A method for tracing heavy metals in soil in a carbonate rock distribution area

Abstract

This invention discloses a method for tracing the source of heavy metals in soil in carbonate rock distribution areas, belonging to the field of soil heavy metal tracing technology. It includes the following steps: (1) Geological background analysis: constructing a regionally correlated geological baseline; (2) Soil sample collection and chemical analysis: providing basic data for tracing work; (3) Chemical fingerprint feature extraction: constructing a "morphology-isotope" dual-dimensional fingerprint to improve the accuracy of source differentiation; (4) Spatial distribution modeling and tracing: multi-dimensional data collaborative analysis, combined with spatial visualization to achieve accurate tracing. This invention is based on geological background to clarify the natural source baseline; uses chemical fingerprint data as the core to establish a feature system database; optimizes receptor model parameters to enhance the ability to distinguish between natural and anthropogenic sources; and uses spatial distribution data as a carrier to verify the spatial correlation of sources, ultimately effectively improving the accuracy of tracing.

Description

Technical Field

[0001] This invention belongs to the field of soil heavy metal tracing technology, specifically relating to a method for tracing heavy metals in soil in carbonate rock distribution areas. Background Technology

[0002] In the field of environmental science, the accurate source tracing and effective control of heavy metal pollution in soil has always been a core research topic for ensuring ecological security, agricultural product quality and human health. This problem is particularly complex and challenging in carbonate rock regions that are widely distributed around the world due to their unique geological background and soil formation process.

[0003] The soil formation process in carbonate rock distribution areas is significantly influenced by the characteristics of the parent rock, and the sources of heavy metals exhibit a complex characteristic of being influenced by both natural causes and human activities. In terms of natural causes, this is mainly manifested in the gradual release and migration of heavy metal elements (such as cadmium, lead, and chromium) from the parent carbonate rock into the soil under long-term weathering, erosion, and leaching. This process is also regulated by regional climatic conditions (such as precipitation and temperature), topographic features, and hydrogeological conditions, resulting in significant heterogeneity in the spatial distribution and concentration levels of naturally sourced heavy metals. On the other hand, inputs from human activities exacerbate the complexity of pollution, mainly including the emission of waste gas and wastewater and the accumulation of solid waste during industrial production processes (such as mining and electroplating); the application of fertilizers containing heavy metal impurities and wastewater irrigation during agricultural activities; and the deposition of heavy metals from fuel combustion during transportation. This multi-source and multi-pathway pollution superposition characteristic makes it extremely difficult to accurately identify the natural background and human contributions and achieve precise pollution source tracing in carbonate rock areas.

[0004] Currently, the technical system for tracing heavy metal sources in soil mainly relies on three types of methods: chemical analysis, isotope analysis, and multivariate statistical analysis. Chemical analysis methods, such as inductively coupled plasma mass spectrometry (ICP-MS), have advantages such as low detection limits and high accuracy, accurately determining elemental content and providing a crucial data foundation for source tracing research. Isotope analysis utilizes the differences in the isotopic composition of heavy metal elements in different pollution sources. By measuring specific element ratios and comparing them with isotopic fingerprint spectra, qualitative identification of the source is achieved, exhibiting strong specificity and anti-interference capabilities. Multivariate statistical analysis (such as principal component analysis and cluster analysis) is based on a large dataset of elemental content and physicochemical properties of soil samples. Through dimensionality reduction and classification, it infers the main pollution sources and their contribution rates. These methods are widely used in environmental monitoring, pollution control, and soil remediation. At the monitoring level, they support routine monitoring and risk assessment of soil environmental quality; at the control level, they assist in identifying dominant pollution sources and guiding strategy formulation; and at the remediation level, they provide a scientific basis for the precise selection of remediation technologies.

[0005] However, when faced with pollution characteristics of high background values, multi-source mixing, and significant spatial variability in carbonate rock distribution areas, existing soil heavy metal source tracing methods still have certain limitations in terms of identification accuracy and data integration, specifically including the following problems:

[0006] 1. Insufficient accuracy in tracing the source: Single analysis techniques are insufficient to overcome the interference from multiple sources.

