrier as a characteristic pollutant.

[0047] This application uses the geo-accumulative index to calculate the degree of heavy metal pollution in farmland soil, and selects N heavy metals with the highest pollution levels as characteristic pollutants. Specifically, it includes: obtaining the degree of heavy metal pollution according to the heavy metal concentration of the soil heavy metal carrier, and further includes: using the box plot method to identify and eliminate the abnormal values ​​in the obtained heavy metal concentration, and then calculating the geo-accumulative index of the heavy metal concentration data to obtain the heavy metal pollution degree.

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

[0049] I geo =log2C i / 1.5×B i (1)

[0050] Among them, I geo Characterizes the degree of heavy metal pollution, C i is the current concentration of heavy metal i, B i is the background concentration of heavy metal i in the soil metal carrier. 1.5 is the correction factor, which indicates the natural fluctuation of heavy metal concentration in the environment, such as Figure 2shown.

[0051] It was calculated that 93.75% of Cd, 87.50% of Pb and 82.14% of Zn were above the moderate pollution level. In contrast, the pollution levels of Cu, As and Cr were relatively low, with 31.25% of Cu, 78.57% of As and 100% of Cr being unpolluted or below the moderate pollution level. Overall, the soil heavy metals in the study area showed different degrees of pollution, with Cd, Pb and Zn being the most seriously polluted, so Cd, Pb and Zn were selected as characteristic pollutants in the mining study area.

[0052] Step 103, matching the distribution characteristics of the selected characteristic pollutants on different soil heavy metal carriers, and selecting the input pathway of the characteristic pollutants based on the matching degree, wherein the input pathway includes flooding and atmospheric deposition.

[0053] Step 103 also includes drawing the distribution characteristics of characteristic pollutants in different soil heavy metal carriers, and superimposing the distribution characteristics of selected characteristic pollutants on different soil heavy metal carriers.

[0054] Step 103 further includes:

[0055] Calculate the probability value of the soil heavy metal carrier confidence interval within which the distribution characteristics of characteristic pollutants fall;

[0056] The input pathway of characteristic pollutants is selected based on probability values.

[0057] This application uses characteristic pollutant fingerprints to analyze pollution sources. This application superimposes the distribution characteristics of selected characteristic pollutants on different heavy metal carriers and compares the homologous relationships between different carriers. Specifically, it includes: performing a logarithmic transformation with a base of 10 on the heavy metal concentrations of different soil heavy metal carriers to eliminate the influence of the order of magnitude, and then obtaining soil sample points, selecting the three heavy metals with the highest degree of farmland soil pollution (i.e., the characteristic pollutants selected in step 102), and plotting their distribution characteristics in different heavy metal carriers, specifically by plotting a scatter plot, wherein the distribution characteristics are the distribution of soil sample points of characteristic pollutants or heavy metals. The soil sample point is a numerical point obtained after data processing of the heavy metal concentration data. On this basis, the confidence interval (95%) of the distribution of heavy metals in soil (including farmland soil, natural background soil), sediment, and atmospheric dustfall is calculated, such as Figure 3 As shown, the characteristic heavy metal fingerprints of the main pollution sources were finally obtained, and the homologous relationship between different heavy metal carriers was obtained.

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

[0059] The lnCd of soil heavy metals is -0.63~1.73, lnCd refers to the soil sample point range or distribution characteristics of the characteristic pollutant Cd, and the lnPb is 1.38~3.77, lnPb refers to the soil sample point range or distribution characteristics of the characteristic pollutant Pb. 91.1% of the soil sample points of the selected characteristic pollutants (Cd, Pb) fall in the confidence ellipse of river sediments (lnCd is 0.18~2.62, lnPb is 1.84~4.01), of which 91.1% is the matching degree of the distribution characteristics of the selected characteristic pollutants in river sediments.

