A source-sink landscape-based soil heavy metal remediation method and system

Abstract

The application provides a source-sink landscape-based soil heavy metal remediation method and system, and relates to the field of ecological environment; the method generates target region small watershed space data by analyzing digital elevation model through GIS; on the other hand, the source-sink landscape type of the target region is determined according to satellite images and remote sensing images, and the pollution source intensity is determined by combining rainfall runoff and soil environment sampling monitoring to establish a source-sink landscape geographic information database; soil heavy metal monitoring and spatial evaluation are carried out by selecting typical landscape types, and a self-regression model is established by spatial autocorrelation analysis of soil heavy metal spatial distribution information and source-sink landscape types, and the best scheme of landscape space optimization is proposed based on the model; based on the optimal scheme, the landscape space pattern most conducive to the soil heavy metal self-repairing process is obtained; finally, physical, chemical and biological treatments are further carried out for different landscape types, and the source-sink landscape-based soil heavy metal remediation application is completed.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental technology, specifically to a method and system for remediating heavy metals in soil based on source-sink landscape. Background Technology

[0002] According to monitoring data from the National Environmental Protection Administration, nearly 10% of farmland in my country has been severely polluted by heavy metals, and the area of ​​land with seriously excessive heavy metal content in the soil accounts for about 12.1% of the total area sampled, especially in more developed regions. Therefore, there is an urgent need to develop an economical, efficient, and green soil remediation technology.

[0003] Currently, common soil remediation technologies include physical, chemical, biological, and electrolytic remediation techniques. Each technology has its own advantages, disadvantages, and applicable scope. For example, patent CN105583222B discloses a method for remediating cadmium-zinc heavy metal contaminated soil. This method utilizes a combination of physical and chemical methods to achieve soil remediation. However, this technique uses a large amount of remediation agents, resulting in high costs and potential for changes in soil structure and secondary pollution, failing to meet the requirements of economical and green remediation goals. In recent years, with the development of landscape ecology, the use of source-sink landscape theory to manage the soil environment has gradually become a research hotspot. By utilizing the components, patterns, and ecological processes of the landscape, the migration and transformation patterns of heavy metals in soil under different landscape patterns are studied, and ecological remediation of the types and enrichment levels of heavy metals in source-sink landscapes is carried out. This aligns with general ecological principles and possesses economic and sustainable characteristics. Furthermore, using source-sink landscape theory to manage the soil environment avoids changes in soil structure or secondary pollution caused by chemical remediation.

[0004] Landscape restoration alone is generally time-consuming, susceptible to seasonality, and limited by the characteristics and landscape type. Therefore, research on soil heavy metal remediation combining landscape restoration with biological and chemical technologies has become a development trend. With the emergence of the "source-sink" landscape theory, watershed-scale research on soil heavy metals has gradually become a research hotspot. Current technologies for soil environmental remediation based on the source-sink landscape theory mainly focus on landscape processes, pollution source apportionment, spatial assessment of pollution sources, and ecosystem assessment, while lacking research on the application of technologies for heavy metal remediation that combine macro-remediation with micro-management based on the source-sink landscape theory. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for soil heavy metal remediation based on source-sink landscape. By combining 3S technology with landscape ecology and environmental science, typical areas are selected to monitor and quantitatively analyze the soil environment, and the application research of soil environment remediation in target areas is proposed from both micro and macro scales.

[0006] To achieve the above objectives, the present invention proposes the following technical solution:

[0007] Firstly, a soil heavy metal remediation method based on source-sink landscapes is proposed, including:

[0008] The target area is identified, and geographic information systems and digital elevation models are used to obtain the geographic information of the small watersheds in the target area, thereby generating a geospatial database of the small watersheds in the target area.

[0009] Acquire satellite and remote sensing images of the target area to determine the geographical information of vegetation cover, topography, spatial distribution of soil environment, and source-sink landscape types of the target area;

[0010] Based on the geographical information of vegetation cover, topography, spatial distribution of soil environment, and source-sink landscape types of the target area, a source-sink landscape geographic information database is established in conjunction with the small watershed geospatial database.

[0011] Based on the source-sink landscape geographic information database, soil samples of typical source-sink landscape types in the target area were selected for analysis to determine the spatial distribution information of heavy metals in soils of typical source-sink landscape types.

[0012] By using spatial autocorrelation analysis, a model of the spatial distribution information of heavy metals in soil and the heavy metal pollution characteristics of source-sink landscape types was established.

[0013] Landscape pattern optimization is carried out based on heavy metal pollution characteristic model, and source-sink landscape scenario simulation and soil environmental response model are established.

[0014] Based on the response results of the response model to landscape pattern optimization, soil heavy metal remediation schemes for different landscape types were selected for soil remediation.

