A method for heavy metal source apportionment based on PMF and geographic detector model

Abstract

This invention relates to the field of data analysis and provides a method for heavy metal source apportionment based on PMF and a geographic detector model. The method includes the following steps: S1, sampling soil in the target area to obtain soil samples; S2, measuring the sample images to obtain heavy metal concentration data; S3, analyzing the spatial distribution characteristics of the heavy metal concentration data to obtain spatial distribution features; S4, based on the heavy metal concentration data and the spatial distribution features, using PMF and a geographic detector model to perform source apportionment to obtain the heavy metal source apportionment result. This invention achieves heavy metal source apportionment by integrating the parameter indicators calculated by PMF and the geographic detector model, providing a better definition of the contribution weights of multiple heavy metal pollution sources, thereby achieving comprehensive and complete soil pollution information, which is more conducive to subsequent environmental remediation.

Description

Technical Field

[0001] This invention relates to the field of data analysis, and in particular to a method for heavy metal source analysis based on PMF and geographic detector models. Background Technology

[0002] With rapid urbanization and industrialization, soil heavy metal pollution and accumulation have become a serious environmental problem. Because the accumulation of heavy metals in soil leads to reduced soil fertility and crop yields, and they can be transferred through the food chain and accumulate through biomagnification, this poses a significant risk to food safety and human health. Therefore, understanding and identifying the sources of heavy metals in soil is crucial to reducing the risk of soil pollution.

[0003] The Positive Matrix Factorization (PMF) model is an effective receptor model for pollution source allocation. This model simplifies high-dimensional variables by using correlation and covariance matrices to ensure non-negative source contributions and factor distributions, ultimately used to identify and quantify heavy metal pollution sources in soil. The Geographic Detector model is used to determine the spatial correlation between each component score and a detailed pollution source dataset.

[0004] Previous studies have typically employed multivariate qualitative or quantitative statistical methods for analyzing heavy metals in soil, such as spatial bias analysis, correlation analysis, cluster analysis, geographic information systems (GIS), and principal component analysis (PAC), combined with multiple linear regression, to determine the variability and potential sources of heavy metals in soil. However, these methods largely fail to analyze the influence of categorical variables such as soil type and parent material, and lack consideration for spatial information of sampling points. Consequently, they cannot define the detailed impact, spatial characteristics, and weight of specific pollution sources on principal components based on spatial analysis data. Summary of the Invention

[0005] The present invention aims to address the problems of existing methods for analyzing heavy metals in soil, such as the lack of consideration for spatial information and the failure to consider weighting in the analysis of detailed characteristics of heavy metal pollution sources.

[0006] To address the shortcomings of the aforementioned technologies, this invention provides a heavy metal source analysis method based on PMF and geographic detectors, the method comprising the following steps:

[0007] S1. Soil samples are taken from the target area to obtain soil samples;

[0008] S2. Measure the image of the sample to obtain the heavy metal concentration data of the soil sample;

[0009] S3. Analyze the spatial distribution characteristics of the heavy metal concentration data to obtain the spatial distribution characteristics;

[0010] S4. Based on the heavy metal concentration data and the spatial distribution characteristics, source analysis is performed using PMF and geographic detector models to obtain heavy metal source analysis results.

[0011] Furthermore, step S1 specifically includes the following sub-steps:

[0012] Determine the scope of the target area for analysis and the sampling method for conducting the soil sampling;

[0013] The soil sample was obtained based on the target area and the sampling method.

[0014] Furthermore, step S2 specifically includes the following sub-steps:

[0015] The soil sample was air-dried and ground.

[0016] The soil sample was digested, and the heavy metal concentration data of the soil sample were obtained using the ICP-AES method.

[0017] Furthermore, in the step of digesting the soil sample, HNO3, HCl, HF, and HClO4 are used sequentially to digest the soil sample.

[0018] Furthermore, the heavy metal concentration data includes the concentration data of Pb, Ni, Zn, Cu, Cr, and Cd.

[0019] Furthermore, step S3 specifically includes:

[0020] The inverse distance weighting method is used, with the target area being analyzed as the distribution point and the heavy metal concentration as the interpolation value of the distribution point, to obtain the spatial distribution characteristics.

[0021] Furthermore, step S1 also includes the following steps:

[0022] Obtain auxiliary data for heavy metal source analysis in the target region.

[0023] Furthermore, step S4 specifically includes the following sub-steps:

[0024] SPSS descriptive statistics and Spearman correlation analysis were performed on the heavy metal concentration data to obtain the basic characteristic information of heavy metals in the target region of the analysis.

