Rapid detection method and system for soil heavy metal ions
Through multispectral imaging technology and mathematical modeling methods, rapid and accurate detection of soil heavy metal ions was achieved, solving the problems of low efficiency and insufficient precision of traditional methods and providing scientific support for on-site detection.
Patent Information
- Application Number
- CN202510860959.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-09-23
AI Technical Summary
Existing technologies make it difficult to achieve rapid and accurate detection of soil heavy metal ions, especially in on-site testing, where efficiency is low. Portable devices also have limited sensitivity and poor anti-interference capabilities, making it difficult to detect multiple heavy metal ions simultaneously.
A multispectral imager is used to scan the soil area to obtain spectral reflectance data. After noise filtering, the soil microstructure and heavy metal adsorption characteristics are analyzed. Combined with the soil chemical property data, heavy metal ion distribution characteristics are extracted, migration rate simulation and pollution risk assessment are carried out, and dynamic diffusion path modeling is performed using formulas.
It achieves rapid and accurate detection of soil heavy metal ions, shortens detection time, improves the reliability and accuracy of detection results, can predict heavy metal migration paths and assess pollution risks, and provide a scientific basis for pollution prevention and control.
Smart Images
Figure CN120685603A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of environmental monitoring and analysis, and specifically relates to a method and system for rapid detection of heavy metal ions in soil. Background Art
[0002] With the rapid development of industrialization and urbanization, solid waste pollution is becoming increasingly serious, with heavy metal pollution posing a particularly significant threat to the soil environment. Heavy metal ions such as lead, cadmium, mercury, and chromium are highly toxic, difficult to degrade, and bioaccumulate. Once in the soil, they not only disrupt the balance of the soil ecosystem but can also harm human health through the food chain. Therefore, rapid and accurate detection of heavy metal ion levels in soil is crucial for assessing soil pollution, developing pollution prevention and control measures, and ensuring ecological and environmental safety.
[0003] Currently, soil heavy metal detection primarily relies on laboratory analysis methods, such as atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS). While these methods offer high accuracy and sensitivity, they suffer from complex procedures, long testing cycles, expensive equipment, and a high demand for specialized personnel. Furthermore, traditional detection methods typically require soil samples to be sent to a laboratory for processing and analysis, making them inefficient for rapid on-site testing and particularly inefficient during sudden pollution incidents or large-scale soil screening.
[0004] In recent years, a number of portable detection technologies and devices have been developed, such as rapid detection methods based on electrochemical sensors or spectral analysis. However, these technologies still have many shortcomings in practical applications, such as limited detection sensitivity, poor anti-interference ability, narrow application scope, and difficulty in simultaneously detecting multiple heavy metal ions. These issues limit their widespread application in complex soil environments. To this end, the present invention provides a method and system for rapid detection of heavy metal ions in soil. Summary of the Invention
[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.
[0006] The technical solution adopted by the present invention to solve the technical problem is: a rapid detection method for heavy metal ions in soil according to the present invention comprises the following steps: Step S1: Scanning the target soil area with a multispectral imager to obtain a soil spectral reflectance dataset; extracting heavy metal ion distribution characteristics based on the soil spectral reflectance dataset to obtain heavy metal ion distribution characteristic data; Step S2: Perform soil micro-area chemical composition difference analysis based on the heavy metal ion distribution characteristic data to obtain micro-area chemical composition difference data; perform heavy metal migration rate simulation calculation based on the micro-area chemical composition difference data to obtain heavy metal migration rate quantification data; Step S3: Perform dynamic diffusion path inference on the heavy metal migration rate quantification data to obtain dynamic diffusion path data; perform heavy metal pollution risk assessment based on the dynamic diffusion path data to obtain heavy metal pollution risk assessment data.
[0007] Preferably, step S1 includes the following steps: step S11: scanning the target soil area through a multispectral imager to obtain a soil spectral reflectance dataset; step S12: performing noise filtering on the soil spectral reflectance dataset to obtain a soil noise-reduced spectral reflectance dataset; step S13: analyzing the soil microstructure and heavy metal adsorption characteristics based on the soil noise-reduced spectral reflectance dataset to obtain soil microscopic adsorption characteristic data; step S14: extracting heavy metal ion distribution characteristics based on the soil microscopic adsorption characteristic data to obtain heavy metal ion distribution characteristic data.