[0015] Furthermore, the content of optimizing landscape patterns based on heavy metal pollution characteristic models and establishing source-sink landscape scenario simulation and soil environmental response models includes:

[0016] The moving window method of landscape analysis is used to perform spatial calculations on the source-sink landscape of the target area. Based on the calculation results of landscape indicators, it is determined whether the source-sink landscape components and spatial patterns within the window meet reasonable standards. The landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation.

[0017] When the source-sink landscape components and spatial pattern within the window reach a reasonable standard, the soil heavy metal content is determined to exceed the pollution index based on the soil environmental spatial distribution information, and environmental remediation is carried out when the content exceeds the pollution index.

[0018] If the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, landscape optimization is performed, and the source-sink landscape geographic information database is updated until the optimized landscape indicators are within a reasonable range.

[0019] Furthermore, the process for obtaining the spatial distribution information of the soil environment is as follows:

[0020] Based on satellite and remote sensing images of the target area, several spatial sampling points are determined using the uniform sampling method.

[0021] By acquiring sampling data from spatial sampling points and performing spatial interpolation, we can monitor and obtain spatial distribution information of heavy metals in soils of different source-sink landscape types, and thus obtain spatial distribution information of the soil environment.

[0022] Furthermore, the process of determining whether the source-sink landscape components and spatial pattern within the window meet reasonable standards based on the calculation results of landscape indicators is as follows:

[0023] The moving window method of landscape analysis is used to perform spatial calculations on the source-sink landscape of the target area to obtain spatial data of source-sink landscape indicators.

[0024] Compare the spatial distribution information of the soil environment with the spatial data of the landscape index, and if the spatial distribution information of the soil environment and the spatial data of the landscape index are positively correlated, then the landscape pattern will continue to be maintained, and the source-sink landscape components and spatial patterns within the window will reach a reasonable standard.

[0025] Furthermore, the spatial calculation of the source-sink landscape of the target area using the moving window method of landscape analysis includes:

[0026] Shannon Diversity Index (SHDI):

[0027]

[0028] Dominance Index D:

[0029]

[0030] Patch shape index S:

[0031]

[0032] Fractal dimension F d :

[0033]

[0034] Spread Index CONTIG:

[0035]

[0036] Cohesion index:

[0037]

[0038] Where n represents the total number of patch types, pi represents the proportion of landscape area occupied by patch i, i represents the i-th patch type, Hmax represents the maximum diversity index, C represents the perimeter of the landscape patch, A represents the area of ​​the landscape patch, k represents the k-th landscape patch that is different from i, p represents the perimeter of the k-th landscape patch, a represents the area of ​​the k-th landscape patch, gik represents the number of adjacent i-th type landscape patches and k-th type landscape patches, pij represents the perimeter of the j-th patch in the i-th type of landscape, and aij represents the area of ​​the j-th patch in the i-th type of landscape.

[0039] Furthermore, the source-sink landscape scenario simulation and soil environmental response model is a cellular automata-Markov chain model;

[0040] The cellular automata-Markov chain model uses the number of cells of each landscape type in the neighborhood as the neighborhood scenario, the central landscape cell to simulate the input landscape as the input variable, and the central landscape cell to simulate the output landscape type. It analyzes the functional relationship of the input land type of the central landscape cell under different neighborhood scenarios, thereby realizing the simulation of different landscape spatial patterns.

[0041] Secondly, a soil heavy metal remediation system based on source-sink landscapes is proposed, including:

[0042] The generation module is used to determine the target area, and uses a geographic information system and a digital elevation model to obtain the small watershed geographic information of the target area, and generate a small watershed geospatial database of the target area.

[0043] The acquisition and determination module is used to acquire satellite and remote sensing images of the target area and determine the geographical information of vegetation cover, topographic information, spatial distribution information of soil environment, and source-sink landscape type of the target area;

[0044] The first module is used to establish a source-sink landscape geographic information database based on the vegetation cover geographic information, topographic information, soil environment spatial distribution information, and source-sink landscape type of the target area, combined with the small watershed geospatial database.

[0045] The analysis and determination module is used to select typical source-sink landscape type soils in the target area for sampling and analysis based on the source-sink landscape geographic information database, and to determine the spatial distribution information of heavy metals in the soils of typical source-sink landscape types.

[0046] The second module is used to establish a model of the spatial distribution information of heavy metals in soil and the heavy metal pollution characteristics of source-sink landscape types through spatial autocorrelation analysis.

[0047] The third module is used to optimize the landscape pattern based on the heavy metal pollution characteristic model and to establish a source-sink landscape scenario simulation and soil environmental response model.

[0048] The selection and remediation module is used to select soil heavy metal remediation schemes for different landscape types based on the response results of the response model to landscape pattern optimization.

