Soil pollution tracing method and device based on causal relationship and medium

Abstract

The embodiment of the invention discloses a causal relationship-based soil pollution tracing method, equipment and medium, and the method comprises the steps: obtaining the soil pollutant concentration detection data of a to-be-traced region, and the data of two emission sources of enterprises and traffic; performing positive matrix factorization on the detection data, and determining contribution of each hidden pollution source to each pollutant; determining at least one pollutant which is contributed by each hidden pollution source and is associated with the enterprise and / or traffic as at least one target pollutant to be traced; aiming at each type of target pollutants, through emission list spatialization and GCCM causal analysis, the causal relationship and the significance degree of the enterprise and the traffic and the target pollutants are verified respectively; and according to a verification result, carrying out auxiliary decision on the preferentially controlled blocks in the region and the types of pollutants and emission sources preferentially controlled by each block. According to the embodiment of the invention, accurate tracing of soil pollution in a complex environment can be realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of pollution detection and control, and in particular, to a soil pollution source tracing method, device and medium based on causal relationship. Background Art

[0002] With the accelerated development of industrialization and urbanization, soil pollution in many areas is becoming increasingly serious. Soil pollution source tracing refers to tracing the source of soil pollution by collecting and analyzing harmful substances in soil samples and combining relevant information such as environmental surveys, so as to provide a reasonable basis for pollution control.

[0003] Current soil pollutant source tracing methods usually perform PMF (Positive Matrix Factorization) on soil pollutant content data to determine hidden soil pollution sources, and then use correlation analysis or geographically weighted regression to verify causal relationships. This method has insufficient causal inference ability. For example, correlation analysis only provides statistical associations, and causal relationships may be masked by other collinear pollution sources, easily leading to "false correlations"; while geographically weighted regression relies on linear assumptions, cannot detect non-linear causal chains in weakly coupled systems, and has high requirements for spatio-temporal data, and the reliability decreases when data is scarce in actual scenarios. Therefore, the above methods cannot achieve accurate soil pollution source tracing in complex environments.

[0004] In the prior art, patent application CN116559148A discloses a soil pollutant source tracing method, and patent application CN118171060A discloses a soil pollutant source tracing and analysis method, both of which cannot solve the above problems. Summary of the Invention

[0005] The embodiments of the present invention provide a soil pollution source tracing method, device and medium based on causal relationship to solve the above problems.

[0006] In a first aspect, the embodiments of the present invention provide a soil pollution source tracing method based on causal relationship, including:

[0007] Obtaining soil pollutant concentration detection data of the area to be traced, as well as data of two emission sources, namely enterprises and traffic;

[0008] Performing positive matrix factorization on the detection data to determine the contribution of each hidden pollution source to each pollutant; determining at least one pollutant with the top-ranked contribution of each hidden pollution source and associated with enterprises and / or traffic as at least one target pollutant to be traced;

[0009] For each target pollutant, perform the following operations respectively:

[0010] S1-1. Interpolate based on the detection data to obtain the first two-dimensional distribution map of the target pollutant concentration;

[0011] S1-2. Execute the emission inventory method according to the data of the two emission sources to obtain the total target pollutant emissions of each enterprise and the total target pollutant emissions of traffic respectively;

[0012] S1-3. Use the kernel density method to allocate the total target pollutant emissions of each enterprise to each grid in the region to obtain the second two-dimensional distribution map of enterprise emissions; use the road network density weight to allocate the total target pollutant emissions of traffic to each grid in the region to obtain the third two-dimensional distribution map of traffic emissions;

[0013] S1-4. Perform GCCM causal analysis on the second two-dimensional distribution map and the third two-dimensional distribution map with the first two-dimensional distribution map respectively to verify the causal relationship and its significance degree between the enterprise and traffic and the target pollutant;

[0014] According to the verification results, assist in making decisions on the blocks with priority control in the region, as well as the types of pollutants and emission source types with priority control in each block.

[0015] In a second aspect, an embodiment of the present invention provides an electronic device, and the electronic device includes:

[0016] One or more processors;

[0017] A memory for storing one or more programs,

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the soil pollution source tracing method based on causal relationship described in any embodiment.

[0019] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the soil pollution source tracing method based on causal relationship described in any embodiment.

[0020] In summary, the embodiment of the present invention provides a method for tracing the source of soil pollution based on causal relationships. For regions with industrial and transportation as the main emission sources, first, PMF decomposition is performed on soil detection data to determine hidden pollution sources, and target pollutants that contribute greatly to the hidden pollution sources and are related to the emission sources are extracted from dozens of pollutants as the objects of source tracing, and the two-dimensional spatial distribution of the target pollutant concentration is obtained through interpolation; then, the emissions of each industrial enterprise and transportation are calculated through the emission inventory method, and according to the characteristics of enterprise emissions and transportation emissions, the emission inventory is spatialized respectively through the kernel density function and road density to obtain a more accurate and refined two-dimensional spatial distribution of emissions; finally, the GCCM model is used to verify the causal relationship between each emission source and the spatial distribution of pollutant concentration, and the non-linear causal chain in the two weakly coupled systems of PMF and emission inventory is detected; and different control strategies are recommended for different blocks according to the detection results to assist in achieving precise targeted control of soil pollution.

[0021] Specifically, compared with the prior art, the embodiment of the present invention has the following advantages:

[0022] 1. The lack of causal inference ability for soil pollution in the prior art. Traditional correlation analysis (such as Pearson correlation coefficient) and MGWR cannot effectively detect non-linear causal relationships in weakly coupled systems and are difficult to reveal the complex interaction chain between pollutant concentration distributions and emission sources. These methods are usually based on linear assumptions and have high requirements for data (such as the need for time series data), and perform poorly in complex environmental systems, especially in the case of scarce spatio-temporal data, and this defect is particularly prominent.

[0023] However, the embodiment of the present invention adopts the GCCM model. Based on the dynamic system theory and the generalized embedding theorem, the state space of the system is reconstructed through spatial cross-sectional data, overcoming the dependence limitation of traditional methods on data, and being able to accurately identify hidden unidirectional causal relationships, that is, being able to clarify the causal direction between enterprise and transportation emission sources and pollutant concentration distributions, avoiding interference from "false correlations", and providing a scientific basis for precise governance.

[0024] 2. The lack of spatialization of emission inventories in the prior art. Although the existing emission inventory method can estimate the total emissions of pollutants, it lacks a refined characterization of its spatial distribution, resulting in difficulty in accurately locating high-emission hotspots.