[0008] Preferably, the step S2 includes the following steps: step S21: acquiring soil basic chemical property data; step S22: performing micro-area chemical composition analysis on the soil microscopic adsorption characteristic data according to the soil spectral reflectance data set to obtain micro-area chemical composition analysis data; step S23: performing micro-area chemical composition difference analysis on the micro-area chemical composition analysis data according to the heavy metal ion distribution characteristic data and the soil spectral reflectance data set to obtain micro-area chemical composition difference data; step S24: performing heavy metal migration rate simulation calculation based on the micro-area chemical composition difference data and the soil microscopic adsorption characteristic data, and quantifying it using the following formula: ; in, represents the migration rate of heavy metals, represents the initial concentration of heavy metals in the soil, represents the effective diffusion coefficient, Indicates the time interval, represents the soil adsorption distribution coefficient.
[0009] Preferably, the step S2 further includes the following steps: Step S25: predicting the migration path of heavy metals based on the micro-region chemical composition difference data and the heavy metal migration rate quantification data to obtain migration path prediction data.
[0010] Preferably, the step S3 includes the following steps: step S31: standardizing the quantified data of heavy metal migration rate to obtain standard data of heavy metal migration rate; step S32: performing dynamic diffusion path inference on the standard data of heavy metal migration rate, and modeling the diffusion path using the following formula: ; in, represents the dynamic diffusion path, represents the decay rate constant.
[0011] Preferably, the step S3 further includes the following steps: Step S33: performing heavy metal pollution risk assessment based on the dynamic diffusion path data to obtain heavy metal pollution risk assessment data.
[0012] The present invention also provides a rapid detection system for heavy metal ions in soil, which is used to implement the above-mentioned rapid detection method for heavy metal ions in soil. The system includes: a heavy metal ion distribution feature extraction module, a heavy metal migration rate quantification module and a heavy metal pollution risk assessment module.
[0013] Preferably, the heavy metal ion distribution feature extraction module is used to scan the target soil area through a multispectral imager to obtain a soil spectral reflectance data set; and extract the heavy metal ion distribution features based on the soil spectral reflectance data set to obtain heavy metal ion distribution feature data.
[0014] Preferably, the heavy metal migration rate quantification module is used to perform soil micro-area chemical composition difference analysis based on heavy metal ion distribution characteristic data to obtain micro-area chemical composition difference data; and perform heavy metal migration rate simulation calculation based on micro-area chemical composition difference data to obtain heavy metal migration rate quantification data.
[0015] Preferably, the heavy metal pollution risk assessment module is used to perform dynamic diffusion path inference on the quantified data of heavy metal migration rate to obtain dynamic diffusion path data; and perform heavy metal pollution risk assessment based on the dynamic diffusion path data to obtain heavy metal pollution risk assessment data.
[0016] The beneficial effects of the present invention are as follows: The rapid soil heavy metal ion detection method and system described in this invention uses a multispectral imaging device to scan the target soil area, rapidly acquiring a soil spectral reflectance dataset. This eliminates the complex sample pretreatment required by traditional detection methods and significantly reduces detection time. From data acquisition through to heavy metal ion distribution feature extraction, migration rate simulation, and pollution risk assessment, this method forms a coherent and efficient detection process, enabling comprehensive soil heavy metal ion detection and analysis in a short period of time, meeting the demand for rapid detection.
[0017] 2. The rapid detection method and system for heavy metal ions in soil described herein effectively removes noise interference from the data by performing noise filtering on the soil spectral reflectance dataset. This makes subsequent analysis of soil microstructure and heavy metal adsorption characteristics based on the de-noised soil spectral reflectance dataset more accurate, thereby ensuring the reliability of the data extracted from the heavy metal ion distribution characteristics. Furthermore, a specific formula is used in the simulation calculation of heavy metal migration rates, comprehensively considering multiple factors such as the initial concentration of heavy metals in the soil, the effective diffusion coefficient, the time interval, and the soil adsorption distribution coefficient. This ensures that the calculated quantitative data on heavy metal migration rates better reflects actual conditions, thereby improving the accuracy of the test results.
