A soil pollution source resolution method, device, medium and equipment

Abstract

The application discloses a soil pollution source analysis method and device, medium and equipment, and relates to the field of environmental science and technology. The application determines the median absolute deviation of the soil background content of the target pollutant in the soil environment background data, considers the uncertainty sources of the target pollutant in different concentration ranges according to the median absolute deviation and the concentration of the method detection limit of the target pollutant, comprehensively considers various uncertainty sources such as laboratory errors, chemical stability and sample uniformity of the target pollutant, constructs a perfect uncertainty calculation method system of spatial heterogeneity, ensures that the uncertainty of the target pollutant can be effectively quantified in various cases, provides a more robust input for the orthogonal matrix decomposition method in soil pollution source analysis, and thus improves the accuracy and reliability of soil pollutant source analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental science and technology, and in particular, to a soil pollution source analysis method, device, medium and equipment. BACKGROUND

[0002] At present, the receptor model is a digital model method for inferring different sources of pollutants and their contribution rates by analyzing the composition and concentration of pollutants in environmental receptors (such as atmosphere and soil). It has been widely used in the identification of atmospheric particulate matter sources since the late 1960s and has been continuously improved. The basic idea of the receptor model is based on the mass balance relationship between the source and the receptor of the pollutant. After the pollutant is discharged from the source, it is mixed by diffusion, and it is assumed that the pollutant is uniformly distributed in the atmosphere. Based on this, it can be considered that the source and mass of atmospheric particulate matter and elements in the receptor are the result of superposition of pollutants transported from surrounding different pollution sources. Therefore, the receptor model can calculate how many different sources of pollutants at the receptor by various mathematical processing calculation methods, such as enrichment factor method, cluster analysis, factor analysis, and orthogonal decomposition method, to determine the relative contribution of different pollution sources to the receptor.

[0003] In the prior art, the positive matrix factorization (PMF) model is a model for decomposing an environmental sample data matrix into a factor contribution matrix and a factor fingerprint spectrum matrix to identify the factors affecting the sample data. This model was first used in the source identification of atmospheric pollution particulate matter. When analyzing the source of atmospheric pollutants, the uncertainty of this model is mainly based on the uncertainty of the laboratory, including the method detection limit (MDL) of sample detection and the uncertainty of the concentration of each chemical component of the pollutant. The existing soil pollution source analysis method is usually simply migrated and applied.

[0004] However, in the existing soil pollution source analysis method, the uncertainty caused by the spatial heterogeneity of the soil sample environmental background is usually ignored, resulting in poor accuracy and reliability of the pollutant receptor model in the application of soil pollution source analysis. SUMMARY

[0005] Therefore, it is necessary to provide a soil pollution source analysis method, device, medium and equipment to solve the above technical problems.

[0006] The present application adopts the following technical solutions:

[0007] The application provides a soil pollution source analysis method, which comprises the following steps: firstly, determining potential pollution emission pathways and potential soil pollutant types in a target region and determining a target pollutant according to a target region of soil pollution source analysis; then, acquiring soil environment background data of the target pollutant under the target region, and determining a median absolute deviation of soil background content of the target pollutant in the soil environment background data; subsequently, acquiring the concentration of the target pollutant of sampling points of each grid after the target region is pre-divided into grids, and constructing a soil pollutant concentration matrix; further, for each sampling point, when the concentration of the target pollutant of the sampling point is greater than the median absolute deviation, determining the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant, a target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation; when the concentration of the target pollutant of the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, determining the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant and the target pollutant concentration uncertainty proportionality coefficient; when the concentration of the target pollutant of the sampling point is less than the concentration of the method detection limit of the target pollutant, determining the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant; obtaining an uncertainty matrix; finally, determining a target function when the soil pollutant concentration matrix is subjected to orthogonal matrix decomposition according to the uncertainty matrix, and performing orthogonal matrix decomposition on the soil pollutant concentration matrix according to the potential pollution emission pathways and the target function, so as to determine a pollution factor of the target pollutant and a factor contribution of the pollution factor.

[0008] The application provides a soil pollution source analysis device, which comprises the following modules:

[0009] A target determination module is configured to determine potential pollution emission pathways and potential soil pollutant types in a target region and determine a target pollutant according to a target region of soil pollution source analysis;

[0010] An acquisition module is configured to acquire soil environment background data of the target pollutant under the target region, and determine a median absolute deviation of soil background content of the target pollutant in the soil environment background data;

[0011] A construction module is configured to acquire the concentration of the target pollutant of sampling points of each grid after the target region is pre-divided into grids, and construct a soil pollutant concentration matrix;

[0012] The uncertainty determination module is configured to, for each sampling point, when the concentration of the target pollutant of the sampling point is greater than the median absolute deviation, determine the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation; when the concentration of the target pollutant of the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant and the target pollutant concentration uncertainty proportionality coefficient; when the concentration of the target pollutant of the sampling point is less than the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant of the sampling point according to the concentration of the method detection limit of the target pollutant; and obtain an uncertainty matrix.