[0025] However, the embodiment of the present invention uses the kernel density allocation and road network weight allocation methods to accurately allocate the emissions of industrial emission sources and transportation emission sources to spatial grids, generating a high-resolution emission inventory map and providing high-quality input data for subsequent causal inference.

[0026] 3. Difficulty in analyzing multi-source complex systems in the prior art. In a multi-source and non-linear coupling complex environment, it is difficult for the prior art to comprehensively analyze the sources of pollutants and their interactions, which may lead to the neglect of secondary pollution sources or the masking of the contributions of primary pollution sources.

[0027] However, through multi-model coupling (PMF + emission inventory + GCCM) and combined with spatial analysis and dynamic causal inference, the embodiments of the present invention can comprehensively analyze the pollution characteristics of multi-source complex systems, and reveal hidden pollution sources and interaction mechanisms.

[0028] 4. Imprecision in pollution control in the prior art. Due to the lack of in-depth understanding of the causal relationship between obvious emission sources and pollutant concentration distribution in the prior art solutions, pollution control measures are often too broad and fail to specifically control key pollution sources.

[0029] However, through integrating PMF (source identification), emission inventory (source calculation) and GCCM (source verification), the embodiments of the present invention construct a complete framework for soil pollution source tracing and control, which can establish the correspondence between hidden pollution sources in the theoretical model and obvious emission sources in reality, accurately lock key pollution sources and their contribution areas, and provide a scientific basis for formulating implementable targeted treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 is a flowchart of a soil pollution source tracing method based on causal relationship provided by an embodiment of the present invention;

[0032] Figure 2 is a two-dimensional distribution diagram of As concentration obtained by interpolation in an exemplary area provided by an embodiment of the present invention;

[0033] Figure 3 is a two-dimensional distribution diagram of the As emissions of enterprises in an exemplary area provided by an embodiment of the present invention;

[0034] Figure 4 is a schematic diagram of the causal relationship verification results with Gejiu City, Yunnan Province as the area to be traced, smelting and mining as the two industries to be controlled, and As and Pb as the two target pollutants; among them, Figure 4 (a) is the verification result of smelting enterprises and As concentration, Figure 4(b) is the verification result of industrial and mining enterprises and As concentration, Figure 4 (c) is the verification result of traffic emission sources and As concentration, Figure 4 (d) is the verification result of smelting enterprises and Pb concentration, Figure 4 (e) is the verification result of industrial and mining enterprises and Pb concentration, Figure 4 (f) is the verification result of traffic emission sources and Pb concentration;

[0035] Figure 5 It is a two-dimensional distribution diagram showing the mixed display of multiple target pollutant concentrations in an exemplary area provided by an embodiment of the present invention;

[0036] Figure 6 It is a technical framework diagram of a soil pollution source tracing method based on causal relationship provided by an embodiment of the present invention;

[0037] Figure 7 It is a schematic diagram of the results of causal relationship verification by taking Gejiu City, Yunnan Province as the area to be traced, and taking the density of smelting enterprises, the density of industrial and mining enterprises, the road network density, and the detected concentration of As as input data respectively; among them, Figure 7 (a) is the verification result of the causal relationship between the density of smelting enterprises and As concentration, Figure 7 (b) is the verification result of the causal relationship between the road network density and As concentration, Figure 7 (c) is the verification result of the causal relationship between the density of industrial and mining enterprises and As concentration;

[0038] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0039] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope protected by the present invention.

[0040] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0041] In the description of the present invention, it should also be noted that, unless otherwise clearly defined and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] Figure 1 It is a flowchart of a soil pollution source tracing method based on causal relationship provided by an embodiment of the present invention. This method is applicable to areas where industry and transportation are the main soil pollutant emission sources and is executed by an electronic device. As Figure 1 shown, this method specifically includes:

[0043] S110. Obtain the soil pollutant concentration detection data of the area to be traced, as well as the data of two emission sources, namely enterprises and transportation.

[0044] In this embodiment, first, a certain area is determined as the object of subsequent soil pollutant source tracing and control. This area can be a certain province or a certain city, and this embodiment does not make specific limitations. Then, the basic data of this area is collected as the data source for the entire method. Optionally, the basic data includes the detection data of soil pollutant concentration, enterprise data, and transportation data. Enterprises and transportation are the main soil pollutant emission sources in this area and are also the objects of control in subsequent control measures.

[0045] In a specific implementation manner, representative sampling points (such as soil pollutant content detection stations) can be selected in this area according to the grid or random sampling method. These sampling points cover different land use types (industrial areas, agricultural areas, traffic-intensive areas, etc.) to ensure balanced sampling. At each sampling point, the soil pollutant concentration data is detected by a standard method. Generally speaking, there are dozens of soil pollutant types detected by the detection station (for example, according to GB36600, 45 items are mandatory for construction land soil detection, and 40 other items), including heavy metals such as arsenic (As), lead (Pb), cadmium, mercury, nickel; organic substances such as carbon tetrachloride, chloroform, and chloromethane.

[0046] Enterprise data includes the location of the enterprise, activity data of the enterprise over a period of time (such as production volume, process parameters), emission factors, as well as the removal efficiency and technology implementation coefficient of the pollution control technologies adopted by the enterprise. These data can be obtained from online public platforms such as web maps POI (Point of Interest) (such as OpenStreetMap), Qichacha (http: / / www.qichacha.com / ), Tianyancha (http: / / www.tianyancha.com / ), and the National Pollution Permit Management Information Platform (https: / / permit.mee.gov.cn / ), and supplemented by the National Enterprise Credit Information Publicity System (http: / / www.gsxt.gov.cn / ). After crawling the corresponding data from each website, it is further cleaned and used. Among them, the enterprise location can be determined according to geographical information data such as industrial layout maps; emission factors, as well as the removal efficiency and technology implementation coefficient of pollution control technologies, can be obtained from the publicly available "Emission Source Statistical Survey Production and Pollutant Discharge Accounting Methods and Coefficient Manual".

[0047] Traffic data includes the vehicle ownership (classified by type: passenger cars, trucks, motorcycles, etc.) and their corresponding emission factors, as well as the road network density map. Among them, vehicle data can be obtained from provincial statistical yearbooks. For example, when the area to be traced is Yunnan Province or a certain city in Yunnan Province, the vehicle data can be obtained from the "Yunnan Statistical Yearbook"; road traffic information can be obtained from OpenStreetMap (https: / / master.apis.dev.openstreetmap.org); emission factors can be obtained from the publicly available "Emission Source Statistical Survey Production and Pollutant Discharge Accounting Methods and Coefficient Manual".