[0025] Using the heavy metal concentration data as input to the PMF model, the heavy metal contribution information in the heavy metal concentration data is obtained;

[0026] A geographic detector was used to perform correlation analysis on the heavy metal concentration data and the heavy metal source apportionment auxiliary data to obtain spatial correlation information of heavy metal sources.

[0027] The basic characteristic information of the heavy metal, the contribution information of the heavy metal, and the spatial correlation information of the heavy metal source are output as the heavy metal source analysis result.

[0028] Furthermore, the geographic detector includes a factor detector, a risk detector, an ecological detector, and an interaction detector.

[0029] The beneficial effects achieved by this invention are as follows: by integrating parameters calculated from PMF and geographic detector models, the contribution rates of various pollution sources to heavy metals are determined, the dominant factors of each environmental covariate are identified, and the spatial correlation between the two is analyzed, ultimately realizing the source apportionment of heavy metals. Furthermore, by including spatial information and various forms of auxiliary data in the model to expand the correlation data, the contribution weights of multiple heavy metal pollution sources are better defined, thereby achieving comprehensive and complete soil pollution information and providing assistance for subsequent environmental remediation.

[0030] Attached Figure

[0031] Figure 1 This is a flowchart of the steps of the heavy metal source analysis method based on PMF and geographic detector provided in the embodiments of the present invention;

[0032] Figure 2 This is a schematic diagram of the basic characteristic information of heavy metals provided in the embodiments of the present invention;

[0033] Figure 3 This is a factor source contribution diagram in the PMF model provided in this embodiment of the invention;

[0034] Figure 4 This is a schematic diagram of the heavy metal source analysis results provided in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0036] For details, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a heavy metal source analysis method based on PMF and geographic detectors provided in an embodiment of the present invention. The method includes the following steps:

[0037] S1. Soil samples are taken from the target area to obtain soil samples.

[0038] Furthermore, step S1 specifically includes the following sub-steps:

[0039] Determine the scope of the target area for analysis and the sampling method for conducting the soil sampling;

[0040] The soil sample was obtained based on the target area and the sampling method.

[0041] For example, in order to eliminate the influence of the objective environment on the data during the soil sampling process, the relationship between time and location should be considered when sampling the soil samples. For example, in this embodiment of the invention, the soil samples were collected from December 2010 to December 2020, that is, the time period is 10 years, and the number of soil samples collected each year is not less than 10.

[0042] Furthermore, step S1 also includes the following steps:

[0043] Obtain auxiliary data for heavy metal source analysis in the target region.

[0044] In this embodiment of the invention, the auxiliary data for heavy metal source apportionment are environmental covariates, such as data on soil parent material, road networks, and industry, as well as data on soil type, soil use, and geospatial information, and pollution characteristic data of surrounding industrial buildings and characteristic pollutants. All of the above data can be obtained through on-site collection or through relevant information dissemination platforms.

[0045] S2. Measure the sample image to obtain the heavy metal concentration data of the sample soil.

[0046] Furthermore, step S2 specifically includes the following sub-steps:

[0047] The soil sample was air-dried and ground.

[0048] The soil sample was digested, and the heavy metal concentration data of the soil sample were obtained using ICP-AES (inductively coupled plasma atomic emission spectrometry).

[0049] Specifically, in this embodiment of the invention, all the sample soils were air-dried at room temperature and ground to a size of 100 mesh.

[0050] Furthermore, in the step of digesting the soil sample, HNO3, HCl, HF, and HClO4 are used sequentially to digest the soil sample.

[0051] Furthermore, the heavy metal concentration data includes the concentration data of Pb, Ni, Zn, Cu, Cr, and Cd.

[0052] S3. Analyze the spatial distribution characteristics of the heavy metal concentration data to obtain the spatial distribution characteristics.

[0053] Furthermore, step S3 specifically includes:

[0054] The inverse distance weighting method is used, with the target area being analyzed as the distribution point and the heavy metal concentration as the interpolation value of the distribution point, to obtain the spatial distribution characteristics.

[0055] Specifically, when using the inverse distance weighting method for spatial distribution analysis of pollutants, the process is fast, convenient, and easy to interpret, and the relationship between interpolation and sampling points can be used as prediction weights. In cases where the target area has complex terrain and unevenly distributed sampling points, for example, this embodiment of the invention uses the inverse distance weighting method in ArcGIS software to derive a geochemical distribution map and interpolates the concentration of heavy metals in the soil between irregularly distributed locations.