[0013] The analysis module is configured to determine a target function when performing orthogonal matrix decomposition on the soil pollutant concentration matrix according to the uncertainty matrix, and perform orthogonal matrix decomposition on the soil pollutant concentration matrix according to the potential pollution emission path and the target function, to determine the pollution factor of the target pollutant and the factor contribution.

[0014] The application provides a computer readable storage medium, the storage medium stores a computer program, the computer program is executed by a processor to realize the soil pollution source analysis method.

[0015] The application provides a computer device, including a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executes the program to realize the soil pollution source analysis method.

[0016] The application adopts the above at least one technical scheme to achieve the following beneficial effects:

[0017] The application determines the median absolute deviation of the soil background content of the target pollutant in the soil environment background data, considers the uncertainty sources of the target pollutant in different concentration ranges according to the median absolute deviation and the concentration of the method detection limit of the target pollutant, comprehensively considers various uncertainty sources such as laboratory errors, chemical stability and sample uniformity of the target pollutant, constructs a perfect spatial heterogeneity uncertainty calculation method system, ensures that the uncertainty of the target pollutant can be effectively quantified in various cases, provides a more robust input for the orthogonal matrix decomposition method in soil pollution source analysis, and thus improves the accuracy and reliability of soil pollutant source analysis. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are included to provide a further understanding of the application and constitute a part of this application, illustrate certain illustrative embodiments of the application and together with the description serve to explain the application. In the drawings:

[0019] Figure 1 A soil pollution source analysis method provided by the present application is shown in the flowchart;

[0020] Figure 2 A regional soil environmental background sample point distribution diagram provided by the present application is shown in the diagram;

[0021] Figure 3 A regional soil pollution source analysis sample point distribution diagram provided by the present application is shown in the diagram;

[0022] Figure 4 A soil pollution factor analysis result diagram provided by the present application is shown in the diagram;

[0023] Figure 5 A soil pollution source analysis device provided by the present application is shown in the diagram. DETAILED DESCRIPTION

[0024] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in detail with reference to specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present application.

[0025] Unlike the application scenarios of atmospheric environmental pollutants, the applicability of the PMF model in soil environmental pollutant source tracing has not been systematically verified. In soil source analysis, the uncertainty of target pollutant source identification, in addition to the laboratory uncertainty, includes the uncertainty caused by the environmental background content of the pollutant and the soil sampling uniformity, which should be considered as the uncertainty of the model.

[0026] The soil environmental background content refers to the content of an element or compound in soil affected only by geochemical processes and non-point source inputs under certain conditions. Especially, the spatial heterogeneity of the sampling point caused by the soil environmental background content produces uncertainty in the model calculation, which is higher than that in the previous atmospheric pollutant source analysis. However, in the existing soil pollution source analysis method, this uncertainty is usually ignored.

[0027] The present application proposes a soil pollutant source analysis accurate identification method for accurately determining the uncertainty caused by the spatial heterogeneity of the soil sample environmental background, so as to improve the accuracy and reliability of the pollutant receptor model in the application of soil pollutant source analysis.

[0028] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 This is a schematic diagram of a soil pollution source apportionment method according to the present invention, which specifically includes the following steps:

[0030] S101: Based on the target area of ​​soil pollution source apportionment, determine the potential pollution emission pathways and types of potential soil pollutants within the target area, and identify the target pollutants.

[0031] S102: Obtain soil environmental background data of the target pollutant in the target area, and determine the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data.

[0032] S103: Obtain the concentration of target pollutants at the sampling points of each grid after the target area is pre-grid divided, and construct a soil pollutant concentration matrix.

[0033] S104: For each sampling point, when the concentration of the target pollutant at that sampling point is greater than the median absolute deviation, determine the uncertainty of the target pollutant at that sampling point based on the concentration of the target pollutant at the method detection limit, the uncertainty proportionality coefficient of the target pollutant concentration, and the median absolute deviation; when the concentration of the target pollutant at that sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the target pollutant at the method detection limit, determine the uncertainty of the target pollutant at that sampling point based on the concentration of the target pollutant at the method detection limit and the uncertainty proportionality coefficient of the target pollutant concentration; when the concentration of the target pollutant at that sampling point is less than the concentration of the target pollutant at the method detection limit, determine the uncertainty of the target pollutant at that sampling point based on the concentration of the target pollutant at the method detection limit; thus obtaining the uncertainty matrix.