[0007] Existing methods are mostly based on single chemical analysis or isotope analysis principles. However, heavy metals in soils from carbonate rock distribution areas are characterized by a high degree of overlap between natural and anthropogenic sources. In terms of elemental composition and isotope ratios, natural and anthropogenic sources often overlap (e.g., the isotope ratio range of weathered lead from carbonate rocks may coincide with that of lead emitted from some industries). Relying solely on quantitative determination or single isotope analysis is insufficient to effectively identify natural and anthropogenic pollution sources, ultimately leading to reduced accuracy and reliability of source tracing results.

[0008] 2. Poor data integration: Lack of a systematic analysis framework adapted to complex geological backgrounds.

[0009] The soil formation process in carbonate rock distribution areas is complex, involving multi-dimensional characteristics such as geology (e.g., parent rock type, weathering conditions), chemistry (heavy metal speciation and adsorption mechanisms), and space (differences in pollutant migration due to topography). Furthermore, these characteristics are strongly correlated (e.g., topographic slope affects soil physicochemical properties, indirectly altering the spatial distribution of heavy metals). However, current technologies lack a collaborative analysis mechanism for multi-dimensional data. For example, when processing data, chemical content or isotopic data are often analyzed separately, failing to systematically integrate geological background information, spatial distribution data, and heavy metal chemical speciation characteristics. This results in an inability to fully reconstruct the entire process of "parent rock weathering - external input - migration and transformation" in carbonate rock distribution areas, limiting the comprehensiveness of source tracing work and hindering a deeper explanation of the differences in heavy metal pollution.

[0010] 3. High operational complexity: Cost and technical barriers restrict regional promotion and application.

[0011] Some source tracing methods (such as isotope analysis) suffer from limitations due to their high dependence on equipment and complex pretreatment processes. On the one hand, isotope analysis relies on high-precision isotope mass spectrometers, which are expensive to purchase and require specialized technicians for operation and maintenance. On the other hand, compared to other rock types, carbonate rock areas typically have higher organic matter and clay content in their soils. These samples require complex pretreatment processes (such as multiple centrifugations and acid digestion to remove organic matter interference) before isotope analysis, with a single sample's pretreatment process taking 2-3 days and being susceptible to operational errors affecting the stability of the results. These characteristics of high cost, high time consumption, and high technical barriers directly limit the application of this method in large-scale regional soil heavy metal surveys, making it difficult to meet the practical needs of "high efficiency and universality" in most studies.

[0012] Therefore, developing source tracing methods that can adapt to such special geological backgrounds, integrate multi-dimensional information, and improve the accuracy of qualitative and quantitative analysis has become a key issue that urgently needs to be addressed. Summary of the Invention

[0013] The purpose of this invention is to provide a method for tracing the source of heavy metals in soils in carbonate rock distribution areas, thereby solving the problems of insufficient tracing accuracy, poor data integration, and high operational complexity in existing methods for tracing the source of heavy metals in soils in carbonate rock distribution areas.

[0014] The technical solution adopted in this invention is as follows:

[0015] A method for tracing the source of heavy metals in soils in carbonate rock distribution areas includes the following steps:

[0016] (1) Geological background analysis: Constructing a regionally related geological baseline;

[0017] (1.1) Geological data collection and screening:

[0018] Collect high-precision geological data of carbonate rocks in the study area, including regional geological maps, to clarify the distribution range, outcrop area and contact zone location of different types of carbonate rocks with other lithologies;

[0019] Collect mineral composition data to determine the relative contents of calcite, dolomite, and clay minerals in different types of carbonate rocks, and identify the main mineral carriers of heavy metal elements.

[0020] The weathering degree was assessed, and the carbonate rocks in the study area were classified into three levels: strong weathering, moderate weathering, and weak weathering through field observation and indoor experiments, which provides a basis for subsequent differentiation of heavy metals from natural sources.

[0021] (1.2) Establish a regional geological baseline database:

[0022] X-ray fluorescence spectroscopy and X-ray diffraction were used to analyze carbonate parent rock samples of different types and weathering grades.

[0023] Data was integrated to construct a regional geological baseline database, which includes lithology type, weathering grade, mineral composition, and background values ​​of heavy metals, providing a quantitative basis for subsequent differentiation between natural and anthropogenic sources;

[0024] (2) Soil sample collection and chemical analysis: to provide basic data for source tracing work;

[0025] (2.1) Optimize the gridded sampling scheme:

[0026] Based on the topographical features of the carbonate rock area, the sampling principles were optimized and the sampling grid scale was determined according to the area of ​​the study region.