[0056] In this embodiment of the invention, the inverse distance weighting interpolation method in ArcGIS software mainly involves four steps:

[0057] First, launch the Geostatistics Wizard in ArcGIS software, select the Inverse Distance Weighting method in the Deterministic Methods, and then select the source dataset and data fields for interpolation;

[0058] Next, in the general properties, set the maximum and minimum number of adjacent features, as well as the sector type, major semi-axis, minor semi-axis, and angle;

[0059] Then cross-validation is performed. This step shows the goodness of fit between the predicted and measured values, as well as the prediction error. If you are satisfied with the model, you can proceed to the next step to view the method report.

[0060] Finally, obtain and view the interpolation results in the data view.

[0061] S4. Based on the heavy metal concentration data and the spatial distribution characteristics, source analysis is performed using PMF and geographic detector models to obtain heavy metal source analysis results.

[0062] Furthermore, step S4 specifically includes the following sub-steps:

[0063] SPSS descriptive statistics and Spearman correlation analysis were performed on the heavy metal concentration data to obtain the basic characteristic information of heavy metals in the target region of the analysis.

[0064] Using the heavy metal concentration data as input to the PMF model, the heavy metal contribution information in the heavy metal concentration data is obtained;

[0065] A geographic detector was used to perform correlation analysis on the heavy metal concentration data and the heavy metal source apportionment auxiliary data to obtain spatial correlation information of heavy metal sources.

[0066] The basic characteristic information of the heavy metal, the contribution information of the heavy metal, and the spatial correlation information of the heavy metal source are output as the heavy metal source analysis result.

[0067] Specifically, this embodiment of the invention uses SPSS software for descriptive statistics and Spearman correlation analysis. Descriptive statistics involves organizing and analyzing data using charts or mathematical methods, and estimating and describing the distribution, numerical characteristics, and relationships between random variables. This embodiment of the invention uses the heavy metal concentration data from the sample soil to summarize and statistically analyze the maximum, minimum, mean, median, variance, and standard coefficient of variation of each heavy metal concentration, thereby determining the spatial distribution of various heavy metal concentrations in the study area. Spearman correlation analysis is used to explore the significant relationships between selected heavy metals; the significance is represented by the p-value, where p < 0.05 indicates a significant correlation, and p < 0.01 indicates a highly significant correlation. For example, this embodiment of the invention provides a schematic diagram of basic heavy metal characteristic information as follows: Figure 2 As shown.

[0068] The PMF model resolves the chemical mass balance between the concentration of substances at the measurement sites and the sources, and uses the uncertainty of the sampling data to weight all data that can analyze the contribution rate of the target variable, i.e., to allocate the source of heavy metals. In this embodiment of the invention, the original heavy metal concentration is used to calculate the uncertainty of each sample. Then, the heavy metal concentration data matrix is ​​decomposed through 20 runs of the PMF model. Subsequently, the optimal factors are set to 3, resulting in two matrices, including the factor distribution F and the factor contribution G with the source number. At the same time, the BS (Bootstrap) and DISP (Displacement) models in PMF are used to evaluate the bias and uncertainty of the PMF results.

[0069] After determining the contribution information of each heavy metal to each factor in the PMF model, the relevant variables of potential pollution sources are obtained using the analysis and extraction tools of ArcGIS software. For example, in this embodiment of the invention, the Euclidean distance tool is used to obtain the distance between polluting industrial buildings and sampling points to represent industrial pollution parameters, and the kernel density tool is used to obtain traffic road density to represent traffic pollution source parameters. It should be noted that the use of analysis and extraction tools to obtain relevant variables of potential pollution sources in this embodiment of the invention is merely an example of a visualization result and is not intended to limit the invention. For example, an example of a factor source contribution diagram in a PMF model provided by this embodiment of the invention is shown below. Figure 3 As shown.

[0070] Furthermore, the geographic detector includes a factor detector, a risk detector, an ecological detector, and an interaction detector.

[0071] For example, embodiments of the present invention use factor detectors and interaction detectors to quantitatively detect the degree of influence of pollution sources on PMF component scores. In this case, the factor detector detects the spatial heterogeneity of the dependent variable Y and how the independent variable X explains the spatial pattern of the dependent variable Y.

[0072] In one possible embodiment, a factor 1 extracted from the PMF can be defined as a specific source. Subsequently, the factor detector determines that factor 1 has a spatial distribution similar to soil parent material by the spatial correlation strength. In this case, factor 1 is very likely to be a natural source. The spatial correlation strength is represented by the power measurement (PD) value. The interaction detector is used to detect the interaction between two different factors, that is, to assess whether the factors working together will increase or decrease the explanatory power of the dependent variable Y, or whether the effects of these factors on the dependent variable Y are independent of each other.