[0049] Furthermore, the third module, based on a heavy metal pollution characteristic model, optimizes the landscape pattern and establishes an execution unit for source-sink landscape scenario simulation and soil environmental response model, including:

[0050] The calculation and judgment unit is used to perform spatial calculations on the source-sink landscape of the target area using the moving window method of landscape analysis, and to judge whether the source-sink landscape components and spatial patterns within the window meet reasonable standards based on the calculation results of landscape indicators; wherein, the landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation.

[0051] The judgment and remediation unit is used to determine whether the soil heavy metal content exceeds the pollution index based on the soil environmental spatial distribution information when the source-sink landscape components and spatial pattern within the window reach a reasonable standard, and to carry out environmental remediation when the content exceeds the pollution index.

[0052] The optimization unit is used to optimize the landscape and update the source-sink landscape geographic information database when the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, until the optimized landscape indicators are within a reasonable range.

[0053] Furthermore, the execution unit for determining the spatial distribution information of the soil environment in the acquisition and determination module includes:

[0054] The determination unit is used to determine several spatial sampling points based on satellite and remote sensing images of the target area using a uniform sampling method.

[0055] The acquisition unit is used to acquire sampling data from spatial sampling points and perform spatial interpolation to monitor and obtain spatial distribution information of heavy metals in soils of different source-sink landscape types, thereby obtaining spatial distribution information of the soil environment.

[0056] Thirdly, an electronic device is proposed, comprising a computer program stored in a computer-readable storage medium; when the processor of the electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of the above-described source-sink landscape-based soil heavy metal remediation method.

[0057] As can be seen from the above technical solutions, the technical solutions of the present invention have achieved the following beneficial effects:

[0058] This invention discloses a method and system for soil heavy metal remediation based on source-sink landscapes. The scheme includes: determining a target area; acquiring small watershed geographic information of the target area using a geographic information system and a digital elevation model (DEM) to generate a small watershed geospatial database for the target area; acquiring satellite and remote sensing images of the target area to determine vegetation cover geographic information, topographic information, soil environmental spatial distribution information, and source-sink landscape types; establishing a source-sink landscape geographic information database based on the vegetation cover geographic information, topographic information, soil environmental spatial distribution information, and source-sink landscape types of the target area, combined with the small watershed geospatial database; sampling and analyzing soil samples from typical source-sink landscape types in the target area based on the source-sink landscape geographic information database to determine the spatial distribution information of heavy metals in the soil of typical source-sink landscape types; establishing a heavy metal pollution characteristic model of soil heavy metal spatial distribution information and source-sink landscape types through spatial autocorrelation analysis; optimizing the landscape pattern based on the heavy metal pollution characteristic model to establish a source-sink landscape scenario simulation and soil environmental response model; and selecting soil heavy metal remediation schemes for different landscape types based on the response model's response to the landscape pattern optimization.

[0059] This invention addresses the complexity and uncertainty of non-point source pollution by comprehensively utilizing multiple disciplines, including remote sensing and geographic information systems, landscape ecology, and environmental science. It addresses the shortcomings of individual technologies by considering the sources of pollution, the mechanisms of action of different landscape types and pollution sources, landscape pattern optimization, and physicochemical treatment methods. Through source-sink landscape theory, this invention proposes landscape pattern optimization, enabling some heavy metal pollutants to self-repair within the landscape based on ecological processes. For source-sink landscapes that cannot fully self-repair, existing methods are used for treatment and remediation, significantly improving the remediation effect of heavy metals in soil. This invention achieves the technical effect of dissolving pollutants at the source, significantly improving remediation precision; it effectively improves the overall environment of the target area and its surroundings, playing a significant role in the protection of important water bodies.

[0060] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below can be considered part of the inventive subject matter of this disclosure, provided that such concepts do not contradict each other.

[0061] The foregoing and other aspects, embodiments, and features of the teachings of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the invention, such as features and / or beneficial effects of exemplary embodiments, will become apparent from the following description or may be learned through practice of specific embodiments according to the teachings of the present invention. Attached Figure Description

[0062] The accompanying drawings are not drawn to scale. In the drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the invention will now be described by way of example and with reference to the accompanying drawings, wherein:

[0063] Figure 1 This is a flowchart of a soil heavy metal remediation method based on source-sink landscape disclosed in an embodiment of the present invention;

[0064] Figure 2 Here is a flowchart of landscape pattern optimization based on a heavy metal pollution characteristic model, as disclosed in the embodiments.

[0065] Figure 3 This is a flowchart illustrating the process of obtaining spatial distribution information of the soil environment as disclosed in an embodiment of the present invention.

[0066] Figure 4 This is a flowchart of the process for determining whether a source-sink landscape meets reasonable standards, as disclosed in an embodiment of the present invention.