[0060] 7.8% of the soil samples of the selected characteristic pollutants (Cd, Pb) fall within the confidence ellipse of atmospheric dustfall (1nCd is 1.69-2.23, lnPb is 3.32-4.00), of which 7.8% is the matching degree of the distribution characteristics of the selected characteristic pollutants on atmospheric dustfall.

[0061] 5.3% of the soil sample points of the selected characteristic pollutants (Cd, Pb) fall within the confidence ellipse of the natural background soil (1nCd is -1.04 to -0.30, lnPb is 1.10 to 2.09), among which 5.3% is the matching degree 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 around the mining area are highly similar to those in river sediments. The results of the fingerprint map show that the heavy metal distribution characteristics of farmland soils are highly consistent with river sediments, followed by atmospheric dust, and the overlap between the natural background value and the distribution characteristics of soil heavy metals is the lowest, indicating that soil heavy metals may come from two composite pollution pathways: river sediments and atmospheric deposition.

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

[0064] This application calculates the input flux of heavy metals in soil by flooding and atmospheric deposition respectively, and then compares the contribution 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 the farmland soil in the study area through the atmospheric deposition pathway. The historical atmospheric deposition data are the annual deposition of atmospheric dust in the study area and the concentration of characteristic pollutants (Cd, Pb, Zn) in the atmospheric deposition in the study area.

[0067] Annual input flux of heavy metals (such as characteristic pollutants (Cd, Pb, Zn)) into agricultural soils through atmospheric deposition (Q a , mg·m -2 ·a -1 )The formula is as follows:

[0068] Q a =C i ×M i (2)

[0069] C i is the concentration of heavy metal element i, mg kg -1 ;M i is the total mass of precipitation per unit area in one year, for example 114.67 ga -1 .

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

[0071] The single input flux of heavy metals into farmland soil via flooding (Q f , mg·m -2 ) is calculated as follows:

[0072] Q a =S i ×V×m i (3)

[0073] S i is the concentration of heavy metal element i, mg kg -1 ; V represents the volume of flood water that can be retained by farmland soil per unit area, for example 0.2m 3 ;m i is the total mass of sediment carried by the flood per unit volume, for example 20 kg m -3 .

[0074] The pathway of flood action entering farmland soil refers to the entry of sediments carried by floods into farmland soil.

[0075] Step 1042, based on the calculated annual input flux of the characteristic pollutant entering the farmland soil via the atmospheric deposition pathway, calculate how many years of atmospheric deposition are required to reach the current concentration of the characteristic pollutant in the farmland soil;

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

[0077] In a specific example, 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 that are mainly engaged in metal smelting, the annual atmospheric dust deposition in the mining activity area is 89.46-114.67 g / m 2 Referring to this data and combining it with the historical atmospheric deposition data collected in this study, the annual input fluxes of characteristic pollutants (Cd, Pb, and Zn) through atmospheric deposition pathways in the study area were calculated to be 11.83, 637.09, and 1085.51 mg·m -2 ·a -1 (Table 2) When calculating flood input flux, according to the standards for rice field construction in Hunan Province, this application assumes that 1m 2 The volume of flood water that can be stored in farmland is 0.2m 3 , while 1m 2 The mass range of flood-carried sediment is 15-20 kg. Based on this assumption, this application calculates the flood input flux of each flood to be 169.24, 6580.56, and 12612.68 mg·m -2 (Table 2). For 1m 2 In terms of the total input of farmland soil (0-20cm), to reach the current pollution level of the soil in the study area, 1417.5mg of Cd, 75.4g of Pb and 106.9g of Zn need to be input. The study area needs to be flooded 8.4-11.5 times or continuous atmospheric deposition for 98.2-119.5 years to reach the current pollution level of the study area. Therefore, this application speculates that in addition to atmospheric deposition, frequent flooding leading to the water-land migration of heavy metals in sediments may be another important factor leading to soil pollution in the study area.