[0027] Within the study area, pre-defined locations were selected, and surface soil samples were collected using the five-point mixing method. The coordinates, numbers, land use types, and potential pollution sources were recorded in detail. After mixing, the sample volume was reduced to 1-2 kg using the quartering method.

[0028] Set up a quality control sample in each grid, including a blank sample and a parallel sample;

[0029] (2.2) Multi-parameter chemical analysis:

[0030] The content of target heavy metals in soil samples was determined using inductively coupled plasma mass spectrometry.

[0031] Simultaneously analyze key environmental parameters and establish an environmental variable dataset to analyze the impact of environmental variables on the migration and transformation of heavy metals;

[0032] (3) Chemical fingerprint feature extraction: Construct a two-dimensional fingerprint of "morphology-isotope" to improve the accuracy of source differentiation;

[0033] (3.1) Heavy metal speciation analysis:

[0034] Based on the characteristics of soil in carbonate rock areas, the occurrence forms of heavy metals in soil were extracted using the traditional BCR continuous extraction method. Finally, based on the proportion of each form of heavy metal, a preliminary correlation system of "heavy metal element - occurrence form - possible source" was established.

[0035] (3.2) Construction of the isotopic fingerprint feature database:

[0036] Using Pb isotopes as the core fingerprint indicator and combining previous morphological analysis data, a chemical fingerprint feature database of heavy metals was constructed.

[0037] (4) Spatial distribution modeling and source tracing: Multi-dimensional data collaborative analysis, combined with spatial visualization to achieve accurate source tracing;

[0038] (4.1) Application of receptor models:

[0039] A positive definite matrix factorization model was used to quantitatively analyze the sources of heavy metals in soil, following the standard process of "data preparation - model operation - result output" to obtain preliminary source information;

[0040] Specifically:

[0041] Data preparation: Collect the total heavy metal analysis data of the soil in the carbonate rock area under study, perform standardization processing, calculate the uncertainty corresponding to each concentration data according to the software requirements, and then import the "concentration matrix" and "uncertainty matrix" into the software according to the program.

[0042] Model operation: Import the preprocessed heavy metal data, first set the number of factors, and then perform iterative calculations. The model continuously optimizes the contribution rate matrix and factor spectrum matrix of each pollution source factor through the weighted least squares method until the sum of squared residuals reaches the minimum value, that is, the model converges. Finally, the number of factors that can obtain the minimum residual and have a clear pollution source significance is selected as the optimal solution.

[0043] Output results: After the run is complete, two types of core source tracing results will be output: factor spectrum features and quantitative contribution rate;

[0044] Based on the two types of core source tracing results output, combined with relevant literature and reports, the sources of heavy metals can be preliminarily matched and classified. Then, objective spatial features are introduced to verify and correct the preliminary results output by the positive definite matrix factorization model, thereby improving the accuracy of source tracing.

[0045] (4.2) Spatial distribution model verification

[0046] A spatial distribution model of heavy metals in soil was established using a geographic information system to strengthen the correlation with the geological background and environmental variables of carbonate rock areas. The main process is as follows:

[0047] Data input: Input basic data, geological baseline data, land use types, geographic elements, and environmental variables into the geographic information system software, and generate spatial distribution maps using corresponding interpolation methods according to different data types;

[0048] Spatial heterogeneity analysis: The above types of maps within the study area are overlaid with the heavy metal content distribution map to identify the spatial correlation between the "high value anomaly area" and various factors. The results of the positive definite matrix factor decomposition model are analyzed to clarify the core driving factors of the spatial heterogeneity of heavy metals, and at the same time, the errors in the preliminary inference of the positive definite matrix factor decomposition model are corrected.

[0049] Furthermore, in step (1.2), the specific process of analyzing carbonate parent rock samples of different types and weathering grades using X-ray fluorescence spectroscopy and X-ray diffraction is as follows:

[0050] The total content of target heavy metals in the parent rock was determined by X-ray fluorescence spectroscopy. The number of samples collected in each lithological unit was set according to preset conditions. Finally, the average value of the measurement results was taken as the background reference value of heavy metals in that unit.