[0048] S120. Perform positive matrix factorization on the detected data to determine the contribution of each latent pollution source to each pollutant; at least one pollutant with the top-ranked contributions of each latent pollution source and associated with enterprises and / or traffic is determined as at least one pollutant to be traced.

[0049] In this embodiment, the PMF receptor model is used to select one or several pollutants from the numerous pollutants in the detected data as the objects for subsequent tracing and control. For the convenience of distinction and description, these pollutants to be traced and controlled are referred to as target pollutants in this embodiment.

[0050] Specifically, PMF is a relatively excellent receptor model for the source analysis of air pollutants. This method decomposes the concentration of pollutants in the sample into the contributions of multiple potential pollution sources through mathematical formulas, and determines the characteristic components and their relative contribution rates of each pollution source. In this embodiment, this method is used to decompose the detected data of pollutant concentrations at sampling points, aiming to determine the target pollutants with serious pollution and controllability.

[0051] In a specific embodiment, the soil pollutant concentration detection data is first standardized to remove outliers, and then a pollutant concentration matrix is constructed based on the standardized data. The matrix dimension is the number of sampling points × type of pollutant, and each row of the matrix is the concentration of each pollutant at the same sampling point, and each column is the concentration of the same pollutant at each sampling point.

[0052] By performing PMF on the matrix, we can get the number of potential pollution sources and the contribution of each pollution source to each pollutant. It should be noted that the pollution sources obtained here are hidden pollution sources analyzed from the pollutant concentration detection data. They are not explicit objects such as a certain block, a certain geographical grid or a certain enterprise, but may be multiple blocks, multiple grids or multiple enterprises, or even the comprehensive result of multiple factors. Therefore, this embodiment refers to the pollution source as a hidden pollution source, and its specific corresponding explicit object will be gradually clarified in the subsequent operations of this embodiment. Specifically, the model formula of PMF is as follows:

[0053] (1)

[0054] in, represents the concentration of pollutant j at sampling point i, represents the contribution ratio of the kth hidden pollution source in sampling point i, represents the characteristic pollutant concentration of the kth hidden pollution source, It represents the residual corresponding to pollutant j in sampling point i, and q represents the number of hidden pollution sources.

[0055] After performing PMF on the pollutant concentration matrix according to the above model, we can get the number of hidden pollution sources in the area and the contribution ratio of each hidden pollution source to each pollutant. At this time, for each hidden pollution source, sort each pollutant according to the contribution ratio of the hidden pollution source to each pollutant, and take the top pollutants. Finally, we get the top pollutants corresponding to each hidden pollution source, and take the union of these pollutants to form a set of pollutants with serious impact.

[0056] Meanwhile, based on the data of the two types of emission sources, at least one pollutant associated with the two emission sources of enterprises or transportation is determined, constituting an associated pollutant set. Optionally, the pollutants associated with each enterprise can be determined from the enterprise data, and the pollutants associated with the transportation emission sources can be extracted from the transportation data, such as the pollutants targeted by each emission factor. These associated pollutants can improve their pollution conditions through the control of enterprises and transportation, and can limit the preliminary scope of the target pollutants. For the convenience of distinction and description, in this embodiment, the determined associated pollutant set here is referred to as the first pollutant set, and the set of pollutants with serious impacts constructed in the previous paragraph is referred to as the second pollutant combination.

[0057] Take the intersection of the first pollutant set and the second pollutant set, and each pollutant in the intersection is a target pollutant to be traced. Exemplarily, taking Gejiu City, Yunnan Province as an example, the finally screened target pollutants include arsenic and lead.

[0058] S130. For each target pollutant, perform the following operations S1-1 to S1-4 respectively to determine the causal relationship and its significance degree between various emission sources and the current target pollutant.

[0059] S1-1. According to the detection data, interpolate to obtain a two-dimensional distribution map of the target pollutant concentration.

[0060] For the target pollutant being currently processed, the detection data includes the concentrations of the target pollutant at multiple sampling points. The inverse distance interpolation method can be used to interpolate these discrete concentration values to form a spatially continuous two-dimensional distribution map of the target pollutant concentration. Figure 2 Exemplarily shows a two-dimensional distribution map of the As concentration interpolated within a rectangular target area, where mg / kg (milligram per kilogram) is the concentration unit, representing the As content in each kilogram of soil; SV represents the standard value, that is, the screening value for the second type of land use.

[0061] S1-2. Execute the emission inventory method according to the data of the two types of emission sources to obtain the total emissions of the target pollutant for each enterprise and the total emissions of the target pollutant for transportation respectively.

[0062] The emission inventory method refers to a method for systematically quantifying the total amount of pollutants emitted by various pollution sources (such as industry, transportation, and life) within a specific time span and spatial range. In this embodiment, for the target pollutant being currently processed, the emission inventory method is used to quantitatively represent the actual emissions brought by each enterprise and transportation to be controlled, providing more accurate data support for source tracing and control.

[0063] In a specific embodiment, the emission inventory method is respectively executed for each enterprise according to the enterprise data. Specifically, first, according to the activity data of each enterprise in the production process and the emission factor of the target pollutant, the production emissions of each enterprise are determined; then, according to the production emissions of each enterprise and the removal efficiency of the pollution control technology, the emission reduction amount of each enterprise is determined; by subtracting the emission reduction amount from the production emissions of each enterprise, the total target pollutant emissions of each enterprise can be obtained. Optionally, for each enterprise, the total pollutant emissions of the enterprise are calculated respectively through the following formula:

[0064] (2)

[0065] Wherein, (kg, kilogram) represents the total target pollutant emissions of the enterprise; represents the activity data of the enterprise in the year of the production process; (kg / unit activity data) is the emission factor of the target pollutant, used to characterize the content of the target pollutant emitted per unit of activity data; (%) represents the removal efficiency of the enterprise's pollution control technology, (value range: 0-1) represents the technology implementation coefficient of the pollution control technology.

[0066] Meanwhile, regarding the traffic in the entire region as an overall emission source, the emission inventory method is executed for the entire traffic source according to the traffic data. Optionally, according to the vehicle ownership of each vehicle type and the emission factor of the target pollutant, the total target pollutant emissions of the entire traffic source are calculated, and the formula is as follows:

[0067] (3)

[0068] Wherein, (kg) represents the total target pollutant emissions of the traffic emission source, (kg / year / vehicle) represents the emission factor of the th vehicle type for the target pollutant, represents the th vehicle type in the year of vehicle ownership.