[0067] Figure 5 This is a conceptual diagram of the soil heavy metal remediation method based on source-sink landscape disclosed in this invention;

[0068] Figure 6 This is a flowchart illustrating the response of soil heavy metal spatial distribution to source-sink landscape patterns as disclosed in this invention.

[0069] Figure 7 This is a schematic diagram illustrating the simulated interactions between various source-sink landscape types disclosed in this invention;

[0070] Figure 8 This is a structural block diagram of a soil heavy metal remediation system based on source-sink landscape disclosed in an embodiment of the present invention;

[0071] Figure 9 This is a structural block diagram of an electronic device disclosed in an embodiment of the present invention. Detailed Implementation

[0072] 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art to which this invention pertains.

[0073] The terms "first," "second," and similar words used in the specification and claims of this patent application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, unless the context clearly indicates otherwise, the singular forms of "an," "a," or "the," etc., do not indicate a quantity limitation, but rather indicate the presence of at least one. Terms such as "comprising" or "including" mean that the element or object preceding "comprising" encompasses the features, integrals, steps, operations, elements, and / or components listed following "comprising" or "including," and do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0074] Existing technologies for modifying soil heavy metal pollution using physical, chemical, and biological techniques can effectively reduce the content of heavy metals in the soil. However, due to the complex composition of the remediation agents used, they can easily cause secondary damage to the soil. For example, chemical remediation may cause changes in soil structure or secondary pollution. Ecological remediation based on source-sink landscapes conforms to general ecological principles and has the characteristics of economy and sustainability. Therefore, this invention aims to propose a method and system for soil heavy metal remediation based on source-sink landscapes. It utilizes source-sink landscape theory to carry out biological remediation, chemical remediation, and landscape restoration of heavy metal-contaminated soil. By combining landscape self-repair with micro-treatment, it maximizes the protection of the original soil structure and achieves cleaner, more economical, and more efficient soil heavy metal remediation.

[0075] The soil heavy metal remediation method and system based on source-sink landscape disclosed in this invention will be further described in detail below with reference to the accompanying drawings.

[0076] Combination Figure 1 and Figure 5 As shown in the embodiment, the soil heavy metal remediation method based on source-sink landscape disclosed includes the following steps:

[0077] Step S102: Determine the target area, use Geographic Information System (GIS) and Digital Elevation Model to obtain the small watershed geographic information of the target area, and generate a small watershed geospatial database of the target area;

[0078] This plan aims to utilize 3S technology combined with landscape ecology and environmental science to monitor and quantitatively analyze the soil environment in selected typical areas, proposing applicable solutions for soil environment remediation in the target areas at both micro and macro scales. During implementation, GIS will be used to analyze a 30m spatial resolution digital elevation model of the target area to obtain small watershed geographic information, including runoff and river information, thereby generating small watershed spatial data for the target area.

[0079] Step S104: Acquire satellite and remote sensing images of the target area to determine the geographical information of vegetation cover, topographic information, spatial distribution information of soil environment, and source-sink landscape type of the target area;

[0080] The satellite imagery is primarily 0.6m resolution QuickBird imagery, and the remote sensing imagery is Landsat 8 remote sensing imagery. First, the vegetation cover and topographic information of the target area are obtained through image analysis, leading to the identification of source-sink landscape types within the area. Soil environmental spatial distribution information requires soil environmental sampling based on topography, which allows for monitoring and determination of pollution source strength. The determination of source strength is as follows: the source strength of point source pollution can be quantitatively determined through pollutant concentration and total amount; non-point source pollution generally enters the environment through surface runoff, soil erosion, and agricultural drainage, exhibiting characteristics such as randomness, widespread impact, lag, ambiguity, and latency.

[0081] Step S106: Based on the geographical information of vegetation cover, topography, spatial distribution information of soil environment, and source-sink landscape type of the target area, a source-sink landscape geographic information database is established in conjunction with the small watershed geospatial database; the data in the small watershed geospatial database is mainly rainfall runoff.

[0082] Step S108: Based on the source-sink landscape geographic information database, select typical source-sink landscape types of soil in the target area for sampling and analysis to determine the spatial distribution information of heavy metals in the soil of typical source-sink landscape types; among them, typical source-sink landscape types include landfills, incinerators, and areas surrounding sewage treatment plants.

[0083] Step S110: Through spatial autocorrelation analysis, establish a heavy metal pollution characteristic model of soil heavy metal spatial distribution information and source-sink landscape type; this heavy metal pollution characteristic model is an autoregressive model.

[0084] Step S112: Based on the heavy metal pollution characteristic model, optimize the landscape pattern and establish a source-sink landscape scenario simulation and soil environmental response model.

[0085] Step S114: Based on the response results of the response model to the landscape pattern optimization, select soil heavy metal remediation schemes for different landscape types for soil remediation; the soil heavy metal remediation schemes include landscape remediation schemes, bioremediation schemes, physical remediation schemes, and chemical remediation schemes for non-core undeveloped areas.