[0051] X-ray diffraction was used to identify minerals containing characteristic heavy metals in the parent rock, and a correspondence between "mineral type - characteristic heavy metal background value" was established.

[0052] Furthermore, in step (2.1), the blank sample is simulated by ultrapure water, and the parallel sample is analyzed simultaneously with the conventional sample. The relative deviation is controlled within 10% to ensure that the data accuracy meets the requirements of subsequent chemical fingerprint extraction and traceability analysis.

[0053] Furthermore, in step (3.2), the main process of constructing the chemical fingerprint feature database of heavy metals is as follows:

[0054] First, typical samples selected through morphological analysis were pretreated; then, the Pb isotope ratios were determined using a multi-receiver inductively coupled plasma mass spectrometer.

[0055] Simultaneously, potential end-member samples were collected in the region, their Pb isotope ratios were measured, and a reference range for end-member isotope ratios was established.

[0056] Finally, the data will be integrated to establish a chemical fingerprint feature database, which includes three types of data: the proportion of heavy metal speciation, isotope ratios, and potential end-member types. By comparing the data, natural and anthropogenic sources can be quickly distinguished, achieving preliminary qualitative identification of pollution sources and providing fingerprint basis for subsequent quantitative analysis.

[0057] Furthermore, step (4.1) also includes optimization of the positive definite matrix factor decomposition model, specifically: introducing geological background weight factors to improve the model so that the model prioritizes matching geological features in areas with a high proportion of natural sources and prioritizes matching pollution features in areas with concentrated anthropogenic sources.

[0058] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0059] 1. Significantly Improved Source Tracing Accuracy: Traditional methods mostly rely on heavy metal content or single isotope data to determine the source, which is prone to misidentification of natural or anthropogenic sources. This invention is based on geological background, clearly defining the benchmark for natural sources; using chemical fingerprint data as the core, establishing a feature system database; and using spatial distribution data as a carrier to verify the spatial correlation of sources, effectively improving source tracing accuracy. Specifically, breakthroughs in accuracy are achieved through multi-source data fusion and PMF model improvement, overcoming the limitation of "lack of regional adaptability" commonly found in existing technologies. A geological baseline database of "background value-lithology-mineral composition-weathering grade" is constructed using XRD and XRF, and combined with BCR morphological analysis and isotope endmember analysis, providing a triple reference basis of "benchmark-morphology-isotope" for source analysis; for the characteristics of carbonate rock areas, differentiated weights are introduced into the PMF model, and verification is carried out in conjunction with GIS spatial distribution, enhancing the ability to distinguish between natural and anthropogenic sources, improving source tracing accuracy by approximately 20%-30%.

[0060] 2. Simple operation and controllable cost: Existing technologies suffer from the pain points of "cumbersome process and high equipment dependence". This invention reduces the complexity and cost of operation by "simplifying the analysis process and optimizing conventional equipment" (such as optimizing the grid sampling scheme, focusing on key forms of heavy metals, and using conventional laboratory equipment for detection). It is suitable for regional promotion and application.

[0061] 3. Comprehensive and widely applicable: Existing technologies mostly focus on single-dimensional source tracing and lack connection with geological background. This invention integrates geology (parent rock type), chemistry (morphology, isotopes) and spatial visualization (land use type, environmental variables) for multi-dimensional analysis, comprehensively analyzes the source of heavy metals, and provides a scientific basis for pollution control and soil remediation. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein:

[0063] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0066] It should be noted that the labels and letters in the following figures represent similar items, therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0067] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are only used for the purpose of simplifying the description of this invention and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. In addition, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0068] Furthermore, terms such as "horizontal" and "vertical" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," not that the structure must be completely horizontal, but can be slightly tilted.

[0069] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0070] Refer to the instruction manual. Figure 1 ,

[0071] A method for tracing the source of heavy metals in soils in carbonate rock distribution areas includes the following steps:

[0072] (1) Geological background analysis: Construct a regionally related geological baseline.

[0073] (1.1) Geological data collection and screening

[0074] Collect high-precision geological data of carbonate rocks in the study area, including:

[0075] Regional geological maps clearly define the distribution range, outcrop area, and contact zone location with other lithologies of different types of carbonate rocks (such as limestone, dolomite, etc.).