[0069] S1-3. Using the kernel density method, distribute the total target pollutant emissions of each enterprise to each grid in the region to obtain a two-dimensional distribution map of enterprise emissions; using the road network density weight, distribute the total target pollutant emissions of the traffic to each grid in the region to obtain a two-dimensional distribution map of traffic emissions.

[0070] In this embodiment, different methods are used to spatially expand the emissions calculated by the enterprise and traffic emission inventories. Optionally, the area to be traced can be rasterized into grid cells of 1 km × 1 km, and continuous spatial expansion can be achieved by calculating the emissions within each grid.

[0071] In a specific embodiment, considering the pollutant diffusion characteristics of fixed emission sources, this embodiment uses the kernel density method to sequentially allocate the total target pollutant emissions of each enterprise to each grid in the area to be traced, realizing the spatial expansion of enterprise emissions. Among them, the kernel density estimation method is used to estimate the probability density function of point geographical data. This method scientifically quantifies the spatial distance attenuation effect of geographical phenomena, and assigns greater weights to objects closer in geographical distance. Its core function is defined as follows:

[0072] (4)

[0073] Among them, represents the kernel density value of the estimation point , represents the search radius (bandwidth), represents the number of point features within the search radius, represents the point to the point distance, is the default spatial weight function;

[0074] (5)

[0075] Among them, represents the standard distance, represents the median distance, with the highest kernel density of point features as the upper limit and 0 as the lower limit. The higher the kernel density, the higher the emission intensity of the pollution source.

[0076] Based on the above core function, each grid in this embodiment can be used as the estimation point , and the total target pollutant emissions of each enterprise are used as point features to execute the kernel density method. Specifically, first, according to the spatial weight function , the total target pollutant emissions of each enterprise are sequentially allocated to multiple grids surrounding the grid where each enterprise is located (i.e., the grids within the above-mentioned search radius). As shown in formula (5), the kernel density method will automatically determine a reasonable search radius according to the overall size of the area to be expanded and the grid size. Eventually, the sum of the emissions allocated to each grid by the same enterprise is equal to the total emissions of the same enterprise, and the farther the distance from each grid to the same enterprise, the greater the corresponding spatial weight and the more total emissions are allocated. Then, the emissions allocated to each grid from each enterprise are added up to obtain the total target pollutant emissions of each grid. By arranging these total emissions values in sequence according to the geographical location of the grids, a two-dimensional distribution map of enterprise emissions can be obtained.

[0077] In practical applications, the above process can also be implemented using the kernel density method in arcgispro. The input is the point elements of the emission inventory of each enterprise (i.e., the emissions with coordinates). By using the kernel density analysis in the spatial analyst toolbox and setting the specified output area unit factor (i.e., the unit, whether it is square meters or square kilometers), and the rest are set as default; the output raster is the emissions of each unit (grid). Further, by using GIS (Geographical Information System) tools to visualize the calculation results, a high-resolution emission inventory map can be generated. Figure 3 Exemplarily shows a two-dimensional distribution map of the emissions of enterprise As within a rectangular area. This embodiment also refers to this process as the spatialization of the enterprise emission inventory.

[0078] The main emission carriers of traffic emission sources are moving vehicles, and their corresponding pollutant diffusion characteristics are not exactly the same as those of fixed emission sources, and the specific diffusion laws are not yet clear. Therefore, in this embodiment, the total emissions of traffic emission sources are allocated to each road section according to the road network density to ensure that the spatial distribution of emissions is consistent with the actual traffic pattern.

[0079] In a specific implementation manner, first, according to the ratio of the total road length of each grid in the area to be traced back to the total road length of the area, the road network density weight of each grid is determined:

[0080] (6)

[0081] Among them, represents the road network density weight (or called road network density) of grid , and respectively represent the total road lengths of grid and grid , represents the number of grids in this area.

[0082] Then, according to the road network density weights of each grid, the total amount of target pollutant emissions from the traffic emission sources is allocated to each grid:

[0083] (7)

[0084] Wherein, represents the amount of target pollutant emissions allocated to grid .

[0085] For the convenience of distinction and description, in this embodiment, the two-dimensional distribution map of the target pollutant concentration obtained by interpolating detection data in S1-1 is called the first two-dimensional distribution map; the two-dimensional distribution map obtained by spatializing the enterprise emission inventory in S1-3 is called the second two-dimensional distribution map, and the two-dimensional distribution map obtained by spatializing the traffic emission inventory is called the third two-dimensional distribution map.

[0086] S1-4. Perform GCCM causal analysis on the second two-dimensional distribution map and the third two-dimensional distribution map respectively with the first two-dimensional distribution map, and verify the causal relationship between the enterprise and the traffic and the target pollutant and its significance level respectively.

[0087] In this embodiment, the GCCM (Geographical Convergent Cross Mapping) model is used to verify the causal relationship between the enterprise emission source and the target pollutant concentration, and the causal relationship between the traffic emission source and the target pollutant concentration respectively, and quantify the strength of their causal relationship. This process is actually coupling the pollution source information obtained by the two source tracing methods of PMF and emission inventory, and comprehensively reflecting the interaction of multiple pollution sources.

[0088] Specifically, GCCM is a spatial causal inference method based on dynamic system theory and generalized embedding theorem. It reconstructs the state space of the system through spatial cross-sectional data and identifies the causal relationship between geographical factors. Different from traditional correlation analysis, causal inference aims to reveal the independent influence of a certain phenomenon (cause) on another phenomenon (effect), that is, how the result variable responds when the cause variable changes, and can distinguish true causal relationships from spurious correlations.

[0089] Furthermore, for two variables X and Y whose causal relationship is to be verified, the core steps of the GCCM model include:

[0090] a) Embedding construction: Determine the spatial lag of each unit (such as pixel or polygon) in the two-dimensional distribution map and organize it into vectors and matrices.

[0091] b) Predict Y based on X: Predict Y based on X with a series of window sizes and calculate the prediction skill ρ for each window.

[0092] c) Predicting X from Y: Use the same method to predict X from Y, obtaining a series of ρ values.

[0093] d) Causal relationship interpretation: Plot the predicted output based on the convergence, significance, and confidence intervals of ρ values at the maximum window size to determine the existence, direction, and strength of the causal relationship. For more details, please refer to the paper Gao, B., Yang, J., Chen, Z., Sugihara, G., Li, M., Stein, A., Kwan, M.-P., Wang, J., 2023. Causal inference from cross-sectional earth system data with geographical convergent cross mapping. Nat. Commun. 14, 5875. https: / / doi.org / 10.1038 / s41467-023-41619-6.