[0034] S105: Determine the objective function for orthogonal matrix decomposition of the soil pollutant concentration matrix based on the uncertainty matrix. Perform orthogonal matrix decomposition of the soil pollutant concentration matrix based on the potential pollution emission pathways and the objective function to determine the pollution factors of the target pollutants and their factor contributions.

[0035] For ease of explanation, the following description focuses solely on the server as the executing entity. The server mentioned in this invention can be a server set up on a business platform, or a device such as a desktop computer or laptop computer capable of executing the solution of this invention.

[0036] In the soil pollution source analysis, the server can first determine the target area and confirm the pollutants in the target area. In one or more embodiments of the present application, the server can explicitly determine the boundary range of the soil pollution source analysis area, demarcate the target area, and obtain the relevant information of the pollution emission of human activities in the target area and the surrounding area according to the boundary range of the target area of the soil pollution source analysis. For example, the relevant information of the pollution emission of human activities such as industry and agriculture in the area and the surrounding area is collected and analyzed. Then, the server can determine the types of potential soil pollutants in the target area according to the relevant information, and determine the toxicity, persistence, and bioaccumulation of each type of soil potential pollutant. In this way, the toxicity, persistence, and bioaccumulation of each type of soil potential pollutant are considered, and the pollutants that are more harmful to the ecological environment and human health are preferentially selected as target pollutants. For example, several soil potential pollutants with the largest toxicity, persistence, and bioaccumulation can be selected as target pollutants.

[0037] After determining the target pollutants to be studied, the server can further obtain the soil environmental background data of the target pollutants in the target area. The soil environmental background data can be directly obtained from existing data or collected in real time.

[0038] For example, the regional soil environmental background content data of the same soil type or soil parent material (rock) type soil in the target area or the surrounding area can be collected. First, it can be determined whether the target area has a soil environmental background data set of the target pollutants. If there is an existing data set, the standardization of the data set can be evaluated. If the data set meets the relevant requirements of regional environmental background investigation sample layout, sample collection and analysis and detection, the subsequent steps can be directly performed. If there is no existing data set or the existing data set cannot fully meet the requirements of the standardization evaluation, the soil environmental background investigation of the target pollutants should be independently carried out or combined with the existing data set until the soil environmental background data set that meets the requirements of the standardization evaluation is obtained.

[0039] When conducting soil environmental background surveys, unlike constructing a soil pollutant concentration matrix for the target area, it is necessary to collect samples of the target pollutant content in soils affected only by geochemical processes and non-point source inputs. This means determining the target pollutant content when virtually unaffected by human activities. The key lies in the selection of sampling sites. On the one hand, the selected sites need to be representative; on the other hand, the sites should avoid the impact of human activities as much as possible. For example, the natural landscape of the sampling sites should meet the requirements of soil environmental background research. Sampling sites should be selected in locations with distinct soil type characteristics, relatively flat and stable terrain, and good vegetation. At the same time, sampling sites should not be set up in areas with significant human interference, such as current and historical towns, residential areas, industrial and mining enterprises, transportation facilities, water conservancy facilities, funeral parlors, and cesspools, or within their influence range. The impact range on the surrounding area should be comprehensively judged based on the actual situation. Agricultural land should generally be sampled before sowing or after crop maturity. Sampling sites should avoid locations where fertilizers and pesticides are concentrated to minimize the impact of human activities.

[0040] Based on appropriate sampling point selection, the soil environmental background data of the target pollutant in the target area can be obtained from the concentration of the target pollutant in the soil samples at the sampling points. The above is only an illustrative example; there are already mature technologies for obtaining the soil environmental background data of the target pollutant in the area, and this invention will not elaborate on them further.

[0041] Based on the soil environmental background data of the target pollutants in the target area, the server can perform environmental background analysis and characterization of the target pollutants in the soil of the target area.

[0042] For example, it can examine the distribution type of soil background content data for target pollutants and identify and process outliers in the dataset.

[0043] The background content of the target pollutant in the soil in the target area is statistically analyzed, and the median sample set and median absolute deviation (u) of the soil background content data (X) of the target pollutant are obtained.

[0044] .

[0045] in, For soil environmental background data, This represents the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data. For the first i The regional background content of the target pollutant in the soil at each sampling point To take the median, To take the absolute value.