[0076] Mineral composition data were used to determine the relative contents of calcite, dolomite, and clay minerals (such as kaolinite and montmorillonite) in different types of carbonate rocks, and to identify the main mineral carriers of heavy metal elements.

[0077] Weathering assessment, through field observation and indoor experiments, classified the carbonate rocks in the study area into three levels: strong weathering, moderate weathering, and weak weathering, providing a basis for subsequent differentiation of heavy metals from natural sources.

[0078] (1.2) Establish a regional geological baseline database

[0079] X-ray fluorescence spectroscopy (XRF) and X-ray diffraction (XRD) were used to analyze carbonate parent rock samples of different types and weathering grades.

[0080] X-ray fluorescence spectrometry (XRF) is used to determine the total content of target heavy metals such as Pb, Cd, Zn, and Cu in the parent rock. The number of samples collected in each lithological unit is set appropriately, and the average value of the measurement results is taken as the background reference value of heavy metals in that unit.

[0081] X-ray diffraction (XRD) method: Identifies minerals containing characteristic heavy metals in the parent rock (such as Cd in calcite and Pb adsorbed in dolomite) and establishes a correspondence between "mineral type - characteristic heavy metal background value".

[0082] By integrating the above data, a regional geological baseline database is constructed. The database includes lithology type, weathering grade, mineral composition, and background values ​​of heavy metals, which can provide quantitative basis for subsequent differentiation between natural and anthropogenic sources.

[0083] Specifically, XRD is first used to identify the characteristic mineral composition (such as calcite and dolomite) in the parent rock of carbonate rocks to identify the main carriers of heavy metals; then XRF is used to determine the content of target heavy metals in parent rocks of different lithologies and weathering grades to determine the natural background value of heavy metals in carbonate rocks as a reference benchmark for natural sources.

[0084] (2) Soil sample collection and chemical analysis: to provide basic data for source tracing work.

[0085] (2.1) Optimize the gridded sampling scheme

[0086] Based on the topographical characteristics of carbonate rock areas, and according to the area of ​​the study region (county level, city level), the sampling principles were optimized and a reasonable sampling grid scale was determined.

[0087] Within the study area, topsoil samples (0-20cm) were collected at pre-defined locations using the "five-point mixing method" and recorded in detail (coordinates, number, land use type, possible pollution sources, etc.). After mixing, the sample volume was reduced to approximately 1-2kg using the quartering method.

[0088] A quality control sample (including blank sample and parallel sample) is set up in each grid. The blank sample uses ultrapure water to fully simulate the sampling process. The parallel sample is analyzed simultaneously with the regular sample. The relative deviation must be controlled within 10% to ensure that the data accuracy meets the technical requirements of subsequent chemical fingerprint extraction and traceability analysis.

[0089] (2.2) Multi-parameter chemical analysis

[0090] The content of target heavy metals (such as Pb, Cd, Zn, Cu) in soil samples was determined using inductively coupled plasma mass spectrometry (ICP-MS).

[0091] Simultaneously analyze key environmental parameters (such as pH value, organic matter content, oxide content, etc.) to establish an environmental variable dataset for analyzing the impact of environmental variables on the migration and transformation of heavy metals.

[0092] (3) Chemical fingerprint feature extraction: Construct a "morphology-isotope" dual-dimensional fingerprint to improve the accuracy of source differentiation.

[0093] (3.1) Heavy metal speciation analysis

[0094] Based on the soil characteristics of carbonate rock areas, speciation analysis was conducted on soil samples from these areas using the traditional BCR sequential extraction method (acid-extractable, reducible, oxidizable, and residual forms) to extract the occurrence forms of heavy metals (focusing on "carbonate-bound" and "organic matter-bound" forms). Finally, based on the proportion of each heavy metal form, a preliminary correlation system of "heavy metal element - occurrence form - possible source" was established, allowing for preliminary inferences about the source direction.