[0094] In this embodiment, the judgment results of the causal relationship may include the following:

[0095] Unidirectional causal relationship (X→Y or Y→X): If the cross-mapping prediction skill (ρ value) of predicting Y from X (Y xmap X) is significantly higher at the maximum library size and shows an obvious upward trend and statistical significance (p<0.05) as the library size (i.e., window size) increases, while the ρ value of the reverse prediction (X xmap Y) is low and not significant (or the significance is much weaker than that of Y xmap X), then it can be inferred that there is a unidirectional causal relationship (e.g., X→Y). Conversely, if the ρ value of X xmap Y is significant and shows a stable upward trend as the library size increases, while the ρ value of Y xmap X is low and not significant, then it can be inferred that Y is the cause of X (Y→X).

[0096] Bidirectional asymmetric causal relationship: When the ρ values of both cross-mapping prediction methods (Y xmap X and X xmap Y) are significant and show statistical significance (p<0.05), but there are obvious differences in their magnitudes, this indicates that there is a bidirectional causal relationship between the two variables, but the causal strengths are asymmetric. For example, if the ρ value of Y xmap X is significantly higher than that of X xmap Y, it can be inferred that X mainly drives Y, while the feedback effect from Y to X is weak. This asymmetry may stem from the strong correlation effect in the dominant causal direction, rather than completely independent bidirectional effects.

[0097] No causal relationship: If the ρ values of both cross - mapping prediction methods (Y xmap X and X xmap Y) do not show a significant upward trend, or their prediction skills (ρ values) are not statistically significant (p≥0.05), then no causal relationship can be inferred between the two variables. At this time, it may indicate a lack of dynamic association between the variables, or that the noise in the data interferes with the validity of causal inference.

[0098] Based on the above - mentioned core steps, in this embodiment, the causal verification between the second two - dimensional distribution map (enterprise emissions) and the first two - dimensional distribution map (pollutant concentration), and the causal relationship verification between the third two - dimensional distribution map (traffic emissions) and the first two - dimensional distribution map (pollutant concentration) are completed respectively. Taking the verification process corresponding to traffic emissions as an example, it specifically includes the following steps:

[0099] Step 1: Input the third two - dimensional distribution map and the first two - dimensional distribution map into the GCCM model. The model predicts the target pollutant concentration based on traffic emissions of a series of window sizes, calculates the prediction skills for each window, and plots a line chart of the prediction skills changing with the window size; at the same time, predicts traffic emissions based on the target pollutant concentration of a series of window sizes, calculates the prediction skills for each window, and plots a line chart of the prediction skills changing with the window size. For the convenience of distinction and description, in this embodiment, the lines in the two line charts mentioned above are respectively called the first line and the second line. Exemplarily, Figure 4 (c) shows the result of the causal verification of the detection concentration of traffic emission sources and the target pollutant As in Gejiu City, Yunnan Province. Among them, the abscissa L is the window size, the ordinate ρ is the prediction skill, As xmapTraffic represents predicting the detection concentration of As based on the As emissions from traffic sources, and its corresponding red line is the first line, Traffic xmap As represents predicting the As emissions from traffic sources based on the detection concentration of As, and its corresponding blue line is the second line.

[0100] Step 2: Judge the causal relationship and significance level according to the two lines. Optionally, if the prediction skill in the first line increases as the window size increases, and the prediction skill of the first line is significantly higher than that of the second line at the same window size, then it is judged that traffic affects the target pollutant concentration; and the greater the distance between the two lines, the more significant the causal relationship. Still taking Figure 4 (c) as an example, the prediction skills of the two lines do not show a significant upward trend, and the prediction skills at the same window are almost the same, indicating that traffic emission sources have little impact on the As concentration. And Figure 4(f) shows the result of causal verification of the detection concentration of traffic emission sources and the target pollutant Pb in Gejiu City, Yunnan Province as the area to be traced. Similarly, Pb xmapTraffic represents the predicted detection concentration of Pb based on the Pb emissions from traffic sources, and the corresponding red broken line is the first broken line. Traffic xmap Pb represents the predicted Pb emissions from traffic sources based on the detected concentration of Pb, and the corresponding blue broken line is the second broken line. It can be seen that the ρ value of the first broken line is relatively high and increases with the increase of the window size, while the ρ value of the second broken line is significantly lower than that of the first broken line, indicating that traffic emission sources affect the Pb concentration. The distance between the two broken lines at the maximum window represents the significance level of the causal relationship.

[0101] For enterprise emission sources, in the case of a large number of enterprises and different industries involved, the enterprises can be pre-classified by industry. Correspondingly, when generating the second two-dimensional distribution map using the kernel density method in S1-3, for each industry, the total emissions of the target pollutants of each enterprise in the industry can be allocated to each grid in the area to be traced to obtain the two-dimensional distribution map corresponding to the enterprise emissions in the industry. Eventually, each industry corresponds to a second two-dimensional distribution map. Correspondingly, when verifying the causal relationship in this step, the causal relationship between the second two-dimensional distribution map of each industry and the first two-dimensional distribution map is also verified respectively to obtain the causal relationship and its significance level between the enterprise emission sources of each industry and the target pollutants.

[0102] After performing the operations from S1-1 to S1-4 for each target pollutant, the verification results of each industry's enterprises and each target pollutant, as well as the causal inference results of traffic and each target pollutant, can be obtained. Exemplarily, Figure 4 shows the verification results with Gejiu City, Yunnan Province as the area to be traced, the non-ferrous metal smelting and rolling processing industry (hereinafter referred to as "smelting") and the non-ferrous metal mining and dressing industry (hereinafter referred to as "mining and industrial") as the two industries to be controlled, and As and Pb as the two target pollutants. Among them, Figure 4 (a) shows the verification result of smelting enterprises (i.e., non-ferrous metal smelting and rolling processing enterprises) and the As concentration. Asxmap NFM Smelting represents the predicted detection concentration of As based on the As emissions of smelting enterprises, and NFM Smelting xmapAs represents the predicted As emissions of smelting enterprises based on the detected concentration of As; Figure 4 (b) shows the verification result of mining and industrial enterprises (i.e., non-ferrous metal mining and dressing enterprises) and the As concentration. As xmap NFM Mining represents the predicted detection concentration of As based on the As emissions of mining and industrial enterprises, and NFM Mining xmap As represents the predicted As emissions of mining and industrial enterprises based on the detected concentration of As; Figure 4(d) shows the verification results of smelting enterprises and Pb concentration. Pb xmap NFM Smelting represents the predicted Pb detection concentration based on the Pb emissions of smelting enterprises, and NFM Smelting xmap Pb represents the predicted Pb emissions of smelting enterprises based on the detected Pb concentration; Figure 4 (e) shows the verification results of industrial and mining enterprises and Pb concentration. Pb xmap NFM Mining represents the predicted Pb detection concentration based on the Pb emissions of industrial and mining enterprises, and NFM Mining xmap Pb represents the predicted Pb emissions of industrial and mining enterprises based on the detected Pb concentration. It can be seen that in the causal relationship results of each figure, the changing trends and distances of the two broken lines are different. Among them, Figure 4 (a) shows the most significant causal relationship between smelting enterprises and As.