[0046] Following environmental background analysis, the server can further collect and analyze soil source apportionment samples in the target area. In one or more embodiments of this invention, a systematic sampling method can be employed, such as a systematic grid sampling method, to deploy soil sampling points for pollution source apportionment. The target area is divided into regular grids of equal size at certain intervals. The size of the grid can be determined based on the area of ​​the target area, the complexity of the pollution source distribution, and the required accuracy of the investigation. Within each grid, the most representative sampling points are set according to factors such as regional topography and land use patterns. Surface soil samples are collected at each point according to relevant sample collection technical guidelines. The content of target pollutants in the surface soil samples is detected and analyzed according to relevant national standards. Finally, a sample set suitable for regional soil pollution source identification is obtained.

[0047] Furthermore, the server can construct an uncertainty system based on the spatial heterogeneity of the soil environment.

[0048] (1) Construction of pollutant concentration matrix of PMF model.

[0049] The PMF model can be represented by the following equation:

[0050] .

[0051] in, It is the first i In the nth sample j The concentration of the target pollutant; It is the number of pollutants (sources) identified; It is the first k The pollution source affects the first i The contribution of each sample; Indicates the first k The first of the pollution sources j The concentration of the pollutants; It is the first i In the nth sample j The residuals corresponding to each target pollutant in the PMF model. and The value is restricted to non-negative, and the uncertainty of each data point is weighted. Q serves as the primary criterion for calculating the PMF model; further analysis is only possible when it converges stepwise. The calculation ends when the Q value converges below a certain set value, and the obtained result... and The corresponding matrices are the factor contribution and factor fingerprint matrix obtained from pollution source analysis, respectively.

[0052] The objective function Q of the model can be derived using the following formula: .

[0053] wherein, is the concentration of the target pollutant in the i th sampling point, i is the uncertainty of the target pollutant in the i th sampling point, and is one of the most important inputs in the model. j The uncertainty value is crucial for the calculation of Q and directly affects the results of PMF source apportionment. The present application establishes a method system for calculating uncertainty by considering multiple possible sources of uncertainty. The uncertainty in the system comprehensively considers uncertainty from laboratory errors, chemical stability, and environmental background spatial heterogeneity in the sample area. Through the method system, the minimum objective function is achieved, and the uncertainty of the data is continuously optimized, thereby improving the accuracy of the source apportionment results. (2) Construction of an uncertainty system for spatial heterogeneity of PMF source apportionment model.

[0054] The system method for data uncertainty in the present application is as follows:

[0055] ① When the concentration of the target pollutant in the sampling point is greater than the median absolute deviation, i.e.

[0056] the uncertainty of the target pollutant in the sampling point can be determined according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient, and the median absolute deviation by the following formula:

[0057] .

[0058] wherein, is the concentration of the method detection limit of the target pollutant in the laboratory, is the concentration of the target pollutant in the i th sampling point, i is the target pollutant concentration uncertainty proportionality coefficient, i.e., the relative uncertainty of each pollutant component. According to the standard operating procedures of the laboratory, the value of each pollutant is different, is the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data, indicating the degree of dispersion relative to the regional soil pollutant background concentration. ② When the concentration of the target pollutant in the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, i.e. the uncertainty of the target pollutant in the sampling point can be determined according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient, and the median absolute deviation by the following formula:

[0059]

[0060] .

[0061] ​​​③ When the concentration of the target pollutant at the sampling point is less than the concentration of the method detection limit of the target pollutant, i.e. , the concentration and uncertainty of the target pollutant at the sampling point can be determined by the following formula: , ; that is, the point concentration is set to half, at which time the uncertainty is expressed as 5 / 6 .

[0062] Finally, the target pollutant can be quantitatively source-identified based on the PMF model in combination with the uncertainty setting of the spatial heterogeneity of the soil environment background. According to the pre-information collection of the target region (such as according to the local emission source list, pollution source survey data, industrial layout, energy structure, motor vehicle ownership, etc.), the range of the number of factors is preliminarily determined, the number of pollution sources (factors) is set, the number of simulation attempts is preliminarily determined, the random seed mode is adopted, the minimum robust convergence Q value of the pollution source (factor) is calculated, the regional soil source analysis is realized, and the pollution factor of the target pollutant and the factor contribution are determined.

[0063] The multiple calculation results obtained by the basic calculation need to be further judged and analyzed. Through the difference analysis of the Q calculation value (Q true ) and the prediction value (Q exp ), whether it converges (Converge) or not, the rationality of the calculation result is judged. The uncertainty of the source analysis result is analyzed and evaluated by using the error estimation method of the PMF model, such as the resampling method (Bootstrap, BS), Displacement (DISP) and the like, the optimal solution affecting the source analysis is identified, and the credibility and accuracy of the model are verified.