[0078] Table 1

[0079] Heavy metal concentrations in sediment 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, using a hydrodynamic model to identify potential flooding range and flooding depth in the mining study area;

[0085] The hydrodynamic model was used to obtain the correlation between the flooded area and the characteristic pollutant contamination in the study area. The hydrodynamic model was used to identify the potential flooding range and flooding depth in the mining study area and to evaluate the soil's ability to carry pollutants.

[0086] Step 105 further includes:

[0087] The DMF model was selected, and the 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, flood range, and flood depth of the flooded area.

[0088] The inundation range and inundation depth of the inundated area are superimposed with the geoaccumulative index of characteristic pollutants at different sampling points, so as to obtain the correlation between the inundated area and the pollution of characteristic pollutants from the perspective of spatial distribution, see Figure 4 shown.

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

[0090] The color of the patches in the flooded area represents the depth of flooding, with darker colors representing higher flooding heights;

[0091] The color of the soil sample indicates the geoaccumulation index of the characteristic pollutant (e.g. Cd). The darker the color, the higher the pollution degree of the characteristic pollutant Cd.

[0092] The sampling points with higher pollution levels of the characteristic pollutant Cd show a distribution trend of spreading to both sides along the water system of the inundated area. In areas with higher catchment depths (areas with high inundation depths), the pollution level of the characteristic pollutant Cd is also higher.

[0093] The water system is the channel for transporting polluted sediments to farmland soil. The farther away from the water system, the less sediment the soil receives.

[0094] This application uses a hydrodynamic model to identify the potential flooding range and depth in the study area, and the correlation between the flooding area and characteristic pollutant pollution, so as to evaluate the pollutant-carrying capacity of farmland soil, including:

[0095] The purpose of using Deltt3D Flexible Mesh (DFM model) in this study is to simulate the scope and depth of land inundation when the study area faces a 50-year flood. The land flood model (DFM model) combines regular and irregular triangular grids. It is a surface runoff numerical model with a grid resolution set to 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 impact of the terrain. The basin flood peak height is set to 61.03m as a boundary condition. This application conducts field visits in the study area to verify the prediction results of the model.

[0096] The DFM model is used to superimpose the simulated flooding range and flooding depth of the flooded area with the geo-accumulative index of characteristic pollutants at different sampling points, so as to obtain the correlation between the flooded area and the pollution of characteristic pollutants from the perspective of spatial distribution.

[0097] The exemplary DFM model is used to superimpose the simulated flooded area with the geoaccumulation index of soil heavy metal Cd at different sampling points, aiming to study the correlation between the flooded area and heavy metal Cd pollution from the perspective of spatial distribution. Figure 4 The figure shows the inundation depth and pollution degree of heavy metal Cd in the inundation area. The figure shows the inundation area of ​​the simulated study area that encounters a flood with a return period of 50 years. The color of the patch represents the inundation depth. The darker the color, the higher the inundation height. The color of the soil sample represents the geoaccumulation index of Cd. The darker the color, the higher the pollution degree of Cd. Figure 4 It can be clearly observed that the soil sampling points with severe Cd pollution show a distribution trend of spreading to both sides along the water system. On the other hand, in areas with higher water collection depths, the pollution level of soil Cd is also higher. This is consistent with the results of the correlation analysis. The water system is a channel for transporting contaminated sediments to the soil. The farther away from the water system, the less sediment the soil receives. Similarly, the water collection depth also reflects the capacity of farmland soil to accommodate contaminated sediments. The greater the water collection depth, the more sediments the area can retain, thus facing a higher degree of heavy metal pollution risk.

[0098] Step 106, using a random forest model to evaluate 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 soil heavy metals. As an extension of the classification and regression tree model, the random forest model has stronger predictive power. When building a 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 prediction.