[0103] S140. According to the verification results, assist in making decisions on the priority-controlled blocks in the area, as well as the types of pollutants and emission source types that should be prioritized for control in each block.

[0104] In this embodiment, based on the detection data and the verification results of causal relationships, different control strategies are recommended for different blocks to assist in achieving precise targeted control of soil pollution.

[0105] In a specific implementation manner, the first two-dimensional distribution maps of all target pollutants can be mixed and displayed. Figure 5 This is a two-dimensional distribution map showing the mixed display of the concentrations of multiple target pollutants provided by the embodiments of the present invention. Different colors in the figure represent different target pollutant concentration situations, and three representative colors are marked with 1, 2, and 3 respectively. From this mixed concentration map, at least one block with excessive target pollutant concentration can be extracted as at least one target block to be controlled.

[0106] For each target block, control strategies can be recommended according to the following two situations:

[0107] Situation 1. If there is only one target pollutant exceeding the standard in a certain target block, then one item with the most significant causal relationship with the target pollutant can be extracted from several emission sources such as industrial enterprises and transportation, and recommended as the priority control object for this target block. For example, for Figure 5 the target block formed by aggregating the grids with color 1, the target pollutants exceeding the standard only include As, so As is the pollutant to be controlled. At the same time, it can be seen from Figure 4 that the degree of influence of each emission source on the As concentration is: smelting enterprises > industrial and mining enterprises > transportation. Therefore, the smelting enterprises in this target block are taken as the priority control object for this target block, and then the industrial and mining enterprises are controlled, while transportation has little influence on the As concentration and can be not taken as a control object.

[0108] Case 2: If there are multiple target pollutants exceeding the standard in a certain target block, then extract the most significant one from all the verified causal relationships for all the pollutants exceeding the standard, and recommend the target pollutant corresponding to the most significant one as the priority control object for the target block. For example Figure 5 For the block aggregated by the grids with color 3 in Figure 5 , both As and Pb exceed the standard. Then in Figure 4 among the 6 causal relationships verified for these two target pollutants in Figure 4 , select the one with the most significant causal relationship Figure 4 (a) The corresponding target pollutant As is used as the object to be controlled preferentially. This is because the more significant the causal relationship is, the more obvious the expected control effect will be, and it should be implemented preferentially. In the specific control of As, the same method as in Case 1 is also adopted, and the control is carried out in the order of smelting enterprises first and then industrial and mining enterprises. After the control measures for As are implemented, the control of Pb can be carried out. Still according to the method described in Case 1, in the order of mining enterprises, transportation, and smelting enterprises, the limited control objects are determined.

[0109] Furthermore, Figure 6 The flow from data collection to causal inference in the above whole method is shown in the form of a technical framework diagram. As Figure 6 shown, the technical framework of this embodiment includes the following stages:

[0110] Source identification: Based on the soil detection data and PMF receptor model of a specific area, identify and quantify the pollution sources, and these sources are all hidden pollution sources and cannot be directly associated with the actual controllable objects.

[0111] Source inventory calculation: By systematically collecting emission factors and activity data of emission sources such as industrial activities and traffic flows in a specific area, estimate the total emissions of pollutants. The emission inventory method can directly associate with the explicit emission sources and at the same time identify the high-emission hotspots.

[0112] Source verification: Source verification is a spatial causal inference method based on dynamic system theory and generalized embedding theorem, which is used to verify the causal relationship between emission sources and pollutant distribution. In this embodiment, this process is also a process of making the hidden pollution sources explicit. Specifically, the hidden pollution sources come from the actual detection data, which are relatively accurate pollution sources, but it is difficult to correspond to the explicit positions or controllable factors. However, in this embodiment, through GCCM causal inference, the hidden pollution sources are corresponded to the explicit emission sources such as enterprises, industries, and transportation, realizing the explicit traceability of the hidden pollution sources and providing a basis for formulating implementable and refined control.

[0113] Furthermore, to prove the advantages of the method in this embodiment, taking Gejiu City, Yunnan Province as an example, the Pearson correlation coefficient method and the MGWR (Multiscale Geographically Weighted Regression) method were used to conduct correlation analysis on two pollutants, As and Pb, respectively, with three types of data: emissions from smelting enterprises, emissions from industrial and mining enterprises, and traffic emissions. Specifically, the pollutant concentration and emissions (if any) of each grid were used as data points in the correlation analysis. The verification results obtained by the three methods are shown in the following table respectively:

[0114]

[0115] In the table, ** indicates significant correlation at the 0.01 level, * indicates significant correlation at the 0.05 level; r represents the Pearson correlation coefficient, ARC represents the average regression coefficient in MGWR, and r / ARC / ρ represents the corresponding parameters in different methods; p represents the significance test p-value in the Pearson correlation coefficient and GCCM; SRR represents the proportion of the significant area, that is, in the MGWR analysis, the proportion of the spatial area where the p-value is less than the significance level in the total area of the study area.

[0116] It can be seen from the above table that the traditional method has insufficient causal inference ability. Among them, the Pearson correlation analysis only provides statistical relevance, and the causal relationship may be masked by other collinear pollution sources, easily leading to "false correlation" (for example, two types of data with correlation actually have no causal relationship). The MGWR method relies on linear assumptions, cannot detect the nonlinear causal chain of weakly coupled systems, and has high requirements for spatio-temporal data, and its reliability decreases when data is scarce in actual scenarios. Therefore, in this embodiment, the results of the above two methods are not significant or the significant results are relatively low (no asterisk or <0.01), and it is impossible to determine which is the cause and which is the effect; while the significance result of the GCCM method is significantly improved, and through the rule that "the effect of predicting Y from X is significant and convergent, while the effect of predicting X from Y is not significant or approaches zero", the one-way causal relationship from X to Y (X is the cause, Y is the effect) can be determined.