[0064] Based on the soil pollution source analysis method shown in Figure 1 , the present application determines the median absolute deviation of the soil background content of the target pollutant in the soil environment background data, considers the uncertainty sources of the target pollutant in different concentration ranges according to the median absolute deviation and the concentration of the method detection limit of the target pollutant, comprehensively considers various uncertainty sources of the target pollutant such as laboratory error, chemical stability and sample uniformity, constructs a perfect uncertainty calculation method system of spatial heterogeneity, ensures that the uncertainty of the target pollutant can be effectively quantified under various conditions, provides a more robust input for the orthogonal matrix decomposition method in soil pollution source analysis, and thus improves the accuracy and reliability of the soil pollutant source analysis.

[0065] Different uncertainty calculation methods are developed for different concentration ranges of pollutants. For example, when the concentration of the pollutant is higher than the background concentration, between the detection limit and the background concentration, or lower than the detection limit, different specific processing methods are used. This comprehensive and detailed uncertainty processing method ensures that the uncertainty of the data can be effectively quantified in various situations to provide more robust input.

[0066] The source analysis method that comprehensively considers the sources of data uncertainty enables the soil pollution source analysis model to more accurately weight the uncertainty of each data point, thereby optimizing the objective function Q of the analysis model. By continuously optimizing the uncertainty of the data, the uncertainty of different pollutants or the same pollutant at different concentrations is comparable. This avoids the difficulty of comparing the contributions of different factors due to different concentration units or error ranges, thereby enabling more accurate assessment of the contribution rate of each pollution source to soil pollution, ultimately improving the scientificity and reliability of the source analysis results.

[0067] Based on more accurate and reliable source analysis results, environmental protection departments and relevant decision-makers can more clearly understand the sources and contribution proportions of regional soil pollution. This helps to develop more targeted and effective soil pollution prevention and control strategies, reasonably allocate resources, and prioritize control of pollution sources that contribute more to soil pollution, thereby improving the efficiency and effectiveness of soil pollution control.

[0068] In the application of the soil pollution source analysis method provided by the present application, the concentration of each data point can be determined according to the actual situation of the case region, and the concentration of each data point can be determined according to the actual situation of the case region. Figure 1 The order of execution of each step shown in the above table can be executed according to the actual situation of the case region, and the order of execution of each step can be determined according to the actual situation of the case region. The present application does not limit this.

[0069] In addition, the present application also provides an embodiment of the application of the method of the present application:

[0070] A certain city administrative region is selected as a case region. The administrative area of the region is about 33 square kilometers, and the boundary of the region is designated as the boundary range of the present case, as shown in Figure 2 Figure 2 A regional soil environmental background sample distribution diagram in the present application.

[0071] According to the regional statistical yearbook, the economic structure of the region is mainly the tertiary industry, the secondary industry is mainly the light textile industry, and the number of heavy industrial factories and enterprises is relatively small and the industrial added value is very low. In 2023, the total value of the tertiary industry in the region accounted for more than 90% of the total value of the overall industrial added value, while the secondary industry accounted for less than 10%, of which the manufacturing industry accounted for only 1.0%.

[0072] ​According to the characteristics of human activities in the region, the main potential pollution emission pathways in this region are traffic emissions and daily life emissions of residents. There is no evidence of direct industrial emissions into the soil in this region. Therefore, the main potential pollutants generated by relevant human activities are mainly heavy metals and organic pollutants such as polycyclic aromatic hydrocarbons.

[0073] Considering the toxicity, persistence, and bioaccumulation of the relevant pollutants, the main heavy metal pollutants of urban soil are preferentially selected, including cadmium (Cd), copper (Cu), mercury (Hg), lead (Pb), and zinc (Zn), as well as high-ring polycyclic aromatic hydrocarbon organic pollutants, including fluoranthene (FLT), pyrene (PYR), chrysene (CHR), benzo(a) anthracene (BaA), benzo(k) fluoranthene (BkF), benzo(b) fluorantene (BbF), benzo(a) pyrene (BaP), dibenzo(a,h) anthracene (DBA), indeno(1, 2, 3-cd) pyrene (IND), and benzo(g, h, i) perylene (BghiP), etc.

[0074] Since no historical data on the background content of the soil environment in this region was collected, this example independently conducted an investigation to obtain the background content of the soil environment in this region. Due to the small area of the region, the various types of land use are evenly distributed and not dense, and the soil type and soil parent material (rock) type in this region are similar, and there is no significant difference in the factors affecting the background content of the soil in this region. Therefore, the region was taken as a whole background investigation unit.