[0086] The above-described method for remediating heavy metal pollution in soil based on source-sink landscapes is based on landscape ecology and environmental science theories. It remediates heavy metal pollution in soil in target areas by combining 3S technology with landscape ecology and environmental science to select typical source-sink landscape types in the target area for monitoring and quantitative analysis of the soil environment. From a macroscopic perspective, the landscape pattern is optimized to enable some heavy metal pollutants to self-repair within the landscape based on landscape ecological processes. From a microscopic perspective, heavy metal pollutants that cannot fully self-repair are treated and remediated through physical and chemical methods, maximizing the protection of the original soil structure from damage and achieving soil remediation in environmentally sensitive areas.

[0087] Combination Figure 2 and Figure 6 As shown, step S112, based on the heavy metal pollution characteristic model, optimizes the landscape pattern and establishes a source-sink landscape scenario simulation and soil environmental response model. This includes: Step S202, using the moving window method of landscape analysis to perform spatial calculations on the source-sink landscape of the target area, and judging whether the source-sink landscape components and spatial patterns within the window meet reasonable standards based on the calculation results of landscape indicators; wherein, the landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation; in implementation, the moving window method of the landscape analysis software Fragstats 3.3 is used to evaluate the source-sink landscape of the target area; Step S204, when the source-sink landscape components and spatial patterns within the window meet reasonable standards, judging whether the soil heavy metal content exceeds the pollution index based on the soil environmental spatial distribution information, and carrying out environmental remediation when the content exceeds the pollution index; Step S204, when the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, landscape optimization is performed, and the source-sink landscape geographic information database is updated until the optimized landscape indicators are within a reasonable range. In other words, in the scenario simulation of source-sink landscape pattern optimization, it is necessary to verify the spatial distribution information of the changing soil environment in real time to help determine whether the current optimized landscape pattern meets the requirements.

[0088] As an optional implementation, the process of acquiring the spatial distribution information of the soil environment can be as follows: Figure 3The illustrated process includes: Step S302, determining several spatial sampling points based on satellite and remote sensing images of the target area using a uniform sampling method; in this embodiment, when setting spatial sampling points, several spatial sampling points need to be uniformly set for any typical source-sink landscape to ensure data reliability while monitoring the spatial distribution of heavy metals in soil of different landscape types based on uniform sampling. Step S304, acquiring sampling data from the spatial sampling points and performing spatial interpolation to monitor and obtain spatial distribution information of heavy metals in soil of different source-sink landscape types, thereby obtaining spatial distribution information of the soil environment; in step S304, if ArcGIS 10.2 software is used for spatial interpolation, the spatial distribution information of the soil environment, i.e., the spatial distribution information of heavy metals, is obtained.

[0089] This implementation example Figure 4 As shown, the process of determining whether the source-sink landscape components and spatial pattern within the window meet the reasonable standard based on the calculation results of landscape indicators includes: Step S402, using the moving window method of landscape analysis to perform spatial calculations on the source-sink landscape of the target area to obtain spatial data of source-sink landscape indicators; Step S404, comparing the spatial distribution information of the soil environment with the spatial data of the landscape index, and if the spatial distribution information of the soil environment and the spatial data of the landscape index are positively correlated, then the landscape pattern is maintained, and the source-sink landscape components and spatial pattern within the window meet the reasonable standard; otherwise, landscape pattern optimization is required.

[0090] The specific parameters calculated in step S402 above include the Shannon Diversity Index (SHDI), Dominance Index (D), Patch Shape Index (S), and Fractal Dimension (F). d The spread index CONTIG and the plaque binding index COHESION are calculated using the following formulas:

[0091]

[0092]

[0093] Where n represents the total number of patch types, pi represents the proportion of landscape area occupied by patch i, i represents the i-th patch type, Hmax represents the maximum diversity index, C represents the perimeter of the landscape patch, A represents the area of ​​the landscape patch, k represents the k-th landscape patch that is different from i, p represents the perimeter of the k-th landscape patch, a represents the area of ​​the k-th landscape patch, gik represents the number of adjacent i-th type landscape patches and k-th type landscape patches, pij represents the perimeter of the j-th patch in the i-th type of landscape, and aij represents the area of ​​the j-th patch in the i-th type of landscape.

[0094] The source-sink landscape scenario simulation and soil environmental response model disclosed in this invention is a cellular automata-Markov chain model. This model uses the number of cells for each landscape type within a neighborhood as the neighborhood scenario, the central landscape cell simulating the input landscape as the input variable, and the central landscape cell simulating the output landscape type. It analyzes the functional relationship between the input land types of the central landscape cell under different neighborhood scenarios, thereby simulating different landscape spatial patterns. Different landscape spatial patterns are simulated under natural and human disturbance conditions. By coupling with soil environmental quality, the source-sink landscape contribution rate under different landscape spatial pattern conditions is determined, revealing the distribution information of heavy metal pollution in the soil.