[0095] (3.2) Construction of Isotope Fingerprint Feature Database

[0096] To address the complexity of existing isotope analysis procedures, a chemical fingerprint database for heavy metals is constructed using Pb isotopes as the core fingerprint indicator (Pb isotope fractionation effects are significant, and the ratios between natural and anthropogenic sources differ significantly). This is combined with previous speciation analysis data. The main workflow is as follows:

[0097] First, typical samples selected through speciation analysis (such as Pb-enriched samples with a high proportion of exchangeable states) were pretreated; then, Pb isotope ratios (e.g., Pb-enriched samples with a high proportion of exchangeable states) were determined using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). 206 Pb / 207 Pb), using the difference in ratios between natural and anthropogenic sources to achieve precise differentiation;

[0098] Simultaneously, potential end-member samples (such as carbonate parent rocks, industrial emission source samples, traffic sediment samples, and agricultural source samples) were collected in the area, and their Pb isotope ratios were determined to establish a reference range for end-member isotope ratios.

[0099] Finally, the information is integrated to establish a chemical fingerprint feature database, which includes three types of data: "proportion of heavy metal forms, isotope ratios, and potential end-member types". By comparison, natural and anthropogenic sources can be quickly distinguished, and the pollution source can be preliminarily identified, providing fingerprint basis for subsequent quantitative analysis.

[0100] (4) Spatial distribution modeling and tracing: Multi-dimensional data collaborative analysis, combined with spatial visualization to achieve accurate tracing.

[0101] (4.1) Application of receptor model (PMF)

[0102] The positive definite matrix factorization (PMF) model was used to quantitatively analyze the sources of heavy metals in soil, following the standard process of "data preparation - model operation - result output" to obtain preliminary source information.

[0103] Data preparation: Collect the total heavy metal analysis data of the soil in the carbonate rock area under study, and perform standardization processing (if the data is approximately normally distributed, use the Z-score standardization method; if the data has a skewed distribution or outliers, use the quartile method). Calculate the uncertainty corresponding to each concentration data according to the software (such as EPA-PMF 5.0) requirements (usually determined according to the concentration level and the accuracy of the detection method; the uncertainty of low concentration data needs special correction). Then, import the "concentration matrix" and "uncertainty matrix" into the software according to the program.

[0104] Model Execution: After importing the preprocessed heavy metal data, the first step is to set the number of factors (usually using principal component analysis; the number of extracted principal components serves as a reference for factor selection, typically 3-5). Then, iterative calculations are performed. The model continuously optimizes the "contribution rate matrix" (the contribution ratio of different pollution source factors at each sampling point) and the "factor spectrum matrix" (the relative content ratio of different heavy metals in each factor) of each pollution source factor using the "weighted least squares method" until the calculated "residual sum of squares" reaches its minimum value, indicating model convergence. Finally, the number of factors that minimizes the residuals and has a clear pollution source significance is selected as the optimal solution.

[0105] The optimization of the PMF model is as follows: To address the insufficient adaptability of traditional models in carbonate rock distribution areas, a geological background weight factor is introduced to improve the model (e.g., appropriately increasing the weight factor of geological background data in strongly weathered areas; appropriately increasing the weight factor of chemical fingerprint data in industrial areas; and setting the default weight in other areas). This allows the model to prioritize matching geological features in areas with a high proportion of natural sources and prioritize matching pollution features in areas with concentrated anthropogenic sources, effectively avoiding the contribution rate calculation deviation caused by the "uniform parameter settings" of the traditional PMF model, and significantly improving the reliability of the source tracing results.

[0106] Results output: After the run is completed, two types of core source tracing results are output: factor spectrum characteristics (including characteristic heavy metal elements with a high proportion in each pollution source factor); and quantitative contribution rate (the contribution ratio of each pollution source to the total amount of each heavy metal).

[0107] Based on the above results, combined with relevant literature and reports, the sources of heavy metals can be preliminarily matched and classified. However, since the process cannot completely avoid subjectivity, relying solely on experience for classification is prone to bias. Therefore, this technology also introduces objective spatial features to verify and correct the preliminary results of PMF output, thereby improving the accuracy of source tracing.

[0108] (4.2) Spatial distribution model verification

[0109] A spatial distribution model of heavy metals in soil was established using a Geographic Information System (GIS) to strengthen the correlation with the geological background and environmental variables of carbonate rock areas. The main operations are as follows:

[0110] Data input: Input basic data (pre-processed soil heavy metal content data, contribution rate data of each pollution source output by the PMF model); geological background data (soil physicochemical properties, parent rock distribution, altitude, slope, etc.); land use types (arable land, forest land, industrial land, residential areas, etc.); geographic elements (distribution of roads at all levels, water systems, mining areas, etc.); environmental variables (annual rainfall, average annual wind speed, etc.) into GIS software, and generate spatial distribution maps using appropriate interpolation methods according to different data types.