[0117] In addition, taking Gejiu City, Yunnan Province as an example, the density of industrial and mining enterprises, the density of smelting enterprises, and the road network density were used as variables to be verified, and their causal relationships with the concentration of target pollutants were verified respectively. Taking the target pollutant As as an example, the verification results are as Figure 7 shown. Specifically, the two-dimensional distribution map formed by arranging the density of smelting enterprises in each grid and the two-dimensional distribution map of the detected concentration of As were used for GCCM causal inference, and Figure 7(a) The verification results of the causal relationship are shown, where xxmap y represents predicting the detected concentration of As based on the density of smelting enterprises, and y xmap x represents predicting the density of smelting enterprises based on the detected concentration of As; the two-dimensional distribution map formed by arranging the densities of industrial and mining enterprises in each grid is subjected to GCCM causal inference with the two-dimensional distribution map of the detected concentration of As, and the result is as shown in Figure 7 (b) The verification results of the causal relationship are shown, where x xmap y represents predicting the detected concentration of As based on the density of industrial and mining enterprises, and y xmap x represents predicting the density of industrial and mining enterprises based on the detected concentration of As; the two-dimensional distribution map formed by arranging the road network densities in each grid is subjected to GCCM causal inference with the two-dimensional distribution map of the detected concentration of As, and the result is as shown in Figure 7 (c) The verification results of the causal relationship are shown, where x xmap y represents predicting the detected concentration of As based on the road network density, and y xmap x represents predicting the road network density based on the detected concentration of As. It can be seen that for areas where industry and transportation are the main pollutant emission sources, there is no significant causal relationship between enterprise density, road network density and pollutant concentration. This is because although there are many enterprises, the impacts of different enterprise scales and emissions on soil pollution are different; although there are many roads, the impacts of emissions from different roads on soil pollution are also different. Therefore, in this embodiment, through the emission inventory and spatial allocation, the spatial distribution of enterprise emissions in the kernel density model and the spatial distribution of traffic emissions in the road network model are used as the verification objects for causal inference, which can not only establish the coupling relationship between hidden pollution sources and explicit emission sources, but also conform to the action laws of industry and transportation on the surrounding environment (such as fixed emission sources diffusing pollutants point by point and mobile emission sources emitting pollutants in a blurred manner), realizing more accurate and implementable explicit tracing of pollutants.

[0118] In summary, this embodiment provides a soil pollution tracing method based on causal relationship. For areas where industry and transportation are the main emission sources, first, PMF decomposition is performed on soil detection data to determine hidden pollution sources, and target pollutants that contribute greatly to hidden pollution sources and are related to emission sources are extracted from dozens of pollutants as the tracing objects, and the two-dimensional spatial distribution of the target pollutant concentration is obtained through interpolation; then, through the emission inventory method, the emissions of each industrial enterprise and traffic are obtained, and according to the characteristics of enterprise emissions and traffic emissions, the emission inventory is spatialized respectively through the kernel density function and road density to obtain a more accurate and refined two-dimensional spatial distribution of emissions; finally, the GCCM model is used to verify the causal relationship between various emission sources and the spatial distribution of pollutant concentration, identify the non-linear causal chain in the two weakly coupled systems of PMF and emission inventory; and different control strategies are recommended for different blocks according to the identification results to assist in realizing the precise targeted control of soil pollution.

[0119] Specifically, compared with the prior art, this embodiment has the following advantages:

[0120] 1. The lack of causal inference ability for soil pollution situations in the prior art. Traditional correlation analysis (such as Pearson correlation coefficient) and MGWR are unable to effectively detect non - linear causal relationships in weakly coupled systems and are difficult to reveal the complex interaction chains between pollutant concentration distributions and emission sources. These methods are usually based on linear assumptions and have high requirements for data (such as the need for time - series data), performing poorly in complex environmental systems, especially when spatio - temporal data is scarce, and this defect is particularly prominent.

[0121] In this embodiment, the GCCM model is adopted. Based on the dynamic system theory and the generalized embedding theorem, the state space of the system is reconstructed through spatial cross - section data, overcoming the dependence limitation of traditional methods on data. It can accurately identify hidden one - way causal relationships, that is, it can clarify the causal directions among enterprises, traffic emission sources and pollutant concentration distributions, avoid the interference of "false correlation", and provide a scientific basis for precise governance.

[0122] 2. The lack of spatialization of emission inventories in the prior art. Although the existing emission inventory method can estimate the total emissions of pollutants, it lacks a refined characterization of their spatial distribution, resulting in difficulty in accurately locating high - emission hotspots.

[0123] In this embodiment, the kernel density allocation and road network weight allocation methods are used to accurately allocate the emissions of industrial emission sources and traffic emission sources into spatial grids, generating a high - resolution emission inventory map, providing high - quality input data for subsequent causal inference.

[0124] 3. The difficulty in analyzing multi - source complex systems in the prior art. In a multi - source, non - linear coupled complex environment, the prior art is difficult to comprehensively analyze pollutant sources and their interactions, which may lead to the neglect of secondary pollution sources or the masking of the contributions of major pollution sources.

[0125] In this embodiment, through multi - model coupling (PMF + emission inventory + GCCM), combined with spatialization analysis and dynamic causal inference, it can comprehensively analyze the pollution characteristics of multi - source complex systems and reveal hidden pollution sources and interaction mechanisms.

[0126] 4. The inaccuracy of pollution control in the prior art. Due to the lack of in - depth understanding of the causal relationship between obvious emission sources and pollutant concentration distributions in the existing technical solutions, pollution control measures are often too broad and fail to target key pollution sources.

[0127] In the embodiments of the present invention, by integrating PMF (source identification), emission inventory (source calculation), and GCCM (source verification), a complete framework for soil pollution source tracing and control is constructed, which can establish the correspondence between the hidden pollution sources in the theoretical model and the explicit emission sources in reality, accurately lock the key pollution sources and their contribution areas, and provide a scientific basis for formulating implementable targeted treatment strategies.

[0128] Figure 8 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 8 shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more, Figure 8 and here, one processor 60 is taken as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected through a bus or other means, Figure 8 and here, connection through a bus is taken as an example.

[0129] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the soil pollution source tracing method based on causal relationships in the embodiments of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, that is, to implement the above-mentioned soil pollution source tracing method based on causal relationships.

[0130] The memory 61 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory 61 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 61 can further include a memory remotely set relative to the processor 60, and these remote memories can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and their combinations.

[0131] The input device 62 can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the device. The output device 63 can include a display device such as a display screen.