[0018] 3. The rapid soil heavy metal ion detection method and system described in this invention combines heavy metal ion distribution characteristic data, soil spectral reflectance datasets, and soil microscopic adsorption characteristic data to analyze and differentiate the chemical composition of micro-regions, providing a deep understanding of the chemical composition changes at different locations in the soil micro-region. The heavy metal migration rate simulation and migration path prediction performed on this basis not only quantifies the migration rate of heavy metals but also predicts their migration paths, providing strong support for a comprehensive understanding of the migration characteristics of heavy metals in soil and facilitating in-depth research on the behavior of heavy metals in soil.
[0019] 4. The rapid soil heavy metal ion detection method and system described herein standardize quantified data on heavy metal migration rates and then employ a specific formula to model dynamic diffusion pathways, enabling scientific and rational deduction of the dynamic diffusion pathways of heavy metals in soil. Heavy metal pollution risk assessment based on dynamic diffusion pathway data can accurately determine the risk level of heavy metal contamination in soil, providing a scientific basis for the prevention, control, and management of soil heavy metal pollution, and facilitating the development of targeted pollution control measures to reduce pollution risks.
[0020] 5. The rapid detection method and system for heavy metal ions in soil described in the present invention comprises a soil heavy metal ion rapid detection system comprising a heavy metal ion distribution feature extraction module, a heavy metal migration rate quantification module, and a heavy metal pollution risk assessment module. Each module has a clear division of labor and close collaboration. The heavy metal ion distribution feature extraction module provides basic data for subsequent modules, the heavy metal migration rate quantification module performs in-depth analysis and calculation based on the data from the previous module, and the heavy metal pollution risk assessment module uses the results of the previous modules to complete the final risk assessment, forming a complete detection and analysis system, ensuring efficient and orderly detection work. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The present invention will be further described below with reference to the accompanying drawings.
[0022] Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the system flow in the present invention. DETAILED DESCRIPTION
[0023] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.
[0024] First, as Figure 1 The figure shows the overall process diagram of the rapid detection method of heavy metal ions in soil according to an embodiment of the present invention. In actual operation, first, a spectral reflectance data set of the target soil area is obtained through step S1. This step involves using a multispectral imager to scan the target soil area and obtain spectral reflectance information of the soil surface. In order to ensure data quality, clear and cloudless weather conditions need to be selected during the acquisition process, and the multispectral imager is set to high-resolution mode to obtain more detailed data. There may be noise interference in the original spectral reflectance data set collected, so it needs to be subjected to noise filtering. Specifically, the wavelet transform algorithm is used to reduce the noise of the spectral reflectance data and extract the spectral reflectance data. The main signal components are extracted and high-frequency noise is removed, and finally the soil noise-reduced spectral reflectance dataset is obtained. Subsequently, the soil microstructure and heavy metal adsorption characteristics are analyzed based on the noise-reduced spectral reflectance dataset. For example, by comparing the changes in spectral reflectance in different bands, the ability of soil particle surfaces to adsorb heavy metals and their spatial distribution characteristics can be identified. Combined with the soil physical and chemical properties database, soil microscopic adsorption characteristic data are further extracted. These data will serve as the basis for subsequent extraction of heavy metal ion distribution characteristics. Finally, by performing cluster analysis on the soil microscopic adsorption characteristic data, the heavy metal ion distribution characteristic data are extracted to clarify the spatial distribution law of heavy metal ions in the target area.
[0025] In step S2, the soil micro-region chemical composition difference analysis is performed based on the heavy metal ion distribution characteristic data obtained in step S1. This process includes multiple sub-steps. First, the basic soil chemical property data, such as soil pH value, organic matter content, and cation exchange capacity, need to be obtained. These data can be measured by conventional laboratory analysis methods and stored in the system database for call. Next, the soil microscopic adsorption characteristic data are analyzed for micro-region chemical composition using the soil spectral reflectance data set to generate micro-region chemical composition analysis data. On this basis, the heavy metal ion distribution characteristic data and the soil spectral reflectance data set are combined to further analyze the differences in micro-region chemical composition. For example, by calculating the difference in chemical component concentration between different micro-regions, the degree of chemical composition difference between each micro-region is quantified to form micro-region chemical composition difference data. Subsequently, based on the micro-region chemical composition difference data and the soil microscopic adsorption characteristic data, the formula is used: The migration rate of heavy metals was simulated and calculated, where It represents the initial concentration of heavy metals in the soil and can be obtained by inverting spectral reflectance data; represents the effective diffusion coefficient, whose value depends on the soil porosity and moisture content; Indicates the time interval, usually set to 1 hour; It represents the soil adsorption distribution coefficient, which is obtained by experimental measurement or literature review. The quantitative data of heavy metal migration rate calculated by the above formula can reflect the migration ability of heavy metals in soil and provide a basis for further prediction of migration path.