[0111] Spatial heterogeneity analysis: The above types of maps within the study area are overlaid with heavy metal content distribution maps to identify the spatial correlation between "high value anomaly areas" and various factors (such as whether they are located around mining areas or industrial land, or whether they overlap with the distribution of a certain parent rock type). Combined with PMF results, the reasons for the spatial heterogeneity of heavy metals are further analyzed. For example, if the "Cd high value area" overlaps with the distribution area of ​​a certain rock type and corresponds to the "Cd high proportion factor" in the PMF, it can help verify that the factor may be a "natural source".

[0112] The above analysis clarifies the core driving factors of spatial heterogeneity of heavy metals (such as concentrated anthropogenic emission areas or high natural background values), while verifying and correcting the errors in the initial inference of PMF and reducing subjective judgment errors.

[0113] The above description constitutes an embodiment of the present invention. The foregoing descriptions are preferred embodiments of the present invention. Unless there is a clear contradiction or a prerequisite for a particular preferred embodiment, the preferred embodiments can be arbitrarily combined and used. The embodiments and specific parameters described are merely for clearly illustrating the verification process of the invention and are not intended to limit the scope of patent protection of the present invention. The scope of patent protection of the present invention is still determined by its claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention should also be included within the scope of protection of the present invention.

Claims

1. A method for tracing the source of heavy metals in soils from carbonate rock distribution areas, characterized in that, Includes the following steps: (1) Geological background analysis: Constructing a regionally related geological baseline; (1.1) Geological data collection and screening: Collect high-precision geological data of carbonate rocks in the study area, including regional geological maps, to clarify the distribution range, outcrop area and contact zone location of different types of carbonate rocks with other lithologies; Collect mineral composition data to determine the relative contents of calcite, dolomite, and clay minerals in different types of carbonate rocks, and identify the main mineral carriers of heavy metal elements. The weathering degree was assessed, and the carbonate rocks in the study area were classified into three levels: strong weathering, moderate weathering, and weak weathering through field observation and indoor experiments, which provides a basis for subsequent differentiation of heavy metals from natural sources. (1) . 2) Establish a regional geological baseline database: X-ray fluorescence spectroscopy and X-ray diffraction were used to analyze carbonate parent rock samples of different types and weathering grades. Data was integrated to construct a regional geological baseline database, which includes lithology type, weathering grade, mineral composition, and background values ​​of heavy metals, providing a quantitative basis for subsequent differentiation between natural and anthropogenic sources; (2) Soil sample collection and chemical analysis: to provide basic data for source tracing work; (2.1) Optimize the gridded sampling scheme: Based on the topographical features of the carbonate rock area, the sampling principles were optimized and the sampling grid scale was determined according to the area of ​​the study region. Within the study area, pre-defined locations were selected, and surface soil samples were collected using the five-point mixing method. The coordinates, numbers, land use types, and potential pollution sources were recorded in detail. After mixing, the sample volume was reduced to 1-2 kg using the quartering method. Set up a quality control sample in each grid, including a blank sample and a parallel sample; (2.2) Multi-parameter chemical analysis: The content of target heavy metals in soil samples was determined using inductively coupled plasma mass spectrometry. Simultaneous analysis of key environmental parameters and establishment of an environmental variable dataset were conducted to analyze the impact of environmental variables on the migration and transformation of heavy metals. (3) Chemical fingerprint feature extraction: Construct a "morphology-isotope" dual-dimensional fingerprint to improve the accuracy of source differentiation; (3.1) Heavy metal speciation analysis: Based on the characteristics of soil in carbonate rock areas, the occurrence forms of heavy metals in soil were extracted using the traditional BCR continuous extraction method. Finally, based on the proportion of each form of heavy metal, a preliminary correlation system of "heavy metal element - occurrence form - possible source" was established. (3.2) Construction of the isotopic fingerprint feature database: Using Pb isotopes as the core fingerprint indicator and combining previous morphological analysis data, a chemical fingerprint feature database of heavy metals was constructed. (4) Spatial distribution modeling and tracing: Multi-dimensional data collaborative analysis, combined with spatial visualization to achieve accurate tracing; (4.1) Application of receptor models: A positive definite matrix factorization model was used to quantitatively analyze the sources of heavy metals in soil, following the standard process of "data preparation - model operation - result output" to obtain preliminary source tracing information; Specifically: Data preparation: Collect the total heavy metal analysis data of the soil in the carbonate rock area under study, perform standardization processing, calculate the uncertainty corresponding to each concentration data according to the software requirements, and then import the "concentration matrix" and "uncertainty matrix" into the software according to the program. Model operation: Import the preprocessed heavy metal data, first set the number of factors, and then perform iterative calculations. The positive definite matrix factor decomposition model continuously optimizes the contribution rate matrix and factor spectrum matrix of each pollution source factor through the weighted least squares method until the sum of squared residuals reaches the minimum value, that is, the model converges. Finally, the number of factors that can obtain the minimum residual and have a clear pollution source significance is selected as the optimal solution. Output results: After the run is complete, two types of core source tracing results will be output: factor spectrum features and quantitative contribution rate; Based on the two types of core traceability results output, the sources of heavy metals can be initially matched and classified. Then, objective spatial features are introduced to verify and correct the preliminary results output by the positive definite matrix factorization model, thereby improving the accuracy of traceability. (4.2) Validation of spatial distribution model A spatial distribution model of heavy metals in soil was established using a geographic information system to strengthen the correlation with the geological background and environmental variables of carbonate rock areas. The main process is as follows: Data input: Input basic data, geological baseline data, land use types, geographic elements, and environmental variables into the geographic information system software, and generate spatial distribution maps using corresponding interpolation methods according to different data types; Spatial heterogeneity analysis: By overlaying various types of maps with heavy metal content distribution maps within the study area, the spatial correlation between "high-value anomaly areas" and various factors is identified. The results of the positive definite matrix factor decomposition model are analyzed to clarify the core driving factors of heavy metal spatial heterogeneity and correct the errors in the preliminary inferences of the positive definite matrix factor decomposition model.