[0095] Simulate the interaction of various landscape types, such as Figure 7 As shown, for example, if the input landscape type of the central landscape cell is agricultural land, the number of landscape cells for construction land in the neighboring scenarios is 2, the number of agricultural land cells is 3, and the number of forest land cells is 3. Due to the influence of the neighboring scenarios, the probability of the central landscape type output being construction land, agricultural land, or forest land differs. The probability of the central landscape land type under different neighboring scenarios is calculated using at least two periods of landscape types. The specific calculation method is as follows:

[0096] If we denote a single landscape change process as Entry(), then:

[0097]

[0098] In the formula, LUi→LUj represents the process of the central landscape unit type changing from i to j, and LUk=m represents the neighborhood scenario with m cells of land use type k. Based on this, the input landscape, output landscape, and neighborhood scenario of all landscape units in the target area are calculated as follows:

[0099]

[0100] Based on this, we can further calculate the potential for the central landscape cell to transform from landscape i to landscape j when the number of landscape types k in the neighborhood is n:

[0101]

[0102] Finally, the probability of landscape i transforming into landscape j is calculated using a conceptual statistical model, different landscape spatial pattern schemes are generated, and soil environmental protection schemes under different landscape spatial pattern schemes are established.

[0103] In embodiments of this application, an electronic device is also provided, including a computer program stored in a computer-readable storage medium. When a processor of the electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of the above-described source-sink landscape-based soil heavy metal remediation method. Taking an electronic device running on a computer as an example, such as... Figure 9 As shown, the electronic device may include one or more (only one is shown in the figure) processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory for storing data, and a transmission device for communication functions. Those skilled in the art will understand that... Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device.

[0104] The aforementioned programs may run in a processor or be stored in memory, i.e., in a computer-readable medium. Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include temporary computer-readable media such as modulated data signals and carrier waves. These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus provide for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented through different modules, corresponding to different method steps.

[0105] In this embodiment, such an apparatus or system is provided, which can be referred to as a source-sink landscape-based soil heavy metal remediation system, such as... Figure 8As shown, it includes: a determination and generation module, used to determine the target area, acquire small watershed geographic information of the target area using a geographic information system and a digital elevation model, and generate a small watershed geospatial database of the target area; an acquisition and determination module, used to acquire satellite imagery and remote sensing imagery of the target area, and determine the vegetation cover geographic information, topographic information, soil environmental spatial distribution information, and source-sink landscape type of the target area; a first establishment module, used to establish a source-sink landscape geographic information database based on the vegetation cover geographic information, topographic information, soil environmental spatial distribution information, and source-sink landscape type of the target area, combined with the small watershed geospatial database; and an analysis and determination module, using... Based on the source-sink landscape geographic information database, soil samples from typical source-sink landscape types in the target area are selected for analysis to determine the spatial distribution information of heavy metals in soils of typical source-sink landscape types. The second module is used to establish a model of the spatial distribution information of heavy metals in soils and the heavy metal pollution characteristics of source-sink landscape types through spatial autocorrelation analysis. The third module is used to optimize the landscape pattern based on the heavy metal pollution characteristic model and establish a source-sink landscape scenario simulation and soil environmental response model. The remediation selection module is used to select soil heavy metal remediation schemes for different landscape types based on the response results of the landscape pattern optimization of the response model.

[0106] The steps of the system for implementing the source-sink landscape-based soil heavy metal remediation method disclosed in the above embodiments have already been described and will not be repeated here.

[0107] For example, the third module, which optimizes landscape patterns based on a heavy metal pollution characteristic model, is an execution unit for establishing a source-sink landscape scenario simulation and soil environmental response model. This unit includes: a calculation and judgment unit, used to perform spatial calculations of the source-sink landscape in the target area using the moving window method of landscape analysis, and to determine whether the source-sink landscape components and spatial patterns within the window meet reasonable standards based on the calculation results of landscape indicators; wherein the landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation; a judgment and remediation unit, used to determine whether the soil heavy metal content exceeds pollution indicators based on the soil environmental spatial distribution information when the source-sink landscape components and spatial patterns within the window meet reasonable standards, and to perform environmental remediation when the content exceeds pollution indicators; and an optimization unit, used to perform landscape optimization and update the source-sink landscape geographic information database when the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, until the optimized landscape indicators are within a reasonable range.