[0100] Based on the results of floodplain identification, i.e. the correlation between flooded areas and characteristic pollutant pollution, this application uses sediment heavy metal concentration, atmospheric dustfall heavy metal concentration, sampling point distance from the river, sampling point distance from the mining enterprise and water collection depth as independent variables, and constructs a statistical model based on the random forest algorithm to calculate the importance of these factors to the heavy metal concentration of farmland soil in the study area, and analyze the main input pathways of Cd, Pb and Zn in the soil of the study area. Figure 5 As shown in the results, the concentration of heavy metal Cd in sediments is the most important factor affecting soil Cd concentration, and the influence of river distance on soil Cd concentration is also greater than that of enterprise distance. This shows that flooding is the main input pathway of soil Cd in the study area, and the sediments carried into the soil by floods are the main source of soil Cd. For Pb and Zn, the effects of atmospheric dustfall and enterprise distance on soil heavy metals are greater than those of sediments and river distance, respectively, indicating that Pb and Zn in the soil of the study area are mainly transferred to the soil in the form of atmospheric dustfall. With the continuous input of atmospheric dustfall, Pb and Zn continue to accumulate in the soil.

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

[0102] This application uses Positive matrix factorization (PMF model) to quantify the contribution of different heavy metal sources. The PMF model does not need to measure the source component spectrum. The PMF model is a positive definite matrix factorization (PMF) model, which estimates the error of each heavy metal content to reduce omissions in data processing, thereby improving accuracy and reliability. Input uncertainty data and concentration data, iterate the factor calculation at different frequencies, and continuously adjust the parameters of the PMF model. The concentration data mentioned below is the concentration of heavy metals in the sample. When the source factor count is set to 3, the model shows the smallest and most stable Q value, indicating that the results at this time can better explain each factor.

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

[0104]

[0105] Among them, X ij represents the concentration of the jth metal in the ith sample, p is the number of main factors, G ik represents the concentration of the kth source in the ith sample, F kj represents the concentration of the jth metal in the kth source, and e ij is the residual error for each sample.

[0106] PMF minimizes the objective function Q by continuously decomposing the concentration data matrix X, and finally determines the contribution and characteristics of the factors. Q is calculated as follows:

[0107]

[0108] Among them, n is the number of samples, m is the type of metal, and u is ij Represents the uncertainty of the jth metal in the ith 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] Wherein, EF represents the error fraction in chemical analysis, and X is the concentration of heavy metals. The concentrations (U) of all heavy metals in this application exceed the detection limit of the measuring instrument, so the uncertainty is calculated using Formula 7.

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

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

[0115] Figure 2 A schematic diagram showing the structure of a system for analyzing the contribution of heavy metal input to farmland soil around a mining area according to an embodiment of the present application is shown. Figure 2 It can be seen that the analysis system 300 for the contribution of heavy metal input to farmland soil around the mining area includes:

[0116] The sample acquisition unit obtains the heavy metal concentrations and sampling point coordinate information of multiple sampling points of four soil metal carriers, namely farmland soil, river sediment, atmospheric deposition and natural background soil in the study area;

[0117] Screening units, calculating the cumulative index to obtain the heavy metal pollution degree of farmland soil, and selecting the heavy metal with the highest pollution degree as the characteristic pollutant of the study area;

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

[0119] For the comparison unit, the input flux of heavy metals from flooding and atmospheric deposition to farmland soil in the study area was calculated 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 flooding depth of the study area;

[0121] The second model calculation unit takes the input contribution of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the flooding depth as independent variables, and constructs a statistical model based on the random forest algorithm to obtain 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 the input pathways of each characteristic pollutant.

[0123] It should be noted that the above-mentioned analysis system for the contribution of heavy metal input to farmland soil around mining areas can realize the above-mentioned analysis method based on the contribution of heavy metal input to farmland soil around mining areas one by one, which will not be repeated here.