[0075] The selection of the sampling points of the regional soil environmental background samples was based on a 1 km x 1 km grid system, and a total of 33 sampling points were set. The specific location of each sampling point ensured that the spatial distribution of the sampling points was uniform and representative. The setting of the regional soil environmental background sampling points is shown in Figure 2 The methods of point layout, sample collection, and analysis and testing in this example meet the relevant national or local standards and guidelines. The obtained soil environmental background data set can reflect the overall soil environmental background conditions in the region.

[0076] Carrying out soil human activity background sample analysis and data processing. The sample point analysis data in this embodiment meet the normal distribution, can be used as a human activity background value sample, and are suitable for regional pollutant index analysis. Statistical analysis is carried out on the sample data set, and the median value (Median) and the median absolute deviation (u) of the human obtained background content of the target pollutant of the regional soil are obtained.

[0077] Table 1 Statistical data of regional human activity soil background content

[0078]

[0079] A systematic random point distribution method is used to arrange sampling points for soil pollution source analysis in the region. The sampling point arrangement is as shown in Figure 3 . Figure 3 It is a regional soil pollution source analysis point distribution diagram in the application. Surface soil samples for regional soil pollution source analysis are collected. In this embodiment, the arranged points comprehensively cover different functional areas in the region, including residential areas, commercial areas, industrial areas and green lands, so as to comprehensively reflect the influence of pollution source emissions on the global soil.

[0080] Based on the analysis and test results of the regional soil source analysis samples, a regional soil pollutant concentration matrix is constructed. Based on the characterization results of the soil environmental background of the target pollutant in the region, a matrix of uncertainty generated due to the spatial heterogeneity of the sample environmental background is constructed.

[0081] 2-5 pollution sources are preferentially set. 100 simulation attempts are set respectively, the minimum robust convergence Q value of the pollution source (factor) is calculated, and the factor distribution and contribution ratio of the regional soil pollutant concentration are calculated.

[0082] According to the pre-information collection of the target region, the soil pollution sources of the case region are identified. The main potential pollution sources of the target pollutants in the region identified include urban traffic emissions, urban harmful substance use and resident daily garbage emissions. The specific pollution factor analysis results are as shown in Figure 4 . Figure 4 It is a soil pollution factor analysis result diagram in the application.

[0083] The first factor is a traffic emission source.

[0084] The characteristic pollutants mainly include high molecular weight polycyclic aromatic hydrocarbons and the like, which account for 51.3% of the total pollutant chemical component contribution.

[0085] The characteristic pollutants of the pollution source are the main evidence of diesel / gasoline combustion pollution and are closely related to traffic exhaust. These characteristic pollutants are also significantly related to the concentrations of heavy metals such as cadmium, mercury and zinc, because these heavy metals also exist in vehicle tires, brakes, lubricating oil and fuel exhaust, and all these compounds and elements are significantly related to traffic density.

[0086] The second factor: the source of urban harmful material use.

[0087] The characteristic pollutants mainly include mercury and lead and account for 24.8% of the contribution of the total pollutant chemical components.

[0088] The characteristic pollutants in the factor can be traced back to materials such as pesticides, fertilizers, paints and coatings. In the process of afforestation and urbanization, harmful materials containing related substances are used in large quantities in the region and are finally released into the soil.

[0089] The second factor: the source of daily garbage discharge of residents.

[0090] The characteristic pollutants mainly include cadmium, copper and zinc and account for 23.9% of the contribution of the total pollutant chemical components.

[0091] The characteristic elements of the pollution source are the main evidence of human habitation and daily activities, and multiple point sources may cause the concentration of these elements in the soil of the dispersed point in the city to increase. This source may be closely related to some point source pollution emissions in municipal or domestic waste.

[0092] The Q calculated values (Q true ) of the results of the analysis of each calculation solution, in which three pollution sources are set, and the predicted values (Q exp ) are 117%, and the simulation results converge. Through the model error estimation method, the uncertainty of the source resolution result is further evaluated. In the BS error evaluation, by comparing the factor contributions under different settings, the BS running results under 3 to 5 factors are mapped to the basic running results. All samples in the 3-factor BS result can be mapped on different factors. However, for the four-factor or five-factor setting, the BS mapping ratio is less than 70%. Therefore, the 3-factor solution is more stable than other solutions.