[0026] The core of step S3 is to normalize the quantitative data of heavy metal migration rate and infer the dynamic diffusion path based on the processing results. First, the quantitative data of heavy metal migration rate is normalized and converted into a unified standard data form for subsequent modeling. Then, the formula is used: The normalized migration rate data were used to perform dynamic diffusion path modeling, where Represents the dynamic diffusion path, which is used to describe the diffusion range and direction of heavy metals in soil; Indicates the total time, which can be set to several days to several months according to actual monitoring needs; It represents the attenuation rate constant, which reflects the strength of the obstruction effect during the diffusion of heavy metals. Its value can be obtained through experimental fitting. In specific implementation, assuming that the total time of a monitoring task is set to 30 days and the attenuation rate constant is 0.05, the dynamic diffusion path of heavy metals within 30 days can be calculated by substituting it into the formula. In addition, in order to improve the accuracy of modeling, the finite element analysis method can be introduced to divide the soil into several grid units, calculate the diffusion behavior in each unit separately, and finally integrate the overall diffusion path data. Based on the dynamic diffusion path data, the heavy metal pollution risk assessment is further carried out. For example, by analyzing whether the diffusion path covers sensitive areas such as farmland and water sources, combined with the heavy metal toxicity threshold, the pollution risk level is comprehensively judged to form heavy metal pollution risk assessment data. like Figure 2 As shown, the present invention also provides a rapid detection system for heavy metal ions in soil, which is used to perform the above detection method. The system mainly includes three functional modules: a heavy metal ion distribution feature extraction module, a heavy metal migration rate quantification module and a heavy metal pollution risk assessment module. Figure 1 As shown in the figure, the heavy metal ion distribution feature extraction module is responsible for extracting heavy metal ion distribution feature data from the spectral reflectance data set collected by the multispectral imager; the heavy metal migration rate quantification module uses the formula: Calculate quantitative data on heavy metal migration rates; the heavy metal pollution risk assessment module assesses the pollution risk level based on dynamic diffusion path data. In addition, the system is equipped with a central processor to coordinate data transmission and computing tasks between modules to ensure the efficient operation of the entire detection process.
[0027] In practical applications, the method and system of the present invention can be used to monitor soil heavy metal pollution in a variety of scenarios. For example, in a soil pollution survey project around an industrial park, technicians used a multispectral imager to scan the soil within a 1 square kilometer area around the park, collected a spectral reflectance data set, and uploaded it to the detection system. The system automatically completed a series of operations such as noise filtering, heavy metal ion distribution feature extraction, micro-area chemical composition difference analysis, and migration rate calculation, ultimately generating a dynamic diffusion path map and pollution risk assessment report. The report showed that the lead ion concentration in the soil on the southeast side of the park was high and there was a trend of diffusion to groundwater, prompting relevant departments to take emergency treatment measures. Through the application of the present invention, not only was the detection efficiency significantly improved, but it also provided a scientific basis for pollution prevention and control.
[0028] In summary, the present invention achieves rapid and accurate detection of heavy metal ions in soil through innovative technical means, solving the problems of low efficiency and insufficient precision of traditional methods. By combining multispectral imaging technology and mathematical modeling methods, the present invention can complete the entire process from data collection to pollution risk assessment in a short period of time, providing important technical support for the field of environmental monitoring.
[0029] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.
Claims
1. A rapid detection method for heavy metal ions in soil, characterized in that: The following steps are involved: Step S1: Scanning the target soil area with a multispectral imager to obtain a soil spectral reflectance dataset; extracting heavy metal ion distribution characteristics based on the soil spectral reflectance dataset to obtain heavy metal ion distribution characteristic data; Step S2: performing a soil micro-area chemical composition difference analysis based on the heavy metal ion distribution characteristic data to obtain micro-area chemical composition difference data; performing a heavy metal migration rate simulation calculation based on the micro-area chemical composition difference data to obtain heavy metal migration rate quantification data; Step S3: performing a dynamic diffusion path inference on the heavy metal migration rate quantification data to obtain dynamic diffusion path data; Heavy metal pollution risk assessment is performed based on dynamic diffusion path data to obtain heavy metal pollution risk assessment data.