[0124] The invention discloses an analytical method for identifying the contribution of flood inundation to the input of heavy metals in farmland soil around a mining area, comprising: obtaining the heavy metal content and coordinate information of multiple sampling points of four carriers, namely, farmland soil, river sediment, historical atmospheric deposition and natural background soil in a mining area study area; calculating the ground cumulative index to obtain the pollution degree of heavy metals in farmland soil, and selecting three heavy metal elements with the highest pollution degree as characteristic pollutants in the mining area study area; drawing the distribution characteristics of characteristic pollutants in different carriers (farmland soil, sediment, atmospheric deposition and natural soil), and superimposing them to determine the homology relationship between different carriers; respectively calculating the input flux of heavy metals in farmland soil in the study area through flood inundation and atmospheric deposition, and comparing them; using a hydrodynamic model to simulate flood inundation, and identifying the potential inundation range and inundation depth of the study area; constructing a random forest model to identify the main environmental influencing factors of heavy metals in soil in the study area, and respectively determining the main input pathways of 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, and at the same time provide a new perspective for the source analysis of farmland soil, and provide theoretical support for decision makers in future mineral 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 the present application. Figure 8 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0126] The processor, network interface and memory can be interconnected through 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. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0127] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0128] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an analysis system for the input contribution of heavy metals in farmland soil around the mining area at the logical level. The processor executes the program stored in the memory and is specifically used to execute the above method.

[0129] The above application Figure 2The method disclosed in the illustrated embodiment for the analysis system for the contribution of heavy metal input to farmland soil around the mining area can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above 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 gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0130] The electronic device may also perform Figure 1 The invention discloses a method for implementing an analysis system for the contribution of heavy metal input to farmland soil around mining areas, and realizes an analysis system for the contribution of heavy metal input to farmland soil around mining areas in Figure 7 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0131] The present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 8 The method implemented by the analysis system for the contribution of heavy metal input to farmland soil around a mining area in the illustrated embodiment is specifically used to implement the aforementioned method.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function 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] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0138] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity or device including the element.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0141] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for analyzing the contribution of heavy metal input to farmland soil around mining areas, characterized in that: include: Obtain the heavy metal concentrations and sampling point coordinate information of multiple sampling points of four soil metal carriers: farmland soil, river sediments, atmospheric deposition, and natural background soil in the study area; The heavy metal pollution degree of farmland soil is obtained by calculating the cumulative index, and the heavy metal with the highest pollution degree is selected as the characteristic pollutant of the study area; Matching the distribution characteristics of the selected characteristic pollutants on different soil heavy metal carriers, and selecting the input pathways of the characteristic pollutants based on the matching degree, wherein the input pathways include flooding and atmospheric deposition; By calculating the input flux of heavy metals from flooding and atmospheric deposition to farmland soil in the study area, the input contribution of the two input pathways to characteristic pollutants was determined. Using hydrodynamic models to identify the inundation depth of the study area; The input contribution of the two input pathways to characteristic pollutants, sampling point coordinate information, and flooding depth were used as independent variables to construct a statistical model based on the random forest algorithm to obtain the input pathways of each characteristic pollutant in the study area. The positive definite factor matrix model was used to quantitatively analyze the relative contribution of the input pathways of each characteristic pollutant.

2. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed 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 comprises: The geoaccumulative index is used to calculate the degree of heavy metal pollution in soil metal carriers. The calculation formula of the geoaccumulative index is as follows: I geo =log2c i / 1.5×B i (1) C i is the current concentration of heavy metal i, B i is the background concentration of heavy metal i in the medium, and 1.5 is the correction factor, which indicates the natural fluctuation of heavy metal content in the environment.

3. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 1, characterized in that: The matching of the distribution characteristics of the 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 based on the heavy metal pollution degree of selected characteristic pollutants; Obtaining the distribution characteristics of the pollution degree of characteristic pollutants; Calculate confidence intervals for heavy metal distributions in farmland soils, natural background soils, river sediments, and atmospheric dust; Based on the proportion of the distribution characteristics of characteristic pollutants falling within the confidence interval, the matching degree between the characteristic pollutants and the soil heavy metal carriers is obtained.

4. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 1, characterized in that: The step of determining the input contribution of the two pathways to characteristic pollutants in farmland soil further includes: Based on the historical atmospheric deposition data collected in the study area, the annual input flux of characteristic pollutants entering the farmland soil through the atmospheric deposition pathway in the study area is calculated. The historical atmospheric deposition data is the annual deposition of atmospheric dust in the study area and the concentration of characteristic pollutants in the atmospheric deposition in the study area; The single input flux of characteristic pollutants entering the farmland soil through flooding in the study area is calculated based on the volume of flood water retained in the farmland soil per unit area in the study area, the concentration of characteristic pollutants in river sediments, and the total mass of sediments carried by flood water per unit volume; Based on the calculated annual input flux of characteristic pollutants entering farmland soil through atmospheric deposition, calculate how many years of atmospheric deposition are required to reach the current concentration of the characteristic pollutants in farmland soil; Based on the calculated single input flux of heavy metals into farmland soil via flooding, it was calculated how many floods are needed to reach the current concentration of the characteristic pollutants in farmland soil.

5. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 1, characterized in that: The identifying the flooding depth of the study area using the hydrodynamic model further comprises: Select the DMF model, import the 30m high-resolution topographic map of the study area into the DFM model, set the highest flood peak height in the study area as a boundary condition, and obtain the water system, flood range and flood depth of the flooded area; The inundation range and inundation depth of the inundated area obtained are superimposed with the geo-accumulative 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 method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 4, characterized in that: Annual input flux of heavy metals into farmland soil via atmospheric deposition (Q a , mg·m -2 ·a -1 ) Formula 2 is as follows: Q a =C i ×M i (2) Ci is the concentration of heavy metal element i (mg kg) -1 ; Mi is the total mass of precipitation per unit area in one year.

7. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 4, characterized in that: Single input flux of heavy metals into farmland soil via flooding (Qf, mg·m- 2 ) is calculated as follows: Q a =S i ×V×m i (3) S i is the concentration of heavy metal element i, mg kg -1 ; V represents the volume of flood that can be retained by farmland soil per unit area; mi is the total mass of sediment carried by the flood per unit volume.

8. The method for analyzing the contribution of heavy metal input to farmland soil around a mining area as claimed in claim 1, characterized in that: The method of using a positive definite factor matrix model to quantitatively analyze the relative contribution of the input pathways of each characteristic pollutant 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 part matrix F and a residual matrix e, as shown in Formula 4: Among them, X ij represents the concentration of the jth metal in the ith sample, p is the number of main factors, G ik represents the concentration of the kth source in the ith sample, F kj represents the concentration of the jth metal in the kth source, and e ij is the residual of 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 pathway.

9. An analysis system for the contribution of heavy metal input to farmland soil around mining areas, characterized in that: include: The sample acquisition unit obtains the heavy metal concentrations and sampling point coordinate information of multiple sampling points of four soil metal carriers, namely farmland soil, river sediment, atmospheric deposition and natural background soil in the study area; Screening units, calculating the cumulative index to obtain the heavy metal pollution degree of farmland soil, and selecting the heavy metal with the highest pollution degree as the characteristic pollutant of the study area; A matching unit matches the distribution characteristics of the selected characteristic pollutants on different soil heavy metal carriers, and selects the input pathway of the characteristic pollutants based on the matching degree, wherein the input pathway includes flooding and atmospheric deposition; For the comparison unit, the input flux of heavy metals from flooding and atmospheric deposition to farmland soil in the study area was calculated 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 flooding depth of the study area; The second model calculation unit takes the input contribution of the two input pathways to the characteristic pollutants, the sampling point coordinate information, and the flooding depth as independent variables, and constructs a statistical model based on the random forest algorithm to obtain 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 the input pathways of each characteristic pollutant.

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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