[0073] In one possible embodiment of using the PMF in conjunction with the geographic detector, the interaction strength of these factors is calculated based on the number of factors extracted by the PMF model. For example, the values ​​of each pair of pollution sources are labeled as PD = X1 and PD = X2, and their interaction influence value PD = X1 ∩ X2 is calculated by the interaction detector. Finally, the interaction relationship between the two sources is evaluated by comparing the values ​​of X1, X2, and X1 ∩ X2.

[0074] For example, a schematic diagram of heavy metal source analysis results provided in an embodiment of the present invention is shown below. Figure 4 As shown, this embodiment of the invention can visualize the distribution of heavy metal pollution sources in the target area by combining the results of PMF and geographic detectors.

[0075] The beneficial effects achieved by this invention are as follows: by integrating parameters calculated from PMF and geographic detector models, the contribution rates of various pollution sources to heavy metals are determined, the dominant factors of each environmental covariate are identified, and the spatial correlation between the two is analyzed, ultimately realizing the source apportionment of heavy metals. Furthermore, by including spatial information and various forms of auxiliary data in the model to expand the correlation data, the contribution weights of multiple heavy metal pollution sources are better defined, thereby achieving the comprehensiveness and completeness of soil pollution information and providing assistance for subsequent environmental remediation.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. For example, in one possible implementation, the computer-readable storage medium stores a computer program that, when executed by a processor, implements the various processes and steps in the heavy metal source analysis method based on PMF and geographic detectors provided in the embodiments of the present invention, and achieves the same technical effects. To avoid repetition, further details are omitted here.

[0077] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0078] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0079] The embodiments of the present invention have been described above with reference to the accompanying drawings. The disclosed embodiments are merely preferred embodiments of the present invention. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many equivalent changes in form without departing from the spirit and scope of the claims of the present invention, and all such changes are within the protection scope of the present invention.

Claims

1. A method for resolving heavy metal sources based on PMF and geoprobe model, characterized in that, The method comprises the following steps: S1, soil sampling is performed on an analysis target area to obtain sample soil; S2, a sample image is determined to obtain heavy metal concentration data of the sample soil; S3, spatial distribution characteristics of the heavy metal concentration data are analyzed to obtain spatial distribution characteristics; S4, source analysis is performed on the heavy metal concentration data and the spatial distribution characteristics by using a PMF and a geographic detector model to obtain a heavy metal source analysis result; Step S3 is specifically: An inverse distance weighting method is used, the analysis target area is taken as a distribution point, and the heavy metal concentration is taken as an interpolation value of the distribution point to obtain the spatial distribution characteristics; Step S1 further comprises the following steps: Heavy metal source analysis auxiliary data of the analysis target area are obtained; Step S4 specifically comprises the following sub-steps: SPSS descriptive statistics and Spearman correlation analysis are performed on the heavy metal concentration data to obtain basic characteristic information of heavy metals in the analysis target area; The heavy metal concentration data are taken as an input of a PMF model to obtain heavy metal contribution information in the heavy metal concentration data; The heavy metal concentration data and the heavy metal source analysis auxiliary data are analyzed by using a geographic detector to obtain spatial correlation information of heavy metal sources; The basic characteristic information of heavy metals, the heavy metal contribution information, and the spatial correlation information of heavy metal sources are output as the heavy metal source analysis result.

2. The PMF and geoprober model based heavy metal source apportionment method of claim 1, wherein, Step S1 specifically comprises the following sub-steps: The range of the analysis target area and a sampling method for soil sampling are determined; The sample soil is obtained according to the analysis target area and the sampling method.

3. The PMF and geoprober model based heavy metal source apportionment method of claim 1, wherein, Step S2 specifically comprises the following sub-steps: The sample soil is subjected to air-drying and grinding treatment; The sample soil is subjected to digestion treatment, and the heavy metal concentration data of the sample soil are obtained by using an ICP-AES method.

4. The PMF and geoprober model based heavy metal source apportionment method of claim 3, wherein, In the step of subjecting the sample soil to digestion treatment, the sample soil is subjected to digestion treatment by using HNO3, HCl, HF, and HClO4 in sequence.

5. The PMF and geoprober model based heavy metal source apportionment method of claim 4, wherein, The heavy metal concentration data comprise concentration data of Pb, Ni, Zn, Cu, Cr, and Cd.

6. The PMF and geoprober model based heavy metal source apportionment method of claim 1, wherein, The geographic detector comprises a factor detector, a risk detector, an ecological detector, and an interaction detector.

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