[0108] Among them, the established source-sink landscape scenario simulation and soil environmental response model is a cellular automata-Markov chain model;

[0109] The cellular automata-Markov chain model uses the number of cells of each landscape type in the neighborhood as the neighborhood scenario, the central landscape cell to simulate the input landscape as the input variable, and the central landscape cell to simulate the output landscape type. It analyzes the functional relationship of the input land type of the central landscape cell under different neighborhood scenarios, thereby realizing the simulation of different landscape spatial patterns.

[0110] For example, the execution unit for determining the spatial distribution information of the soil environment in the acquisition and determination module includes: a determination unit, used to determine a number of spatial sampling points based on satellite images and remote sensing images of the target area using a uniform sampling method; and an acquisition unit, used to acquire the sampling data of the spatial sampling points and perform spatial interpolation, monitor and obtain the spatial distribution information of soil heavy metals in different source-sink landscape types, and obtain the spatial distribution information of the soil environment.

[0111] For example, the process by which the calculation and judgment unit judges whether the source-sink landscape components and spatial pattern within the window meet the reasonable standard based on the calculation results of the landscape index is as follows: the moving window method of landscape analysis is used to perform spatial calculations on the source-sink landscape of the target area to obtain spatial data of the source-sink landscape index; the spatial distribution information of the soil environment is compared with the spatial data of the landscape index, and when the spatial distribution information of the soil environment is positively correlated with the spatial data of the landscape index, the landscape pattern is maintained, and the source-sink landscape components and spatial pattern within the window meet the reasonable standard.

[0112] This invention discloses a method and system for remediating heavy metals in soil based on source-sink landscapes. It proposes landscape pattern optimization based on source-sink landscape theory, enabling some heavy metal pollutants to self-repair within the landscape through ecological processes. For source-sink landscapes that cannot fully self-repair, existing methods are used for treatment and remediation. Addressing the complexity and uncertainty of non-point source pollution, this invention comprehensively utilizes remote sensing and geographic information systems, landscape ecology, environmental science, and other disciplines, employing a cross-disciplinary approach. It addresses the shortcomings of individual technologies by considering pollution sources, the mechanisms of action of different landscape types and pollution sources, landscape pattern optimization, and physicochemical treatment methods. This significantly improves the remediation effect of heavy metals in soil and has substantial implications for environmental improvement and water source protection.

[0113] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for remediating heavy metals in soil based on source-sink landscapes, characterized in that, include: The target area is identified, and geographic information of the small watersheds in the target area is obtained using geographic information systems and digital elevation models to generate a geospatial database of the small watersheds in the target area. Acquire satellite and remote sensing images of the target area to determine the geographical information of vegetation cover, topography, spatial distribution of soil environment, and source-sink landscape types of the target area; Based on the geographical information of vegetation cover, topography, spatial distribution of soil environment, and source-sink landscape types of the target area, a source-sink landscape geographic information database is established in conjunction with the small watershed geospatial database. Based on the source-sink landscape geographic information database, soil samples of typical source-sink landscape types in the target area were selected for analysis to determine the spatial distribution information of heavy metals in soils of typical source-sink landscape types. By using spatial autocorrelation analysis, a model of the spatial distribution information of heavy metals in soil and the heavy metal pollution characteristics of source-sink landscape types was established. Landscape pattern optimization is carried out based on heavy metal pollution characteristic model, and source-sink landscape scenario simulation and soil environmental response model are established. Based on the response results of the response model to landscape pattern optimization, soil heavy metal remediation schemes for different landscape types are selected for soil remediation. The content of optimizing landscape patterns based on heavy metal pollution characteristic models and establishing source-sink landscape scenario simulation and soil environmental response models includes: The moving window method of landscape analysis is used to perform spatial calculations on the source-sink landscape of the target area. Based on the calculation results of landscape indicators, it is determined whether the source-sink landscape components and spatial patterns within the window meet reasonable standards. The landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation. When the source-sink landscape components and spatial pattern within the window reach a reasonable standard, the soil heavy metal content is determined to exceed the pollution index based on the soil environmental spatial distribution information, and environmental remediation is carried out when the content exceeds the pollution index. If the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, landscape optimization is performed, and the source-sink landscape geographic information database is updated until the optimized landscape indicators are within a reasonable range.

2. The soil heavy metal remediation method based on source-sink landscape according to claim 1, characterized in that, The process for obtaining the spatial distribution information of the soil environment is as follows: Based on satellite and remote sensing images of the target area, several spatial sampling points are determined using the uniform sampling method. By acquiring sampling data from spatial sampling points and performing spatial interpolation, we can monitor and obtain spatial distribution information of heavy metals in soils of different source-sink landscape types, and thus obtain spatial distribution information of the soil environment.