[0093] In the BS error evaluation, the rationality of the Q value in the basic calculation is evaluated through the distribution of the Q correction value. The Q value obtained by the basic calculation is between the 25th and 75th percentile values after the BS correction. It is proved that the uncertainty of the calculation result of the chemical component model is acceptable, and the calculation result of the resolution model is good.

[0094] The soil pollution source resolution method provided by one or more embodiments of the present application is based on the same idea, and the present application also provides a corresponding soil pollution source resolution device, as shown in Figure 5 .

[0095] Figure 5 A soil pollution source analysis device provided by the present application, a schematic diagram thereof comprises:

[0096] The target determination module 201 is configured to determine potential pollution discharge pathways and potential soil pollutant types in a target region according to a target region of soil pollution source analysis, and determine a target pollutant.

[0097] The acquisition module 202 is configured to acquire soil environmental background data of the target pollutant in the target region, and determine a median absolute deviation of soil background content of the target pollutant in the soil environmental background data.

[0098] The construction module 203 is configured to acquire concentrations of the target pollutant at sampling points of each grid after the target region is pre-divided into grids, and construct a soil pollutant concentration matrix.

[0099] The uncertainty determination module 204 is configured to, for each sampling point, when the concentration of the target pollutant at the sampling point is greater than the median absolute deviation, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant, a target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation; when the concentration of the target pollutant at the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant and the target pollutant concentration uncertainty proportionality coefficient; when the concentration of the target pollutant at the sampling point is less than the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant; and obtain an uncertainty matrix.

[0100] The analysis module 205 is configured to determine a target function when performing orthogonal matrix decomposition on the soil pollutant concentration matrix according to the uncertainty matrix, perform orthogonal matrix decomposition on the soil pollutant concentration matrix according to the potential pollution discharge pathways and the target function, and determine a pollution factor of the target pollutant and a factor contribution of the pollution factor.

[0101] The specific limitations of the soil pollution source analysis device can be referred to the limitations of the soil pollution source analysis method in the foregoing, which will not be repeated here. The modules in the soil pollution source analysis device described above can be realized by software, hardware and combinations thereof in whole or in part. The modules described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0102] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the aboveFigure 1 The provided soil pollution source analysis method.

[0103] The computer device provided by the present application comprises a processor, an internal bus, a network interface, a memory and a nonvolatile memory at the hardware level, and can also comprise other hardware required by the business. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs to realize the above-mentioned Figure 1 The provided soil pollution source analysis method.

[0104] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a nonvolatile computer readable storage medium, and when the computer program is executed, the computer program can include the processes of the above-mentioned embodiment methods. In the embodiments of the present application, any reference to the memory, storage, database or other medium can include at least one of the nonvolatile and volatile memories. The nonvolatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory or an optical memory. The volatile memory can include a random access memory (RAM) or an external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM).

[0105] The technical features of the above embodiments can be combined in any manner. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

Claims

1. A method for source apportionment of soil pollution, characterized in that, The method comprises the following steps: According to the target area of soil pollution source analysis, determine the potential pollution emission path and the potential soil pollutant type in the target area and determine the target pollutant; Obtain the soil environmental background data of the target pollutant in the target area, and determine the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data; Obtain the concentration of the target pollutant of each sampling point in each grid after the target area is pre-grid divided, and construct a soil pollutant concentration matrix; For each sampling point, when the concentration of the target pollutant of the sampling point is greater than the median absolute deviation, the uncertainty of the target pollutant of the sampling point is determined according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation; when the concentration of the target pollutant of the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, the uncertainty of the target pollutant of the sampling point is determined according to the concentration of the method detection limit of the target pollutant and the target pollutant concentration uncertainty proportionality coefficient; when the concentration of the target pollutant of the sampling point is less than the concentration of the method detection limit of the target pollutant, the uncertainty of the target pollutant of the sampling point is determined according to the concentration of the method detection limit of the target pollutant; Obtain the uncertainty matrix; According to the uncertainty matrix, determine the objective function when the soil pollutant concentration matrix is subjected to orthogonal matrix decomposition, and according to the potential pollution emission path and the objective function, the soil pollutant concentration matrix is subjected to orthogonal matrix decomposition to determine the pollution factor of the target pollutant and the factor contribution.

2. The soil pollution source resolution method according to claim 1, wherein, According to the target area of soil pollution source analysis, determine the potential soil pollutant type in the target area and determine the target pollutant, specifically comprising: According to the boundary range of the target area of soil pollution source analysis, obtain the related information of the pollution emission of human activities in the target area and the surrounding area; According to the related information, determine the potential soil potential pollutant type in the target area, and determine the toxicity, persistence and biological accumulation of each type of soil potential pollutant; The several soil potential pollutants with the largest toxicity, persistence and biological accumulation are taken as the target pollutants.