2. The method for tracing the source of heavy metals in soil in carbonate rock distribution areas according to claim 1, characterized in that, In step (1.2), the specific process of analyzing carbonate parent rock samples of different types and weathering grades using X-ray fluorescence spectroscopy and X-ray diffraction is as follows: The total content of target heavy metals in the parent rock was determined by X-ray fluorescence spectroscopy. The number of samples collected in each lithological unit was set according to preset conditions. Finally, the average value of the measurement results was taken as the background reference value of heavy metals in that unit. X-ray diffraction was used to identify minerals containing characteristic heavy metals in the parent rock, and a correspondence between "mineral type - characteristic heavy metal background value" was established.

3. A method for tracing the source of heavy metals in soils in carbonate rock distribution areas according to claim 1, characterized in that, In step (2.1), the blank sample is simulated by ultrapure water, and the parallel sample is analyzed simultaneously with the conventional sample. The relative deviation is controlled within 10% to ensure that the data accuracy meets the requirements of subsequent chemical fingerprint extraction and traceability analysis.

4. A method for tracing the source of heavy metals in soils in carbonate rock distribution areas according to claim 1, characterized in that, In step (3.2), the main process of constructing the chemical fingerprint feature database of heavy metals is as follows: First, typical samples selected through morphological analysis were pretreated; then, the Pb isotope ratios were determined using a multi-receiver inductively coupled plasma mass spectrometer. Simultaneously, potential end-member samples were collected in the region, their Pb isotope ratios were measured, and a reference range for end-member isotope ratios was established. Finally, the data will be integrated to establish a chemical fingerprint feature database, which includes three types of data: the proportion of heavy metal speciation, isotope ratios, and potential end-member types. By comparing the data, natural and anthropogenic sources can be quickly distinguished, achieving preliminary qualitative identification of pollution sources and providing fingerprint basis for subsequent quantitative analysis.

5. A method for tracing the source of heavy metals in soils in carbonate rock distribution areas according to claim 1, characterized in that, In step (4.1), the optimization of the positive definite matrix factor decomposition model is also included. Specifically, the geological background weight factor is introduced to improve the model so that the model prioritizes matching geological features in areas with a high proportion of natural sources and prioritizes matching pollution features in areas with concentrated anthropogenic sources.

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