3. The soil heavy metal remediation method based on source-sink landscape according to claim 2, characterized in that, The process of determining whether the source-sink landscape components and spatial pattern within the window meet reasonable standards based on the calculation results of landscape indicators is as follows: The moving window method of landscape analysis is used to perform spatial calculations on the source-sink landscape of the target area to obtain spatial data of source-sink landscape indicators. Compare the spatial distribution information of the soil environment with the spatial data of the landscape index, and if the spatial distribution information of the soil environment and the spatial data of the landscape index are positively correlated, then the landscape pattern will continue to be maintained, and the source-sink landscape components and spatial patterns within the window will reach a reasonable standard.

4. The method for remediating heavy metals in soil based on source-sink landscapes according to claim 1, characterized in that, The content of spatial calculation of the source-sink landscape of the target area using the moving window method of landscape analysis includes: Shannon Diversity Index (SHDI): ； Dominance Index D: ； Patch shape index S: ； Fractal dimension F d : ； Spread Index : ×100 ； Plaque Binding Index : ； Where n represents the total number of patch types, pi represents the proportion of landscape area occupied by patch i, i represents the i-th patch type, and Hmax represents the maximum diversity index. C represents the perimeter of the landscape patch, A represents the area of ​​the landscape patch, k represents the k landscape patch that is different from i, p represents the perimeter of the k landscape patch, a represents the area of ​​the k landscape patch, gik represents the number of adjacent i-type landscape patches and k-type landscape patches, pij represents the perimeter of the j-th patch in the i-th landscape, and aij represents the area of ​​the j-th patch in the i-th landscape.

5. The soil heavy metal remediation method based on source-sink landscape according to claim 1, characterized in that, The source-sink landscape scenario simulation and soil environmental response model is a cellular automata-Markov chain model. The cellular automata-Markov chain model uses the number of cells of each landscape type in the neighborhood as the neighborhood scenario, the central landscape cell to simulate the input landscape as the input variable, and the central landscape cell to simulate the output landscape type. It analyzes the functional relationship of the input land type of the central landscape cell under different neighborhood scenarios, thereby realizing the simulation of different landscape spatial patterns.

6. A soil heavy metal remediation system based on source-sink landscape, characterized in that, include: The generation module is used to determine the target area, and uses a geographic information system and a digital elevation model to obtain the small watershed geographic information of the target area, and generate a small watershed geospatial database of the target area. The acquisition and determination module is used to acquire satellite and remote sensing images of the target area and determine the geographical information of vegetation cover, topographic information, spatial distribution information of soil environment, and source-sink landscape type of the target area; The first module is used to establish a source-sink landscape geographic information database based on the vegetation cover geographic information, topographic information, soil environment spatial distribution information, and source-sink landscape type of the target area, combined with the small watershed geospatial database. The analysis and determination module is used to select typical source-sink landscape type soils in the target area for sampling and analysis based on the source-sink landscape geographic information database, and to determine the spatial distribution information of heavy metals in the soils of typical source-sink landscape types. The second module is used to establish a model of the spatial distribution information of heavy metals in soil and the heavy metal pollution characteristics of source-sink landscape types through spatial autocorrelation analysis. The third module is used to optimize the landscape pattern based on the heavy metal pollution characteristic model and to establish a source-sink landscape scenario simulation and soil environmental response model. The selection and remediation module is used to select soil heavy metal remediation schemes for different landscape types based on the response results of the response model to landscape pattern optimization. The third module, which optimizes landscape patterns based on a heavy metal pollution characteristic model, is an execution unit for establishing source-sink landscape scenario simulation and soil environmental response models, and includes: The calculation and judgment unit is used to perform spatial calculations on the source-sink landscape of the target area using the moving window method of landscape analysis, and to judge whether the source-sink landscape components and spatial patterns within the window meet reasonable standards based on the calculation results of landscape indicators; wherein, the landscape indicators include diversity, fractal dimension, spatial isolation, and fragmentation. The judgment and remediation unit is used to determine whether the soil heavy metal content exceeds the pollution index based on the soil environmental spatial distribution information when the source-sink landscape components and spatial pattern within the window reach a reasonable standard, and to carry out environmental remediation when the content exceeds the pollution index. The optimization unit is used to optimize the landscape and update the source-sink landscape geographic information database when the source-sink landscape components and spatial patterns within the window do not meet reasonable standards, until the optimized landscape indicators are within a reasonable range.

7. The soil heavy metal remediation system based on source-sink landscape according to claim 6, characterized in that, The execution unit for determining the spatial distribution information of the soil environment in the acquisition and determination module includes: The determination unit is used to determine several spatial sampling points based on satellite and remote sensing images of the target area using a uniform sampling method. The acquisition unit is used to acquire sampling data from spatial sampling points and perform spatial interpolation to monitor and obtain spatial distribution information of heavy metals in soils of different source-sink landscape types, thereby obtaining spatial distribution information of the soil environment.

8. An electronic device, characterized in that, The method includes a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of the soil heavy metal remediation method based on source-sink landscape as described in any one of claims 1-5.

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