3. The soil pollution source apportionment method of claim 1, wherein, The median absolute deviation of the soil background content of the target pollutant in the soil environmental background data is determined by the following formula: When the concentration of the target pollutant of the sampling point is greater than the median absolute deviation, the uncertainty of the target pollutant of the sampling point is determined according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation, specifically comprising: ； wherein, is the soil environmental background data, is the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data, is the regional background content of the target pollutant in the soil of the i th sampling point, is the median, is the absolute value.

4. The soil pollution source apportionment method of claim 1, wherein, When the concentration of the target pollutant of the sampling point is greater than the median absolute deviation, the uncertainty of the target pollutant of the sampling point is determined according to the concentration of the method detection limit of the target pollutant, the target pollutant concentration uncertainty proportionality coefficient and the median absolute deviation by the following formula: ​ ； in, For the first i The sampling point of the nth sampling point j Uncertainty regarding the target pollutant The concentration of the target pollutant at the method detection limit. The proportionality coefficient for the uncertainty of the target pollutant concentration. For the first i The concentration of the target pollutant at each sampling point. This represents the median absolute deviation of the soil background content of the target pollutant in the soil environmental background data.

5. The soil pollution source apportionment method of claim 1, wherein, When the concentration of the target pollutant at the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, the uncertainty of the target pollutant at the sampling point is determined according to the concentration of the method detection limit of the target pollutant and the uncertainty proportionality coefficient of the target pollutant concentration, and specifically includes: When the concentration of the target pollutant at the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, the uncertainty of the target pollutant at the sampling point is determined according to the concentration of the method detection limit of the target pollutant and the uncertainty proportionality coefficient of the target pollutant concentration, and specifically includes: ； in, For the first i The sampling point of the nth sampling point j Uncertainty regarding the target pollutant The concentration of the target pollutant at the method detection limit. The proportionality coefficient for the uncertainty of the target pollutant concentration. For the first i The concentration of the target pollutant at each sampling point.

6. The soil pollution source apportionment method of claim 1, wherein, When the concentration of the target pollutant at the sampling point is less than the concentration of the method detection limit of the target pollutant, the concentration and uncertainty of the target pollutant at the sampling point are determined by the following formula: When the concentration of the target pollutant at the sampling point is less than the concentration of the method detection limit of the target pollutant, the concentration and uncertainty of the target pollutant at the sampling point are determined by the following formula: ， ； wherein, is the method detection limit of the target pollutant, i is the uncertainty of the target pollutant in the i-th sampling point, j is the concentration of the target pollutant in the i-th sampling point. is the concentration of the target pollutant in the i-th sampling point. is the concentration of the target pollutant in the i-th sampling point. i is the concentration of the target pollutant in the i-th sampling point.

7. A soil pollution source apportionment device, comprising: Including: The target determination module is configured to determine potential pollution emission pathways and potential soil pollutant species in a target region according to a soil pollution source analysis of the target region, and determine a target pollutant. The acquisition module is configured to acquire soil environmental background data of the target pollutant in the target region, and determine a median absolute deviation of a soil background content of the target pollutant in the soil environmental background data. The construction module is configured to acquire the concentration of the target pollutant at the sampling points of each grid after the target region is pre-divided into grids, and construct a soil pollutant concentration matrix. The uncertainty determination module is configured to, for each sampling point, when the concentration of the target pollutant at the sampling point is greater than the median absolute deviation, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant, the uncertainty proportionality coefficient of the target pollutant concentration, and the median absolute deviation; when the concentration of the target pollutant at the sampling point is less than or equal to the median absolute deviation and greater than or equal to the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant and the uncertainty proportionality coefficient of the target pollutant concentration; and when the concentration of the target pollutant at the sampling point is less than the concentration of the method detection limit of the target pollutant, determine the uncertainty of the target pollutant at the sampling point according to the concentration of the method detection limit of the target pollutant. An uncertainty matrix is obtained. The analysis module is configured to determine a target function when the soil pollutant concentration matrix is subjected to orthogonal matrix decomposition according to the uncertainty matrix, and perform orthogonal matrix decomposition on the soil pollutant concentration matrix according to the potential pollution emission pathways and the target function, to determine a pollution factor of the target pollutant and a factor contribution of the pollution factor.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1-6.

9. A computer device, comprising: The computer program is stored in the memory and executable on the processor, and the processor implements the method of any one of claims 1-6 when executing the computer program.

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