2. The rapid detection method for heavy metal ions in soil according to claim 1, wherein: The step S1 includes the following steps: step S11: scanning the target soil area with a multispectral imager to obtain a soil spectral reflectance dataset; step S12: performing noise filtering on the soil spectral reflectance dataset to obtain a soil noise-reduced spectral reflectance dataset; step S13: analyzing the soil microstructure and heavy metal adsorption characteristics based on the soil noise-reduced spectral reflectance dataset to obtain soil micro-adsorption characteristic data; step S14: extracting heavy metal ion distribution characteristics based on the soil micro-adsorption characteristic data to obtain heavy metal ion distribution characteristic data.
3. The rapid detection method for heavy metal ions in soil according to claim 1, wherein: The step S2 comprises the following steps: step S21: acquiring soil basic chemical property data; step S22: performing micro-region chemical composition analysis on the soil microscopic adsorption characteristic data according to the soil spectral reflectance data set to obtain micro-region chemical composition analysis data; step S23: performing micro-region chemical composition difference analysis on the micro-region chemical composition analysis data according to the heavy metal ion distribution characteristic data and the soil spectral reflectance data set to obtain micro-region chemical composition difference data; step S24: performing heavy metal migration rate simulation calculation based on the micro-region chemical composition difference data and the soil microscopic adsorption characteristic data, and quantifying it using the following formula: ； in, represents the migration rate of heavy metals, represents the initial concentration of heavy metals in the soil, represents the effective diffusion coefficient, Indicates the time interval, represents the soil adsorption partition coefficient.
4. The rapid detection method for heavy metal ions in soil according to claim 3, wherein: The step S2 further includes the following steps: Step S25: predicting the migration path of heavy metals based on the micro-region chemical composition difference data and the heavy metal migration rate quantification data to obtain migration path prediction data.
5. The rapid detection method for heavy metal ions in soil according to claim 1, wherein: The step S3 comprises the following steps: step S31: standardizing the quantified data of heavy metal migration rate to obtain standard data of heavy metal migration rate; step S32: performing dynamic diffusion path inference on the standard data of heavy metal migration rate, and modeling the diffusion path using the following formula: ； in, represents the dynamic diffusion path, represents the decay rate constant.
6. The method for rapid detection of heavy metal ions in soil according to claim 5, wherein: The step S3 further includes the following steps: Step S33: performing heavy metal pollution risk assessment based on the dynamic diffusion path data to obtain heavy metal pollution risk assessment data.
7. A rapid detection system for heavy metal ions in soil, for implementing a rapid detection method for heavy metal ions in soil as claimed in any one of claims 1 to 6, the system comprising a heavy metal ion distribution feature extraction module (1), a heavy metal migration rate quantification module (2) and a heavy metal pollution risk assessment module (3).
8. A rapid detection system for heavy metal ions in soil according to claim 7, characterized in that: The heavy metal ion distribution feature extraction module (1) is used to scan the target soil area through a multispectral imager to obtain a soil spectral reflectance data set; and to extract the heavy metal ion distribution features based on the soil spectral reflectance data set to obtain heavy metal ion distribution feature data.
9. A rapid detection system for heavy metal ions in soil according to claim 7, characterized in that: The heavy metal migration rate quantification module (2) is used to perform soil micro-area chemical composition difference analysis based on heavy metal ion distribution characteristic data to obtain micro-area chemical composition difference data; and perform heavy metal migration rate simulation calculation based on the micro-area chemical composition difference data to obtain heavy metal migration rate quantification data.
10. The rapid detection system for heavy metal ions in soil according to claim 7, characterized in that: The heavy metal pollution risk assessment module (3) is used to perform dynamic diffusion path inference on the heavy metal migration rate quantification data to obtain dynamic diffusion path data; and perform heavy metal pollution risk assessment based on the dynamic diffusion path data to obtain heavy metal pollution risk assessment data.