[0132] The embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the soil pollution source tracing method based on causal relationships in any embodiment.

[0133] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device, or component.

[0134] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0135] The program codes contained on the computer-readable media may be transmitted by any appropriate media, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0136] The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as C language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for tracing the source of soil pollution based on causal relationships, characterized in that, Including: Obtaining the soil pollutant concentration detection data of the area to be traced, as well as the data of two emission sources, namely enterprises and traffic; Performing positive matrix factorization on the detection data to determine the contribution of each hidden pollution source to each pollutant; Determining at least one pollutant with the top contributions from hidden pollution sources and associated with enterprises and / or traffic as at least one target pollutant to be traced; For each target pollutant, perform the following operations respectively: S1-1: According to the detection data, interpolate to obtain the first two-dimensional distribution map of the target pollutant concentration; S1-2: Execute the emission inventory method based on the data of the two emission sources to obtain the total emissions of the target pollutant from each enterprise and the total emissions of the target pollutant from traffic respectively; S1-3: Using the kernel density method, allocate the total emissions of the target pollutant from each enterprise to each grid in the area to obtain the second two-dimensional distribution map of enterprise emissions; using the road network density weight, allocate the total emissions of the target pollutant from traffic to each grid in the area to obtain the third two-dimensional distribution map of traffic emissions; S1-4: Perform GCCM causal analysis on the second two-dimensional distribution map and the third two-dimensional distribution map with the first two-dimensional distribution map respectively to verify the causal relationship and its significance level between enterprises and traffic and the target pollutant; According to the verification results, assist in making decisions on the areas to be preferentially controlled in the area, as well as the types of pollutants and emission source types to be preferentially controlled in each area.

2. The method according to claim 1, wherein The step of determining at least one pollutant with the top contributions from hidden pollution sources and associated with enterprises and / or traffic as at least one target pollutant to be traced includes: According to the data of the two emission sources, determining at least one pollutant associated with enterprises or traffic to form a first pollutant set; For each hidden pollution source: Sort the multiple pollutants according to the contribution ratio of the hidden pollution source to each pollutant, and select several pollutants ranked at the top; Taking the union of the several pollutants ranked at the top corresponding to each hidden pollution source to form a second pollutant set; Taking the intersection of the first pollutant set and the second pollutant set, and determining at least one pollutant in the intersection as at least one target pollutant to be traced.

3. The method according to claim 1, wherein The step of executing the emission inventory method based on the data of the two emission sources to obtain the total emissions of the target pollutant from each enterprise and the total emissions of the target pollutant from traffic respectively includes: According to the activity data of each enterprise in the production process and the emission coefficient of the target pollutant, determining the production emissions of each enterprise; According to the production emissions of each enterprise and the removal efficiency of pollution control technologies, determining the total emissions of the target pollutant from each enterprise.

4. The method according to claim 1, characterized in that, The step of using the kernel density method to allocate the total emissions of the target pollutant from each enterprise to each grid in the area includes: According to the preset spatial weight function, sequentially allocate the total emissions of the target pollutant from each enterprise to multiple grids centered on each enterprise in the area, where the sum of the emissions allocated to each grid by the same enterprise is equal to the total emissions of the same enterprise, and the farther the distance from each grid to the same enterprise, the greater the corresponding spatial weight; Add up the emissions allocated to each grid from each enterprise to obtain the total target pollutant emissions of each grid.

5. The method according to claim 1, wherein The step of using the road network density weight to allocate the total target pollutant emissions of the traffic to each grid in the region includes: Determine the road network density weight of each grid according to the ratio of the total road length of each grid in the region to the total road length of the region; Allocate the total target pollutant emissions of the traffic to each grid according to the road network density weight of each grid.

6. The method according to claim 1, wherein The step of performing GCCM causal analysis on the second two-dimensional distribution map and the third two-dimensional distribution map respectively with the first two-dimensional distribution map to verify the causal relationship and its significance level between the enterprise and the traffic and the target pollutant includes: Input the third two-dimensional distribution map and the first two-dimensional distribution map into the GCCM causal analysis model. The model predicts the target pollutant concentration based on the traffic emissions of a series of window sizes, calculates the prediction skill under each window, and draws the first broken line of the change of the prediction skill with the window size; at the same time, the model predicts the traffic emissions based on the target pollutant concentration of a series of window sizes, calculates the prediction skill under each window, and draws the second broken line of the change of the prediction skill with the window size; If the prediction skill in the first broken line increases with the increase of the window size, and the prediction skill of the first broken line at the maximum window is significantly higher than that of the second broken line, it is determined that the traffic affects the target pollutant concentration, and the greater the distance between the two broken lines, the more significant the causal relationship.

7. The method according to claim 1, characterized in that, The step of using the kernel density method to allocate the total target pollutant emissions of each enterprise to each grid in the region to obtain the second two-dimensional distribution map of the enterprise emissions includes: classifying each enterprise by industry; using the kernel density method to allocate the total target pollutant emissions of each enterprise in the same industry to each grid in the region to obtain the second two-dimensional distribution map of the emissions of the enterprises in the same industry; Correspondingly, the step of performing GCCM causal analysis on the second two-dimensional distribution map and the third two-dimensional distribution map respectively with the first two-dimensional distribution map to verify the causal relationship and its significance level between the enterprise and the traffic and the target pollutant includes: performing GCCM causal analysis on the second two-dimensional distribution map of the emissions of the enterprises in each industry respectively with the first two-dimensional distribution map to verify the causal relationship and its significance level between the enterprises in each industry and the target pollutant respectively.

8. The method according to claim 7, wherein The step of assisting in making decisions on the priority control blocks in the region, as well as the types of pollutants and emission source types to be preferentially controlled in each block according to the verification results includes: Mix and display the first two-dimensional distribution maps of the at least one target pollutant, and extract at least one target block with the target pollutant concentration exceeding the standard from them; For the target block with only one target pollutant exceeding the standard, extract one type of emission source type with the most significant causal relationship with the one target pollutant from several emission source types such as enterprises in each industry and traffic, and recommend it as the priority control object of the target block; For a target block with multiple exceeded target pollutants, extract the most significant one from all verified causal relationships for all exceeded pollutants, and recommend the target pollutant corresponding to the most significant one as the priority control object for the target block.

9. An electronic device, characterized in that, Including: One or more processors; A memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the causal relationship-based soil pollution source tracing method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the program is executed by a processor, it implements the causal relationship-based soil pollution source tracing method according to any one of claims 1-8.

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